Metabolic disease group

We are a multidisciplinary team that combines large-scale genetic and genomic approaches, and studies in model organisms, to understand the aetiology of various metabolic diseases. We are also actively engaged in developing partnerships with collaborators in Africa focused on applying genomic approaches to study diseases of relevance to Africa and its peoples.

The knowledge of genetic predisposition is important to help those at high risk for these disorders to develop healthier lifestyles and to avoid risky behaviours (such as high fat diets). It can also lead to the development of better drugs that work in each affected individual.

[Anna Tanczos, Wellcome Images]

Background

Prevalence of Diabetes Worldwide.

Prevalence of Diabetes Worldwide.

zoom

Type 2 diabetes and obesity are complex disorders whose prevalence has reached epidemic proportions.

In 1985 the number of individuals with type 2 diabetes worldwide was 30 million, the more recent number is more than 150 million, and in the next 20 or so years this disease is expected to double in prevalence to 300 million (See graph). The increase in the number of subjects with diabetes will not be confined to the Western world and in fact it is expected that most of the rise in prevalence will come from developing countries.

Traditionally thought of as a disease of old age, troublingly it is on the rise in younger age groups. This increase is the result of lifestyle and dietary changes over the last few decades acting on a background of genes that have evolved in an environment where diet and physical activity where very different from today. These changes have led to a rapid rise in the prevalence of obesity which has been paralleled by a rise in diabetes prevalence, for which obesity is a significant risk factor.

Because of the complex interaction between environment and genes, identifying genes with a role in the susceptibility to these diseases has been a difficult task. However significant progress has recently been made in the identification of genes with a role in predisposition to type 2 diabetes, obesity and related traits.

Team members

  • Bill Bottomley
  • Allan Daly
  • Felicity Payne
  • Rachel Watson
  • Eleanor Wheeler
  • Gaelle Marenne
  • Jennifer Asimit
  • Audrey Hendricks
  • Chris Franklin
  • Neneh Sallah
  • Laure Lam Hung
Our group is also supported by
  • Carol Dunbar (Personal Assistant)
  • Nicola Corton (Research Administrator)

Research

Approach

We have been using genome-wide association and sequencing approaches to identify genes with a role in obesity, type 2 diabetes and related quantitative traits, as well as rare extreme forms of these diseases. To further the knowledge from a statistical association to a biologically relevant finding it is imperative to determine the functional implications of those variants in terms of protein structure, activity and action in vivo. To functionally evaluate those genes with genetic and statistical associations with disease will be the next great challenge in complex disease. We have established collaborations with other Sanger Institute researchers, and have the ability to study novel genes implicated in disease in model organisms such as zebrafish (collaboration with Derek Stemple's group) and mouse (Sanger Institute Mouse genetics programme). The ultimate aim will be to elucidate how those variants are acting at the cellular and organismal level to increase individual predisposition to disease.

Ongoing projects

Genome-wide Association Studies

We work with many other groups across the world in large consortia, such as GIANT (Genetic Investigation of ANthropometric Traits) and MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium), that aim to increase statistical power by performing meta-analyses of GWAS data across many different studies.

We are also conducting a genome-wide association study of scarring trachoma in Gambians (collaboration with Dr David Mabey, Professor Robin Bailey and Professor Dominic Kwiatkowski at WTSI) to identify human polymorphisms that predispose to scarring and potentially identify molecules and mechanisms that play an important role in either protection or pathogenesis of this disease.

Next Generation Sequencing

In collaboration with Professor Stephen O'Rahilly", Dr David Savage, Dr Rob Semple and Professor Sadaf Farooqi we have conducted many candidate gene sequencing projects in patients with severe insulin resistance and severe childhood onset obesity, which have led to the discovery of causal mutations underlying the phenotype in affected individuals. As part of these ongoing collaborations, and within the UK10K project, we have more recently obtained exome sequences, using next generation sequencing techniques, for a number of patients with syndromes of insulin resistance and severe childhood onset obesity. This will allow us to conduct unbiased approaches to identify novel rare variants (mutations) that may underlie disease in affected individuals with these extreme forms of disease.

We are leading the 500 Exome project, a collaborative project with investigators at WTSI as well as in Lausanne and GSK. The project sequenced the exomes of ~500 individuals from the Lausanne cohort and is currently analysing these data for association with a number of traits and performing imputation studies.

Collaborations

We have close collaborative links with many colleagues and are always open to extending our collaborative network to others where there is mutual scientific benefit.

  • 1. Collaborators at the Institute of Metabolic Sciences including Professor Stephen O'Rahilly, Dr David Savage, Dr Robert Semple and Professor Sadaf Farooqi, as well as Dr Claudia Langenberg, Dr Ruth Loos and Professor Nick Wareham at the MRC Epidemiology Unit.
  • 2. MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium).
  • 3. DIAGRAM (Diabetes Genetics Replication And Meta-analysis Consortium).
  • 4. GIANT (The Genetic Investigation of ANthropometric Traits).
  • 5. The 500 Exome Project with collaborators from WTSI, GSK and Lausanne University.
  • 6. UK10K - Rare Genetic Variants in Health and Disease.
  • 7. GlobalBPGen: Global Blood Pressure Genetics consortium.
  • 8. GDC: Global Diabetes Consortium.
  • 9. We are also key partners in the InterAct project, an EU FP6 Integrated Project which is a collaboration among 36 partners from 30 European Institutions.
  • 10. Dr David Mabey and Professor Robin Bailey at London School of Hygiene and Tropical Medicine.
  • 11. Collaboration with Dr Julie Makani Clinical Research Fellow in the Nuffield Department of Medicine who is based in the Department of Haematology and Blood Transfusion at Muhimbili University of Health and Allied Sciences and Dr Jeff Barrett (WTSI) to employ a GWAS design to study Sickle Cell Disease.
  • 12. APCDR: The African Partnership for Chronic Disease Research.

Publications

2014 Publications

  • Mutations disrupting the Kennedy phosphatidylcholine pathway in humans with congenital lipodystrophy and fatty liver disease.

    Payne F, Lim K, Girousse A, Brown RJ, Kory N, Robbins A, Xue Y, Sleigh A, Cochran E, Adams C, Dev Borman A, Russel-Jones D, Gorden P, Semple RK, Saudek V, O'Rahilly S, Walther TC, Barroso I and Savage DB

    Metabolic Disease Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, United Kingdom;

    Phosphatidylcholine (PC) is the major glycerophospholipid in eukaryotic cells and is an essential component in all cellular membranes. The biochemistry of de novo PC synthesis by the Kennedy pathway is well established, but less is known about the physiological functions of PC. We identified two unrelated patients with defects in the Kennedy pathway due to biallellic loss-of-function mutations in phosphate cytidylyltransferase 1 alpha (PCYT1A), the rate-limiting enzyme in this pathway. The mutations lead to a marked reduction in PCYT1A expression and PC synthesis. The phenotypic consequences include some features, such as severe fatty liver and low HDL cholesterol levels, that are predicted by the results of previously reported liver-specific deletion of murine Pcyt1a. Both patients also had lipodystrophy, severe insulin resistance, and diabetes, providing evidence for an additional and essential role for PCYT1A-generated PC in the normal function of white adipose tissue and insulin action.

    Funded by: Medical Research Council; NHLBI NIH HHS: HL-102923, HL-102926, HL-103010; NIGMS NIH HHS: R01 GM09719; Wellcome Trust: 098498, WT091310, WT091551, WT095515, WT098051, WT098498

    Proceedings of the National Academy of Sciences of the United States of America 2014;111;24;8901-6

  • Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.

    Dimas AS, Lagou V, Barker A, Knowles JW, Mägi R, Hivert MF, Benazzo A, Rybin D, Jackson AU, Stringham HM, Song C, Fischer-Rosinsky A, Boesgaard TW, Grarup N, Abbasi FA, Assimes TL, Hao K, Yang X, Lecoeur C, Barroso I, Bonnycastle LL, Böttcher Y, Bumpstead S, Chines PS, Erdos MR, Graessler J, Kovacs P, Morken MA, Narisu N, Payne F, Stancakova A, Swift AJ, Tönjes A, Bornstein SR, Cauchi S, Froguel P, Meyre D, Schwarz PE, Häring HU, Smith U, Boehnke M, Bergman RN, Collins FS, Mohlke KL, Tuomilehto J, Quertemous T, Lind L, Hansen T, Pedersen O, Walker M, Pfeiffer AF, Spranger J, Stumvoll M, Meigs JB, Wareham NJ, Kuusisto J, Laakso M, Langenberg C, Dupuis J, Watanabe RM, Florez JC, Ingelsson E, McCarthy MI, Prokopenko I and MAGIC Investigators

    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K.Alexander Fleming, Biomedical Sciences Research Center, Vari, Athens, Greece.

    Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

    Funded by: NCRR NIH HHS: 2 M01 RR000070, RR01066; NHGRI NIH HHS: 1 Z01 HG000024; NHLBI NIH HHS: N01-HC-25195, N02-HL-6-4278; NIDA NIH HHS: U54 DA021519; NIDDK NIH HHS: DK-062370, DK-069922, DK-072193, K24 DK-080140, K24 DK080140, R01 DK-078616, R01 DK062370, R01 DK072193, R01 DK078616, R01 DK093757, R56 DK062370, U01 DK062370; Wellcome Trust: 090532, 098381

    Diabetes 2014;63;6;2158-71

  • Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

    Langenberg C, Sharp SJ, Franks PW, Scott RA, Deloukas P, Forouhi NG, Froguel P, Groop LC, Hansen T, Palla L, Pedersen O, Schulze MB, Tormo MJ, Wheeler E, Agnoli C, Arriola L, Barricarte A, Boeing H, Clarke GM, Clavel-Chapelon F, Duell EJ, Fagherazzi G, Kaaks R, Kerrison ND, Key TJ, Khaw KT, Kröger J, Lajous M, Morris AP, Navarro C, Nilsson PM, Overvad K, Palli D, Panico S, Quirós JR, Rolandsson O, Sacerdote C, Sánchez MJ, Slimani N, Spijkerman AM, Tumino R, van der A DL, van der Schouw YT, Barroso I, McCarthy MI, Riboli E and Wareham NJ

    Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.

    Background: Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.

    The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.

    Conclusions: The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.

    Funded by: Cancer Research UK: 16491; Medical Research Council: G0601261; Wellcome Trust: 083270/Z/07/Z, 090532, 098017, WT090532, WT098017

    PLoS medicine 2014;11;5;e1001647

  • A genome-wide association analysis of a broad psychosis phenotype identifies three loci for further investigation.

    Psychosis Endophenotypes International Consortium, Wellcome Trust Case-Control Consortium 2, Bramon E, Pirinen M, Strange A, Lin K, Freeman C, Bellenguez C, Su Z, Band G, Pearson R, Vukcevic D, Langford C, Deloukas P, Hunt S, Gray E, Dronov S, Potter SC, Tashakkori-Ghanbaria A, Edkins S, Bumpstead SJ, Arranz MJ, Bakker S, Bender S, Bruggeman R, Cahn W, Chandler D, Collier DA, Crespo-Facorro B, Dazzan P, de Haan L, Di Forti M, Dragović M, Giegling I, Hall J, Iyegbe C, Jablensky A, Kahn RS, Kalaydjieva L, Kravariti E, Lawrie S, Linszen DH, Mata I, McDonald C, McIntosh A, Myin-Germeys I, Ophoff RA, Pariante CM, Paunio T, Picchioni M, Psychiatric Genomics Consortium, Ripke S, Rujescu D, Sauer H, Shaikh M, Sussmann J, Suvisaari J, Tosato S, Toulopoulou T, Van Os J, Walshe M, Weisbrod M, Whalley H, Wiersma D, Blackwell JM, Brown MA, Casas JP, Corvin A, Duncanson A, Jankowski JA, Markus HS, Mathew CG, Palmer CN, Plomin R, Rautanen A, Sawcer SJ, Trembath RC, Wood NW, Barroso I, Peltonen L, Lewis CM, Murray RM, Donnelly P, Powell J and Spencer CC

    Background: Genome-wide association studies (GWAS) have identified several loci associated with schizophrenia and/or bipolar disorder. We performed a GWAS of psychosis as a broad syndrome rather than within specific diagnostic categories.

    Methods: 1239 cases with schizophrenia, schizoaffective disorder, or psychotic bipolar disorder; 857 of their unaffected relatives, and 2739 healthy controls were genotyped with the Affymetrix 6.0 single nucleotide polymorphism (SNP) array. Analyses of 695,193 SNPs were conducted using UNPHASED, which combines information across families and unrelated individuals. We attempted to replicate signals found in 23 genomic regions using existing data on nonoverlapping samples from the Psychiatric GWAS Consortium and Schizophrenia-GENE-plus cohorts (10,352 schizophrenia patients and 24,474 controls).

    Results: No individual SNP showed compelling evidence for association with psychosis in our data. However, we observed a trend for association with same risk alleles at loci previously associated with schizophrenia (one-sided p = .003). A polygenic score analysis found that the Psychiatric GWAS Consortium's panel of SNPs associated with schizophrenia significantly predicted disease status in our sample (p = 5 × 10(-14)) and explained approximately 2% of the phenotypic variance.

    Conclusions: Although narrowly defined phenotypes have their advantages, we believe new loci may also be discovered through meta-analysis across broad phenotypes. The novel statistical methodology we introduced to model effect size heterogeneity between studies should help future GWAS that combine association evidence from related phenotypes. Applying these approaches, we highlight three loci that warrant further investigation. We found that SNPs conveying risk for schizophrenia are also predictive of disease status in our data.

    Funded by: Department of Health: PDA/02/06/016; Medical Research Council: G0000934, G0901310, G1100583; Wellcome Trust: 064971, 068545/Z/02, 072894/Z/03/Z, 075491/Z/04/B, 085475/B/08/Z, 085475/Z/08/Z, 090532, 090532/Z/09/Z, 095552, 097364/Z/11/Z

    Biological psychiatry 2014;75;5;386-97

  • A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility.

    Damotte V, Guillot-Noel L, Patsopoulos NA, Madireddy L, El Behi M, International Multiple Sclerosis Genetics Consortium, Wellcome Trust Case Control Consortium 2, De Jager PL, Baranzini SE, Cournu-Rebeix I and Fontaine B

    UPMC-INSERM-UMR_S 1127-CNRS UMR7225, Paris, France.

    Genome-wide association studies (GWASs) perform per-SNP association tests to identify variants involved in disease or trait susceptibility. However, such an approach is not powerful enough to unravel genes that are not individually contributing to the disease/trait, but that may have a role in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted of ITGAL, ICAM1 and ICAM3 genes, could be of interest to develop novel therapeutic targets.

    Funded by: Wellcome Trust: 090532

    Genes and immunity 2014;15;2;126-32

  • Loss of FTO antagonises Wnt signaling and leads to developmental defects associated with ciliopathies.

    Osborn DP, Roccasecca RM, McMurray F, Hernandez-Hernandez V, Mukherjee S, Barroso I, Stemple D, Cox R, Beales PL and Christou-Savina S

    Biomedical Sciences, St George's University of London, London, United Kingdom.

    Common intronic variants in the Human fat mass and obesity-associated gene (FTO) are found to be associated with an increased risk of obesity. Overexpression of FTO correlates with increased food intake and obesity, whilst loss-of-function results in lethality and severe developmental defects. Despite intense scientific discussions around the role of FTO in energy metabolism, the function of FTO during development remains undefined. Here, we show that loss of Fto leads to developmental defects such as growth retardation, craniofacial dysmorphism and aberrant neural crest cells migration in Zebrafish. We find that the important developmental pathway, Wnt, is compromised in the absence of FTO, both in vivo (zebrafish) and in vitro (Fto(-/-) MEFs and HEK293T). Canonical Wnt signalling is down regulated by abrogated β-Catenin translocation to the nucleus whilst non-canonical Wnt/Ca(2+) pathway is activated via its key signal mediators CaMKII and PKCδ. Moreover, we demonstrate that loss of Fto results in short, absent or disorganised cilia leading to situs inversus, renal cystogenesis, neural crest cell defects and microcephaly in Zebrafish. Congruently, Fto knockout mice display aberrant tissue specific cilia. These data identify FTO as a protein-regulator of the balanced activation between canonical and non-canonical branches of the Wnt pathway. Furthermore, we present the first evidence that FTO plays a role in development and cilia formation/function.

    Funded by: Medical Research Council: G0801843

    PloS one 2014;9;2;e87662

2013 Publications

  • KSR2 mutations are associated with obesity, insulin resistance, and impaired cellular fuel oxidation.

    Pearce LR, Atanassova N, Banton MC, Bottomley B, van der Klaauw AA, Revelli JP, Hendricks A, Keogh JM, Henning E, Doree D, Jeter-Jones S, Garg S, Bochukova EG, Bounds R, Ashford S, Gayton E, Hindmarsh PC, Shield JP, Crowne E, Barford D, Wareham NJ, UK10K consortium, O'Rahilly S, Murphy MP, Powell DR, Barroso I and Farooqi IS

    Kinase suppressor of Ras 2 (KSR2) is an intracellular scaffolding protein involved in multiple signaling pathways. Targeted deletion of Ksr2 leads to obesity in mice, suggesting a role in energy homeostasis. We explored the role of KSR2 in humans by sequencing 2,101 individuals with severe early-onset obesity and 1,536 controls. We identified multiple rare variants in KSR2 that disrupt signaling through the Raf-MEKERK pathway and impair cellular fatty acid oxidation and glucose oxidation in transfected cells; effects that can be ameliorated by the commonly prescribed antidiabetic drug, metformin. Mutation carriers exhibit hyperphagia in childhood, low heart rate, reduced basal metabolic rate and severe insulin resistance. These data establish KSR2 as an important regulator of energy intake, energy expenditure, and substrate utilization in humans. Modulation of KSR2-mediated effects may represent a novel therapeutic strategy for obesity and type 2 diabetes.

    Funded by: Cancer Research UK: 14109; Medical Research Council: MC_U106179471; Wellcome Trust: 077016/Z/05/Z, 096106/Z/11/Z, 098497, 098497/Z/12/Z, WT091310

    Cell 2013;155;4;765-77

  • Common variants associated with plasma triglycerides and risk for coronary artery disease.

    Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen LP, Magnusson PK, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O'Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney AS, Döring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJ, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin SY, Lindström J, Loos RJ, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stančáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YD, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin MR, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PE, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BH, Altshuler D, Ordovas JM, Boerwinkle E, Palmer CN, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Mohlke KL, Ingelsson E, Abecasis GR, Daly MJ, Neale BM and Kathiresan S

    1] Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA. [2] Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA. [3] Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. [4] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.

    Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.

    Funded by: British Heart Foundation: PG/08/094/26019, RG/08/014/24067; Canadian Institutes of Health Research; Chief Scientist Office: CZB/4/672, CZB/4/710; Medical Research Council: G0801566, G1000143, MC_U106179471, MC_U106179472, MC_U106188470, MC_U123092720, MC_U950080926; NCATS NIH HHS: UL1 TR000124; NHLBI NIH HHS: R01 HL105756, R01 HL107816, R01HL107816, T32HL007208, U01 HL069757; NIDDK NIH HHS: P30 DK063491, P30 DK072488, R01 DK072193; Wellcome Trust: 090532

    Nature genetics 2013;45;11;1345-52

  • Discovery and refinement of loci associated with lipid levels.

    Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen LP, Magnusson PK, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O'Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney AS, Döring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJ, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin SY, Lindström J, Loos RJ, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stancáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YD, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin MR, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PE, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BH, Ordovas JM, Boerwinkle E, Palmer CN, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E and Abecasis GR

    1] Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA. [2] Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. [3] Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA. [4] Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA. [5] [6].

    Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.

    Funded by: British Heart Foundation: PG/08/094/26019, RG/08/008/25291, RG/08/014/24067; Chief Scientist Office: CZB/4/672, CZB/4/710; Medical Research Council: G0801566, G0901213, G1000143, MC_U106179471, MC_U106179472, MC_U106188470, MC_U123092720, MC_U950080926; NCATS NIH HHS: UL1 TR000124; NHLBI NIH HHS: R00 HL094535, R01 HL105756, R01 HL109946, U01 HL069757; NIDDK NIH HHS: P30 DK063491, P30 DK072488, R01 DK072193; Wellcome Trust: 090532

    Nature genetics 2013;45;11;1274-83

  • Genome-wide association analysis identifies 13 new risk loci for schizophrenia.

    Ripke S, O'Dushlaine C, Chambert K, Moran JL, Kähler AK, Akterin S, Bergen SE, Collins AL, Crowley JJ, Fromer M, Kim Y, Lee SH, Magnusson PK, Sanchez N, Stahl EA, Williams S, Wray NR, Xia K, Bettella F, Borglum AD, Bulik-Sullivan BK, Cormican P, Craddock N, de Leeuw C, Durmishi N, Gill M, Golimbet V, Hamshere ML, Holmans P, Hougaard DM, Kendler KS, Lin K, Morris DW, Mors O, Mortensen PB, Neale BM, O'Neill FA, Owen MJ, Milovancevic MP, Posthuma D, Powell J, Richards AL, Riley BP, Ruderfer D, Rujescu D, Sigurdsson E, Silagadze T, Smit AB, Stefansson H, Steinberg S, Suvisaari J, Tosato S, Verhage M, Walters JT, Multicenter Genetic Studies of Schizophrenia Consortium, Levinson DF, Gejman PV, Kendler KS, Laurent C, Mowry BJ, O'Donovan MC, Owen MJ, Pulver AE, Riley BP, Schwab SG, Wildenauer DB, Dudbridge F, Holmans P, Shi J, Albus M, Alexander M, Campion D, Cohen D, Dikeos D, Duan J, Eichhammer P, Godard S, Hansen M, Lerer FB, Liang KY, Maier W, Mallet J, Nertney DA, Nestadt G, Norton N, O'Neill FA, Papadimitriou GN, Ribble R, Sanders AR, Silverman JM, Walsh D, Williams NM, Wormley B, Psychosis Endophenotypes International Consortium, Arranz MJ, Bakker S, Bender S, Bramon E, Collier D, Crespo-Facorro B, Hall J, Iyegbe C, Jablensky A, Kahn RS, Kalaydjieva L, Lawrie S, Lewis CM, Lin K, Linszen DH, Mata I, McIntosh A, Murray RM, Ophoff RA, Powell J, Rujescu D, Van Os J, Walshe M, Weisbrod M, Wiersma D, Wellcome Trust Case Control Consortium 2, Donnelly P, Barroso I, Blackwell JM, Bramon E, Brown MA, Casas JP, Corvin AP, Deloukas P, Duncanson A, Jankowski J, Markus HS, Mathew CG, Palmer CN, Plomin R, Rautanen A, Sawcer SJ, Trembath RC, Viswanathan AC, Wood NW, Spencer CC, Band G, Bellenguez C, Freeman C, Hellenthal G, Giannoulatou E, Pirinen M, Pearson RD, Strange A, Su Z, Vukcevic D, Donnelly P, Langford C, Hunt SE, Edkins S, Gwilliam R, Blackburn H, Bumpstead SJ, Dronov S, Gillman M, Gray E, Hammond N, Jayakumar A, McCann OT, Liddle J, Potter SC, Ravindrarajah R, Ricketts M, Tashakkori-Ghanbaria A, Waller MJ, Weston P, Widaa S, Whittaker P, Barroso I, Deloukas P, Mathew CG, Blackwell JM, Brown MA, Corvin AP, McCarthy MI, Spencer CC, Bramon E, Corvin AP, O'Donovan MC, Stefansson K, Scolnick E, Purcell S, McCarroll SA, Sklar P, Hultman CM and Sullivan PF

    1] Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA. [2] Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [3].

    Schizophrenia is an idiopathic mental disorder with a heritable component and a substantial public health impact. We conducted a multi-stage genome-wide association study (GWAS) for schizophrenia beginning with a Swedish national sample (5,001 cases and 6,243 controls) followed by meta-analysis with previous schizophrenia GWAS (8,832 cases and 12,067 controls) and finally by replication of SNPs in 168 genomic regions in independent samples (7,413 cases, 19,762 controls and 581 parent-offspring trios). We identified 22 loci associated at genome-wide significance; 13 of these are new, and 1 was previously implicated in bipolar disorder. Examination of candidate genes at these loci suggests the involvement of neuronal calcium signaling. We estimate that 8,300 independent, mostly common SNPs (95% credible interval of 6,300-10,200 SNPs) contribute to risk for schizophrenia and that these collectively account for at least 32% of the variance in liability. Common genetic variation has an important role in the etiology of schizophrenia, and larger studies will allow more detailed understanding of this disorder.

    Funded by: Medical Research Council: G0600429, G0601635, G0800509, G1000718, G1100583; NIMH NIH HHS: K01 MH094406, R01 MH077139, R01 MH083094, R01 MH095034, U01 MH094421; Wellcome Trust: 085475/B/08/Z, 085475/Z/08/Z, 090532, 095552

    Nature genetics 2013;45;10;1150-9

  • Rare variants in single-minded 1 (SIM1) are associated with severe obesity.

    Ramachandrappa S, Raimondo A, Cali AM, Keogh JM, Henning E, Saeed S, Thompson A, Garg S, Bochukova EG, Brage S, Trowse V, Wheeler E, Sullivan AE, Dattani M, Clayton PE, Datta V, Datta V, Bruning JB, Wareham NJ, O'Rahilly S, Peet DJ, Barroso I, Whitelaw ML and Farooqi IS

    University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.

    Single-minded 1 (SIM1) is a basic helix-loop-helix transcription factor involved in the development and function of the paraventricular nucleus of the hypothalamus. Obesity has been reported in Sim1 haploinsufficient mice and in a patient with a balanced translocation disrupting SIM1. We sequenced the coding region of SIM1 in 2,100 patients with severe, early onset obesity and in 1,680 controls. Thirteen different heterozygous variants in SIM1 were identified in 28 unrelated severely obese patients. Nine of the 13 variants significantly reduced the ability of SIM1 to activate a SIM1-responsive reporter gene when studied in stably transfected cells coexpressing the heterodimeric partners of SIM1 (ARNT or ARNT2). SIM1 variants with reduced activity cosegregated with obesity in extended family studies with variable penetrance. We studied the phenotype of patients carrying variants that exhibited reduced activity in vitro. Variant carriers exhibited increased ad libitum food intake at a test meal, normal basal metabolic rate, and evidence of autonomic dysfunction. Eleven of the 13 probands had evidence of a neurobehavioral phenotype. The phenotypic similarities between patients with SIM1 deficiency and melanocortin 4 receptor (MC4R) deficiency suggest that some of the effects of SIM1 deficiency on energy homeostasis are mediated by altered melanocortin signaling.

    Funded by: Medical Research Council: G9824984, MC_U106179471, MC_U106179473, MC_U106188470; NHLBI NIH HHS: HL-102923, HL-102924, HL-102925, HL-102926, HL-103010; Wellcome Trust: 077016/Z/05/Z, 082390/Z/07/Z, 098497

    The Journal of clinical investigation 2013;123;7;3042-50

  • Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake.

    Tanaka T, Ngwa JS, van Rooij FJ, Zillikens MC, Wojczynski MK, Frazier-Wood AC, Houston DK, Kanoni S, Lemaitre RN, Luan J, Mikkilä V, Renstrom F, Sonestedt E, Zhao JH, Chu AY, Qi L, Chasman DI, de Oliveira Otto MC, Dhurandhar EJ, Feitosa MF, Johansson I, Khaw KT, Lohman KK, Manichaikul A, McKeown NM, Mozaffarian D, Singleton A, Stirrups K, Viikari J, Ye Z, Bandinelli S, Barroso I, Deloukas P, Forouhi NG, Hofman A, Liu Y, Lyytikäinen LP, North KE, Dimitriou M, Hallmans G, Kähönen M, Langenberg C, Ordovas JM, Uitterlinden AG, Hu FB, Kalafati IP, Raitakari O, Franco OH, Johnson A, Emilsson V, Schrack JA, Semba RD, Siscovick DS, Arnett DK, Borecki IB, Franks PW, Kritchevsky SB, Lehtimäki T, Loos RJ, Orho-Melander M, Rotter JI, Wareham NJ, Witteman JC, Ferrucci L, Dedoussis G, Cupples LA and Nettleton JA

    Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21225, USA. tanakato@mail.nih.gov

    Background: Macronutrient intake varies substantially between individuals, and there is evidence that this variation is partly accounted for by genetic variants.

    Objective: The objective of the study was to identify common genetic variants that are associated with macronutrient intake.

    Design: We performed 2-stage genome-wide association (GWA) meta-analysis of macronutrient intake in populations of European descent. Macronutrients were assessed by using food-frequency questionnaires and analyzed as percentages of total energy consumption from total fat, protein, and carbohydrate. From the discovery GWA (n = 38,360), 35 independent loci associated with macronutrient intake at P < 5 × 10(-6) were identified and taken forward to replication in 3 additional cohorts (n = 33,533) from the DietGen Consortium. For one locus, fat mass obesity-associated protein (FTO), cohorts with Illumina MetaboChip genotype data (n = 7724) provided additional replication data.

    Results: A variant in the chromosome 19 locus (rs838145) was associated with higher carbohydrate (β ± SE: 0.25 ± 0.04%; P = 1.68 × 10(-8)) and lower fat (β ± SE: -0.21 ± 0.04%; P = 1.57 × 10(-9)) consumption. A candidate gene in this region, fibroblast growth factor 21 (FGF21), encodes a fibroblast growth factor involved in glucose and lipid metabolism. The variants in this locus were associated with circulating FGF21 protein concentrations (P < 0.05) but not mRNA concentrations in blood or brain. The body mass index (BMI)-increasing allele of the FTO variant (rs1421085) was associated with higher protein intake (β ± SE: 0.10 ± 0.02%; P = 9.96 × 10(-10)), independent of BMI (after adjustment for BMI, β ± SE: 0.08 ± 0.02%; P = 3.15 × 10(-7)).

    Conclusion: Our results indicate that variants in genes involved in nutrient metabolism and obesity are associated with macronutrient consumption in humans. Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetic and Environmental Determinants of Triglycerides), NCT01331512 (InCHIANTI Study), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

    Funded by: Canadian Institutes of Health Research; Cancer Research UK; Medical Research Council: G1000143, MC_U106179471, MC_U106188470, MC_UP_A100_1003; NCATS NIH HHS: UL1 TR000124; NHLBI NIH HHS: R01 HL105756, T32 HL007575; NIDDK NIH HHS: P30 DK063491, R01 DK091718; NIGMS NIH HHS: T32 GM074905; Wellcome Trust

    The American journal of clinical nutrition 2013;97;6;1395-402

  • Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits.

    Randall JC, Winkler TW, Kutalik Z, Berndt SI, Jackson AU, Monda KL, Kilpeläinen TO, Esko T, Mägi R, Li S, Workalemahu T, Feitosa MF, Croteau-Chonka DC, Day FR, Fall T, Ferreira T, Gustafsson S, Locke AE, Mathieson I, Scherag A, Vedantam S, Wood AR, Liang L, Steinthorsdottir V, Thorleifsson G, Dermitzakis ET, Dimas AS, Karpe F, Min JL, Nicholson G, Clegg DJ, Person T, Krohn JP, Bauer S, Buechler C, Eisinger K, DIAGRAM Consortium, Bonnefond A, Froguel P, MAGIC Investigators, Hottenga JJ, Prokopenko I, Waite LL, Harris TB, Smith AV, Shuldiner AR, McArdle WL, Caulfield MJ, Munroe PB, Grönberg H, Chen YD, Li G, Beckmann JS, Johnson T, Thorsteinsdottir U, Teder-Laving M, Khaw KT, Wareham NJ, Zhao JH, Amin N, Oostra BA, Kraja AT, Province MA, Cupples LA, Heard-Costa NL, Kaprio J, Ripatti S, Surakka I, Collins FS, Saramies J, Tuomilehto J, Jula A, Salomaa V, Erdmann J, Hengstenberg C, Loley C, Schunkert H, Lamina C, Wichmann HE, Albrecht E, Gieger C, Hicks AA, Johansson A, Pramstaller PP, Kathiresan S, Speliotes EK, Penninx B, Hartikainen AL, Jarvelin MR, Gyllensten U, Boomsma DI, Campbell H, Wilson JF, Chanock SJ, Farrall M, Goel A, Medina-Gomez C, Rivadeneira F, Estrada K, Uitterlinden AG, Hofman A, Zillikens MC, den Heijer M, Kiemeney LA, Maschio A, Hall P, Tyrer J, Teumer A, Völzke H, Kovacs P, Tönjes A, Mangino M, Spector TD, Hayward C, Rudan I, Hall AS, Samani NJ, Attwood AP, Sambrook JG, Hung J, Palmer LJ, Lokki ML, Sinisalo J, Boucher G, Huikuri H, Lorentzon M, Ohlsson C, Eklund N, Eriksson JG, Barlassina C, Rivolta C, Nolte IM, Snieder H, Van der Klauw MM, Van Vliet-Ostaptchouk JV, Gejman PV, Shi J, Jacobs KB, Wang Z, Bakker SJ, Mateo Leach I, Navis G, van der Harst P, Martin NG, Medland SE, Montgomery GW, Yang J, Chasman DI, Ridker PM, Rose LM, Lehtimäki T, Raitakari O, Absher D, Iribarren C, Basart H, Hovingh KG, Hyppönen E, Power C, Anderson D, Beilby JP, Hui J, Jolley J, Sager H, Bornstein SR, Schwarz PE, Kristiansson K, Perola M, Lindström J, Swift AJ, Uusitupa M, Atalay M, Lakka TA, Rauramaa R, Bolton JL, Fowkes G, Fraser RM, Price JF, Fischer K, Krjutå Kov K, Metspalu A, Mihailov E, Langenberg C, Luan J, Ong KK, Chines PS, Keinanen-Kiukaanniemi SM, Saaristo TE, Edkins S, Franks PW, Hallmans G, Shungin D, Morris AD, Palmer CN, Erbel R, Moebus S, Nöthen MM, Pechlivanis S, Hveem K, Narisu N, Hamsten A, Humphries SE, Strawbridge RJ, Tremoli E, Grallert H, Thorand B, Illig T, Koenig W, Müller-Nurasyid M, Peters A, Boehm BO, Kleber ME, März W, Winkelmann BR, Kuusisto J, Laakso M, Arveiler D, Cesana G, Kuulasmaa K, Virtamo J, Yarnell JW, Kuh D, Wong A, Lind L, de Faire U, Gigante B, Magnusson PK, Pedersen NL, Dedoussis G, Dimitriou M, Kolovou G, Kanoni S, Stirrups K, Bonnycastle LL, Njølstad I, Wilsgaard T, Ganna A, Rehnberg E, Hingorani A, Kivimaki M, Kumari M, Assimes TL, Barroso I, Boehnke M, Borecki IB, Deloukas P, Fox CS, Frayling T, Groop LC, Haritunians T, Hunter D, Ingelsson E, Kaplan R, Mohlke KL, O'Connell JR, Schlessinger D, Strachan DP, Stefansson K, van Duijn CM, Abecasis GR, McCarthy MI, Hirschhorn JN, Qi L, Loos RJ, Lindgren CM, North KE and Heid IM

    Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom.

    Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10(-8)), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.

    Funded by: AHRQ HHS: R01HS006516; British Heart Foundation: PG/07/133/24260, PG/11/63/29011; CSR NIH HHS: RG2008/014, RG2008/08; Cancer Research UK: C490/A10124, C490/A8339; Chief Scientist Office: CZB/4/672, CZB/4/710; Medical Research Council: 85374, G0000649, G0000934, G0500539, G0600237, G0600705, G0601261, G1000143, G9521010, G9521010D, MC_U106179471, MC_U106179472, MC_U106188470; NCATS NIH HHS: UL1 TR000124; NCI NIH HHS: P01CA87969, R01CA047988, R01CA049449, R01CA050385, R01CA065725, R01CA067262, U01CA098233; NCRR NIH HHS: M01RR16500, U54RR020278, UL1RR025005, UL1RR033176; NHGRI NIH HHS: N01HG65403, R01HG002651, U01HG004402, Z01HG000024; NHLBI NIH HHS: 5R01HL087679-02, N01HC25195, N01HC35129, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85084, N01HC85085, N01HC85086, N01HC85239, N02HL64278, R01HL036310, R01HL043851, R01HL059367, R01HL071981, R01HL075366, R01HL086694, R01HL087641, R01HL087647, R01HL087652, R01HL087676, R01HL087679, R01HL087700, R01HL088215, R01HL105756, U01HL069757, U01HL072515, U01HL080295, U01HL084729, U01HL084756; NIA NIH HHS: N01AG12100, N01AG12109, R01AG004563, R01AG008724, R01AG008861, R01AG010175, R01AG013196, R01AG015928, R01AG020098, R01AG023629, R01AG027058, R01AG028555; NIAAA NIH HHS: R01AA007535, R01AA013320, R01AA013321, R01AA013326, R01AA014041; NICHD NIH HHS: R01HD042157; NIDA NIH HHS: R01DA12854; NIDDK NIH HHS: K23DK080145, P01 DK088761, P30 DK063491, P30 DK072488, P30DK063491, P30DK072488, R01 DK072193, R01 DK075787, R01 DK091718, R01DK062370, R01DK072193, R01DK073490, R01DK075681, R01DK075787, R01DK089256, U01DK062418; NIGMS NIH HHS: T32 GM007092; NIMH NIH HHS: 1RL1MH083268-01, 5R01MH63706, R01MH059565, R01MH059566, R01MH059571, R01MH059586, R01MH059587, R01MH059588, R01MH060870, R01MH060879, R01MH061675, R01MH067257, R01MH079469, R01MH079470, R01MH081800, RL1MH083268, U24MH068457; NLM NIH HHS: R01LM010098; OAPP OPHS HHS: PG/02/128; PHS HHS: 268200625226C, 268201100005C, 268201100006C, 268201100007C, 268201100008C, 268201100009C, 268201100010C, 268201100011C, 268201100012C; Wellcome Trust: 068545/Z/02, 069224, 072960, 075491, 076113/B/04/Z, 076113/K/04/Z, 079557, 079895, 081682, 083270, 084183/Z/07/Z, 085301, 086596/Z/08/Z, 089061/Z/09/Z, 089062/Z/09/Z, 090532, 090532/Z/09/Z, 098051

    PLoS genetics 2013;9;6;e1003500

  • Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture.

    Berndt SI, Gustafsson S, Mägi R, Ganna A, Wheeler E, Feitosa MF, Justice AE, Monda KL, Croteau-Chonka DC, Day FR, Esko T, Fall T, Ferreira T, Gentilini D, Jackson AU, Luan J, Randall JC, Vedantam S, Willer CJ, Winkler TW, Wood AR, Workalemahu T, Hu YJ, Lee SH, Liang L, Lin DY, Min JL, Neale BM, Thorleifsson G, Yang J, Albrecht E, Amin N, Bragg-Gresham JL, Cadby G, den Heijer M, Eklund N, Fischer K, Goel A, Hottenga JJ, Huffman JE, Jarick I, Johansson Å, Johnson T, Kanoni S, Kleber ME, König IR, Kristiansson K, Kutalik Z, Lamina C, Lecoeur C, Li G, Mangino M, McArdle WL, Medina-Gomez C, Müller-Nurasyid M, Ngwa JS, Nolte IM, Paternoster L, Pechlivanis S, Perola M, Peters MJ, Preuss M, Rose LM, Shi J, Shungin D, Smith AV, Strawbridge RJ, Surakka I, Teumer A, Trip MD, Tyrer J, Van Vliet-Ostaptchouk JV, Vandenput L, Waite LL, Zhao JH, Absher D, Asselbergs FW, Atalay M, Attwood AP, Balmforth AJ, Basart H, Beilby J, Bonnycastle LL, Brambilla P, Bruinenberg M, Campbell H, Chasman DI, Chines PS, Collins FS, Connell JM, Cookson WO, de Faire U, de Vegt F, Dei M, Dimitriou M, Edkins S, Estrada K, Evans DM, Farrall M, Ferrario MM, Ferrières J, Franke L, Frau F, Gejman PV, Grallert H, Grönberg H, Gudnason V, Hall AS, Hall P, Hartikainen AL, Hayward C, Heard-Costa NL, Heath AC, Hebebrand J, Homuth G, Hu FB, Hunt SE, Hyppönen E, Iribarren C, Jacobs KB, Jansson JO, Jula A, Kähönen M, Kathiresan S, Kee F, Khaw KT, Kivimäki M, Koenig W, Kraja AT, Kumari M, Kuulasmaa K, Kuusisto J, Laitinen JH, Lakka TA, Langenberg C, Launer LJ, Lind L, Lindström J, Liu J, Liuzzi A, Lokki ML, Lorentzon M, Madden PA, Magnusson PK, Manunta P, Marek D, März W, Mateo Leach I, McKnight B, Medland SE, Mihailov E, Milani L, Montgomery GW, Mooser V, Mühleisen TW, Munroe PB, Musk AW, Narisu N, Navis G, Nicholson G, Nohr EA, Ong KK, Oostra BA, Palmer CN, Palotie A, Peden JF, Pedersen N, Peters A, Polasek O, Pouta A, Pramstaller PP, Prokopenko I, Pütter C, Radhakrishnan A, Raitakari O, Rendon A, Rivadeneira F, Rudan I, Saaristo TE, Sambrook JG, Sanders AR, Sanna S, Saramies J, Schipf S, Schreiber S, Schunkert H, Shin SY, Signorini S, Sinisalo J, Skrobek B, Soranzo N, Stančáková A, Stark K, Stephens JC, Stirrups K, Stolk RP, Stumvoll M, Swift AJ, Theodoraki EV, Thorand B, Tregouet DA, Tremoli E, Van der Klauw MM, van Meurs JB, Vermeulen SH, Viikari J, Virtamo J, Vitart V, Waeber G, Wang Z, Widén E, Wild SH, Willemsen G, Winkelmann BR, Witteman JC, Wolffenbuttel BH, Wong A, Wright AF, Zillikens MC, Amouyel P, Boehm BO, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Cupples LA, Cusi D, Dedoussis GV, Erdmann J, Eriksson JG, Franks PW, Froguel P, Gieger C, Gyllensten U, Hamsten A, Harris TB, Hengstenberg C, Hicks AA, Hingorani A, Hinney A, Hofman A, Hovingh KG, Hveem K, Illig T, Jarvelin MR, Jöckel KH, Keinanen-Kiukaanniemi SM, Kiemeney LA, Kuh D, Laakso M, Lehtimäki T, Levinson DF, Martin NG, Metspalu A, Morris AD, Nieminen MS, Njølstad I, Ohlsson C, Oldehinkel AJ, Ouwehand WH, Palmer LJ, Penninx B, Power C, Province MA, Psaty BM, Qi L, Rauramaa R, Ridker PM, Ripatti S, Salomaa V, Samani NJ, Snieder H, Sørensen TI, Spector TD, Stefansson K, Tönjes A, Tuomilehto J, Uitterlinden AG, Uusitupa M, van der Harst P, Vollenweider P, Wallaschofski H, Wareham NJ, Watkins H, Wichmann HE, Wilson JF, Abecasis GR, Assimes TL, Barroso I, Boehnke M, Borecki IB, Deloukas P, Fox CS, Frayling T, Groop LC, Haritunian T, Heid IM, Hunter D, Kaplan RC, Karpe F, Moffatt MF, Mohlke KL, O'Connell JR, Pawitan Y, Schadt EE, Schlessinger D, Steinthorsdottir V, Strachan DP, Thorsteinsdottir U, van Duijn CM, Visscher PM, Di Blasio AM, Hirschhorn JN, Lindgren CM, Morris AP, Meyre D, Scherag A, McCarthy MI, Speliotes EK, North KE, Loos RJ and Ingelsson E

    US Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA.

    Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

    Funded by: British Heart Foundation: PG/11/63/29011; Cancer Research UK; Chief Scientist Office: CZB/4/710; Medical Research Council: G0600237, G0601261, G1000143, G9521010, MC_PC_U127561128, MC_U105260558, MC_U106179471, MC_U106179472, MC_U106188470, MC_U123092720; NHLBI NIH HHS: R01 HL105756; NIAAA NIH HHS: K05 AA017688; NIDDK NIH HHS: R01 DK072193, R01 DK075787; NIGMS NIH HHS: T32 GM074905; Wellcome Trust: 090532, 097117, 098017

    Nature genetics 2013;45;5;501-12

  • Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity.

    Wheeler E, Huang N, Bochukova EG, Keogh JM, Lindsay S, Garg S, Henning E, Blackburn H, Loos RJ, Wareham NJ, O'Rahilly S, Hurles ME, Barroso I and Farooqi IS

    Wellcome Trust Sanger Institute, Cambridge, UK.

    Common and rare variants associated with body mass index (BMI) and obesity account for <5% of the variance in BMI. We performed SNP and copy number variation (CNV) association analyses in 1,509 children with obesity at the extreme tail (>3 s.d. from the mean) of the BMI distribution and 5,380 controls. Evaluation of 29 SNPs (P < 1 × 10(-5)) in an additional 971 severely obese children and 1,990 controls identified 4 new loci associated with severe obesity (LEPR, PRKCH, PACS1 and RMST). A previously reported 43-kb deletion at the NEGR1 locus was significantly associated with severe obesity (P = 6.6 × 10(-7)). However, this signal was entirely driven by a flanking 8-kb deletion; absence of this deletion increased risk for obesity (P = 6.1 × 10(-11)). We found a significant burden of rare, single CNVs in severely obese cases (P < 0.0001). Integrative gene network pathway analysis of rare deletions indicated enrichment of genes affecting G protein-coupled receptors (GPCRs) involved in the neuronal regulation of energy homeostasis.

    Funded by: Cancer Research UK; Medical Research Council: G0900554, G9824984, MC_U106179471, MC_U106188470; NIDA NIH HHS: R25 DA027995; Wellcome Trust: 084713, 098497, WT098051

    Nature genetics 2013;45;5;513-7

  • Large-scale association analysis identifies new risk loci for coronary artery disease.

    CARDIoGRAMplusC4D Consortium, Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, Ingelsson E, Saleheen D, Erdmann J, Goldstein BA, Stirrups K, König IR, Cazier JB, Johansson A, Hall AS, Lee JY, Willer CJ, Chambers JC, Esko T, Folkersen L, Goel A, Grundberg E, Havulinna AS, Ho WK, Hopewell JC, Eriksson N, Kleber ME, Kristiansson K, Lundmark P, Lyytikäinen LP, Rafelt S, Shungin D, Strawbridge RJ, Thorleifsson G, Tikkanen E, Van Zuydam N, Voight BF, Waite LL, Zhang W, Ziegler A, Absher D, Altshuler D, Balmforth AJ, Barroso I, Braund PS, Burgdorf C, Claudi-Boehm S, Cox D, Dimitriou M, Do R, DIAGRAM Consortium, CARDIOGENICS Consortium, Doney AS, El Mokhtari N, Eriksson P, Fischer K, Fontanillas P, Franco-Cereceda A, Gigante B, Groop L, Gustafsson S, Hager J, Hallmans G, Han BG, Hunt SE, Kang HM, Illig T, Kessler T, Knowles JW, Kolovou G, Kuusisto J, Langenberg C, Langford C, Leander K, Lokki ML, Lundmark A, McCarthy MI, Meisinger C, Melander O, Mihailov E, Maouche S, Morris AD, Müller-Nurasyid M, MuTHER Consortium, Nikus K, Peden JF, Rayner NW, Rasheed A, Rosinger S, Rubin D, Rumpf MP, Schäfer A, Sivananthan M, Song C, Stewart AF, Tan ST, Thorgeirsson G, van der Schoot CE, Wagner PJ, Wellcome Trust Case Control Consortium, Wells GA, Wild PS, Yang TP, Amouyel P, Arveiler D, Basart H, Boehnke M, Boerwinkle E, Brambilla P, Cambien F, Cupples AL, de Faire U, Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrières J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen SL, Holm H, Iribarren C, Jang Y, Kähönen M, Kee F, Kim HS, Klopp N, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lee JY, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Salomaa V, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Trégouët DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvänen AC, Hovingh GK, Dedoussis G, Franks PW, Lehtimäki T, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Perola M, Clarke R, Boehm BO, O'Donnell C, Reilly MP, März W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H and Samani NJ

    Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r(2) < 0.2) strongly associated with CAD at a 5% false discovery rate (FDR). Together, these variants explain approximately 10.6% of CAD heritability. Of the 46 genome-wide significant lead SNPs, 12 show a significant association with a lipid trait, and 5 show a significant association with blood pressure, but none is significantly associated with diabetes. Network analysis with 233 candidate genes (loci at 10% FDR) generated 5 interaction networks comprising 85% of these putative genes involved in CAD. The four most significant pathways mapping to these networks are linked to lipid metabolism and inflammation, underscoring the causal role of these activities in the genetic etiology of CAD. Our study provides insights into the genetic basis of CAD and identifies key biological pathways.

    Funded by: British Heart Foundation: PG/08/094/26019, RG/08/014/24067; Medical Research Council: G0801566; NHLBI NIH HHS: K24 HL107643, R00 HL094535, R01 HL111694; NIDDK NIH HHS: R01 DK062370

    Nature genetics 2013;45;1;25-33

2012 Publications

  • Human SH2B1 mutations are associated with maladaptive behaviors and obesity.

    Doche ME, Bochukova EG, Su HW, Pearce LR, Keogh JM, Henning E, Cline JM, Saeed S, Dale A, Cheetham T, Barroso I, Argetsinger LS, O'Rahilly S, Rui L, Carter-Su C and Farooqi IS

    Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan 48109-5622, USA.

    Src homology 2 B adapter protein 1 (SH2B1) modulates signaling by a variety of ligands that bind to receptor tyrosine kinases or JAK-associated cytokine receptors, including leptin, insulin, growth hormone (GH), and nerve growth factor (NGF). Targeted deletion of Sh2b1 in mice results in increased food intake, obesity, and insulin resistance, with an intermediate phenotype seen in heterozygous null mice on a high-fat diet. We identified SH2B1 loss-of-function mutations in a large cohort of patients with severe early-onset obesity. Mutation carriers exhibited hyperphagia, childhood-onset obesity, disproportionate insulin resistance, and reduced final height as adults. Unexpectedly, mutation carriers exhibited a spectrum of behavioral abnormalities that were not reported in controls, including social isolation and aggression. We conclude that SH2B1 plays a critical role in the control of human food intake and body weight and is implicated in maladaptive human behavior.

    Funded by: Medical Research Council: G0900554, G9824984; NCI NIH HHS: P30-CA46592; NIDDK NIH HHS: P60 DK020572, P60-DK20572, R01 DK054222, R01 DK065122, R01 DK073601, R01-DK065122, R01-DK073601, R01-DK54222; NIGMS NIH HHS: T32 GM008322, T32-GM008322; Wellcome Trust: 077016/Z/05/Z, 082390/Z/07/Z, 098497

    The Journal of clinical investigation 2012;122;12;4732-6

  • Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

    Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J, Mägi R, Strawbridge RJ, Rehnberg E, Gustafsson S, Kanoni S, Rasmussen-Torvik LJ, Yengo L, Lecoeur C, Shungin D, Sanna S, Sidore C, Johnson PC, Jukema JW, Johnson T, Mahajan A, Verweij N, Thorleifsson G, Hottenga JJ, Shah S, Smith AV, Sennblad B, Gieger C, Salo P, Perola M, Timpson NJ, Evans DM, Pourcain BS, Wu Y, Andrews JS, Hui J, Bielak LF, Zhao W, Horikoshi M, Navarro P, Isaacs A, O'Connell JR, Stirrups K, Vitart V, Hayward C, Esko T, Mihailov E, Fraser RM, Fall T, Voight BF, Raychaudhuri S, Chen H, Lindgren CM, Morris AP, Rayner NW, Robertson N, Rybin D, Liu CT, Beckmann JS, Willems SM, Chines PS, Jackson AU, Kang HM, Stringham HM, Song K, Tanaka T, Peden JF, Goel A, Hicks AA, An P, Müller-Nurasyid M, Franco-Cereceda A, Folkersen L, Marullo L, Jansen H, Oldehinkel AJ, Bruinenberg M, Pankow JS, North KE, Forouhi NG, Loos RJ, Edkins S, Varga TV, Hallmans G, Oksa H, Antonella M, Nagaraja R, Trompet S, Ford I, Bakker SJ, Kong A, Kumari M, Gigante B, Herder C, Munroe PB, Caulfield M, Antti J, Mangino M, Small K, Miljkovic I, Liu Y, Atalay M, Kiess W, James AL, Rivadeneira F, Uitterlinden AG, Palmer CN, Doney AS, Willemsen G, Smit JH, Campbell S, Polasek O, Bonnycastle LL, Hercberg S, Dimitriou M, Bolton JL, Fowkes GR, Kovacs P, Lindström J, Zemunik T, Bandinelli S, Wild SH, Basart HV, Rathmann W, Grallert H, DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, Maerz W, Kleber ME, Boehm BO, Peters A, Pramstaller PP, Province MA, Borecki IB, Hastie ND, Rudan I, Campbell H, Watkins H, Farrall M, Stumvoll M, Ferrucci L, Waterworth DM, Bergman RN, Collins FS, Tuomilehto J, Watanabe RM, de Geus EJ, Penninx BW, Hofman A, Oostra BA, Psaty BM, Vollenweider P, Wilson JF, Wright AF, Hovingh GK, Metspalu A, Uusitupa M, Magnusson PK, Kyvik KO, Kaprio J, Price JF, Dedoussis GV, Deloukas P, Meneton P, Lind L, Boehnke M, Shuldiner AR, van Duijn CM, Morris AD, Toenjes A, Peyser PA, Beilby JP, Körner A, Kuusisto J, Laakso M, Bornstein SR, Schwarz PE, Lakka TA, Rauramaa R, Adair LS, Smith GD, Spector TD, Illig T, de Faire U, Hamsten A, Gudnason V, Kivimaki M, Hingorani A, Keinanen-Kiukaanniemi SM, Saaristo TE, Boomsma DI, Stefansson K, van der Harst P, Dupuis J, Pedersen NL, Sattar N, Harris TB, Cucca F, Ripatti S, Salomaa V, Mohlke KL, Balkau B, Froguel P, Pouta A, Jarvelin MR, Wareham NJ, Bouatia-Naji N, McCarthy MI, Franks PW, Meigs JB, Teslovich TM, Florez JC, Langenberg C, Ingelsson E, Prokopenko I and Barroso I

    Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.

    Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

    Funded by: AHRQ HHS: HS06516; Biotechnology and Biological Sciences Research Council: G20234; British Heart Foundation: PG/07/133/24260, RG/07/008/23674; Chief Scientist Office: CZB/4/672, CZB/4/710; Department of Health; FIC NIH HHS: TW05596; Medical Research Council: 74882, 85374, G0500539, G0600705, G0701863, G0800582, G0902037, G19/35, G8802774, G9521010, MC_PC_U127561128, MC_U106179471, MC_U127561128, MC_U127592696, MC_UP_A100_1003, U.1061.00.001 (79471); NCATS NIH HHS: UL1 TR000130; NCRR NIH HHS: M01 RR 16500, M01-RR00425, RR20649, UL1RR025005; NHGRI NIH HHS: 1Z01-HG000024, N01-HG-65403, U01HG004402; NHLBI NIH HHS: 5R01HL087679-02, HL075366, HL080295, HL085144, HL087652, HL087660, HL100245, HL105756, N01 HC-15103, N01 HC-55222, N01-HC-25195, N01-HC-35129, N01-HC-45133, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N01-HC-75150, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, N01-HC-85239, N02-HL-6-4278, R01 HL088119, R01-HL-087700, R01-HL-088215, R01HL086694, R01HL087641, R01HL59367, U01 HL072515-06, U01 HL84756; NIA NIH HHS: 1R01AG032098-01A1, AG-023629, AG-027058, AG-15928, AG-20098, AG028555, AG04563, AG08724, AG08861, AG10175, AG13196, N01-AG-1-2100, N01-AG-1-2109, N01AG62101, N01AG62103, N01AG62106, R01 AG18728, T32 AG000219; NIAMS NIH HHS: K08AR055688; NICHD NIH HHS: R24 HD050924; NIDDK NIH HHS: DK063491, DK078150, DK56350, K24 DK080140, P30 DK72488, P60DK79637, R01 DK072193, R01 DK078150, R01 DK078616, R01 DK54261, R01-DK-075681, R01-DK-8925601, R01-DK062370, R01-DK072193; NIEHS NIH HHS: ES10126, P30 ES010126; NIGMS NIH HHS: U01 GM074518; NIMH NIH HHS: 1RL1MH083268-01, 5R01MH63706:02, U24 MH068457-06; NLM NIH HHS: LM010098; PHS HHS: HHSN268200625226C, HHSN268200782096C, R01D0042157-01A; Wellcome Trust: 075491/Z/04, 076467, 081682, 083948, 090532, 092731, 098017, 098051, GR069224

    Nature genetics 2012;44;9;991-1005

  • Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes.

    Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, Strawbridge RJ, Khan H, Grallert H, Mahajan A, Prokopenko I, Kang HM, Dina C, Esko T, Fraser RM, Kanoni S, Kumar A, Lagou V, Langenberg C, Luan J, Lindgren CM, Müller-Nurasyid M, Pechlivanis S, Rayner NW, Scott LJ, Wiltshire S, Yengo L, Kinnunen L, Rossin EJ, Raychaudhuri S, Johnson AD, Dimas AS, Loos RJ, Vedantam S, Chen H, Florez JC, Fox C, Liu CT, Rybin D, Couper DJ, Kao WH, Li M, Cornelis MC, Kraft P, Sun Q, van Dam RM, Stringham HM, Chines PS, Fischer K, Fontanillas P, Holmen OL, Hunt SE, Jackson AU, Kong A, Lawrence R, Meyer J, Perry JR, Platou CG, Potter S, Rehnberg E, Robertson N, Sivapalaratnam S, Stančáková A, Stirrups K, Thorleifsson G, Tikkanen E, Wood AR, Almgren P, Atalay M, Benediktsson R, Bonnycastle LL, Burtt N, Carey J, Charpentier G, Crenshaw AT, Doney AS, Dorkhan M, Edkins S, Emilsson V, Eury E, Forsen T, Gertow K, Gigante B, Grant GB, Groves CJ, Guiducci C, Herder C, Hreidarsson AB, Hui J, James A, Jonsson A, Rathmann W, Klopp N, Kravic J, Krjutškov K, Langford C, Leander K, Lindholm E, Lobbens S, Männistö S, Mirza G, Mühleisen TW, Musk B, Parkin M, Rallidis L, Saramies J, Sennblad B, Shah S, Sigurðsson G, Silveira A, Steinbach G, Thorand B, Trakalo J, Veglia F, Wennauer R, Winckler W, Zabaneh D, Campbell H, van Duijn C, Uitterlinden AG, Hofman A, Sijbrands E, Abecasis GR, Owen KR, Zeggini E, Trip MD, Forouhi NG, Syvänen AC, Eriksson JG, Peltonen L, Nöthen MM, Balkau B, Palmer CN, Lyssenko V, Tuomi T, Isomaa B, Hunter DJ, Qi L, Wellcome Trust Case Control Consortium, Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Asian Genetic Epidemiology Network–Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Shuldiner AR, Roden M, Barroso I, Wilsgaard T, Beilby J, Hovingh K, Price JF, Wilson JF, Rauramaa R, Lakka TA, Lind L, Dedoussis G, Njølstad I, Pedersen NL, Khaw KT, Wareham NJ, Keinanen-Kiukaanniemi SM, Saaristo TE, Korpi-Hyövälti E, Saltevo J, Laakso M, Kuusisto J, Metspalu A, Collins FS, Mohlke KL, Bergman RN, Tuomilehto J, Boehm BO, Gieger C, Hveem K, Cauchi S, Froguel P, Baldassarre D, Tremoli E, Humphries SE, Saleheen D, Danesh J, Ingelsson E, Ripatti S, Salomaa V, Erbel R, Jöckel KH, Moebus S, Peters A, Illig T, de Faire U, Hamsten A, Morris AD, Donnelly PJ, Frayling TM, Hattersley AT, Boerwinkle E, Melander O, Kathiresan S, Nilsson PM, Deloukas P, Thorsteinsdottir U, Groop LC, Stefansson K, Hu F, Pankow JS, Dupuis J, Meigs JB, Altshuler D, Boehnke M, McCarthy MI and DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium

    Wellcome Trust Centre for Human Genetics, University of Oxford, UK. amorris@well.ox.ac.uk

    To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.

    Funded by: British Heart Foundation: RG/07/008/23674, RG/08/014/24067, RG/98002, RG2008/08; Cancer Research UK; Chief Scientist Office: CZB/4/672, CZB/4/710; Department of Health: DHCS/07/07/008; Medical Research Council: G0000649, G0401527, G0601261, G0701863, G0902037, G1000143, G19/35, G8802774, MC_U106179471, MC_UP_A100_1003; NCI NIH HHS: CA055075; NCRR NIH HHS: UL1 RR029887, UL1RR025005; NHGRI NIH HHS: 1Z01HG000024, N01HG65403, U01HG004399, U01HG004402; NHLBI NIH HHS: N01HC25195, N02HL64278, R01HL086694, R01HL087641, R01HL59367; NIA NIH HHS: AG028555, AG04563, AG08724, AG08861, AG10175; NIDDK NIH HHS: DK058845, DK062370, DK072193, DK073490, DK078616, DK080140, K24 DK080140, R01 DK072193, R01 DK073490, U01 DK085545; NIGMS NIH HHS: T32 GM007753; NINDS NIH HHS: 1R21NS064908; PHS HHS: HHSN268200625226C, HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C; Wellcome Trust: 064890, 081682, 090367, 090532, 098017, 098395, GR072960, GR076113, GR077016, GR081682, GR083270, GR083948, GR084711, GR086596, GR090532, GR098051

    Nature genetics 2012;44;9;981-90

  • A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.

    Manning AK, Hivert MF, Scott RA, Grimsby JL, Bouatia-Naji N, Chen H, Rybin D, Liu CT, Bielak LF, Prokopenko I, Amin N, Barnes D, Cadby G, Hottenga JJ, Ingelsson E, Jackson AU, Johnson T, Kanoni S, Ladenvall C, Lagou V, Lahti J, Lecoeur C, Liu Y, Martinez-Larrad MT, Montasser ME, Navarro P, Perry JR, Rasmussen-Torvik LJ, Salo P, Sattar N, Shungin D, Strawbridge RJ, Tanaka T, van Duijn CM, An P, de Andrade M, Andrews JS, Aspelund T, Atalay M, Aulchenko Y, Balkau B, Bandinelli S, Beckmann JS, Beilby JP, Bellis C, Bergman RN, Blangero J, Boban M, Boehnke M, Boerwinkle E, Bonnycastle LL, Boomsma DI, Borecki IB, Böttcher Y, Bouchard C, Brunner E, Budimir D, Campbell H, Carlson O, Chines PS, Clarke R, Collins FS, Corbatón-Anchuelo A, Couper D, de Faire U, Dedoussis GV, Deloukas P, Dimitriou M, Egan JM, Eiriksdottir G, Erdos MR, Eriksson JG, Eury E, Ferrucci L, Ford I, Forouhi NG, Fox CS, Franzosi MG, Franks PW, Frayling TM, Froguel P, Galan P, de Geus E, Gigante B, Glazer NL, Goel A, Groop L, Gudnason V, Hallmans G, Hamsten A, Hansson O, Harris TB, Hayward C, Heath S, Hercberg S, Hicks AA, Hingorani A, Hofman A, Hui J, Hung J, Jarvelin MR, Jhun MA, Johnson PC, Jukema JW, Jula A, Kao WH, Kaprio J, Kardia SL, Keinanen-Kiukaanniemi S, Kivimaki M, Kolcic I, Kovacs P, Kumari M, Kuusisto J, Kyvik KO, Laakso M, Lakka T, Lannfelt L, Lathrop GM, Launer LJ, Leander K, Li G, Lind L, Lindstrom J, Lobbens S, Loos RJ, Luan J, Lyssenko V, Mägi R, Magnusson PK, Marmot M, Meneton P, Mohlke KL, Mooser V, Morken MA, Miljkovic I, Narisu N, O'Connell J, Ong KK, Oostra BA, Palmer LJ, Palotie A, Pankow JS, Peden JF, Pedersen NL, Pehlic M, Peltonen L, Penninx B, Pericic M, Perola M, Perusse L, Peyser PA, Polasek O, Pramstaller PP, Province MA, Räikkönen K, Rauramaa R, Rehnberg E, Rice K, Rotter JI, Rudan I, Ruokonen A, Saaristo T, Sabater-Lleal M, Salomaa V, Savage DB, Saxena R, Schwarz P, Seedorf U, Sennblad B, Serrano-Rios M, Shuldiner AR, Sijbrands EJ, Siscovick DS, Smit JH, Small KS, Smith NL, Smith AV, Stančáková A, Stirrups K, Stumvoll M, Sun YV, Swift AJ, Tönjes A, Tuomilehto J, Trompet S, Uitterlinden AG, Uusitupa M, Vikström M, Vitart V, Vohl MC, Voight BF, Vollenweider P, Waeber G, Waterworth DM, Watkins H, Wheeler E, Widen E, Wild SH, Willems SM, Willemsen G, Wilson JF, Witteman JC, Wright AF, Yaghootkar H, Zelenika D, Zemunik T, Zgaga L, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Multiple Tissue Human Expression Resource (MUTHER) Consortium, Wareham NJ, McCarthy MI, Barroso I, Watanabe RM, Florez JC, Dupuis J, Meigs JB and Langenberg C

    Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.

    Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

    Funded by: British Heart Foundation: RG/07/008/23674; Chief Scientist Office: CZB/4/710; Medical Research Council: G0100222, G0701863, G0900339, G0902037, G19/35, G8802774, MC_PC_U127561128, MC_U106179471, MC_U106179472, MC_U127561128, MC_U127592696, MC_UP_A100_1003; NCATS NIH HHS: UL1 TR000124; NCRR NIH HHS: S10 RR029392; NHLBI NIH HHS: R01 HL105756; NIDDK NIH HHS: K24 DK080140, P30 DK063491, R01 DK072193, R01 DK078616; NIMH NIH HHS: R37 MH059490; Wellcome Trust: 090532, 091551

    Nature genetics 2012;44;6;659-69

  • No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels.

    Scott RA, Chu AY, Grarup N, Manning AK, Hivert MF, Shungin D, Tönjes A, Yesupriya A, Barnes D, Bouatia-Naji N, Glazer NL, Jackson AU, Kutalik Z, Lagou V, Marek D, Rasmussen-Torvik LJ, Stringham HM, Tanaka T, Aadahl M, Arking DE, Bergmann S, Boerwinkle E, Bonnycastle LL, Bornstein SR, Brunner E, Bumpstead SJ, Brage S, Carlson OD, Chen H, Chen YD, Chines PS, Collins FS, Couper DJ, Dennison EM, Dowling NF, Egan JS, Ekelund U, Erdos MR, Forouhi NG, Fox CS, Goodarzi MO, Grässler J, Gustafsson S, Hallmans G, Hansen T, Hingorani A, Holloway JW, Hu FB, Isomaa B, Jameson KA, Johansson I, Jonsson A, Jørgensen T, Kivimaki M, Kovacs P, Kumari M, Kuusisto J, Laakso M, Lecoeur C, Lévy-Marchal C, Li G, Loos RJ, Lyssenko V, Marmot M, Marques-Vidal P, Morken MA, Müller G, North KE, Pankow JS, Payne F, Prokopenko I, Psaty BM, Renström F, Rice K, Rotter JI, Rybin D, Sandholt CH, Sayer AA, Shrader P, Schwarz PE, Siscovick DS, Stancáková A, Stumvoll M, Teslovich TM, Waeber G, Williams GH, Witte DR, Wood AR, Xie W, Boehnke M, Cooper C, Ferrucci L, Froguel P, Groop L, Kao WH, Vollenweider P, Walker M, Watanabe RM, Pedersen O, Meigs JB, Ingelsson E, Barroso I, Florez JC, Franks PW, Dupuis J, Wareham NJ and Langenberg C

    Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK. robert.scott@mrc-epid.cam.ac.uk

    Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

    Funded by: British Heart Foundation: RG/07/008/23674; Medical Research Council: G0701863, G0902037, G19/35, G8802774, MC_U106179471, MC_U106179473, MC_UP_A100_1003, MC_UP_A620_1014, MC_UP_A620_1015; NCATS NIH HHS: UL1 TR000124; NCRR NIH HHS: UL1 RR024148; NHLBI NIH HHS: T32 HL007575; NIDDK NIH HHS: K24 DK080140, P30 DK063491, R01 DK072041

    Diabetes 2012;61;5;1291-6

  • A genome-wide association search for type 2 diabetes genes in African Americans.

    Palmer ND, McDonough CW, Hicks PJ, Roh BH, Wing MR, An SS, Hester JM, Cooke JN, Bostrom MA, Rudock ME, Talbert ME, Lewis JP, DIAGRAM Consortium, MAGIC Investigators, Ferrara A, Lu L, Ziegler JT, Sale MM, Divers J, Shriner D, Adeyemo A, Rotimi CN, Ng MC, Langefeld CD, Freedman BI, Bowden DW, Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Boström KB, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney AS, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PR, Jørgensen T, Kao WH, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JR, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CN, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann HE, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI, Soranzo N, Wheeler E, Glazer NL, Bouatia-Naji N, Mägi R, Randall J, Johnson T, Elliott P, Rybin D, Henneman P, Dehghan A, Hottenga JJ, Song K, Goel A, Egan JM, Lajunen T, Doney A, Kanoni S, Cavalcanti-Proença C, Kumari M, Timpson NJ, Zabena C, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Roccasecca RM, Pattou F, Sethupathy P, Ariyurek Y, Barter P, Beilby JP, Ben-Shlomo Y, Bergmann S, Bochud M, Bonnefond A, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Crisponi L, Day IN, de Geus EJ, Delplanque J, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Grundy S, Gwilliam R, Hallmans G, Hammond N, Han X, Hartikainen AL, Hayward C, Heath SC, Hercberg S, Hicks AA, Hillman DR, Hingorani AD, Hui J, Hung J, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Mahley R, Mangino M, Manning AK, Martínez-Larrad MT, McAteer JB, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Mukherjee S, Naitza S, Neville MJ, Oostra BA, Orrù M, Pakyz R, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Seedorf U, Sharp SJ, Shields B, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Tönjes A, Uitterlinden AG, van Dijk KW, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Ward KL, Watkins H, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Ríos M, Lind L, Palmer LJ, Franks PW, Ebrahim S, Marmot M, Kao WH, Pramstaller PP, Wright AF, Stumvoll M, Hamsten A, Buchanan TA, Valle TT, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Ferrucci L, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V and Sladek R

    Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America. nallred@wfubmc.edu

    African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10(-8)). SNP rs7560163 (P = 7.0×10(-9), OR (95% CI) = 0.75 (0.67-0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10(-5)) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations.

    Funded by: British Heart Foundation: RG/07/008/23674; Department of Health: DHCS/07/07/008; Medical Research Council: G0001164, G0801056, G0902037, G19/35, G8802774, MC_PC_U127561128, MC_U106179471, MC_U106179474, MC_U127561128, MC_U127592696, MC_UP_A100_1003, MC_UP_A620_1015; NCRR NIH HHS: M01 RR07122; NHLBI NIH HHS: K99 HL098459, R01 HL56266; NIDDK NIH HHS: K24 DK080140, K99 DK081350, P30 DK063491, R00 DK081350, R01 DK053591, R01 DK066358, R01 DK070941; PHS HHS: HHSC268200782096C; Wellcome Trust: 090532

    PloS one 2012;7;1;e29202

  • The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits.

    Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, Burtt NP, Fuchsberger C, Li Y, Erdmann J, Frayling TM, Heid IM, Jackson AU, Johnson T, Kilpeläinen TO, Lindgren CM, Morris AP, Prokopenko I, Randall JC, Saxena R, Soranzo N, Speliotes EK, Teslovich TM, Wheeler E, Maguire J, Parkin M, Potter S, Rayner NW, Robertson N, Stirrups K, Winckler W, Sanna S, Mulas A, Nagaraja R, Cucca F, Barroso I, Deloukas P, Loos RJ, Kathiresan S, Munroe PB, Newton-Cheh C, Pfeufer A, Samani NJ, Schunkert H, Hirschhorn JN, Altshuler D, McCarthy MI, Abecasis GR and Boehnke M

    Medical Population Genetics, The Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

    Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the "Metabochip," a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.

    Funded by: British Heart Foundation; Medical Research Council: MC_U106188470; NHGRI NIH HHS: HG000376, HG005214, HG005581, R01 HG000376; NIA NIH HHS: N01-AG-1-2109; NIDDK NIH HHS: DK062370; Wellcome Trust: 064890, 081682, 090532, 098051

    PLoS genetics 2012;8;8;e1002793

  • Mosaic overgrowth with fibroadipose hyperplasia is caused by somatic activating mutations in PIK3CA.

    Lindhurst MJ, Parker VE, Payne F, Sapp JC, Rudge S, Harris J, Witkowski AM, Zhang Q, Groeneveld MP, Scott CE, Daly A, Huson SM, Tosi LL, Cunningham ML, Darling TN, Geer J, Gucev Z, Sutton VR, Tziotzios C, Dixon AK, Helliwell T, O'Rahilly S, Savage DB, Wakelam MJ, Barroso I*, Biesecker LG* and Semple RK*

    The National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA.

    The phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway is critical for cellular growth and metabolism. Correspondingly, loss of function of PTEN, a negative regulator of PI3K, or activating mutations in AKT1, AKT2 or AKT3 have been found in distinct disorders featuring overgrowth or hypoglycemia. We performed exome sequencing of DNA from unaffected and affected cells from an individual with an unclassified syndrome of congenital progressive segmental overgrowth of fibrous and adipose tissue and bone and identified the cancer-associated mutation encoding p.His1047Leu in PIK3CA, the gene that encodes the p110α catalytic subunit of PI3K, only in affected cells. Sequencing of PIK3CA in ten additional individuals with overlapping syndromes identified either the p.His1047Leu alteration or a second cancer-associated alteration, p.His1047Arg, in nine cases. Affected dermal fibroblasts showed enhanced basal and epidermal growth factor (EGF)-stimulated phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) generation and concomitant activation of downstream signaling relative to their unaffected counterparts. Our findings characterize a distinct overgrowth syndrome, biochemically demonstrate activation of PI3K signaling and thereby identify a rational therapeutic target.

    Funded by: Biotechnology and Biological Sciences Research Council; Medical Research Council; Wellcome Trust: 078986/Z/06/Z, 091551/Z/10/Z, 097721/Z/11/Z, 098051/Z/05/Z, 80952/Z/06/Z

    Nature genetics 2012;44;8;928-33

    (* Equal communicating author)

    • Genome-wide meta-analysis of common variant differences between men and women.

      Boraska V, Jerončić A, Colonna V, Southam L, Nyholt DR, Rayner NW, Perry JR, Toniolo D, Albrecht E, Ang W, Bandinelli S, Barbalic M, Barroso I, Beckmann JS, Biffar R, Boomsma D, Campbell H, Corre T, Erdmann J, Esko T, Fischer K, Franceschini N, Frayling TM, Girotto G, Gonzalez JR, Harris TB, Heath AC, Heid IM, Hoffmann W, Hofman A, Horikoshi M, Zhao JH, Jackson AU, Hottenga JJ, Jula A, Kähönen M, Khaw KT, Kiemeney LA, Klopp N, Kutalik Z, Lagou V, Launer LJ, Lehtimäki T, Lemire M, Lokki ML, Loley C, Luan J, Mangino M, Mateo Leach I, Medland SE, Mihailov E, Montgomery GW, Navis G, Newnham J, Nieminen MS, Palotie A, Panoutsopoulou K, Peters A, Pirastu N, Polasek O, Rehnström K, Ripatti S, Ritchie GR, Rivadeneira F, Robino A, Samani NJ, Shin SY, Sinisalo J, Smit JH, Soranzo N, Stolk L, Swinkels DW, Tanaka T, Teumer A, Tönjes A, Traglia M, Tuomilehto J, Valsesia A, van Gilst WH, van Meurs JB, Smith AV, Viikari J, Vink JM, Waeber G, Warrington NM, Widen E, Willemsen G, Wright AF, Zanke BW, Zgaga L, Wellcome Trust Case Control Consortium, Boehnke M, d'Adamo AP, de Geus E, Demerath EW, den Heijer M, Eriksson JG, Ferrucci L, Gieger C, Gudnason V, Hayward C, Hengstenberg C, Hudson TJ, Järvelin MR, Kogevinas M, Loos RJ, Martin NG, Metspalu A, Pennell CE, Penninx BW, Perola M, Raitakari O, Salomaa V, Schreiber S, Schunkert H, Spector TD, Stumvoll M, Uitterlinden AG, Ulivi S, van der Harst P, Vollenweider P, Völzke H, Wareham NJ, Wichmann HE, Wilson JF, Rudan I, Xue Y and Zeggini E

      Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. vboraska@mefst.hr

      The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 × 10(-8)) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across ~115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.

      Funded by: Canadian Institutes of Health Research: MOP-82893; Cancer Research UK; Chief Scientist Office: CZB/4/710; Medical Research Council: G0401527, G1000143, G1001799, MC_PC_U127561128, MC_U106179471, MC_U127561128; NCRR NIH HHS: RR018787, UL1RR025005; NHGRI NIH HHS: U01HG004402; NHLBI NIH HHS: HL65234, HL67466, R01HL086694, R01HL087641, R01HL59367; NIA NIH HHS: N.1-AG-1-1, N.1-AG-1-2111, N01-AG-1-2100, N01-AG-5-0002; NIAAA NIH HHS: AA07535, AA10248, AA13320, AA13321, AA13326, AA14041, K05 AA017688; NIDDK NIH HHS: DK062370; NIMH NIH HHS: MH081802, MH66206, R01 MH059160, U24 MH068457-06; NLM NIH HHS: LM010098; PHS HHS: HHSN268200625226C, HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C; Wellcome Trust: 076113, 089062/Z/09/Z, 092447/Z/10/Z, 095831, 098051, 89061/Z/09/Z

      Human molecular genetics 2012;21;21;4805-15

    • A genome-wide association meta-analysis identifies new childhood obesity loci.

      Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, Warrington NM, Hypponen E, Holst C, Valcarcel B, Thiering E, Salem RM, Schumacher FR, Cousminer DL, Sleiman PM, Zhao J, Berkowitz RI, Vimaleswaran KS, Jarick I, Pennell CE, Evans DM, St Pourcain B, Berry DJ, Mook-Kanamori DO, Hofman A, Rivadeneira F, Uitterlinden AG, van Duijn CM, van der Valk RJ, de Jongste JC, Postma DS, Boomsma DI, Gauderman WJ, Hassanein MT, Lindgren CM, Mägi R, Boreham CA, Neville CE, Moreno LA, Elliott P, Pouta A, Hartikainen AL, Li M, Raitakari O, Lehtimäki T, Eriksson JG, Palotie A, Dallongeville J, Das S, Deloukas P, McMahon G, Ring SM, Kemp JP, Buxton JL, Blakemore AI, Bustamante M, Guxens M, Hirschhorn JN, Gillman MW, Kreiner-Møller E, Bisgaard H, Gilliland FD, Heinrich J, Wheeler E, Barroso I, O'Rahilly S, Meirhaeghe A, Sørensen TI, Power C, Palmer LJ, Hinney A, Widen E, Farooqi IS, McCarthy MI, Froguel P, Meyre D, Hebebrand J, Jarvelin MR, Jaddoe VW, Smith GD, Hakonarson H, Grant SF and Early Growth Genetics Consortium

      Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

      Multiple genetic variants have been associated with adult obesity and a few with severe obesity in childhood; however, less progress has been made in establishing genetic influences on common early-onset obesity. We performed a North American, Australian and European collaborative meta-analysis of 14 studies consisting of 5,530 cases (≥95th percentile of body mass index (BMI)) and 8,318 controls (<50th percentile of BMI) of European ancestry. Taking forward the eight newly discovered signals yielding association with P < 5 × 10(-6) in nine independent data sets (2,818 cases and 4,083 controls), we observed two loci that yielded genome-wide significant combined P values near OLFM4 at 13q14 (rs9568856; P = 1.82 × 10(-9); odds ratio (OR) = 1.22) and within HOXB5 at 17q21 (rs9299; P = 3.54 × 10(-9); OR = 1.14). Both loci continued to show association when two extreme childhood obesity cohorts were included (2,214 cases and 2,674 controls). These two loci also yielded directionally consistent associations in a previous meta-analysis of adult BMI(1).

      Funded by: British Heart Foundation: PG/09/023, PG/09/023/26806, PG/1996183/9569; Canadian Institutes of Health Research: MOP-82893; Medical Research Council: 74882, G0000934, G0100103, G0500539, G0600705, G0601653, G0800582, G0801056, G0900554, MC_UP_A620_1014; NHLBI NIH HHS: 1RC2HL101543, 1RC2HL101651, 5R01HL061768, 5R01HL076647, 5R01HL087679-02, 5R01HL087680; NICHD NIH HHS: R01 HD056465, R01 HD056465-01A1, R01 HD056465-02, R01 HD056465-03, R01 HD056465-04, R01 HD056465-05, R24 HD050924; NIDDK NIH HHS: R01 DK075787, U01 DK062418; NIEHS NIH HHS: 5P01ES009581, 5P01ES011627, 5P30ES007048, 5R01ES014447, 5R01ES014708, 5R01ES016535, 5R03ES014046; NIMH NIH HHS: 1RL1MH083268-01, 5R01MH63706:02; ORD VA: RD831861-01; PHS HHS: R826708-01; Wellcome Trust: 052515/2/97/2, 068545/Z/02, 076467, 077016/Z/05/Z, 083948, 086596/Z/08/Z, 090532, 092731, GR069224, WT088431MA

      Nature genetics 2012;44;5;526-31

    • Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes.

      Bonnefond A, Clément N, Fawcett K, Yengo L, Vaillant E, Guillaume JL, Dechaume A, Payne F, Roussel R, Czernichow S, Hercberg S, Hadjadj S, Balkau B, Marre M, Lantieri O, Langenberg C, Bouatia-Naji N, Meta-Analysis of Glucose and Insulin-Related Traits Consortium (MAGIC), Charpentier G, Vaxillaire M, Rocheleau G, Wareham NJ, Sladek R, McCarthy MI, Dina C, Barroso I, Jockers R and Froguel P

      Centre National de la Recherche Scientifique Unité Mixte de Recherche, Lille Pasteur Institute, France.

      Genome-wide association studies have revealed that common noncoding variants in MTNR1B (encoding melatonin receptor 1B, also known as MT(2)) increase type 2 diabetes (T2D) risk(1,2). Although the strongest association signal was highly significant (P < 1 × 10(-20)), its contribution to T2D risk was modest (odds ratio (OR) of ∼1.10-1.15)(1-3). We performed large-scale exon resequencing in 7,632 Europeans, including 2,186 individuals with T2D, and identified 40 nonsynonymous variants, including 36 very rare variants (minor allele frequency (MAF) <0.1%), associated with T2D (OR = 3.31, 95% confidence interval (CI) = 1.78-6.18; P = 1.64 × 10(-4)). A four-tiered functional investigation of all 40 mutants revealed that 14 were non-functional and rare (MAF < 1%), and 4 were very rare with complete loss of melatonin binding and signaling capabilities. Among the very rare variants, the partial- or total-loss-of-function variants but not the neutral ones contributed to T2D (OR = 5.67, CI = 2.17-14.82; P = 4.09 × 10(-4)). Genotyping the four complete loss-of-function variants in 11,854 additional individuals revealed their association with T2D risk (8,153 individuals with T2D and 10,100 controls; OR = 3.88, CI = 1.49-10.07; P = 5.37 × 10(-3)). This study establishes a firm functional link between MTNR1B and T2D risk.

      Funded by: Medical Research Council: MC_U106179471; Wellcome Trust: 077016, 077016/Z/05/Z, 090532

      Nature genetics 2012;44;3;297-301

    • Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals.

      Dastani Z, Hivert MF, Timpson N, Perry JR, Yuan X, Scott RA, Henneman P, Heid IM, Kizer JR, Lyytikäinen LP, Fuchsberger C, Tanaka T, Morris AP, Small K, Isaacs A, Beekman M, Coassin S, Lohman K, Qi L, Kanoni S, Pankow JS, Uh HW, Wu Y, Bidulescu A, Rasmussen-Torvik LJ, Greenwood CM, Ladouceur M, Grimsby J, Manning AK, Liu CT, Kooner J, Mooser VE, Vollenweider P, Kapur KA, Chambers J, Wareham NJ, Langenberg C, Frants R, Willems-Vandijk K, Oostra BA, Willems SM, Lamina C, Winkler TW, Psaty BM, Tracy RP, Brody J, Chen I, Viikari J, Kähönen M, Pramstaller PP, Evans DM, St Pourcain B, Sattar N, Wood AR, Bandinelli S, Carlson OD, Egan JM, Böhringer S, van Heemst D, Kedenko L, Kristiansson K, Nuotio ML, Loo BM, Harris T, Garcia M, Kanaya A, Haun M, Klopp N, Wichmann HE, Deloukas P, Katsareli E, Couper DJ, Duncan BB, Kloppenburg M, Adair LS, Borja JB, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, Wilson JG, Musani S, Guo X, Johnson T, Semple R, Teslovich TM, Allison MA, Redline S, Buxbaum SG, Mohlke KL, Meulenbelt I, Ballantyne CM, Dedoussis GV, Hu FB, Liu Y, Paulweber B, Spector TD, Slagboom PE, Ferrucci L, Jula A, Perola M, Raitakari O, Florez JC, Salomaa V, Eriksson JG, Frayling TM, Hicks AA, Lehtimäki T, Smith GD, Siscovick DS, Kronenberg F, van Duijn C, Loos RJ, Waterworth DM, Meigs JB, Dupuis J, Richards JB, Voight BF, Scott LJ, Steinthorsdottir V, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Hofmann OM, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Boström KB, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Crawford G, Doney AS, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PR, Jørgensen T, Kao WH, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Morris AD, Palmer CN, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Pedersen O, Barroso I, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI, Soranzo N, Wheeler E, Glazer NL, Bouatia-Naji N, Mägi R, Randall J, Elliott P, Rybin D, Dehghan A, Hottenga JJ, Song K, Goel A, Lajunen T, Doney A, Cavalcanti-Proença C, Kumari M, Timpson NJ, Zabena C, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Roccasecca RM, Pattou F, Sethupathy P, Ariyurek Y, Barter P, Beilby JP, Ben-Shlomo Y, Bergmann S, Bochud M, Bonnefond A, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Crisponi L, Day IN, de Geus EJ, Delplanque J, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Grundy S, Gwilliam R, Hallmans G, Hammond N, Han X, Hartikainen AL, Hayward C, Heath SC, Hercberg S, Hillman DR, Hingorani AD, Hui J, Hung J, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Mahley R, Mangino M, Martínez-Larrad MT, McAteer JB, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Mukherjee S, Naitza S, Neville MJ, Orrù M, Pakyz R, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Seedorf U, Sharp SJ, Shields B, Sigurðsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tönjes A, Uitterlinden AG, van Dijk KW, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Ward KL, Watkins H, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Borecki IB, Meneton P, Magnusson PK, Nathan DM, Williams GH, Silander K, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Serrano-Ríos M, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WH, Pramstaller PP, Wright AF, Stumvoll M, Hamsten A, Procardis Consortium, Buchanan TA, Valle TT, Rotter JI, Penninx BW, Boomsma DI, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Peltonen L, Mooser V, Sladek R, MAGIC investigators, GLGC Consortium, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Chasman DI, Johansen CT, Fouchier SW, Peloso GM, Barbalic M, Ricketts SL, Bis JC, Feitosa MF, Orho-Melander M, Melander O, Li X, Li M, Cho YS, Go MJ, Kim YJ, Lee JY, Park T, Kim K, Sim X, Ong RT, Croteau-Chonka DC, Lange LA, Smith JD, Ziegler A, Zhang W, Zee RY, Whitfield JB, Thompson JR, Surakka I, Spector TD, Smit JH, Sinisalo J, Scott J, Saharinen J, Sabatti C, Rose LM, Roberts R, Rieder M, Parker AN, Pare G, O'Donnell CJ, Nieminen MS, Nickerson DA, Montgomery GW, McArdle W, Masson D, Martin NG, Marroni F, Lucas G, Luben R, Lokki ML, Lettre G, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, König IR, Khaw KT, Kaplan LM, Johansson Å, Janssens AC, Igl W, Hovingh GK, Hengstenberg C, Havulinna AS, Hastie ND, Harris TB, Haritunians T, Hall AS, Groop LC, Gonzalez E, Freimer NB, Erdmann J, Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de Faire U, Crawford G, Chen YD, Caulfield MJ, Boekholdt SM, Assimes TL, Quertermous T, Seielstad M, Wong TY, Tai ES, Feranil AB, Kuzawa CW, Taylor HA, Gabriel SB, Holm H, Gudnason V, Krauss RM, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele RA, Kastelein JJ, Schadt EE, Strachan DP, Reilly MP, Samani NJ, Schunkert H, Cupples LA, Sandhu MS, Ridker PM, Rader DJ and Kathiresan S

      Department of Epidemiology, Biostatistics, and Occupational Health, Jewish General Hospital, Lady Davis Institute, McGill University, Montreal, Canada.

      Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10(-3), n = 22,044), increased triglycerides (p = 2.6×10(-14), n = 93,440), increased waist-to-hip ratio (p = 1.8×10(-5), n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10(-3), n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10(-13), n = 96,748) and decreased BMI (p = 1.4×10(-4), n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.

      Funded by: Biotechnology and Biological Sciences Research Council: G20234; British Heart Foundation: PG/08/094/26019, PG/09/002/26056, RG/07/008/23674; Canadian Institutes of Health Research; Chief Scientist Office: CZB/4/710; Department of Health: DHCS/07/07/008; FIC NIH HHS: TW05596; Medical Research Council: G0401527, G0600705, G0601966, G0700931, G0701863, G0800582, G0801056, G0900339, G0901213, G0902037, G1000143, G19/35, G8802774, MC_PC_U127561128, MC_U106179471, MC_U127561128, MC_U127592696, MC_UP_A100_1003, MC_UP_A620_1014, MC_UP_A620_1015; NCATS NIH HHS: UL1 TR000124; NCRR NIH HHS: M01-RR00425, RR-024156, RR20649, UL1 RR025008, UL1RR025005; NHGRI NIH HHS: U01HG004402; NHLBI NIH HHS: HL085144, HL094555, HL105756, N01 HC-15103, N01 HC-55222, N01 HC-95159, N01-HC-25195, N01-HC-35129, N01-HC-45133, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N01-HC-65226, N01-HC-75150, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, N01-HC-95170, N01-HC-95171, N01-HC-95172, N02-HL-6-4278, R01 HL087647, R01 HL087652, R01-HL085251, R01HL086694, R01HL087641, R01HL59367, RC2 HL102419, U01 HL080295; NIA NIH HHS: 1R01AG032098-01A1, N01AG62101, N01AG62103, N01AG62106; NICHD NIH HHS: R24 HD050924; NIDDK NIH HHS: 1 R01 DK075787-01A1, DK063491, DK078150, DK56350, K24 DK080140, P30 DK072488, R01 DK078150, R01DK056918; NIEHS NIH HHS: ES10126, P30 ES010126; PHS HHS: HHSN268200625226C, HHSN268200782096C; The Dunhill Medical Trust: R69/0208; Wellcome Trust: 064890, 081682, 090532, 092731

      PLoS genetics 2012;8;3;e1002607

2011 Publications

  • Mendelian randomization study of B-type natriuretic peptide and type 2 diabetes: evidence of causal association from population studies.

    Pfister R, Sharp S, Luben R, Welsh P, Barroso I, Salomaa V, Meirhaeghe A, Khaw KT, Sattar N, Langenberg C and Wareham NJ

    Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom. rp415@mrc-epid.cam.ac.uk

    Background: Genetic and epidemiological evidence suggests an inverse association between B-type natriuretic peptide (BNP) levels in blood and risk of type 2 diabetes (T2D), but the prospective association of BNP with T2D is uncertain, and it is unclear whether the association is confounded.

    We analysed the association between levels of the N-terminal fragment of pro-BNP (NT-pro-BNP) in blood and risk of incident T2D in a prospective case-cohort study and genotyped the variant rs198389 within the BNP locus in three T2D case-control studies. We combined our results with existing data in a meta-analysis of 11 case-control studies. Using a Mendelian randomization approach, we compared the observed association between rs198389 and T2D to that expected from the NT-pro-BNP level to T2D association and the NT-pro-BNP difference per C allele of rs198389. In participants of our case-cohort study who were free of T2D and cardiovascular disease at baseline, we observed a 21% (95% CI 3%-36%) decreased risk of incident T2D per one standard deviation (SD) higher log-transformed NT-pro-BNP levels in analysis adjusted for age, sex, body mass index, systolic blood pressure, smoking, family history of T2D, history of hypertension, and levels of triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The association between rs198389 and T2D observed in case-control studies (odds ratio = 0.94 per C allele, 95% CI 0.91-0.97) was similar to that expected (0.96, 0.93-0.98) based on the pooled estimate for the log-NT-pro-BNP level to T2D association derived from a meta-analysis of our study and published data (hazard ratio = 0.82 per SD, 0.74-0.90) and the difference in NT-pro-BNP levels (0.22 SD, 0.15-0.29) per C allele of rs198389. No significant associations were observed between the rs198389 genotype and potential confounders.

    Conclusions: Our results provide evidence for a potential causal role of the BNP system in the aetiology of T2D. Further studies are needed to investigate the mechanisms underlying this association and possibilities for preventive interventions. Please see later in the article for the Editors' Summary.

    Funded by: British Heart Foundation: FS/10/005/28147; Medical Research Council: G0401527, G0601463, G1000143; Wellcome Trust: 077016/Z/05/Z

    PLoS medicine 2011;8;10;e1001112

  • Early Diagnosis of Werner's Syndrome Using Exome-Wide Sequencing in a Single, Atypical Patient.

    Raffan E, Hurst LA, Turki SA, Carpenter G, Scott C, Daly A, Coffey A, Bhaskar S, Howard E, Khan N, Kingston H, Palotie A, Savage DB, O'Driscoll M, Smith C, O'Rahilly S, Barroso I and Semple RK

    Institute of Metabolic Science, University of Cambridge Metabolic Research Laboratories Cambridge, UK.

    Genetic diagnosis of inherited metabolic disease is conventionally achieved through syndrome recognition and targeted gene sequencing, but many patients receive no specific diagnosis. Next-generation sequencing allied to capture of expressed sequences from genomic DNA now offers a powerful new diagnostic approach. Barriers to routine diagnostic use include cost, and the complexity of interpreting results arising from simultaneous identification of large numbers of variants. We applied exome-wide sequencing to an individual, 16-year-old daughter of consanguineous parents with a novel syndrome of short stature, severe insulin resistance, ptosis, and microcephaly. Pulldown of expressed sequences from genomic DNA followed by massively parallel sequencing was undertaken. Single nucleotide variants were called using SAMtools prior to filtering based on sequence quality and existence in control genomes and exomes. Of 485 genetic variants predicted to alter protein sequence and absent from control data, 24 were homozygous in the patient. One mutation - the p.Arg732X mutation in the WRN gene - has previously been reported in Werner's syndrome (WS). On re-evaluation of the patient several early features of WS were detected including loss of fat from the extremities and frontal hair thinning. Lymphoblastoid cells from the proband exhibited a defective decatenation checkpoint, consistent with loss of WRN activity. We have thus diagnosed WS some 15 years earlier than average, permitting aggressive prophylactic therapy and screening for WS complications, illustrating the potential of exome-wide sequencing to achieve early diagnosis and change management of rare autosomal recessive disease, even in individual patients of consanguineous parentage with apparently novel syndromes.

    Funded by: Cancer Research UK: 8300; Medical Research Council: G0700733

    Frontiers in endocrinology 2011;2;8

  • An activating mutation of AKT2 and human hypoglycemia.

    Hussain K, Challis B, Rocha N, Payne F, Minic M, Thompson A, Daly A, Scott C, Harris J, Smillie BJ, Savage DB, Ramaswami U, De Lonlay P, O'Rahilly S*, Barroso I* and Semple RK*

    Clinical and Molecular Genetics Unit, Developmental Endocrinology Research Group, Institute of Child Health, University College London, London WC1N 1EH, UK.

    Pathological fasting hypoglycemia in humans is usually explained by excessive circulating insulin or insulin-like molecules or by inborn errors of metabolism impairing liver glucose production. We studied three unrelated children with unexplained, recurrent, and severe fasting hypoglycemia and asymmetrical growth. All were found to carry the same de novo mutation, p.Glu17Lys, in the serine/threonine kinase AKT2, in two cases as heterozygotes and in one case in mosaic form. In heterologous cells, the mutant AKT2 was constitutively recruited to the plasma membrane, leading to insulin-independent activation of downstream signaling. Thus, systemic metabolic disease can result from constitutive, cell-autonomous activation of signaling pathways normally controlled by insulin.

    Funded by: Medical Research Council; Wellcome Trust: 077016, 077016/Z/05/Z, 078986, 078986/Z/06/Z, 080952, 080952/Z/06/Z, 091551, 091551/Z/10/Z

    Science (New York, N.Y.) 2011;334;6055;474

    (* Equal communicating author)

    • Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function.

      Soler Artigas M, Loth DW, Wain LV, Gharib SA, Obeidat M, Tang W, Zhai G, Zhao JH, Smith AV, Huffman JE, Albrecht E, Jackson CM, Evans DM, Cadby G, Fornage M, Manichaikul A, Lopez LM, Johnson T, Aldrich MC, Aspelund T, Barroso I, Campbell H, Cassano PA, Couper DJ, Eiriksdottir G, Franceschini N, Garcia M, Gieger C, Gislason GK, Grkovic I, Hammond CJ, Hancock DB, Harris TB, Ramasamy A, Heckbert SR, Heliövaara M, Homuth G, Hysi PG, James AL, Jankovic S, Joubert BR, Karrasch S, Klopp N, Koch B, Kritchevsky SB, Launer LJ, Liu Y, Loehr LR, Lohman K, Loos RJ, Lumley T, Al Balushi KA, Ang WQ, Barr RG, Beilby J, Blakey JD, Boban M, Boraska V, Brisman J, Britton JR, Brusselle GG, Cooper C, Curjuric I, Dahgam S, Deary IJ, Ebrahim S, Eijgelsheim M, Francks C, Gaysina D, Granell R, Gu X, Hankinson JL, Hardy R, Harris SE, Henderson J, Henry A, Hingorani AD, Hofman A, Holt PG, Hui J, Hunter ML, Imboden M, Jameson KA, Kerr SM, Kolcic I, Kronenberg F, Liu JZ, Marchini J, McKeever T, Morris AD, Olin AC, Porteous DJ, Postma DS, Rich SS, Ring SM, Rivadeneira F, Rochat T, Sayer AA, Sayers I, Sly PD, Smith GD, Sood A, Starr JM, Uitterlinden AG, Vonk JM, Wannamethee SG, Whincup PH, Wijmenga C, Williams OD, Wong A, Mangino M, Marciante KD, McArdle WL, Meibohm B, Morrison AC, North KE, Omenaas E, Palmer LJ, Pietiläinen KH, Pin I, Pola Sbreve Ek O, Pouta A, Psaty BM, Hartikainen AL, Rantanen T, Ripatti S, Rotter JI, Rudan I, Rudnicka AR, Schulz H, Shin SY, Spector TD, Surakka I, Vitart V, Völzke H, Wareham NJ, Warrington NM, Wichmann HE, Wild SH, Wilk JB, Wjst M, Wright AF, Zgaga L, Zemunik T, Pennell CE, Nyberg F, Kuh D, Holloway JW, Boezen HM, Lawlor DA, Morris RW, Probst-Hensch N, International Lung Cancer Consortium, GIANT consortium, Kaprio J, Wilson JF, Hayward C, Kähönen M, Heinrich J, Musk AW, Jarvis DL, Gläser S, Järvelin MR, Ch Stricker BH, Elliott P, O'Connor GT, Strachan DP, London SJ, Hall IP, Gudnason V and Tobin MD

      Department of Health Sciences, University of Leicester, Leicester, UK.

      Pulmonary function measures reflect respiratory health and are used in the diagnosis of chronic obstructive pulmonary disease. We tested genome-wide association with forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity in 48,201 individuals of European ancestry with follow up of the top associations in up to an additional 46,411 individuals. We identified new regions showing association (combined P < 5 × 10(-8)) with pulmonary function in or near MFAP2, TGFB2, HDAC4, RARB, MECOM (also known as EVI1), SPATA9, ARMC2, NCR3, ZKSCAN3, CDC123, C10orf11, LRP1, CCDC38, MMP15, CFDP1 and KCNE2. Identification of these 16 new loci may provide insight into the molecular mechanisms regulating pulmonary function and into molecular targets for future therapy to alleviate reduced lung function.

      Funded by: Biotechnology and Biological Sciences Research Council: BB/F019394/1, G20234; British Heart Foundation: FS05/125, PG/06/154/22043, PG/97012, RG/08/013/25942; Canadian Institutes of Health Research: MOP-82893; Cancer Research UK; Chief Scientist Office: CZB/4/710, CZD/16/6, CZD/16/6/2, CZD/16/6/4; Department of Health; Medical Research Council: G0000934, G0401540, G0500539, G0501942, G0600705, G0701863, G0800582, G0801056, G0902125, G0902313, G1000861, G9815508, G9901462, MC_PC_U127561128, MC_U106188470, MC_U123092720, MC_U123092721, MC_U127561128, MC_UP_A620_1014, MC_UP_A620_1015; NCI NIH HHS: 1P50 CA70907, CA127219, CA55769, R01 CA121197, R01CA111703, U19 CA148127; NCRR NIH HHS: 5M01 RR00997, M01-RR00425, RR-024156, UL1RR025005; NHGRI NIH HHS: U01-HG-004402, U01-HG-004729; NHLBI NIH HHS: 1K23HL094531-01, 5R01HL087679-02, HL075336, HL080295, HL087652, HL088133, HL105756, N01 HC-15103, N01 HC-25195, N01 HC-55222, N01-HC-05187, N01-HC-35129, N01-HC-45133, N01-HC-45134, N01-HC-45204, N01-HC-45205, N01-HC-48047, N01-HC-48048, N01-HC-48049, N01-HC-48050, N01-HC-75150, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, N01-HC-95095, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, N02-HL-6-4278, R01 HL-071022, R01 HL-074104, R01 HL-077612, R01 HL075476, R01 HL077612, R01 HL093081, R01-HL-084099, R01-HL084099, R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071252, R01HL071258, R01HL071259, R01HL086694, R01HL087641, R01HL59367, RC1 HL100543; NIA NIH HHS: 1R01AG032098-01A1, AG-023269, AG-027058, AG-15928, AG-20098, AG035835, N01AG12100, N01AG62101, N01AG62103, N01AG62106, R01 AG032098, RC1 AG035835, RC1 AG035835-01; NIDDK NIH HHS: DK063491; NIEHS NIH HHS: ES015794, ZO1 ES49019; NIMH NIH HHS: 1RL1MH083268-01, 5R01MH63706:02; PHS HHS: 268200625226C, 268200782096C, 268201100005C, 268201100006C, 268201100007C, 268201100008C, 268201100009C, 268201100010C, 268201100011C, 268201100012C; Wellcome Trust: 068545/Z/02, 076113/B/04/Z, 077016/Z/05/Z, 079895, 090532, 092731, GR069224

      Nature genetics 2011;43;11;1082-90

    • Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.

      International Consortium for Blood Pressure Genome-Wide Association Studies, Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, Smith AV, Tobin MD, Verwoert GC, Hwang SJ, Pihur V, Vollenweider P, O'Reilly PF, Amin N, Bragg-Gresham JL, Teumer A, Glazer NL, Launer L, Zhao JH, Aulchenko Y, Heath S, Sõber S, Parsa A, Luan J, Arora P, Dehghan A, Zhang F, Lucas G, Hicks AA, Jackson AU, Peden JF, Tanaka T, Wild SH, Rudan I, Igl W, Milaneschi Y, Parker AN, Fava C, Chambers JC, Fox ER, Kumari M, Go MJ, van der Harst P, Kao WH, Sjögren M, Vinay DG, Alexander M, Tabara Y, Shaw-Hawkins S, Whincup PH, Liu Y, Shi G, Kuusisto J, Tayo B, Seielstad M, Sim X, Nguyen KD, Lehtimäki T, Matullo G, Wu Y, Gaunt TR, Onland-Moret NC, Cooper MN, Platou CG, Org E, Hardy R, Dahgam S, Palmen J, Vitart V, Braund PS, Kuznetsova T, Uiterwaal CS, Adeyemo A, Palmas W, Campbell H, Ludwig B, Tomaszewski M, Tzoulaki I, Palmer ND, CARDIoGRAM consortium, CKDGen Consortium, KidneyGen Consortium, EchoGen consortium, CHARGE-HF consortium, Aspelund T, Garcia M, Chang YP, O'Connell JR, Steinle NI, Grobbee DE, Arking DE, Kardia SL, Morrison AC, Hernandez D, Najjar S, McArdle WL, Hadley D, Brown MJ, Connell JM, Hingorani AD, Day IN, Lawlor DA, Beilby JP, Lawrence RW, Clarke R, Hopewell JC, Ongen H, Dreisbach AW, Li Y, Young JH, Bis JC, Kähönen M, Viikari J, Adair LS, Lee NR, Chen MH, Olden M, Pattaro C, Bolton JA, Köttgen A, Bergmann S, Mooser V, Chaturvedi N, Frayling TM, Islam M, Jafar TH, Erdmann J, Kulkarni SR, Bornstein SR, Grässler J, Groop L, Voight BF, Kettunen J, Howard P, Taylor A, Guarrera S, Ricceri F, Emilsson V, Plump A, Barroso I, Khaw KT, Weder AB, Hunt SC, Sun YV, Bergman RN, Collins FS, Bonnycastle LL, Scott LJ, Stringham HM, Peltonen L, Perola M, Vartiainen E, Brand SM, Staessen JA, Wang TJ, Burton PR, Soler Artigas M, Dong Y, Snieder H, Wang X, Zhu H, Lohman KK, Rudock ME, Heckbert SR, Smith NL, Wiggins KL, Doumatey A, Shriner D, Veldre G, Viigimaa M, Kinra S, Prabhakaran D, Tripathy V, Langefeld CD, Rosengren A, Thelle DS, Corsi AM, Singleton A, Forrester T, Hilton G, McKenzie CA, Salako T, Iwai N, Kita Y, Ogihara T, Ohkubo T, Okamura T, Ueshima H, Umemura S, Eyheramendy S, Meitinger T, Wichmann HE, Cho YS, Kim HL, Lee JY, Scott J, Sehmi JS, Zhang W, Hedblad B, Nilsson P, Smith GD, Wong A, Narisu N, Stančáková A, Raffel LJ, Yao J, Kathiresan S, O'Donnell CJ, Schwartz SM, Ikram MA, Longstreth WT, Mosley TH, Seshadri S, Shrine NR, Wain LV, Morken MA, Swift AJ, Laitinen J, Prokopenko I, Zitting P, Cooper JA, Humphries SE, Danesh J, Rasheed A, Goel A, Hamsten A, Watkins H, Bakker SJ, van Gilst WH, Janipalli CS, Mani KR, Yajnik CS, Hofman A, Mattace-Raso FU, Oostra BA, Demirkan A, Isaacs A, Rivadeneira F, Lakatta EG, Orru M, Scuteri A, Ala-Korpela M, Kangas AJ, Lyytikäinen LP, Soininen P, Tukiainen T, Würtz P, Ong RT, Dörr M, Kroemer HK, Völker U, Völzke H, Galan P, Hercberg S, Lathrop M, Zelenika D, Deloukas P, Mangino M, Spector TD, Zhai G, Meschia JF, Nalls MA, Sharma P, Terzic J, Kumar MV, Denniff M, Zukowska-Szczechowska E, Wagenknecht LE, Fowkes FG, Charchar FJ, Schwarz PE, Hayward C, Guo X, Rotimi C, Bots ML, Brand E, Samani NJ, Polasek O, Talmud PJ, Nyberg F, Kuh D, Laan M, Hveem K, Palmer LJ, van der Schouw YT, Casas JP, Mohlke KL, Vineis P, Raitakari O, Ganesh SK, Wong TY, Tai ES, Cooper RS, Laakso M, Rao DC, Harris TB, Morris RW, Dominiczak AF, Kivimaki M, Marmot MG, Miki T, Saleheen D, Chandak GR, Coresh J, Navis G, Salomaa V, Han BG, Zhu X, Kooner JS, Melander O, Ridker PM, Bandinelli S, Gyllensten UB, Wright AF, Wilson JF, Ferrucci L, Farrall M, Tuomilehto J, Pramstaller PP, Elosua R, Soranzo N, Sijbrands EJ, Altshuler D, Loos RJ, Shuldiner AR, Gieger C, Meneton P, Uitterlinden AG, Wareham NJ, Gudnason V, Rotter JI, Rettig R, Uda M, Strachan DP, Witteman JC, Hartikainen AL, Beckmann JS, Boerwinkle E, Vasan RS, Boehnke M, Larson MG, Järvelin MR, Psaty BM, Abecasis GR, Chakravarti A, Elliott P, van Duijn CM, Newton-Cheh C, Levy D, Caulfield MJ and Johnson T

      Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or  ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention.

      Funded by: AHRQ HHS: HS06516; Biotechnology and Biological Sciences Research Council: G20234; British Heart Foundation: CH/03/001, FS05/125, G0501942, PG/02/128, PG97012, PG97027, RG/07/005/23633, RG/08/008/25291, RG/08/013/25942, RG/08/014/24067, RG/98002, RG08/01, SP/04/002, SP/08/005/25115; Canadian Institutes of Health Research: MOP-82810, MOP172605, MOP77682; Chief Scientist Office: CZB/4/276, CZB/4/710; FIC NIH HHS: R03 TW007165, TW008288, TW05596; Howard Hughes Medical Institute: 55005617; Medical Research Council: G0000934, G0400874, G0401527, G0500539, G0501942, G0600331, G0600705, G0601966, G0700931, G0701863, G0801056, G0902037, G0902313, G1000143, G19/35, G9521010, G9521010D, MC_PC_U127561128, MC_U106179471, MC_U106188470, MC_U123092720, MC_U123092723, MC_U127561128, MC_UP_A100_1003; NCI NIH HHS: 5U01CA086308, P01CA055075, P01CA087969; NCRR NIH HHS: 2M01RR010284, K12RR023250, M01 RR16500, M01-RR00425, RR-024156, RR20649, U54 RR020278, UL1RR025005; NHGRI NIH HHS: HG003054, HG005581, U01HG004399, U01HG004402, U01HG004415, U01HG004422, U01HG004423, U01HG004436, U01HG004438, U01HG004446, U01HG004726, U01HG004728, U01HG004729, U01HG004735, U01HG004738; NHLBI NIH HHS: 5R01HL086694-03, 5R01HL087679-02, 5R01HL08770002, HL 54512, HL-87660, HL043851, HL080025, HL084729, HL085144, HL086718, HL087647, HL098283, HL36310, HL45508, HL53353, HL54512, N01 HC-15103, N01 HC-55222, N01 HC-95159, N01 HC-95169, N01-HC-25195, N01-HC-35129, N01-HC-45133, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N01-HC-75150, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N02-HL-6-4278, R01 HL073410, R01 HL085251, R01 HL086694, R01 HL086694-03, R01 HL086694-04A1, R01 HL086694-05, R01 HL087647, R01 HL087652, R01 HL088119, R01HL056931, R01HL060894, R01HL060919, R01HL06094, R01HL061019, R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071252, R01HL071258, R01HL071259, R01HL086694, R01HL087641, R01HL089650-02, R01HL59367, R37HL051021, U01 HL054466, U01 HL054466-11, U01 HL054471, U01 HL054473, U01 HL054527, U01 HL072515-06, U01 HL080295, U01 HL084756, U10 HL054512, U10HL054512; NIA NIH HHS: 1R01AG032098-01A, AG13196, N01-AG-1-2109, N01-AG-12100, N01AG6210, N01AG62101, N01AG62103, R01 AG017644-09S1, R01 AG18728; NICHD NIH HHS: N01-HD-1-3107; NIDCR NIH HHS: U01DE018903, U01DE01899; NIDDK NIH HHS: DK062370, DK063491, DK072193, DK075787, DK078150, DK56350, P30 DK072488, R01 DK072193, R01 DK078150, R01DK058845, R01DK066574, U01 DK062418; NIEHS NIH HHS: ES10126, P30 ES010126, P30ES007033; NIGMS NIH HHS: S06GM008016-320107, S06GM008016-380111, U01 GM074518-04; NIMH NIH HHS: 1RL1MH083268-01, 5R01MH63706:02; NIMHD NIH HHS: 263 MD 821336, 263 MD 9164; NINDS NIH HHS: R01 NS39987, R01 NS42733, U01 NS069208, U01 NS069208-01; PHS HHS: 263-MA-410953, 33014, HHSN268200625226C, HHSN268200782096, HHSN268200782096C; Wellcome Trust: 068545/Z/02, 070191/Z/03/Z, 077016/Z/05/Z, 079895, 080747/Z/06/Z, 090532

      Nature 2011;478;7367;103-9

    • Effect of five genetic variants associated with lung function on the risk of chronic obstructive lung disease, and their joint effects on lung function.

      Soler Artigas M, Wain LV, Repapi E, Obeidat M, Sayers I, Burton PR, Johnson T, Zhao JH, Albrecht E, Dominiczak AF, Kerr SM, Smith BH, Cadby G, Hui J, Palmer LJ, Hingorani AD, Wannamethee SG, Whincup PH, Ebrahim S, Smith GD, Barroso I, Loos RJ, Wareham NJ, Cooper C, Dennison E, Shaheen SO, Liu JZ, Marchini J, Medical Research Council National Survey of Health and Development (NSHD) Respiratory Study Team, Dahgam S, Naluai AT, Olin AC, Karrasch S, Heinrich J, Schulz H, McKeever TM, Pavord ID, Heliövaara M, Ripatti S, Surakka I, Blakey JD, Kähönen M, Britton JR, Nyberg F, Holloway JW, Lawlor DA, Morris RW, James AL, Jackson CM, Hall IP, Tobin MD and SpiroMeta Consortium

      Department of Health Sciences, University of Leicester, Leicester, UK.

      Rationale: Genomic loci are associated with FEV1 or the ratio of FEV1 to FVC in population samples, but their association with chronic obstructive pulmonary disease (COPD) has not yet been proven, nor have their combined effects on lung function and COPD been studied.

      Objectives: To test association with COPD of variants at five loci (TNS1, GSTCD, HTR4, AGER, and THSD4) and to evaluate joint effects on lung function and COPD of these single-nucleotide polymorphisms (SNPs), and variants at the previously reported locus near HHIP.

      Methods: By sampling from 12 population-based studies (n = 31,422), we obtained genotype data on 3,284 COPD case subjects and 17,538 control subjects for sentinel SNPs in TNS1, GSTCD, HTR4, AGER, and THSD4. In 24,648 individuals (including 2,890 COPD case subjects and 13,862 control subjects), we additionally obtained genotypes for rs12504628 near HHIP. Each allele associated with lung function decline at these six SNPs contributed to a risk score. We studied the association of the risk score to lung function and COPD.

      Association with COPD was significant for three loci (TNS1, GSTCD, and HTR4) and the previously reported HHIP locus, and suggestive and directionally consistent for AGER and TSHD4. Compared with the baseline group (7 risk alleles), carrying 10-12 risk alleles was associated with a reduction in FEV1 (β = -72.21 ml, P = 3.90 × 10(-4)) and FEV1/FVC (β = -1.53%, P = 6.35 × 10(-6)), and with COPD (odds ratio = 1.63, P = 1.46 × 10(-5)).

      Conclusions: Variants in TNS1, GSTCD, and HTR4 are associated with COPD. Our highest risk score category was associated with a 1.6-fold higher COPD risk than the population average score.

      Funded by: British Heart Foundation: FS05/125, PG/06/154/22043, PG/97012, RG/08/013/25942; Cancer Research UK; Chief Scientist Office: CZD/16/6/2; Department of Health; Medical Research Council: G0600705, G0701863, G1000861, MC_U106188470, MC_UP_A620_1014; Wellcome Trust: 077016/Z/05/Z, 090532

      American journal of respiratory and critical care medicine 2011;184;7;786-95

    • Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

      Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, Petrie JR, Travers ME, Bouatia-Naji N, Dimas AS, Nica A, Wheeler E, Chen H, Voight BF, Taneera J, Kanoni S, Peden JF, Turrini F, Gustafsson S, Zabena C, Almgren P, Barker DJ, Barnes D, Dennison EM, Eriksson JG, Eriksson P, Eury E, Folkersen L, Fox CS, Frayling TM, Goel A, Gu HF, Horikoshi M, Isomaa B, Jackson AU, Jameson KA, Kajantie E, Kerr-Conte J, Kuulasmaa T, Kuusisto J, Loos RJ, Luan J, Makrilakis K, Manning AK, Martínez-Larrad MT, Narisu N, Nastase Mannila M, Ohrvik J, Osmond C, Pascoe L, Payne F, Sayer AA, Sennblad B, Silveira A, Stancáková A, Stirrups K, Swift AJ, Syvänen AC, Tuomi T, van 't Hooft FM, Walker M, Weedon MN, Xie W, Zethelius B, DIAGRAM Consortium, GIANT Consortium, MuTHER Consortium, CARDIoGRAM Consortium, C4D Consortium, Ongen H, Mälarstig A, Hopewell JC, Saleheen D, Chambers J, Parish S, Danesh J, Kooner J, Ostenson CG, Lind L, Cooper CC, Serrano-Ríos M, Ferrannini E, Forsen TJ, Clarke R, Franzosi MG, Seedorf U, Watkins H, Froguel P, Johnson P, Deloukas P, Collins FS, Laakso M, Dermitzakis ET, Boehnke M, McCarthy MI, Wareham NJ, Groop L, Pattou F, Gloyn AL, Dedoussis GV, Lyssenko V, Meigs JB, Barroso I, Watanabe RM, Ingelsson E, Langenberg C, Hamsten A and Florez JC

      Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden.

      Objective: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.

      We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.

      Results: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.

      Conclusions: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

      Funded by: British Heart Foundation: RG/08/014/24067; Medical Research Council: 81696, G0601261, G0601966, G0700222, G0700222(81696), G0700931, G0801056, MC_PC_U127561128, MC_U106188470, MC_U127561128, MC_UP_A620_1014, MC_UP_A620_1015; NHLBI NIH HHS: R01 HL087647, U01 HL054527; NIDDK NIH HHS: DK062370, K24 DK080140, R01 DK078616; NIGMS NIH HHS: T32 GM074905; Wellcome Trust: 077016/Z/05/Z, 083270/Z/07/Z, 090532

      Diabetes 2011;60;10;2624-34

    • Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.

      Wain LV, Verwoert GC, O'Reilly PF, Shi G, Johnson T, Johnson AD, Bochud M, Rice KM, Henneman P, Smith AV, Ehret GB, Amin N, Larson MG, Mooser V, Hadley D, Dörr M, Bis JC, Aspelund T, Esko T, Janssens AC, Zhao JH, Heath S, Laan M, Fu J, Pistis G, Luan J, Arora P, Lucas G, Pirastu N, Pichler I, Jackson AU, Webster RJ, Zhang F, Peden JF, Schmidt H, Tanaka T, Campbell H, Igl W, Milaneschi Y, Hottenga JJ, Vitart V, Chasman DI, Trompet S, Bragg-Gresham JL, Alizadeh BZ, Chambers JC, Guo X, Lehtimäki T, Kühnel B, Lopez LM, Polašek O, Boban M, Nelson CP, Morrison AC, Pihur V, Ganesh SK, Hofman A, Kundu S, Mattace-Raso FU, Rivadeneira F, Sijbrands EJ, Uitterlinden AG, Hwang SJ, Vasan RS, Wang TJ, Bergmann S, Vollenweider P, Waeber G, Laitinen J, Pouta A, Zitting P, McArdle WL, Kroemer HK, Völker U, Völzke H, Glazer NL, Taylor KD, Harris TB, Alavere H, Haller T, Keis A, Tammesoo ML, Aulchenko Y, Barroso I, Khaw KT, Galan P, Hercberg S, Lathrop M, Eyheramendy S, Org E, Sõber S, Lu X, Nolte IM, Penninx BW, Corre T, Masciullo C, Sala C, Groop L, Voight BF, Melander O, O'Donnell CJ, Salomaa V, d'Adamo AP, Fabretto A, Faletra F, Ulivi S, Del Greco F, Facheris M, Collins FS, Bergman RN, Beilby JP, Hung J, Musk AW, Mangino M, Shin SY, Soranzo N, Watkins H, Goel A, Hamsten A, Gider P, Loitfelder M, Zeginigg M, Hernandez D, Najjar SS, Navarro P, Wild SH, Corsi AM, Singleton A, de Geus EJ, Willemsen G, Parker AN, Rose LM, Buckley B, Stott D, Orru M, Uda M, LifeLines Cohort Study, van der Klauw MM, Zhang W, Li X, Scott J, Chen YD, Burke GL, Kähönen M, Viikari J, Döring A, Meitinger T, Davies G, Starr JM, Emilsson V, Plump A, Lindeman JH, Hoen PA, König IR, EchoGen consortium, Felix JF, Clarke R, Hopewell JC, Ongen H, Breteler M, Debette S, Destefano AL, Fornage M, AortaGen Consortium, Mitchell GF, CHARGE Consortium Heart Failure Working Group, Smith NL, KidneyGen consortium, Holm H, Stefansson K, Thorleifsson G, Thorsteinsdottir U, CKDGen consortium, Cardiogenics consortium, CardioGram, Samani NJ, Preuss M, Rudan I, Hayward C, Deary IJ, Wichmann HE, Raitakari OT, Palmas W, Kooner JS, Stolk RP, Jukema JW, Wright AF, Boomsma DI, Bandinelli S, Gyllensten UB, Wilson JF, Ferrucci L, Schmidt R, Farrall M, Spector TD, Palmer LJ, Tuomilehto J, Pfeufer A, Gasparini P, Siscovick D, Altshuler D, Loos RJ, Toniolo D, Snieder H, Gieger C, Meneton P, Wareham NJ, Oostra BA, Metspalu A, Launer L, Rettig R, Strachan DP, Beckmann JS, Witteman JC, Erdmann J, van Dijk KW, Boerwinkle E, Boehnke M, Ridker PM, Jarvelin MR, Chakravarti A, Abecasis GR, Gudnason V, Newton-Cheh C, Levy D, Munroe PB, Psaty BM, Caulfield MJ, Rao DC, Tobin MD, Elliott P and van Duijn CM

      Department of Health Sciences, University of Leicester, Leicester, UK.

      Numerous genetic loci have been associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N = 74,064) and follow-up studies (N = 48,607), we identified at genome-wide significance (P = 2.7 × 10(-8) to P = 2.3 × 10(-13)) four new PP loci (at 4q12 near CHIC2, 7q22.3 near PIK3CG, 8q24.12 in NOV and 11q24.3 near ADAMTS8), two new MAP loci (3p21.31 in MAP4 and 10q25.3 near ADRB1) and one locus associated with both of these traits (2q24.3 near FIGN) that has also recently been associated with SBP in east Asians. For three of the new PP loci, the estimated effect for SBP was opposite of that for DBP, in contrast to the majority of common SBP- and DBP-associated variants, which show concordant effects on both traits. These findings suggest new genetic pathways underlying blood pressure variation, some of which may differentially influence SBP and DBP.

      Funded by: Chief Scientist Office: CZB/4/710; Medical Research Council: G0401527, G0601966, G0700931, G0701863, G0801056, G0902313, G1000143, G9521010, MC_PC_U127561128, MC_U106179471, MC_U106188470, MC_U127561128, MC_U127592696; NHGRI NIH HHS: Z01 HG000024-13; NHLBI NIH HHS: K23 HL080025, N01 HC025195, N01 HC055015, N01 HC085079, N01 HC095159, R01 HL043851, R01 HL086694, R01 HL087647, R01 HL105756, U10 HL054512; NIA NIH HHS: N01 AG012109; NIMHD NIH HHS: 263 MD9164 13; Wellcome Trust: 090532

      Nature genetics 2011;43;10;1005-11

    • Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study.

      InterAct Consortium, Langenberg C, Sharp S, Forouhi NG, Franks PW, Schulze MB, Kerrison N, Ekelund U, Barroso I, Panico S, Tormo MJ, Spranger J, Griffin S, van der Schouw YT, Amiano P, Ardanaz E, Arriola L, Balkau B, Barricarte A, Beulens JW, Boeing H, Bueno-de-Mesquita HB, Buijsse B, Chirlaque Lopez MD, Clavel-Chapelon F, Crowe FL, de Lauzon-Guillan B, Deloukas P, Dorronsoro M, Drogan D, Froguel P, Gonzalez C, Grioni S, Groop L, Groves C, Hainaut P, Halkjaer J, Hallmans G, Hansen T, Huerta Castaño JM, Kaaks R, Key TJ, Khaw KT, Koulman A, Mattiello A, Navarro C, Nilsson P, Norat T, Overvad K, Palla L, Palli D, Pedersen O, Peeters PH, Quirós JR, Ramachandran A, Rodriguez-Suarez L, Rolandsson O, Romaguera D, Romieu I, Sacerdote C, Sánchez MJ, Sandbaek A, Slimani N, Sluijs I, Spijkerman AM, Teucher B, Tjonneland A, Tumino R, van der A DL, Verschuren WM, Tuomilehto J, Feskens E, McCarthy M, Riboli E and Wareham NJ

      Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Box 285, Cambridge CB2 0QQ, UK e-mail: claudia.langenberg@mrc-epid.cam.ac.uk

      Studying gene-lifestyle interaction may help to identify lifestyle factors that modify genetic susceptibility and uncover genetic loci exerting important subgroup effects. Adequately powered studies with prospective, unbiased, standardised assessment of key behavioural factors for gene-lifestyle studies are lacking. This case-cohort study aims to investigate how genetic and potentially modifiable lifestyle and behavioural factors, particularly diet and physical activity, interact in their influence on the risk of developing type 2 diabetes.

      Methods: Incident cases of type 2 diabetes occurring in European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts between 1991 and 2007 from eight of the ten EPIC countries were ascertained and verified. Prentice-weighted Cox regression and random-effects meta-analyses were used to investigate differences in diabetes incidence by age and sex.

      Results: A total of 12,403 verified incident cases of type 2 diabetes occurred during 3.99 million person-years of follow-up of 340,234 EPIC participants eligible for InterAct. We defined a centre-stratified subcohort of 16,154 individuals for comparative analyses. Individuals with incident diabetes who were randomly selected into the subcohort (n = 778) were included as cases in the analyses. All prevalent diabetes cases were excluded from the study. InterAct cases were followed-up for an average of 6.9 years; 49.7% were men. Mean baseline age and age at diagnosis were 55.6 and 62.5 years, mean BMI and waist circumference values were 29.4 kg/m(2) and 102.7 cm in men, and 30.1 kg/m(2) and 92.8 cm in women, respectively. Risk of type 2 diabetes increased linearly with age, with an overall HR of 1.56 (95% CI 1.48-1.64) for a 10 year age difference, adjusted for sex. A male excess in the risk of incident diabetes was consistently observed across all countries, with a pooled HR of 1.51 (95% CI 1.39-1.64), adjusted for age.

      InterAct is a large, well-powered, prospective study that will inform our understanding of the interplay between genes and lifestyle factors on the risk of type 2 diabetes development.

      Funded by: Canadian Institutes of Health Research: G0601261; Cancer Research UK: 11692; Medical Research Council: G0401527, G0601261, G1000143, MC_U106179471, MC_U106179473, MC_U106179474, MC_UP_A090_1006, MC_UP_A100_1003; Wellcome Trust: 083270/083270/z

      Diabetologia 2011;54;9;2272-82

    • Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile.

      Kilpeläinen TO, Zillikens MC, Stančákova A, Finucane FM, Ried JS, Langenberg C, Zhang W, Beckmann JS, Luan J, Vandenput L, Styrkarsdottir U, Zhou Y, Smith AV, Zhao JH, Amin N, Vedantam S, Shin SY, Haritunians T, Fu M, Feitosa MF, Kumari M, Halldorsson BV, Tikkanen E, Mangino M, Hayward C, Song C, Arnold AM, Aulchenko YS, Oostra BA, Campbell H, Cupples LA, Davis KE, Döring A, Eiriksdottir G, Estrada K, Fernández-Real JM, Garcia M, Gieger C, Glazer NL, Guiducci C, Hofman A, Humphries SE, Isomaa B, Jacobs LC, Jula A, Karasik D, Karlsson MK, Khaw KT, Kim LJ, Kivimäki M, Klopp N, Kühnel B, Kuusisto J, Liu Y, Ljunggren O, Lorentzon M, Luben RN, McKnight B, Mellström D, Mitchell BD, Mooser V, Moreno JM, Männistö S, O'Connell JR, Pascoe L, Peltonen L, Peral B, Perola M, Psaty BM, Salomaa V, Savage DB, Semple RK, Skaric-Juric T, Sigurdsson G, Song KS, Spector TD, Syvänen AC, Talmud PJ, Thorleifsson G, Thorsteinsdottir U, Uitterlinden AG, van Duijn CM, Vidal-Puig A, Wild SH, Wright AF, Clegg DJ, Schadt E, Wilson JF, Rudan I, Ripatti S, Borecki IB, Shuldiner AR, Ingelsson E, Jansson JO, Kaplan RC, Gudnason V, Harris TB, Groop L, Kiel DP, Rivadeneira F, Walker M, Barroso I, Vollenweider P, Waeber G, Chambers JC, Kooner JS, Soranzo N, Hirschhorn JN, Stefansson K, Wichmann HE, Ohlsson C, O'Rahilly S, Wareham NJ, Speliotes EK, Fox CS, Laakso M and Loos RJ

      Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK.

      Genome-wide association studies have identified 32 loci influencing body mass index, but this measure does not distinguish lean from fat mass. To identify adiposity loci, we meta-analyzed associations between ∼2.5 million SNPs and body fat percentage from 36,626 individuals and followed up the 14 most significant (P < 10(-6)) independent loci in 39,576 individuals. We confirmed a previously established adiposity locus in FTO (P = 3 × 10(-26)) and identified two new loci associated with body fat percentage, one near IRS1 (P = 4 × 10(-11)) and one near SPRY2 (P = 3 × 10(-8)). Both loci contain genes with potential links to adipocyte physiology. Notably, the body-fat-decreasing allele near IRS1 is associated with decreased IRS1 expression and with an impaired metabolic profile, including an increased visceral to subcutaneous fat ratio, insulin resistance, dyslipidemia, risk of diabetes and coronary artery disease and decreased adiponectin levels. Our findings provide new insights into adiposity and insulin resistance.

      Funded by: Biotechnology and Biological Sciences Research Council: G20234; British Heart Foundation: PG/07/133/24260, RG/07/008/23674, RG/08/008, RG/08/008/25291, SP/04/002, SP/07/007/23671; Cancer Research UK; Chief Scientist Office: CZB/4/710; Department of Health; Medical Research Council: G0401527, G0601966, G0700931, G0701863, G0802051, G0902037, G1000143, G19/35, G8802774, MC_U106179471, MC_U106188470, MC_U127561128; NCRR NIH HHS: M01 RR 16500, M01 RR000425-36, M01 RR016500-04, M01-RR00425; NHLBI NIH HHS: N01 HC015103, N01 HC025195, N01 HC045133, N01 HC055222, N01 HC075150, N01 HC085079, N01 HC085086, N01-HC15103, N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55222, N01-HC75150, N01-HC85079-86, N01HC25195, N02 HL64278, R01 HL087652, R01 HL087652-03, R01 HL087700, R01 HL087700-03, R01 HL088119, R01 HL088119-04, R01-HL036310-20A2, R01-HL087652, R01-HL08770003, R01-HL088119, U01 HL072515, U01 HL072515-06, U01 HL080295-04, U01 HL084756, U01 HL084756-03, U01-HL080295, U01-HL72515, U01-HL84756; NIA NIH HHS: AG13196, N01-AG12100, N01-AG62101, N01-AG62103, N01-AG62106, N01AG12100, N1AG62101A, N1AG62103A, N1AG62106A, R01 AG018728, R01 AG018728-05S1, R01 AG032098, R01 AG032098-01A1, R01-AG031890-01, R01-AG032098-01A1, R01-AG18728, R01-AR/AG41398; NIAMS NIH HHS: R01 AR041398, R01 AR041398-19, R01 AR046838, R01 AR046838-05, R01-AR046838; NIDDK NIH HHS: DK063491, K23 DK080145, K23 DK080145-05, K23-DK080145, P30 DK063491-03, P30 DK072488, P30 DK072488-04S1, P30-DK072488, R01 DK068336, R01 DK068336-03, R01 DK075681, R01 DK075681-04, R01 DK075787, R01 DK075787-05, R01 DK089256, R01-DK06833603, R01-DK07568102, R01-DK075787; Wellcome Trust: 077016/Z/05/Z, 084723/Z/08/Z, 091551, 091746/Z/10/Z

      Nature genetics 2011;43;8;753-60

    • Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.

      Fox ER, Young JH, Li Y, Dreisbach AW, Keating BJ, Musani SK, Liu K, Morrison AC, Ganesh S, Kutlar A, Ramachandran VS, Polak JF, Fabsitz RR, Dries DL, Farlow DN, Redline S, Adeyemo A, Hirschorn JN, Sun YV, Wyatt SB, Penman AD, Palmas W, Rotter JI, Townsend RR, Doumatey AP, Tayo BO, Mosley TH, Lyon HN, Kang SJ, Rotimi CN, Cooper RS, Franceschini N, Curb JD, Martin LW, Eaton CB, Kardia SL, Taylor HA, Caulfield MJ, Ehret GB, Johnson T, International Consortium for Blood Pressure Genome-wide Association Studies (ICBP-GWAS), Chakravarti A, Zhu X and Levy D

      Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA. efox@umc.edu

      The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity.

      Funded by: British Heart Foundation: RG/08/008/25291, RG/08/014/24067; Medical Research Council: G0400874, G0401527, G0600705, G0701863, G0801056, G0902037, G0902313, G1000143, G19/35, G9521010, MC_QA137934, MC_U106179471, MC_U106188470, MC_U123092720, MC_U123092721, MC_U127561128; NHGRI NIH HHS: HG003054; NHLBI NIH HHS: HL074166, HL086694, HL086718, HL087660, HL100245, N01 HC-15103, N01 HC-55222, N01-HB-72982, N01-HB-72991, N01-HB-72992, N01-HB-72993, N01-HB-72994, N01-HB-72995, N01-HB-72996, N01-HB-72997, N01-HB-72998, N01-HB-73000, N01-HB-73001, N01-HB-73002, N01-HB-73003, N01-HB-73004, N01-HB-73005, N01-HB-73006, N01-HB-97051, N01-HB-97053, N01-HB-97054, N01-HB-97056, N01-HB-97058, N01-HB-97060, N01-HB-97061, N01-HB-97062, N01-HB-97064, N01-HB-97066, N01-HB-97068, N01-HB-97069, N01-HB-9707, N01-HB-97071, N01-HB-97072, N01-HB-97073, N01-HB-97085, N01-HC-05187, N01-HC-25195, N01-HC-35129, N01-HC-45133, N01-HC-45134, N01-HC-45204, N01-HC-45205, N01-HC-48047, N01-HC-48048, N01-HC-48049, N01-HC-48050, N01-HC-55015, N01-HC-55016, N01-HC-55017, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-65226, N01-HC-75150, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, N01-HC-95095, N01-HC-95100, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, N01-HC-95170, N01-HC-95171, N01-HC-95172, R01 HL46380-01-16, U01 HL053916, U01 HL053934, U01 HL053937, U01 HL053938, U01 HL053941, U01 HL063429, U01 HL063463, U01 HL064360, U01 HL077813, U01-HL080295; NICHD NIH HHS: R24 HD050924, R24 HD050924-07; NIMHD NIH HHS: MD002249; WHI NIH HHS: N01WH22110, N01WH24152, N01WH32100, N01WH32102, N01WH32105, N01WH32106, N01WH32108, N01WH32111, N01WH32112, N01WH32113, N01WH32115, N01WH32118, N01WH32119, N01WH32122, N01WH42107, N01WH42108, N01WH42109, N01WH42110, N01WH42111, N01WH42112, N01WH42113, N01WH42114, N01WH42115, N01WH42116, N01WH42117, N01WH42118, N01WH42119, N01WH42120, N01WH42121, N01WH42122, N01WH42123, N01WH42124, N01WH42125, N01WH42126, N01WH42129, N01WH42130, N01WH42131, N01WH42132, N01WH444221; Wellcome Trust: 090532

      Human molecular genetics 2011;20;11;2273-84

    • Founder effect in the Horn of Africa for an insulin receptor mutation that may impair receptor recycling.

      Raffan E, Soos MA, Rocha N, Tuthill A, Thomsen AR, Hyden CS, Gregory JW, Hindmarsh P, Dattani M, Cochran E, Al Kaabi J, Gorden P, Barroso I, Morling N, O'Rahilly S and Semple RK

      University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Addenbrooke's Hospital B289, Cambridge, CB2 0QR, UK.

      Genetic insulin receptoropathies are a rare cause of severe insulin resistance. We identified the Ile119Met missense mutation in the insulin receptor INSR gene, previously reported in a Yemeni kindred, in four unrelated patients with Somali ancestry. We aimed to investigate a possible genetic founder effect, and to study the mechanism of loss of function of the mutant receptor.

      Methods: Biochemical profiling and DNA haplotype analysis of affected patients were performed. Insulin receptor expression in lymphoblastoid cells from a homozygous p.Ile119Met INSR patient, and in cells heterologously expressing the mutant receptor, was examined. Insulin binding, insulin-stimulated receptor autophosphorylation, and cooperativity and pH dependency of insulin dissociation were also assessed.

      Results: All patients had biochemical profiles pathognomonic of insulin receptoropathy, while haplotype analysis revealed the putative shared region around the INSR mutant to be no larger than 28 kb. An increased insulin proreceptor to β subunit ratio was seen in patient-derived cells. Steady state insulin binding and insulin-stimulated autophosphorylation of the mutant receptor was normal; however it exhibited decreased insulin dissociation rates with preserved cooperativity, a difference accentuated at low pH.

      The p.Ile119Met INSR appears to have arisen around the Horn of Africa, and should be sought first in severely insulin resistant patients with ancestry from this region. Despite collectively compelling genetic, clinical and biochemical evidence for its pathogenicity, loss of function in conventional in vitro assays is subtle, suggesting mildly impaired receptor recycling only.

      Funded by: Medical Research Council; Wellcome Trust: 077016/Z/05/Z, 078986/Z/06/Z, 080952/Z/06/Z, 087678/Z/08/Z

      Diabetologia 2011;54;5;1057-65

    • Perilipin deficiency and autosomal dominant partial lipodystrophy.

      Gandotra S, Le Dour C, Bottomley W, Cervera P, Giral P, Reznik Y, Charpentier G, Auclair M, Delépine M, Barroso I, Semple RK, Lathrop M, Lascols O, Capeau J, O'Rahilly S, Magré J, Savage DB and Vigouroux C

      University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.

      Perilipin is the most abundant adipocyte-specific protein that coats lipid droplets, and it is required for optimal lipid incorporation and release from the droplet. We identified two heterozygous frameshift mutations in the perilipin gene (PLIN1) in three families with partial lipodystrophy, severe dyslipidemia, and insulin-resistant diabetes. Subcutaneous fat from the patients was characterized by smaller-than-normal adipocytes, macrophage infiltration, and fibrosis. In contrast to wild-type perilipin, mutant forms of the protein failed to increase triglyceride accumulation when expressed heterologously in preadipocytes. These findings define a novel dominant form of inherited lipodystrophy and highlight the serious metabolic consequences of a primary defect in the formation of lipid droplets in adipose tissue.

      Funded by: Medical Research Council; Wellcome Trust: 077016, 077016/Z/05/Z, 091551

      The New England journal of medicine 2011;364;8;740-8

    • The architecture of gene regulatory variation across multiple human tissues: the MuTHER study.

      Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, Travers M, Potter S, Grundberg E, Small K, Hedman AK, Bataille V, Tzenova Bell J, Surdulescu G, Dimas AS, Ingle C, Nestle FO, di Meglio P, Min JL, Wilk A, Hammond CJ, Hassanali N, Yang TP, Montgomery SB, O'Rahilly S, Lindgren CM, Zondervan KT, Soranzo N, Barroso I, Durbin R, Ahmadi K, Deloukas P, McCarthy MI, Dermitzakis ET, Spector TD and MuTHER Consortium

      Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom.

      While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis--MCTA) permits immediate replication of eQTLs using co-twins (93%-98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%-20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.

      Funded by: Medical Research Council: G0900339; Wellcome Trust: 077016/Z/05/Z, 085235, 090532

      PLoS genetics 2011;7;2;e1002003

    • A comprehensive evaluation of potential lung function associated genes in the SpiroMeta general population sample.

      Obeidat M, Wain LV, Shrine N, Kalsheker N, Soler Artigas M, Repapi E, Burton PR, Johnson T, Ramasamy A, Zhao JH, Zhai G, Huffman JE, Vitart V, Albrecht E, Igl W, Hartikainen AL, Pouta A, Cadby G, Hui J, Palmer LJ, Hadley D, McArdle WL, Rudnicka AR, Barroso I, Loos RJ, Wareham NJ, Mangino M, Soranzo N, Spector TD, Gläser S, Homuth G, Völzke H, Deloukas P, Granell R, Henderson J, Grkovic I, Jankovic S, Zgaga L, Polašek O, Rudan I, Wright AF, Campbell H, Wild SH, Wilson JF, Heinrich J, Imboden M, Probst-Hensch NM, Gyllensten U, Johansson Å, Zaboli G, Mustelin L, Rantanen T, Surakka I, Kaprio J, Jarvelin MR, Hayward C, Evans DM, Koch B, Musk AW, Elliott P, Strachan DP, Tobin MD, Sayers I, Hall IP and SpiroMeta Consortium

      Nottingham Respiratory Biomedical Research Unit, Division of Therapeutics and Molecular Medicine, University Hospital of Nottingham, Nottingham, United Kingdom.

      Rationale: Lung function measures are heritable traits that predict population morbidity and mortality and are essential for the diagnosis of chronic obstructive pulmonary disease (COPD). Variations in many genes have been reported to affect these traits, but attempts at replication have provided conflicting results. Recently, we undertook a meta-analysis of Genome Wide Association Study (GWAS) results for lung function measures in 20,288 individuals from the general population (the SpiroMeta consortium).

      Objectives: To comprehensively analyse previously reported genetic associations with lung function measures, and to investigate whether single nucleotide polymorphisms (SNPs) in these genomic regions are associated with lung function in a large population sample.

      Methods: We analysed association for SNPs tagging 130 genes and 48 intergenic regions (+/-10 kb), after conducting a systematic review of the literature in the PubMed database for genetic association studies reporting lung function associations.

      Results: The analysis included 16,936 genotyped and imputed SNPs. No loci showed overall significant association for FEV(1) or FEV(1)/FVC traits using a carefully defined significance threshold of 1.3×10(-5). The most significant loci associated with FEV(1) include SNPs tagging MACROD2 (P = 6.81×10(-5)), CNTN5 (P = 4.37×10(-4)), and TRPV4 (P = 1.58×10(-3)). Among ever-smokers, SERPINA1 showed the most significant association with FEV(1) (P = 8.41×10(-5)), followed by PDE4D (P = 1.22×10(-4)). The strongest association with FEV(1)/FVC ratio was observed with ABCC1 (P = 4.38×10(-4)), and ESR1 (P = 5.42×10(-4)) among ever-smokers.

      Conclusions: Polymorphisms spanning previously associated lung function genes did not show strong evidence for association with lung function measures in the SpiroMeta consortium population. Common SERPINA1 polymorphisms may affect FEV(1) among smokers in the general population.

      Funded by: Cancer Research UK; Chief Scientist Office: CZB/4/710; Medical Research Council: G0000934, G0401540, G0600705, G0701863, G0800582, G0801056, G0902125, G0902313, G9815508, G990146, MC_QA137934, MC_U106179471, MC_U106188470; NHLBI NIH HHS: 5R01HL087679-02; NIDDK NIH HHS: U01 DK062418; NIMH NIH HHS: 1RL1MH083268-01; Wellcome Trust: 068545/Z/02, 076113/B/04/Z, 077016/Z/05/Z, 079895, 092731

      PloS one 2011;6;5;e19382

2010 Publications

  • Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies.

    Elks CE, Perry JR, Sulem P, Chasman DI, Franceschini N, He C, Lunetta KL, Visser JA, Byrne EM, Cousminer DL, Gudbjartsson DF, Esko T, Feenstra B, Hottenga JJ, Koller DL, Kutalik Z, Lin P, Mangino M, Marongiu M, McArdle PF, Smith AV, Stolk L, van Wingerden SH, Zhao JH, Albrecht E, Corre T, Ingelsson E, Hayward C, Magnusson PK, Smith EN, Ulivi S, Warrington NM, Zgaga L, Alavere H, Amin N, Aspelund T, Bandinelli S, Barroso I, Berenson GS, Bergmann S, Blackburn H, Boerwinkle E, Buring JE, Busonero F, Campbell H, Chanock SJ, Chen W, Cornelis MC, Couper D, Coviello AD, d'Adamo P, de Faire U, de Geus EJ, Deloukas P, Döring A, Smith GD, Easton DF, Eiriksdottir G, Emilsson V, Eriksson J, Ferrucci L, Folsom AR, Foroud T, Garcia M, Gasparini P, Geller F, Gieger C, GIANT Consortium, Gudnason V, Hall P, Hankinson SE, Ferreli L, Heath AC, Hernandez DG, Hofman A, Hu FB, Illig T, Järvelin MR, Johnson AD, Karasik D, Khaw KT, Kiel DP, Kilpeläinen TO, Kolcic I, Kraft P, Launer LJ, Laven JS, Li S, Liu J, Levy D, Martin NG, McArdle WL, Melbye M, Mooser V, Murray JC, Murray SS, Nalls MA, Navarro P, Nelis M, Ness AR, Northstone K, Oostra BA, Peacock M, Palmer LJ, Palotie A, Paré G, Parker AN, Pedersen NL, Peltonen L, Pennell CE, Pharoah P, Polasek O, Plump AS, Pouta A, Porcu E, Rafnar T, Rice JP, Ring SM, Rivadeneira F, Rudan I, Sala C, Salomaa V, Sanna S, Schlessinger D, Schork NJ, Scuteri A, Segrè AV, Shuldiner AR, Soranzo N, Sovio U, Srinivasan SR, Strachan DP, Tammesoo ML, Tikkanen E, Toniolo D, Tsui K, Tryggvadottir L, Tyrer J, Uda M, van Dam RM, van Meurs JB, Vollenweider P, Waeber G, Wareham NJ, Waterworth DM, Weedon MN, Wichmann HE, Willemsen G, Wilson JF, Wright AF, Young L, Zhai G, Zhuang WV, Bierut LJ, Boomsma DI, Boyd HA, Crisponi L, Demerath EW, van Duijn CM, Econs MJ, Harris TB, Hunter DJ, Loos RJ, Metspalu A, Montgomery GW, Ridker PM, Spector TD, Streeten EA, Stefansson K, Thorsteinsdottir U, Uitterlinden AG, Widen E, Murabito JM, Ong KK and Murray A

    Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.

    To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P = 5.4 × 10⁻⁶⁰) and 9q31.2 (P = 2.2 × 10⁻³³), we identified 30 new menarche loci (all P < 5 × 10⁻⁸) and found suggestive evidence for a further 10 loci (P < 1.9 × 10⁻⁶). The new loci included four previously associated with body mass index (in or near FTO, SEC16B, TRA2B and TMEM18), three in or near other genes implicated in energy homeostasis (BSX, CRTC1 and MCHR2) and three in or near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and gene-set enrichment pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.

    Funded by: Canadian Institutes of Health Research: 166067; Cancer Research UK: 10118, A10119, A10124; Chief Scientist Office: CZB/4/710; Medical Research Council: G0000934, G0401527, G0500539, G0600705, G0701863, G9815508, MC_U106179471, MC_U106179472, MC_U106188470, MC_U127561128; NCI NIH HHS: CA047988, CA089392, CA104021, CA136792, CA40356, CA54281, CA63464, CA98233, P01 CA055075, P01 CA055075-17, P01 CA087969, P01 CA087969-13, P01 CA089392, P01 CA089392-08, P01 CA089392-09, P01CA055075, P01CA087969, R01 CA040356-15S1, R01 CA047988, R01 CA047988-20, R01 CA063464, R01 CA063464-10, R01 CA104021-05, R37 CA054281, R37 CA054281-17, U01 CA098233, U01 CA098233-08, U01 CA136792, U01 CA136792-03, Z01 CP010200-03, Z01CP010200; NCRR NIH HHS: M01 RR 16500, M01 RR-00750, M01 RR000750-31, M01 RR016500-04, U54RR025204-01, UL1 RR025005, UL1 RR025005-05, UL1 RR025774, UL1 RR025774-05, UL1RR025005; NHGRI NIH HHS: U01 HG004399, U01 HG004399-02, U01 HG004402, U01 HG004402-02, U01 HG004415-02, U01 HG004422, U01 HG004422-01, U01 HG004422-02, U01 HG004423, U01 HG004423-01, U01 HG004424-04, U01 HG004436, U01 HG004436-02, U01 HG004438, U01 HG004438-04, U01 HG004446, U01 HG004446-04, U01 HG004726, U01 HG004726-02, U01 HG004728, U01 HG004728-01, U01 HG004729, U01 HG004729-02, U01 HG004735, U01 HG004735-02, U01 HG004738, U01 HG004738-02, U01HG004399, U01HG004402, U01HG004415, U01HG004422, U01HG004423, U01HG004436, U01HG004438, U01HG004446, U01HG004728, U01HG004729, U01HG004735, U01HG004738, U01HG04424; NHLBI NIH HHS: HL 043851, HL087679, HL69757, N01 HC025195, N01 HC055015, N01 HC055016, N01 HC055018, N01 HC055019, N01 HC055020, N01 HC055021, N01 HC055022, N01-HC-25195, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N02 HL64278, R01 HL043851, R01 HL043851-10, R01 HL059367, R01 HL059367-11, R01 HL086694, R01 HL086694-03, R01 HL087641, R01 HL087641-03, R01 HL087679-03, R01 HL088119, R01 HL088119-04, R01HL086694, R01HL087641, R01HL59367, RC2 HL102419, RC2 HL102419-02, U01 HL072515, U01 HL072515-06, U01 HL084756, U01 HL084756-03, U01 HL84756, U01HL72515, U19 HL069757, U19 HL069757-11; NIA NIH HHS: AG-16592, N.1-AG-1-1, N.1-AG-1-2111, N01 AG012100, N01 AG012109, N01 AG050002, N01-AG-1-2109, N01-AG-12100, N01-AG-5-0002, P01 AG018397, P01 AG018397-08, P01 AG025204-03, P01-AG-18397, R01 AG016592, R01 AG016592-10, R01 AG041517, R01 AR/AG 41398, R21 AG032598, R21 AG032598-02, R21AG032598; NIAAA NIH HHS: AA07535, AA10248, AA13320, AA13321, AA13326, AA14041, K05 AA017688, R01 AA007535, R01 AA007535-08, R01 AA013320, R01 AA013320-05, R01 AA013321, R01 AA013321-05, R01 AA013326-05, R01 AA014041-05, U10 AA008401, U10 AA008401-23, U10AA008401; NIAMS NIH HHS: R01 AR041398, R01 AR041398-15, R01 AR041398-20; NICHD NIH HHS: HD-061437, R03 HD061437, R03 HD061437-02; NIDA NIH HHS: R01 DA012854, R01 DA012854-09, R01 DA013423, R01 DA013423-05, R01 DA019963, R01 DA019963-01A2, R01 DA019963-02, R01 DA019963-03, R01-DA013423; NIDCR NIH HHS: U01 DE018903, U01 DE018903-02, U01 DE018993, U01 DE018993-01, U01DE018903, U01DE018993; NIDDK NIH HHS: P30 DK072488, R01 DK058845, R01 DK058845-11, R01DK058845, U01 DK062418, U01 DK062418-06; NIMH NIH HHS: MH66206, R01 MH066206, R01 MH066206-05; NIMHD NIH HHS: 263 MD 821336, 263 MD 9164, 263 MD821336, 263 MD9164 13; PHS HHS: HHSN268200625226C, HHSN268200782096C, R01-088119, RFAHG006033; Wellcome Trust: 068545/Z/02, 076467/Z/05/Z, 077016/Z/05/Z, 079895, 89061/Z/09/Z

    Nature genetics 2010;42;12;1077-85

  • Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.

    Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Lango Allen H, Lindgren CM, Luan J, Mägi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segrè AV, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpeläinen TO, Yang J, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti-Proença C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L, Connell J, Day IN, den Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grässler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jørgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, König IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaløy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimäki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN, Ludwig B, MAGIC, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O'Donnell CJ, O'Rahilly S, Ong KK, Oostra B, Paré G, Parker AN, Perola M, Pichler I, Pietiläinen KH, Platou CG, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstråle M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich TM, Thompson JR, Thomson B, Tönjes A, Tuomi T, van Meurs JB, van Ommen GJ, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CI, Voight BF, Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL, Kähönen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J, Froguel P, Grönberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Procardis Consortium, Wilson JF, Wright AF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E and Loos RJ

    Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.

    Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

    Funded by: British Heart Foundation; Cancer Research UK; Chief Scientist Office: CZB/4/710; Department of Health; Medical Research Council: G0000934, G0401527, G0501184, G0600705, G0601261, G0701863, G0801056, G0900554, G9521010, G9824984, MC_QA137934, MC_U106179471, MC_U106179472, MC_U106188470, MC_U127561128, MC_U137686854; NCI NIH HHS: CA047988, CA49449, CA50385, CA65725, CA67262, CA87969, U01-CA098233; NCRR NIH HHS: M01-RR00425, U54-RR020278, UL1-RR025005; NHGRI NIH HHS: HG002651, N01-HG-65403, T32 HG000040, T32 HG000040-17, T32-HG00040, U01-HG004399, U01-HG004402, Z01-HG000024; NHLBI NIH HHS: HL084729, HL71981, K99-HL094535, N01-HC15103, N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC55018, N01-HC55019, N01-HC55020, N01-HC55022, N01-HC55222, N01-HC75150, N01-HC85079, N01-HC85080, N01-HC85081, N01-HC85082, N01-HC85083, N01-HC85084, N01-HC85085, N01-HC85086, N01-N01HC-55021, N02-HL64278, R01 HL071981, R01 HL087647, R01-HL086694, R01-HL087641, R01-HL087647, R01-HL087652, R01-HL087676, R01-HL087679, R01-HL087700, R01-HL088119, R01-HL59367, U01 HL054527, U01-HL080295, U01-HL084756, U01-HL72515; NIA NIH HHS: N01-AG12100, N01-AG12109, R01-AG031890; NIAAA NIH HHS: AA014041, AA07535, AA10248, AA13320, AA13321, AA13326, K05 AA017688; NIAMS NIH HHS: K08 AR055688, K08 AR055688-03, K08 AR055688-04; NIDA NIH HHS: DA12854, R01 DA012854; NIDDK NIH HHS: DK062370, DK063491, DK072193, DK46200, DK58845, F32 DK079466, F32 DK079466-01, K23 DK080145, K23 DK080145-01, K23-DK080145, P30-DK072488, R01 DK072193, R01 DK072193-05, R01-DK073490, R01-DK075787, R01DK068336, R01DK075681, U01 DK062370, U01 DK062370-08, U01-DK062418; NIGMS NIH HHS: T32 GM074905, U01-GM074518; NIMH NIH HHS: MH084698, R01-MH59160, R01-MH59565, R01-MH59566, R01-MH59571, R01-MH59586, R01-MH59587, R01-MH59588, R01-MH60870, R01-MH60879, R01-MH61675, R01-MH63706, R01-MH67257, R01-MH79469, R01-MH79470, R01-MH81800, RL1-MH083268; PHS HHS: 263-MA-410953; Wellcome Trust: 064890, 068545, 072960, 075491, 076113, 077016, 079557, 079895, 081682, 083270, 085301, 086596

    Nature genetics 2010;42;11;937-48

  • Genetic variants influencing circulating lipid levels and risk of coronary artery disease.

    Waterworth DM, Ricketts SL, Song K, Chen L, Zhao JH, Ripatti S, Aulchenko YS, Zhang W, Yuan X, Lim N, Luan J, Ashford S, Wheeler E, Young EH, Hadley D, Thompson JR, Braund PS, Johnson T, Struchalin M, Surakka I, Luben R, Khaw KT, Rodwell SA, Loos RJ, Boekholdt SM, Inouye M, Deloukas P, Elliott P, Schlessinger D, Sanna S, Scuteri A, Jackson A, Mohlke KL, Tuomilehto J, Roberts R, Stewart A, Kesäniemi YA, Mahley RW, Grundy SM, Wellcome Trust Case Control Consortium, McArdle W, Cardon L, Waeber G, Vollenweider P, Chambers JC, Boehnke M, Abecasis GR, Salomaa V, Järvelin MR, Ruokonen A, Barroso I, Epstein SE, Hakonarson HH, Rader DJ, Reilly MP, Witteman JC, Hall AS, Samani NJ, Strachan DP, Barter P, van Duijn CM, Kooner JS, Peltonen L, Wareham NJ, McPherson R, Mooser V and Sandhu MS

    Genetics Division, GlaxoSmithKline R&D, King of Prussia, PA, USA.

    Objective: Genetic studies might provide new insights into the biological mechanisms underlying lipid metabolism and risk of CAD. We therefore conducted a genome-wide association study to identify novel genetic determinants of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides.

    We combined genome-wide association data from 8 studies, comprising up to 17 723 participants with information on circulating lipid concentrations. We did independent replication studies in up to 37 774 participants from 8 populations and also in a population of Indian Asian descent. We also assessed the association between single-nucleotide polymorphisms (SNPs) at lipid loci and risk of CAD in up to 9 633 cases and 38 684 controls. We identified 4 novel genetic loci that showed reproducible associations with lipids (probability values, 1.6×10(-8) to 3.1×10(-10)). These include a potentially functional SNP in the SLC39A8 gene for HDL-C, an SNP near the MYLIP/GMPR and PPP1R3B genes for LDL-C, and at the AFF1 gene for triglycerides. SNPs showing strong statistical association with 1 or more lipid traits at the CELSR2, APOB, APOE-C1-C4-C2 cluster, LPL, ZNF259-APOA5-A4-C3-A1 cluster and TRIB1 loci were also associated with CAD risk (probability values, 1.1×10(-3) to 1.2×10(-9)).

    Conclusions: We have identified 4 novel loci associated with circulating lipids. We also show that in addition to those that are largely associated with LDL-C, genetic loci mainly associated with circulating triglycerides and HDL-C are also associated with risk of CAD. These findings potentially provide new insights into the biological mechanisms underlying lipid metabolism and CAD risk.

    Funded by: British Heart Foundation: PG/08/094, PG/08/094/26019; Medical Research Council: G0000934, G0401527, G0500539, G0601966, G0700931, G0701863, G0801566, MC_QA137934, MC_U105630924, MC_U106179471, MC_U106188470; NHLBI NIH HHS: 5R01HL087679-02; NIDDK NIH HHS: R01 DK062370, U01 DK062370, U01 DK062418; NIMH NIH HHS: 1RL1MH083268-01; Wellcome Trust: 068545/Z/02, 077016/Z/05/Z, 079895, GR069224

    Arteriosclerosis, thrombosis, and vascular biology 2010;30;11;2264-76

  • Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.

    Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V, Thorleifsson G, Zillikens MC, Speliotes EK, Mägi R, Workalemahu T, White CC, Bouatia-Naji N, Harris TB, Berndt SI, Ingelsson E, Willer CJ, Weedon MN, Luan J, Vedantam S, Esko T, Kilpeläinen TO, Kutalik Z, Li S, Monda KL, Dixon AL, Holmes CC, Kaplan LM, Liang L, Min JL, Moffatt MF, Molony C, Nicholson G, Schadt EE, Zondervan KT, Feitosa MF, Ferreira T, Lango Allen H, Weyant RJ, Wheeler E, Wood AR, MAGIC, Estrada K, Goddard ME, Lettre G, Mangino M, Nyholt DR, Purcell S, Smith AV, Visscher PM, Yang J, McCarroll SA, Nemesh J, Voight BF, Absher D, Amin N, Aspelund T, Coin L, Glazer NL, Hayward C, Heard-Costa NL, Hottenga JJ, Johansson A, Johnson T, Kaakinen M, Kapur K, Ketkar S, Knowles JW, Kraft P, Kraja AT, Lamina C, Leitzmann MF, McKnight B, Morris AP, Ong KK, Perry JR, Peters MJ, Polasek O, Prokopenko I, Rayner NW, Ripatti S, Rivadeneira F, Robertson NR, Sanna S, Sovio U, Surakka I, Teumer A, van Wingerden S, Vitart V, Zhao JH, Cavalcanti-Proença C, Chines PS, Fisher E, Kulzer JR, Lecoeur C, Narisu N, Sandholt C, Scott LJ, Silander K, Stark K, Tammesoo ML, Teslovich TM, Timpson NJ, Watanabe RM, Welch R, Chasman DI, Cooper MN, Jansson JO, Kettunen J, Lawrence RW, Pellikka N, Perola M, Vandenput L, Alavere H, Almgren P, Atwood LD, Bennett AJ, Biffar R, Bonnycastle LL, Bornstein SR, Buchanan TA, Campbell H, Day IN, Dei M, Dörr M, Elliott P, Erdos MR, Eriksson JG, Freimer NB, Fu M, Gaget S, Geus EJ, Gjesing AP, Grallert H, Grässler J, Groves CJ, Guiducci C, Hartikainen AL, Hassanali N, Havulinna AS, Herzig KH, Hicks AA, Hui J, Igl W, Jousilahti P, Jula A, Kajantie E, Kinnunen L, Kolcic I, Koskinen S, Kovacs P, Kroemer HK, Krzelj V, Kuusisto J, Kvaloy K, Laitinen J, Lantieri O, Lathrop GM, Lokki ML, Luben RN, Ludwig B, McArdle WL, McCarthy A, Morken MA, Nelis M, Neville MJ, Paré G, Parker AN, Peden JF, Pichler I, Pietiläinen KH, Platou CG, Pouta A, Ridderstråle M, Samani NJ, Saramies J, Sinisalo J, Smit JH, Strawbridge RJ, Stringham HM, Swift AJ, Teder-Laving M, Thomson B, Usala G, van Meurs JB, van Ommen GJ, Vatin V, Volpato CB, Wallaschofski H, Walters GB, Widen E, Wild SH, Willemsen G, Witte DR, Zgaga L, Zitting P, Beilby JP, James AL, Kähönen M, Lehtimäki T, Nieminen MS, Ohlsson C, Palmer LJ, Raitakari O, Ridker PM, Stumvoll M, Tönjes A, Viikari J, Balkau B, Ben-Shlomo Y, Bergman RN, Boeing H, Smith GD, Ebrahim S, Froguel P, Hansen T, Hengstenberg C, Hveem K, Isomaa B, Jørgensen T, Karpe F, Khaw KT, Laakso M, Lawlor DA, Marre M, Meitinger T, Metspalu A, Midthjell K, Pedersen O, Salomaa V, Schwarz PE, Tuomi T, Tuomilehto J, Valle TT, Wareham NJ, Arnold AM, Beckmann JS, Bergmann S, Boerwinkle E, Boomsma DI, Caulfield MJ, Collins FS, Eiriksdottir G, Gudnason V, Gyllensten U, Hamsten A, Hattersley AT, Hofman A, Hu FB, Illig T, Iribarren C, Jarvelin MR, Kao WH, Kaprio J, Launer LJ, Munroe PB, Oostra B, Penninx BW, Pramstaller PP, Psaty BM, Quertermous T, Rissanen A, Rudan I, Shuldiner AR, Soranzo N, Spector TD, Syvanen AC, Uda M, Uitterlinden A, Völzke H, Vollenweider P, Wilson JF, Witteman JC, Wright AF, Abecasis GR, Boehnke M, Borecki IB, Deloukas P, Frayling TM, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, North KE, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, Hirschhorn JN, Assimes TL, Wichmann HE, Thorsteinsdottir U, van Duijn CM, Stefansson K, Cupples LA, Loos RJ, Barroso I, McCarthy MI, Fox CS, Mohlke KL and Lindgren CM

    Regensburg University Medical Center, Department of Epidemiology and Preventive Medicine, Regensburg, Germany.

    Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

    Funded by: British Heart Foundation; Chief Scientist Office: CZB/4/710; Department of Health; Medical Research Council: G0000934, G0401527, G0500115, G0501184, G0600705, G0601261, G0701863, G0801056, G9521010, MC_QA137934, MC_U106179472, MC_U106188470, MC_U127561128, MC_UP_A390_1107; NCI NIH HHS: CA047988, CA49449, CA50385, CA65725, CA67262, CA87969, P01 CA087969, P01 CA087969-12, R01 CA047988, R01 CA047988-20, R01 CA050385, R01 CA050385-20, R01 CA065725, R01 CA065725-14, R01 CA067262, R01 CA067262-14, U01 CA049449, U01 CA049449-21, U01 CA098233, U01 CA098233-08, ­U01-CA098233; NCRR NIH HHS: UL1 RR025005, UL1 RR025005-04, UL1-RR025005, ­UL1-RR025005; NHGRI NIH HHS: HG002651, HG005581, N01 HG065403, N01-HG-65403, R01 HG002651, R01 HG002651-05, RC2 HG005581, RC2 HG005581-02, T32 HG000040, T32 HG000040-14, U01 HG004399, U01 HG004399-02, U01 HG004402, U01 HG004402-02, Z01 HG000024-14, ­T32-HG00040, ­U01-HG004399, ­U01-HG004402; NHLBI NIH HHS: HL043851, HL084729, HL71981, K99 HL094535, K99 HL094535-02, N01 HC015103, N01 HC025195, N01 HC035129, N01 HC045133, N01 HC055015, N01 HC055016, N01 HC055018, N01 HC055019, N01 HC055020, N01 HC055021, N01 HC055022, N01 HC055222, N01 HC075150, N01 HC085079, N01 HC085080, N01 HC085081, N01 HC085082, N01 HC085083, N01 HC085084, N01 HC085085, N01 HC085086, N01-HC-55018, N01-HC55222, R01 HL043851, R01 HL043851-10, R01 HL059367, R01 HL059367-10, R01 HL071981, R01 HL071981-07, R01 HL086694, R01 HL086694-03, R01 HL087641, R01 HL087641-03, R01 HL087647, R01 HL087647-03, R01 HL087652, R01 HL087652-03, R01 HL087679-03, R01 HL087700, R01 HL087700-03, R01 HL088119, R01 HL088119-04, R01-HL087647, R01-HL59367, U01 HL054527, U01 HL072515, U01 HL072515-06, U01 HL080295, U01 HL080295-04, U01 HL084729, U01 HL084729-03, U01 HL084756, U01 HL084756-03, U01-HL72515, ­K99HL094535, ­N01-HC-25195, ­N01-HC-55019, ­N01-HC-55020, ­N01-HC-55021, ­N01-HC-55022, ­N01-HC15103, ­N01-HC35129, ­N01-HC45133, ­N01-HC55015, ­N01-HC55016, ­N01-HC75150, ­N01-HC85079, ­N01-HC85080, ­N01-HC85081, ­N01-HC85082, ­N01-HC85083, ­N01-HC85084, ­N01-HC85085, ­N01-HC85086, ­R01-HL086694, ­R01-HL087641, ­R01-HL087679, ­R01-HL087700, ­R01-HL088119, ­R01­HL087652, ­U01-HL084756; NIA NIH HHS: N01 AG012100, N01 AG012109, N01-AG-1-2109, R01 AG031890-02, ­N01-AG-12100, ­R01-AG031890; NIDDK NIH HHS: DK062370, DK072193, DK075787, DK58845, F32 DK079466, F32 DK079466-01, K23 DK080145, K23 DK080145-01, K23-DK080145, P30 DK046200, P30 DK046200-14, P30 DK072488, P30 DK072488-06, R01 DK056690, R01 DK058845, R01 DK058845-11, R01 DK068336, R01 DK068336-03, R01 DK072193, R01 DK072193-05, R01 DK073490, R01 DK073490-05, R01 DK075681, R01 DK075681-04, R01 DK075787, R01 DK075787-05, R01 DK089256, R01-DK068336, R01-DK075787, U01 DK062370, U01 DK062370-08, U01 DK062418, U01 DK062418-06, ­K23-DK080145, ­P30-DK072488, ­R01-DK-073490, ­R01-DK075681, ­R01-DK075787, ­U01-DK062418; NIGMS NIH HHS: U01 GM074518, U01 GM074518-05, ­U01-GM074518; NIMH NIH HHS: R01 MH063706-05, R01 MH084698, R01 MH084698-03, RL1 MH083268, RL1 MH083268-05, ­1RL1-MH083268-01, ­MH084698, ­R01-MH63706; PHS HHS: ­263-MA-410953; Wellcome Trust: 064890, 068545, 072960, 075491, 076113, 077011, 077016, 077016/Z/05/Z, 079557, 079895, 081682, 083270, 085235, 085301, 086596, 088885, 089061, 091746, ­068545/Z/02, ­072960, ­076113/B/04/Z, ­091746/Z/10/Z, ­WT086596/Z/08/Z

    Nature genetics 2010;42;11;949-60

  • Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

    Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia-Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL, Stewart AF, Voight BF, Willenborg C, Wright B, Altshuler D, Arking D, Balkau B, Barnes D, Boerwinkle E, Böhm B, Bonnefond A, Bonnycastle LL, Boomsma DI, Bornstein SR, Böttcher Y, Bumpstead S, Burnett-Miller MS, Campbell H, Cao A, Chambers J, Clark R, Collins FS, Coresh J, de Geus EJ, Dei M, Deloukas P, Döring A, Egan JM, Elosua R, Ferrucci L, Forouhi N, Fox CS, Franklin C, Franzosi MG, Gallina S, Goel A, Graessler J, Grallert H, Greinacher A, Hadley D, Hall A, Hamsten A, Hayward C, Heath S, Herder C, Homuth G, Hottenga JJ, Hunter-Merrill R, Illig T, Jackson AU, Jula A, Kleber M, Knouff CW, Kong A, Kooner J, Köttgen A, Kovacs P, Krohn K, Kühnel B, Kuusisto J, Laakso M, Lathrop M, Lecoeur C, Li M, Li M, Loos RJ, Luan J, Lyssenko V, Mägi R, Magnusson PK, Mälarstig A, Mangino M, Martínez-Larrad MT, März W, McArdle WL, McPherson R, Meisinger C, Meitinger T, Melander O, Mohlke KL, Mooser VE, Morken MA, Narisu N, Nathan DM, Nauck M, O'Donnell C, Oexle K, Olla N, Pankow JS, Payne F, Peden JF, Pedersen NL, Peltonen L, Perola M, Polasek O, Porcu E, Rader DJ, Rathmann W, Ripatti S, Rocheleau G, Roden M, Rudan I, Salomaa V, Saxena R, Schlessinger D, Schunkert H, Schwarz P, Seedorf U, Selvin E, Serrano-Ríos M, Shrader P, Silveira A, Siscovick D, Song K, Spector TD, Stefansson K, Steinthorsdottir V, Strachan DP, Strawbridge R, Stumvoll M, Surakka I, Swift AJ, Tanaka T, Teumer A, Thorleifsson G, Thorsteinsdottir U, Tönjes A, Usala G, Vitart V, Völzke H, Wallaschofski H, Waterworth DM, Watkins H, Wichmann HE, Wild SH, Willemsen G, Williams GH, Wilson JF, Winkelmann J, Wright AF, WTCCC, Zabena C, Zhao JH, Epstein SE, Erdmann J, Hakonarson HH, Kathiresan S, Khaw KT, Roberts R, Samani NJ, Fleming MD, Sladek R, Abecasis G, Boehnke M, Froguel P, Groop L, McCarthy MI, Kao WH, Florez JC, Uda M, Wareham NJ*, Barroso I* and Meigs JB*

    Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.

    Objective: Glycated hemoglobin (HbA₁(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA₁(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA₁(c) levels.

    We studied associations with HbA₁(c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA₁(c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.

    Results: Ten loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10⁻²⁶), HFE (rs1800562/P = 2.6 × 10⁻²⁰), TMPRSS6 (rs855791/P = 2.7 × 10⁻¹⁴), ANK1 (rs4737009/P = 6.1 × 10⁻¹²), SPTA1 (rs2779116/P = 2.8 × 10⁻⁹) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10⁻⁹), and four known HbA₁(c) loci: HK1 (rs16926246/P = 3.1 × 10⁻⁵⁴), MTNR1B (rs1387153/P = 4.0 × 10⁻¹¹), GCK (rs1799884/P = 1.5 × 10⁻²⁰) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10⁻¹⁸). We show that associations with HbA₁(c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA₁(c)) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA₁(c).

    Conclusions: GWAS identified 10 genetic loci reproducibly associated with HbA₁(c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA₁(c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA₁(c).

    Diabetes 2010;59;12;3229-39

    (* Equal communicating author)

    • Loss of NPC1 function in a patient with a co-inherited novel insulin receptor mutation does not grossly modify the severity of the associated insulin resistance.

      Kirk J, Porter KM, Parker V, Barroso I, O'Rahilly S, Hendriksz C and Semple RK

      Department of Endocrinology, Birmingham Children's Hospital, Steelhouse Lane, Birmingham B4 6NH, United Kingdom.

      In Npc1 null mice, a model for Niemann Pick Disease Type C1, it has been reported that hepatocyte insulin receptor function is significantly impaired, consistent with growing evidence that membrane fluidity and microdomain structure have an important role in insulin signal transduction. However, whether insulin receptor function is also compromised in human Niemann Pick disease Type C1 is unclear. We now report a girl who developed progressive dementia, ataxia and opthalmoplegia from 9 years old, followed by severe acanthosis nigricans, hirsutism and acne at 11 years old. She was diagnosed with Niemann Pick Disease type C1 (OMIM#257220) based on positive filipin staining and reduced cholesterol-esterifying activity in dermal fibroblasts, and homozygosity for the p.Ile1061Thr NPC1 mutation. Further analysis revealed her also to be heterozygous for a novel trinucleotide deletion (c.3659 + 1_3659 + 3delGTG) at the end of exon 20 of INSR, encoding the insulin receptor, leading to deletion of Trp1193 in the intracellular tyrosine kinase domain. INSR mRNA and protein levels were normal in dermal fibroblasts, consistent with a primary signal transduction defect in the mutant receptor. Although the proband was significantly more insulin resistant than her father, who carried the INSR mutation but was only heterozygous for the NPC1 variant, their respective degrees of IR were very similar to those previously reported in a father-daughter pair with the closely related p.Trp1193Leu INSR mutation. This suggests that loss of NPC1 function, with attendant changes in membrane cholesterol composition, does not significantly modify the IR phenotype, even in the context of severely impaired INSR function.

      Funded by: Medical Research Council; Wellcome Trust: 077016, 078986, 078986/Z/06/Z, 080952, 080952/Z/06/Z

      Journal of inherited metabolic disease 2010;33 Suppl 3;S227-32

    • Hundreds of variants clustered in genomic loci and biological pathways affect human height.

      Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S, Ferreira T, Wood AR, Weyant RJ, Segrè AV, Speliotes EK, Wheeler E, Soranzo N, Park JH, Yang J, Gudbjartsson D, Heard-Costa NL, Randall JC, Qi L, Vernon Smith A, Mägi R, Pastinen T, Liang L, Heid IM, Luan J, Thorleifsson G, Winkler TW, Goddard ME, Sin Lo K, Palmer C, Workalemahu T, Aulchenko YS, Johansson A, Zillikens MC, Feitosa MF, Esko T, Johnson T, Ketkar S, Kraft P, Mangino M, Prokopenko I, Absher D, Albrecht E, Ernst F, Glazer NL, Hayward C, Hottenga JJ, Jacobs KB, Knowles JW, Kutalik Z, Monda KL, Polasek O, Preuss M, Rayner NW, Robertson NR, Steinthorsdottir V, Tyrer JP, Voight BF, Wiklund F, Xu J, Zhao JH, Nyholt DR, Pellikka N, Perola M, Perry JR, Surakka I, Tammesoo ML, Altmaier EL, Amin N, Aspelund T, Bhangale T, Boucher G, Chasman DI, Chen C, Coin L, Cooper MN, Dixon AL, Gibson Q, Grundberg E, Hao K, Juhani Junttila M, Kaplan LM, Kettunen J, König IR, Kwan T, Lawrence RW, Levinson DF, Lorentzon M, McKnight B, Morris AP, Müller M, Suh Ngwa J, Purcell S, Rafelt S, Salem RM, Salvi E, Sanna S, Shi J, Sovio U, Thompson JR, Turchin MC, Vandenput L, Verlaan DJ, Vitart V, White CC, Ziegler A, Almgren P, Balmforth AJ, Campbell H, Citterio L, De Grandi A, Dominiczak A, Duan J, Elliott P, Elosua R, Eriksson JG, Freimer NB, Geus EJ, Glorioso N, Haiqing S, Hartikainen AL, Havulinna AS, Hicks AA, Hui J, Igl W, Illig T, Jula A, Kajantie E, Kilpeläinen TO, Koiranen M, Kolcic I, Koskinen S, Kovacs P, Laitinen J, Liu J, Lokki ML, Marusic A, Maschio A, Meitinger T, Mulas A, Paré G, Parker AN, Peden JF, Petersmann A, Pichler I, Pietiläinen KH, Pouta A, Ridderstråle M, Rotter JI, Sambrook JG, Sanders AR, Schmidt CO, Sinisalo J, Smit JH, Stringham HM, Bragi Walters G, Widen E, Wild SH, Willemsen G, Zagato L, Zgaga L, Zitting P, Alavere H, Farrall M, McArdle WL, Nelis M, Peters MJ, Ripatti S, van Meurs JB, Aben KK, Ardlie KG, Beckmann JS, Beilby JP, Bergman RN, Bergmann S, Collins FS, Cusi D, den Heijer M, Eiriksdottir G, Gejman PV, Hall AS, Hamsten A, Huikuri HV, Iribarren C, Kähönen M, Kaprio J, Kathiresan S, Kiemeney L, Kocher T, Launer LJ, Lehtimäki T, Melander O, Mosley TH, Musk AW, Nieminen MS, O'Donnell CJ, Ohlsson C, Oostra B, Palmer LJ, Raitakari O, Ridker PM, Rioux JD, Rissanen A, Rivolta C, Schunkert H, Shuldiner AR, Siscovick DS, Stumvoll M, Tönjes A, Tuomilehto J, van Ommen GJ, Viikari J, Heath AC, Martin NG, Montgomery GW, Province MA, Kayser M, Arnold AM, Atwood LD, Boerwinkle E, Chanock SJ, Deloukas P, Gieger C, Grönberg H, Hall P, Hattersley AT, Hengstenberg C, Hoffman W, Lathrop GM, Salomaa V, Schreiber S, Uda M, Waterworth D, Wright AF, Assimes TL, Barroso I, Hofman A, Mohlke KL, Boomsma DI, Caulfield MJ, Cupples LA, Erdmann J, Fox CS, Gudnason V, Gyllensten U, Harris TB, Hayes RB, Jarvelin MR, Mooser V, Munroe PB, Ouwehand WH, Penninx BW, Pramstaller PP, Quertermous T, Rudan I, Samani NJ, Spector TD, Völzke H, Watkins H, Wilson JF, Groop LC, Haritunians T, Hu FB, Kaplan RC, Metspalu A, North KE, Schlessinger D, Wareham NJ, Hunter DJ, O'Connell JR, Strachan DP, Wichmann HE, Borecki IB, van Duijn CM, Schadt EE, Thorsteinsdottir U, Peltonen L, Uitterlinden AG, Visscher PM, Chatterjee N, Loos RJ, Boehnke M, McCarthy MI, Ingelsson E, Lindgren CM, Abecasis GR, Stefansson K, Frayling TM and Hirschhorn JN

      Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter EX1 2LU, UK.

      Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

      Funded by: British Heart Foundation: PG/02/128, PG/02/128/14470; Cancer Research UK; Chief Scientist Office: CZB/4/276, CZB/4/279, CZB/4/710; Medical Research Council: G0000649, G0000934, G0500539, G0600331, G0600331(77796), G0601261, G0701863, G9521010, G9521010(63660), G9521010D, MC_QA137934, MC_U106179471, MC_U106188470, MC_U127561128; NCI NIH HHS: CA047988, CA49449, CA50385, CA65725, CA67262, CA87969, P01 CA087969, P01 CA087969-12, R01 CA047988, R01 CA047988-20, R01 CA050385, R01 CA050385-20, R01 CA065725, R01 CA065725-14, R01 CA067262, R01 CA067262-14, R01 CA104021, R01 CA104021-02, U01 CA049449, U01 CA049449-21, U01 CA098233, U01 CA098233-08, U01-CA098233; NCRR NIH HHS: M01-RR00425, U54-RR020278, UL1-RR025005; NHGRI NIH HHS: HG002651, HG005214, HG005581, R01 HG002651, R01 HG002651-05, RC2 HG005581, RC2 HG005581-02, T32-HG00040, U01 HG004399, U01 HG004399-02, U01 HG004402, U01 HG004402-02, U01 HG005214, U01 HG005214-02, U01-HG004399, U01-HG004402, Z01-HG000024; NHLBI NIH HHS: HL043851, HL084729, HL69757, HL71981, K99-HL094535, N01-HC15103, N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC55018, N01-HC55019, N01-HC55020, N01-HC55021, N01-HC55022, N01-HC55222, N01-HC75150, N01-HC85079, N01-HC85080, N01-HC85081, N01-HC85082, N01-HC85083, N01-HC85084, N01-HC85085, N01-HC85086, N02-HL-6-4278, R01 HL043851, R01 HL043851-10, R01 HL059367, R01 HL059367-10, R01 HL071981, R01 HL071981-07, R01 HL086694, R01 HL086694-02, R01 HL087641, R01 HL087641-01, R01 HL087647, R01 HL087647-01, R01 HL087652, R01 HL087652-01, R01 HL087676, R01 HL087676-01, R01 HL087679-01, R01 HL087700, R01 HL087700-03, R01 HL088119, R01 HL088119-01, R01-HL086694, R01-HL087641, R01-HL087647, R01-HL087652, R01-HL087676, R01-HL087679, R01-HL087700, R01-HL088119, R01-HL59367, U01 HL069757, U01 HL069757-10, U01 HL072515, U01 HL072515-06, U01 HL080295, U01 HL080295-04, U01 HL084729, U01 HL084729-03, U01 HL084756, U01 HL084756-03, U01-HL080295, U01-HL084756, U01-HL72515; NIA NIH HHS: N01-AG12100, N01-AG12109, R01 AG031890-02, R01-AG031890, Z01-AG00675, Z01-AG007380; NIAAA NIH HHS: AA014041, AA07535, AA10248, AA13320, AA13321, AA13326, K05 AA017688, R01 AA007535, R01 AA007535-08, R01 AA013320-04, R01 AA013321, R01 AA013321-05, R01 AA013326-05, R01 AA014041-05; NIAMS NIH HHS: K08 AR055688, K08 AR055688-03, K08 AR055688-04, K08-AR055688; NIDA NIH HHS: DA12854, R01 DA012854, R01 DA012854-09; NIDDK NIH HHS: DK062370, DK063491, DK072193, DK079466, DK080145, DK46200, DK58845, F32 DK079466, F32 DK079466-01, K23 DK080145, K23 DK080145-01, K23-DK080145, P30 DK072488, R01 DK058845, R01 DK058845-11, R01 DK068336, R01 DK068336-01, R01 DK072193, R01 DK072193-05, R01 DK073490, R01 DK073490-01, R01 DK075681, R01 DK075681-02, R01 DK075787, R01 DK075787-03, R01 DK089256, R01 DK091718, R01-DK068336, R01-DK073490, R01-DK075681, R01-DK075787, U01 DK062370, U01 DK062370-08, U01 DK062418; NIGMS NIH HHS: U01 GM074518, U01 GM074518-05, U01-GM074518; NIMH NIH HHS: MH084698, R01 MH059160, R01 MH059160-04, R01 MH059565, R01 MH059565-06, R01 MH059566, R01 MH059566-08, R01 MH059571, R01 MH059571-05, R01 MH059586, R01 MH059586-08, R01 MH059587-09, R01 MH059588-08, R01 MH060870-09, R01 MH060879-08, R01 MH061675, R01 MH061675-09, R01 MH067257-04, R01 MH081800, R01 MH081800-01, R01-MH059160, R01-MH59565, R01-MH59566, R01-MH59571, R01-MH59586, R01-MH59587, R01-MH59588, R01-MH60870, R01-MH60879, R01-MH61675, R01-MH63706, R01-MH67257, R01-MH79469, R01-MH81800, RL1 MH083268, RL1 MH083268-05, RL1-MH083268, U01 MH079469, U01 MH079469-03, U01 MH079470, U01 MH079470-03, U01-MH79469, U01-MH79470; PHS HHS: 263-MA-410953, HHSN268200625226C, N01-G65403; Wellcome Trust: 064890, 068545, 068545/Z/02, 072856, 072960, 075491, 076113, 076113/B/04/Z, 076113/C/04/Z, 077016, 077016/Z/05/Z, 079557, 079771, 079895, 081682, 081682/Z/06/Z, 083270, 084183/Z/07/Z, 085301, 085301/Z/08/Z, 086596, 086596/Z/08/Z, 088885, 091746, 091746/Z/10/Z

      Nature 2010;467;7317;832-8

    • Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population.

      Fontaine-Bisson B, Renström F, Rolandsson O, MAGIC, Payne F, Hallmans G, Barroso I and Franks PW

      Department of Nutrition Sciences, University of Ottawa, Ottawa, ON, Canada.

      We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score.

      Methods: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test.

      Results: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626.

      Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabetes loci.

      Funded by: Wellcome Trust: 077016/Z/05/Z

      Diabetologia 2010;53;10;2155-62

    • Biological, clinical and population relevance of 95 loci for blood lipids.

      Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, Johansen CT, Fouchier SW, Isaacs A, Peloso GM, Barbalic M, Ricketts SL, Bis JC, Aulchenko YS, Thorleifsson G, Feitosa MF, Chambers J, Orho-Melander M, Melander O, Johnson T, Li X, Guo X, Li M, Shin Cho Y, Jin Go M, Jin Kim Y, Lee JY, Park T, Kim K, Sim X, Twee-Hee Ong R, Croteau-Chonka DC, Lange LA, Smith JD, Song K, Hua Zhao J, Yuan X, Luan J, Lamina C, Ziegler A, Zhang W, Zee RY, Wright AF, Witteman JC, Wilson JF, Willemsen G, Wichmann HE, Whitfield JB, Waterworth DM, Wareham NJ, Waeber G, Vollenweider P, Voight BF, Vitart V, Uitterlinden AG, Uda M, Tuomilehto J, Thompson JR, Tanaka T, Surakka I, Stringham HM, Spector TD, Soranzo N, Smit JH, Sinisalo J, Silander K, Sijbrands EJ, Scuteri A, Scott J, Schlessinger D, Sanna S, Salomaa V, Saharinen J, Sabatti C, Ruokonen A, Rudan I, Rose LM, Roberts R, Rieder M, Psaty BM, Pramstaller PP, Pichler I, Perola M, Penninx BW, Pedersen NL, Pattaro C, Parker AN, Pare G, Oostra BA, O'Donnell CJ, Nieminen MS, Nickerson DA, Montgomery GW, Meitinger T, McPherson R, McCarthy MI, McArdle W, Masson D, Martin NG, Marroni F, Mangino M, Magnusson PK, Lucas G, Luben R, Loos RJ, Lokki ML, Lettre G, Langenberg C, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, Kronenberg F, König IR, Khaw KT, Kaprio J, Kaplan LM, Johansson A, Jarvelin MR, Janssens AC, Ingelsson E, Igl W, Kees Hovingh G, Hottenga JJ, Hofman A, Hicks AA, Hengstenberg C, Heid IM, Hayward C, Havulinna AS, Hastie ND, Harris TB, Haritunians T, Hall AS, Gyllensten U, Guiducci C, Groop LC, Gonzalez E, Gieger C, Freimer NB, Ferrucci L, Erdmann J, Elliott P, Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de Geus EJ, de Faire U, Crawford G, Collins FS, Chen YD, Caulfield MJ, Campbell H, Burtt NP, Bonnycastle LL, Boomsma DI, Boekholdt SM, Bergman RN, Barroso I, Bandinelli S, Ballantyne CM, Assimes TL, Quertermous T, Altshuler D, Seielstad M, Wong TY, Tai ES, Feranil AB, Kuzawa CW, Adair LS, Taylor HA, Borecki IB, Gabriel SB, Wilson JG, Holm H, Thorsteinsdottir U, Gudnason V, Krauss RM, Mohlke KL, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele RA, Kastelein JJ, Schadt EE, Rotter JI, Boerwinkle E, Strachan DP, Mooser V, Stefansson K, Reilly MP, Samani NJ, Schunkert H, Cupples LA, Sandhu MS, Ridker PM, Rader DJ, van Duijn CM, Peltonen L, Abecasis GR, Boehnke M and Kathiresan S

      Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA.

      Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.

      Funded by: British Heart Foundation: PG/02/128, PG/08/094, PG/08/094/26019, RG/07/005/23633, SP/08/005/25115; Chief Scientist Office: CZB/4/710; FIC NIH HHS: TW05596; Medical Research Council: G0000934, G0401527, G0601966, G0700931, G0701863, G0801056, G0801566, G9521010, G9521010D, MC_QA137934, MC_U106179471, MC_U106188470, MC_U127527180, MC_U127561128; NCI NIH HHS: CA 047988; NCRR NIH HHS: M01-RR00425, RR20649, U54 RR020278, UL1RR025005; NHGRI NIH HHS: 1Z01 HG000024, N01-HG-65403, T32 HG00040, U01HG004402; NHLBI NIH HHS: 5R01HL087679-02, 5R01HL08770003, 5R01HL08821502, HL 04381, HL 080467, HL-54776, HL085144, K99 HL098364, K99 HL098364-01, K99HL094535, N01 HC-15103, N01 HC-55222, N01-HC-25195, N01-HC-35129, N01-HC-45133, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N01-HC-75150, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, N02-HL-6-4278, R01 HL087647, R01 HL087676, R01 HL089650, R01HL086694, R01HL087641, R01HL087652, R01HL59367, RC1 HL099634, RC1 HL099634-02, RC1 HL099793, RC2 HL101864,, RC2 HL102419, T32HL007208, U01 HL069757, U01 HL080295; NIA NIH HHS: N01-AG-12100; NICHD NIH HHS: R24 HD050924, R24 HD050924-07; NIDDK NIH HHS: 5R01DK06833603, 5R01DK07568102, DK062370, DK063491, DK072193, DK078150, DK56350, R01 DK072193, R01 DK078150, U01 DK062370, U01 DK062418; NIEHS NIH HHS: ES10126; NIGMS NIH HHS: T32 GM007092; PHS HHS: HHSN268200625226C; Wellcome Trust: 068545/Z/02, 076113/B/04/Z, 077016/Z/05/Z, 079895

      Nature 2010;466;7307;707-13

    • Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

      Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Bengtsson Boström K, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney AS, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PR, Jørgensen T, Kao WH, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JR, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CN, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann HE, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI, MAGIC investigators and GIANT Consortium

      Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.

      By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.

      Funded by: Chief Scientist Office: CZB/4/710; Department of Health: DHCS/07/07/008; Medical Research Council: G0601261, G0700222, G0700222(81696), G0701863, MC_U106179471, MC_U106179474, MC_U127592696; NCRR NIH HHS: UL1RR025005; NHGRI NIH HHS: 1 Z01 HG000024, U01HG004171, U01HG004399, U01HG004402; NHLBI NIH HHS: 1K99HL094535-01A1, N01-HC-25195, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N02-HL-6-4278, R01HL086694, R01HL087641, R01HL59367; NIAMS NIH HHS: 1K08AR055688, K08 AR055688, K08 AR055688-03; NIDA NIH HHS: U54 DA021519; NIDDK NIH HHS: DK062370, DK069922, DK072193, DK073490, DK078616, DK58845, K23-DK65978, K24-DK080140, R01 DK029867, R01 DK072193; PHS HHS: HHSN268200625226C; Wellcome Trust: 064890, 072960, 075491, 076113, 077016, 079557, 081682, 083270, 086596, 088885

      Nature genetics 2010;42;7;579-89

    • Detailed physiologic characterization reveals diverse mechanisms for novel genetic Loci regulating glucose and insulin metabolism in humans.

      Ingelsson E, Langenberg C, Hivert MF, Prokopenko I, Lyssenko V, Dupuis J, Mägi R, Sharp S, Jackson AU, Assimes TL, Shrader P, Knowles JW, Zethelius B, Abbasi FA, Bergman RN, Bergmann A, Berne C, Boehnke M, Bonnycastle LL, Bornstein SR, Buchanan TA, Bumpstead SJ, Böttcher Y, Chines P, Collins FS, Cooper CC, Dennison EM, Erdos MR, Ferrannini E, Fox CS, Graessler J, Hao K, Isomaa B, Jameson KA, Kovacs P, Kuusisto J, Laakso M, Ladenvall C, Mohlke KL, Morken MA, Narisu N, Nathan DM, Pascoe L, Payne F, Petrie JR, Sayer AA, Schwarz PE, Scott LJ, Stringham HM, Stumvoll M, Swift AJ, Syvänen AC, Tuomi T, Tuomilehto J, Tönjes A, Valle TT, Williams GH, Lind L, Barroso I, Quertermous T, Walker M, Wareham NJ, Meigs JB, McCarthy MI, Groop L, Watanabe RM, Florez JC and MAGIC investigators

      Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. erik.ingelsson@ki.se

      OBJECTIVE Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action. RESEARCH DESIGN AND METHODS We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084). RESULTS The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 x 10(-71)). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction. CONCLUSIONS Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.

      Funded by: Medical Research Council: G0701863, MC_U106179471, MC_U147574213, MC_U147574239, MC_UP_A620_1014, MC_UP_A620_1015; NHLBI NIH HHS: R01 HL087647; NIDDK NIH HHS: R01 DK029867

      Diabetes 2010;59;5;1266-75

    • Meta-analysis and imputation refines the association of 15q25 with smoking quantity.

      Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G, Vollenweider P, Preisig M, Wareham NJ, Zhao JH, Loos RJ, Barroso I, Khaw KT, Grundy S, Barter P, Mahley R, Kesaniemi A, McPherson R, Vincent JB, Strauss J, Kennedy JL, Farmer A, McGuffin P, Day R, Matthews K, Bakke P, Gulsvik A, Lucae S, Ising M, Brueckl T, Horstmann S, Wichmann HE, Rawal R, Dahmen N, Lamina C, Polasek O, Zgaga L, Huffman J, Campbell S, Kooner J, Chambers JC, Burnett MS, Devaney JM, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Epstein S, Wilson JF, Wild SH, Campbell H, Vitart V, Reilly MP, Li M, Qu L, Wilensky R, Matthai W, Hakonarson HH, Rader DJ, Franke A, Wittig M, Schäfer A, Uda M, Terracciano A, Xiao X, Busonero F, Scheet P, Schlessinger D, St Clair D, Rujescu D, Abecasis GR, Grabe HJ, Teumer A, Völzke H, Petersmann A, John U, Rudan I, Hayward C, Wright AF, Kolcic I, Wright BJ, Thompson JR, Balmforth AJ, Hall AS, Samani NJ, Anderson CA, Ahmad T, Mathew CG, Parkes M, Satsangi J, Caulfield M, Munroe PB, Farrall M, Dominiczak A, Worthington J, Thomson W, Eyre S, Barton A, Wellcome Trust Case Control Consortium, Mooser V, Francks C and Marchini J

      Department of Statistics, University of Oxford, Oxford, UK.

      Smoking is a leading global cause of disease and mortality. We established the Oxford-GlaxoSmithKline study (Ox-GSK) to perform a genome-wide meta-analysis of SNP association with smoking-related behavioral traits. Our final data set included 41,150 individuals drawn from 20 disease, population and control cohorts. Our analysis confirmed an effect on smoking quantity at a locus on 15q25 (P = 9.45 x 10(-19)) that includes CHRNA5, CHRNA3 and CHRNB4, three genes encoding neuronal nicotinic acetylcholine receptor subunits. We used data from the 1000 Genomes project to investigate the region using imputation, which allowed for analysis of virtually all common SNPs in the region and offered a fivefold increase in marker density over HapMap2 (ref. 2) as an imputation reference panel. Our fine-mapping approach identified a SNP showing the highest significance, rs55853698, located within the promoter region of CHRNA5. Conditional analysis also identified a secondary locus (rs6495308) in CHRNA3.

      Funded by: Chief Scientist Office: CZB/4/540, CZB/4/710, ETM/137, ETM/75; Medical Research Council: G0401527, G0600329, G0701863, G0800759, G9521010, MC_U106179471, MC_U106188470, MC_U127561128; NIA NIH HHS: Z99 AG999999, ZIA AG000196-03, ZIA AG000196-04

      Nature genetics 2010;42;5;436-40

    • Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations.

      Nica AC, Montgomery SB, Dimas AS, Stranger BE, Beazley C, Barroso I and Dermitzakis ET

      Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

      The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r(2)) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits.

      Funded by: Wellcome Trust

      PLoS genetics 2010;6;4;e1000895

    • Detailed investigation of the role of common and low-frequency WFS1 variants in type 2 diabetes risk.

      Fawcett KA, Wheeler E, Morris AP, Ricketts SL, Hallmans G, Rolandsson O, Daly A, Wasson J, Permutt A, Hattersley AT, Glaser B, Franks PW, McCarthy MI, Wareham NJ, Sandhu MS and Barroso I

      Metabolic Disease Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK.

      Objective: Wolfram syndrome 1 (WFS1) single nucleotide polymorphisms (SNPs) are associated with risk of type 2 diabetes. In this study we aimed to refine this association and investigate the role of low-frequency WFS1 variants in type 2 diabetes risk.

      For fine-mapping, we sequenced WFS1 exons, splice junctions, and conserved noncoding sequences in samples from 24 type 2 diabetic case and 68 control subjects, selected tagging SNPs, and genotyped these in 959 U.K. type 2 diabetic case and 1,386 control subjects. The same genomic regions were sequenced in samples from 1,235 type 2 diabetic case and 1,668 control subjects to compare the frequency of rarer variants between case and control subjects.

      Results: Of 31 tagging SNPs, the strongest associated was the previously untested 3' untranslated region rs1046320 (P = 0.008); odds ratio 0.84 and P = 6.59 x 10(-7) on further replication in 3,753 case and 4,198 control subjects. High correlation between rs1046320 and the original strongest SNP (rs10010131) (r2 = 0.92) meant that we could not differentiate between their effects in our samples. There was no difference in the cumulative frequency of 82 rare (minor allele frequency [MAF] <0.01) nonsynonymous variants between type 2 diabetic case and control subjects (P = 0.79). Two intermediate frequency (MAF 0.01-0.05) nonsynonymous changes also showed no statistical association with type 2 diabetes.

      Conclusions: We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations will require large sample sizes (>100,000) or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on type 2 diabetes risk in white U.K. populations, highlighting the complexities of undertaking association studies with low-frequency variants identified by resequencing.

      Funded by: British Heart Foundation; Medical Research Council: MC_U106179471; Wellcome Trust: 064890, 077016, 077016/Z/05/Z, 081682

      Diabetes 2010;59;3;741-6

    • Genetic evidence that raised sex hormone binding globulin (SHBG) levels reduce the risk of type 2 diabetes.

      Perry JR, Weedon MN, Langenberg C, Jackson AU, Lyssenko V, Sparsø T, Thorleifsson G, Grallert H, Ferrucci L, Maggio M, Paolisso G, Walker M, Palmer CN, Payne F, Young E, Herder C, Narisu N, Morken MA, Bonnycastle LL, Owen KR, Shields B, Knight B, Bennett A, Groves CJ, Ruokonen A, Jarvelin MR, Pearson E, Pascoe L, Ferrannini E, Bornstein SR, Stringham HM, Scott LJ, Kuusisto J, Nilsson P, Neptin M, Gjesing AP, Pisinger C, Lauritzen T, Sandbaek A, Sampson M, MAGIC, Zeggini E, Lindgren CM, Steinthorsdottir V, Thorsteinsdottir U, Hansen T, Schwarz P, Illig T, Laakso M, Stefansson K, Morris AD, Groop L, Pedersen O, Boehnke M, Barroso I, Wareham NJ, Hattersley AT, McCarthy MI and Frayling TM

      Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Magdalen Road, Exeter, UK.

      Epidemiological studies consistently show that circulating sex hormone binding globulin (SHBG) levels are lower in type 2 diabetes patients than non-diabetic individuals, but the causal nature of this association is controversial. Genetic studies can help dissect causal directions of epidemiological associations because genotypes are much less likely to be confounded, biased or influenced by disease processes. Using this Mendelian randomization principle, we selected a common single nucleotide polymorphism (SNP) near the SHBG gene, rs1799941, that is strongly associated with SHBG levels. We used data from this SNP, or closely correlated SNPs, in 27 657 type 2 diabetes patients and 58 481 controls from 15 studies. We then used data from additional studies to estimate the difference in SHBG levels between type 2 diabetes patients and controls. The SHBG SNP rs1799941 was associated with type 2 diabetes [odds ratio (OR) 0.94, 95% CI: 0.91, 0.97; P = 2 x 10(-5)], with the SHBG raising allele associated with reduced risk of type 2 diabetes. This effect was very similar to that expected (OR 0.92, 95% CI: 0.88, 0.96), given the SHBG-SNP versus SHBG levels association (SHBG levels are 0.2 standard deviations higher per copy of the A allele) and the SHBG levels versus type 2 diabetes association (SHBG levels are 0.23 standard deviations lower in type 2 diabetic patients compared to controls). Results were very similar in men and women. There was no evidence that this variant is associated with diabetes-related intermediate traits, including several measures of insulin secretion and resistance. Our results, together with those from another recent genetic study, strengthen evidence that SHBG and sex hormones are involved in the aetiology of type 2 diabetes.

      Funded by: Department of Health: DHCS/07/07/008; Medical Research Council: G0000649, G016121, G0601261, MC_U106179471; NHGRI NIH HHS: 1 Z01 HG000024; NIA NIH HHS: R01 AG24233-0; NIDA NIH HHS: U54 DA021519; NIDDK NIH HHS: DK062370, DK069922, DK072193; Wellcome Trust: 076113, 077016/Z/05/Z, 083270/Z/07/Z, GR072960

      Human molecular genetics 2010;19;3;535-44

    • Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge.

      Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P, Lyssenko V, Bouatia-Naji N, Dupuis J, Jackson AU, Kao WH, Li M, Glazer NL, Manning AK, Luan J, Stringham HM, Prokopenko I, Johnson T, Grarup N, Boesgaard TW, Lecoeur C, Shrader P, O'Connell J, Ingelsson E, Couper DJ, Rice K, Song K, Andreasen CH, Dina C, Köttgen A, Le Bacquer O, Pattou F, Taneera J, Steinthorsdottir V, Rybin D, Ardlie K, Sampson M, Qi L, van Hoek M, Weedon MN, Aulchenko YS, Voight BF, Grallert H, Balkau B, Bergman RN, Bielinski SJ, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Böttcher Y, Brunner E, Buchanan TA, Bumpstead SJ, Cavalcanti-Proença C, Charpentier G, Chen YD, Chines PS, Collins FS, Cornelis M, J Crawford G, Delplanque J, Doney A, Egan JM, Erdos MR, Firmann M, Forouhi NG, Fox CS, Goodarzi MO, Graessler J, Hingorani A, Isomaa B, Jørgensen T, Kivimaki M, Kovacs P, Krohn K, Kumari M, Lauritzen T, Lévy-Marchal C, Mayor V, McAteer JB, Meyre D, Mitchell BD, Mohlke KL, Morken MA, Narisu N, Palmer CN, Pakyz R, Pascoe L, Payne F, Pearson D, Rathmann W, Sandbaek A, Sayer AA, Scott LJ, Sharp SJ, Sijbrands E, Singleton A, Siscovick DS, Smith NL, Sparsø T, Swift AJ, Syddall H, Thorleifsson G, Tönjes A, Tuomi T, Tuomilehto J, Valle TT, Waeber G, Walley A, Waterworth DM, Zeggini E, Zhao JH, GIANT consortium, MAGIC investigators, Illig T, Wichmann HE, Wilson JF, van Duijn C, Hu FB, Morris AD, Frayling TM, Hattersley AT, Thorsteinsdottir U, Stefansson K, Nilsson P, Syvänen AC, Shuldiner AR, Walker M, Bornstein SR, Schwarz P, Williams GH, Nathan DM, Kuusisto J, Laakso M, Cooper C, Marmot M, Ferrucci L, Mooser V, Stumvoll M, Loos RJ, Altshuler D, Psaty BM, Rotter JI, Boerwinkle E, Hansen T, Pedersen O, Florez JC, McCarthy MI, Boehnke M, Barroso I, Sladek R, Froguel P, Meigs JB, Groop L, Wareham NJ and Watanabe RM

      Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

      Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958-30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, beta (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 x 10(-15)). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 x 10(-17); ratio of insulin to glucose area under the curve, P = 1.3 x 10(-16)) and diminished incretin effect (n = 804; P = 4.3 x 10(-4)). We also identified variants at ADCY5 (rs2877716, P = 4.2 x 10(-16)), VPS13C (rs17271305, P = 4.1 x 10(-8)), GCKR (rs1260326, P = 7.1 x 10(-11)) and TCF7L2 (rs7903146, P = 4.2 x 10(-10)) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09-1.15, P = 4.8 x 10(-18)).

      Funded by: Chief Scientist Office: CZB/4/710; Medical Research Council: G0701863, G19/35, MC_U106179471, MC_U106188470, MC_UP_A620_1014, MC_UP_A620_1015; NCI NIH HHS: P01 CA087969, P01 CA087969-12; NCRR NIH HHS: M01 RR000052, M01 RR000052-46, M01 RR001066-26, M01 RR016500-08; NHGRI NIH HHS: U01 HG004399, U01 HG004399-02, U01 HG004402, U01 HG004402-02, Z01 HG000024-14; NHLBI NIH HHS: N01 HC015103, N01 HC025195, N01 HC035129, N01 HC045133, N01 HC055015, N01 HC055016, N01 HC055018, N01 HC055019, N01 HC055020, N01 HC055021, N01 HC055022, N01 HC055222, N01 HC075150, N01 HC085079, N01 HC085080, N01 HC085081, N01 HC085082, N01 HC085083, N01 HC085084, N01 HC085085, N01 HC085086, N02 HL64278, R01 HL036310, R01 HL036310-21, R01 HL059367, R01 HL059367-10, R01 HL086694, R01 HL086694-03, R01 HL087641, R01 HL087641-03, R01 HL087652, R01 HL087652-03, U01 HL072515, U01 HL072515-06, U01 HL080295, U01 HL080295-04; NIA NIH HHS: R01 AG013196, R01 AG013196-16; NIDA NIH HHS: U54 DA021519, U54 DA021519-04; NIDDK NIH HHS: K23 DK065978, K23 DK065978-05, K24 DK080140, K24 DK080140-04, P30 DK072488, P30 DK072488-06, P60 DK079637, P60 DK079637-04, R01 DK029867, R01 DK054261, R01 DK054261-09, R01 DK058845, R01 DK058845-11, R01 DK062370, R01 DK062370-05, R01 DK069922-03, R01 DK072193, R01 DK072193-04, R01 DK078616, R01 DK078616-03, R01 DK091718; Wellcome Trust: 077016, 088885

      Nature genetics 2010;42;2;142-8

    • Genome-wide association study identifies five loci associated with lung function.

      Repapi E, Sayers I, Wain LV, Burton PR, Johnson T, Obeidat M, Zhao JH, Ramasamy A, Zhai G, Vitart V, Huffman JE, Igl W, Albrecht E, Deloukas P, Henderson J, Granell R, McArdle WL, Rudnicka AR, Wellcome Trust Case Control Consortium, Barroso I, Loos RJ, Wareham NJ, Mustelin L, Rantanen T, Surakka I, Imboden M, Wichmann HE, Grkovic I, Jankovic S, Zgaga L, Hartikainen AL, Peltonen L, Gyllensten U, Johansson A, Zaboli G, Campbell H, Wild SH, Wilson JF, Gläser S, Homuth G, Völzke H, Mangino M, Soranzo N, Spector TD, Polasek O, Rudan I, Wright AF, Heliövaara M, Ripatti S, Pouta A, Naluai AT, Olin AC, Torén K, Cooper MN, James AL, Palmer LJ, Hingorani AD, Wannamethee SG, Whincup PH, Smith GD, Ebrahim S, McKeever TM, Pavord ID, MacLeod AK, Morris AD, Porteous DJ, Cooper C, Dennison E, Shaheen S, Karrasch S, Schnabel E, Schulz H, Grallert H, Bouatia-Naji N, Delplanque J, Froguel P, Blakey JD, NSHD Respiratory Study Team, Britton JR, Morris RW, Holloway JW, Lawlor DA, Hui J, Nyberg F, Jarvelin MR, Jackson C, Kähönen M, Kaprio J, Probst-Hensch NM, Koch B, Hayward C, Evans DM, Elliott P, Strachan DP, Hall IP and Tobin MD

      Departments of Health Sciences and Genetics, Adrian Building, University of Leicester, Leicester, UK.

      Pulmonary function measures are heritable traits that predict morbidity and mortality and define chronic obstructive pulmonary disease (COPD). We tested genome-wide association with forced expiratory volume in 1 s (FEV(1)) and the ratio of FEV(1) to forced vital capacity (FVC) in the SpiroMeta consortium (n = 20,288 individuals of European ancestry). We conducted a meta-analysis of top signals with data from direct genotyping (n < or = 32,184 additional individuals) and in silico summary association data from the CHARGE Consortium (n = 21,209) and the Health 2000 survey (n < or = 883). We confirmed the reported locus at 4q31 and identified associations with FEV(1) or FEV(1)/FVC and common variants at five additional loci: 2q35 in TNS1 (P = 1.11 x 10(-12)), 4q24 in GSTCD (2.18 x 10(-23)), 5q33 in HTR4 (P = 4.29 x 10(-9)), 6p21 in AGER (P = 3.07 x 10(-15)) and 15q23 in THSD4 (P = 7.24 x 10(-15)). mRNA analyses showed expression of TNS1, GSTCD, AGER, HTR4 and THSD4 in human lung tissue. These associations offer mechanistic insight into pulmonary function regulation and indicate potential targets for interventions to alleviate respiratory disease.

      Funded by: Biotechnology and Biological Sciences Research Council; British Heart Foundation: PG/06/154/22043, PG/97012, RG/08/013/25942; Cancer Research UK; Chief Scientist Office: CZB/4/710, CZD/16/6/2, CZD/16/6/4; Department of Health: 0020029; Medical Research Council: G0000934, G0000943, G0401540, G0500539, G0501942, G0600705, G0800582, G0801056, G0902125, G9815508, G990146, MC_U106179471, MC_U106188470, MC_U123092720, MC_U123092721, MC_U127561128, MC_UP_A620_1014, U.1230.00.008.00005.02; NHLBI NIH HHS: 5R01HL087679-02; NIDDK NIH HHS: U01 DK062418; NIMH NIH HHS: 1RL1MH083268-01, 5R01MH63706:02; Wellcome Trust: 068545/Z/02, 075883, 076113/B/04/Z, 077016/Z/05/Z, 079895, 086160/Z/08/A

      Nature genetics 2010;42;1;36-44

    • New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

      Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Mägi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JR, Egan JM, Lajunen T, Grarup N, Sparsø T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proença C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day IN, de Geus EJ, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S, Hallmans G, Hammond N, Han X, Hartikainen AL, Hassanali N, Hayward C, Heath SC, Hercberg S, Herder C, Hicks AA, Hillman DR, Hingorani AD, Hofman A, Hui J, Hung J, Isomaa B, Johnson PR, Jørgensen T, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Lyssenko V, Mahley R, Mangino M, Manning AK, Martínez-Larrad MT, McAteer JB, McCulloch LJ, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, Narisu N, Neville MJ, Oostra BA, Orrù M, Pakyz R, Palmer CN, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, Seedorf U, Sharp SJ, Shields B, Sigurethsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Thorand B, Tichet J, Tönjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, DIAGRAM Consortium, GIANT Consortium, Global BPgen Consortium, Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Hattersley AT, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Ríos M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WH, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF, Anders Hamsten on behalf of Procardis Consortium, MAGIC investigators, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M*, McCarthy MI*, Florez JC* and Barroso I*

      Department of Biostatistics, Boston University School of Public Health, Massachusetts, USA.

      Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

      Funded by: Medical Research Council: G0700222(81696); NIDDK NIH HHS: K24 DK080140-05, P30 DK040561-14, R01 DK078616-01A1; Wellcome Trust: 064890, 077011, 077016, 081682, 088885, 089061, 091746

      Nature genetics 2010;42;2;105-16

      (* Equal communicating author)

      • Analysis of TBC1D4 in patients with severe insulin resistance.

        Dash S, Langenberg C, Fawcett KA, Semple RK, Romeo S, Sharp S, Sano H, Lienhard GE, Rochford JJ, Howlett T, Massoud AF, Hindmarsh P, Howell SJ, Wilkinson RJ, Lyssenko V, Groop L, Baroni MG, Barroso I, Wareham NJ, O'Rahilly S and Savage DB

        Funded by: Medical Research Council: G0600414, G0800203, MC_U106179471, MC_U117588499; NIDDK NIH HHS: DK25336, R01 DK025336, R56 DK025336; Wellcome Trust: 072070, 077016, 088316

        Diabetologia 2010;53;6;1239-42

      • Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.

        Wellcome Trust Case Control Consortium, Craddock N, Hurles ME, Cardin N, Pearson RD, Plagnol V, Robson S, Vukcevic D, Barnes C, Conrad DF, Giannoulatou E, Holmes C, Marchini JL, Stirrups K, Tobin MD, Wain LV, Yau C, Aerts J, Ahmad T, Andrews TD, Arbury H, Attwood A, Auton A, Ball SG, Balmforth AJ, Barrett JC, Barroso I, Barton A, Bennett AJ, Bhaskar S, Blaszczyk K, Bowes J, Brand OJ, Braund PS, Bredin F, Breen G, Brown MJ, Bruce IN, Bull J, Burren OS, Burton J, Byrnes J, Caesar S, Clee CM, Coffey AJ, Connell JM, Cooper JD, Dominiczak AF, Downes K, Drummond HE, Dudakia D, Dunham A, Ebbs B, Eccles D, Edkins S, Edwards C, Elliot A, Emery P, Evans DM, Evans G, Eyre S, Farmer A, Ferrier IN, Feuk L, Fitzgerald T, Flynn E, Forbes A, Forty L, Franklyn JA, Freathy RM, Gibbs P, Gilbert P, Gokumen O, Gordon-Smith K, Gray E, Green E, Groves CJ, Grozeva D, Gwilliam R, Hall A, Hammond N, Hardy M, Harrison P, Hassanali N, Hebaishi H, Hines S, Hinks A, Hitman GA, Hocking L, Howard E, Howard P, Howson JM, Hughes D, Hunt S, Isaacs JD, Jain M, Jewell DP, Johnson T, Jolley JD, Jones IR, Jones LA, Kirov G, Langford CF, Lango-Allen H, Lathrop GM, Lee J, Lee KL, Lees C, Lewis K, Lindgren CM, Maisuria-Armer M, Maller J, Mansfield J, Martin P, Massey DC, McArdle WL, McGuffin P, McLay KE, Mentzer A, Mimmack ML, Morgan AE, Morris AP, Mowat C, Myers S, Newman W, Nimmo ER, O'Donovan MC, Onipinla A, Onyiah I, Ovington NR, Owen MJ, Palin K, Parnell K, Pernet D, Perry JR, Phillips A, Pinto D, Prescott NJ, Prokopenko I, Quail MA, Rafelt S, Rayner NW, Redon R, Reid DM, Renwick, Ring SM, Robertson N, Russell E, St Clair D, Sambrook JG, Sanderson JD, Schuilenburg H, Scott CE, Scott R, Seal S, Shaw-Hawkins S, Shields BM, Simmonds MJ, Smyth DJ, Somaskantharajah E, Spanova K, Steer S, Stephens J, Stevens HE, Stone MA, Su Z, Symmons DP, Thompson JR, Thomson W, Travers ME, Turnbull C, Valsesia A, Walker M, Walker NM, Wallace C, Warren-Perry M, Watkins NA, Webster J, Weedon MN, Wilson AG, Woodburn M, Wordsworth BP, Young AH, Zeggini E, Carter NP, Frayling TM, Lee C, McVean G, Munroe PB, Palotie A, Sawcer SJ, Scherer SW, Strachan DP, Tyler-Smith C, Brown MA, Burton PR, Caulfield MJ, Compston A, Farrall M, Gough SC, Hall AS, Hattersley AT, Hill AV, Mathew CG, Pembrey M, Satsangi J, Stratton MR, Worthington J, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand W, Parkes M, Rahman N, Todd JA, Samani NJ and Donnelly P

        Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed approximately 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated approximately 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

        Funded by: Arthritis Research UK: 17552; Chief Scientist Office: CZB/4/540, ETM/137, ETM/75; Medical Research Council: G0000934, G0400874, G0500115, G0501942, G0600329, G0600705, G0700491, G0701003, G0701420, G0701810, G0701810(85517), G0800383, G0800759, G19/9, G90/106, G9521010, MC_UP_A390_1107; Wellcome Trust: 061858, 083948, 089989

        Nature 2010;464;7289;713-20

2009 Publications

  • Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

    Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, Berndt SI, Elliott AL, Jackson AU, Lamina C, Lettre G, Lim N, Lyon HN, McCarroll SA, Papadakis K, Qi L, Randall JC, Roccasecca RM, Sanna S, Scheet P, Weedon MN, Wheeler E, Zhao JH, Jacobs LC, Prokopenko I, Soranzo N, Tanaka T, Timpson NJ, Almgren P, Bennett A, Bergman RN, Bingham SA, Bonnycastle LL, Brown M, Burtt NP, Chines P, Coin L, Collins FS, Connell JM, Cooper C, Smith GD, Dennison EM, Deodhar P, Elliott P, Erdos MR, Estrada K, Evans DM, Gianniny L, Gieger C, Gillson CJ, Guiducci C, Hackett R, Hadley D, Hall AS, Havulinna AS, Hebebrand J, Hofman A, Isomaa B, Jacobs KB, Johnson T, Jousilahti P, Jovanovic Z, Khaw KT, Kraft P, Kuokkanen M, Kuusisto J, Laitinen J, Lakatta EG, Luan J, Luben RN, Mangino M, McArdle WL, Meitinger T, Mulas A, Munroe PB, Narisu N, Ness AR, Northstone K, O'Rahilly S, Purmann C, Rees MG, Ridderstråle M, Ring SM, Rivadeneira F, Ruokonen A, Sandhu MS, Saramies J, Scott LJ, Scuteri A, Silander K, Sims MA, Song K, Stephens J, Stevens S, Stringham HM, Tung YC, Valle TT, Van Duijn CM, Vimaleswaran KS, Vollenweider P, Waeber G, Wallace C, Watanabe RM, Waterworth DM, Watkins N, Wellcome Trust Case Control Consortium, Witteman JC, Zeggini E, Zhai G, Zillikens MC, Altshuler D, Caulfield MJ, Chanock SJ, Farooqi IS, Ferrucci L, Guralnik JM, Hattersley AT, Hu FB, Jarvelin MR, Laakso M, Mooser V, Ong KK, Ouwehand WH, Salomaa V, Samani NJ, Spector TD, Tuomi T, Tuomilehto J, Uda M, Uitterlinden AG, Wareham NJ, Deloukas P, Frayling TM, Groop LC, Hayes RB, Hunter DJ, Mohlke KL, Peltonen L, Schlessinger D, Strachan DP, Wichmann HE, McCarthy MI*, Boehnke M*, Barroso I*, Abecasis GR*, Hirschhorn JN* and Genetic Investigation of ANthropometric Traits Consortium

    Genetic Investigation of ANthropometric Traits Consortium.

    Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

    Funded by: British Heart Foundation: FS/05/061/19501; Medical Research Council: G0000649, G0000934, G0601261; NCI NIH HHS: 5UO1CA098233, CA49449, CA50385, CA65725, CA67262, CA87969; NHGRI NIH HHS: 01-HG-65403, 1Z01 HG000024, HG02651; NHLBI NIH HHS: HL084729, HL087679; NIA NIH HHS: N01-AG-1-2109; NIDDK NIH HHS: DK062370, DK072193, DK075787, F32 DK079466, F32 DK079466-01, K23 DK067288, K23 DK080145, K23 DK080145-01, R01 DK072193-01, R01 DK072193-02, R01 DK072193-03, T32DK07191; NIMH NIH HHS: 1RL1MH083268; Wellcome Trust: 068545/Z/02, 076113, 076467/Z/05/Z, 077011, 077016, 079557, 082390, 089061

    Nature genetics 2009;41;1;25-34

    (* Equal communicating author)

    • Underlying genetic models of inheritance in established type 2 diabetes associations.

      Salanti G, Southam L, Altshuler D, Ardlie K, Barroso I, Boehnke M, Cornelis MC, Frayling TM, Grallert H, Grarup N, Groop L, Hansen T, Hattersley AT, Hu FB, Hveem K, Illig T, Kuusisto J, Laakso M, Langenberg C, Lyssenko V, McCarthy MI, Morris A, Morris AD, Palmer CN, Payne F, Platou CG, Scott LJ, Voight BF, Wareham NJ, Zeggini E and Ioannidis JP

      Clinical and Molecular Epidemiology Unit and Clinical Trials and Evidence-Based Medicine Unit, Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece.

      For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.

      Funded by: Medical Research Council: MC_U106179471; NCRR NIH HHS: P20 RR021954, P20 RR021954-02, UL1 RR025752; Wellcome Trust: 077016, 079557, 088885, WT088885/Z/09/Z

      American journal of epidemiology 2009;170;5;537-45

    • Is the thrifty genotype hypothesis supported by evidence based on confirmed type 2 diabetes- and obesity-susceptibility variants?

      Southam L, Soranzo N, Montgomery SB, Frayling TM, McCarthy MI, Barroso I and Zeggini E

      Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.

      According to the thrifty genotype hypothesis, the high prevalence of type 2 diabetes and obesity is a consequence of genetic variants that have undergone positive selection during historical periods of erratic food supply. The recent expansion in the number of validated type 2 diabetes- and obesity-susceptibility loci, coupled with access to empirical data, enables us to look for evidence in support (or otherwise) of the thrifty genotype hypothesis using proven loci.

      Methods: We employed a range of tests to obtain complementary views of the evidence for selection: we determined whether the risk allele at associated 'index' single-nucleotide polymorphisms is derived or ancestral, calculated the integrated haplotype score (iHS) and assessed the population differentiation statistic fixation index (F (ST)) for 17 type 2 diabetes and 13 obesity loci.

      Results: We found no evidence for significant differences for the derived/ancestral allele test. None of the studied loci showed strong evidence for selection based on the iHS score. We find a high F (ST) for rs7901695 at TCF7L2, the largest type 2 diabetes effect size found to date.

      Our results provide some evidence for selection at specific loci, but there are no consistent patterns of selection that provide conclusive confirmation of the thrifty genotype hypothesis. Discovery of more signals and more causal variants for type 2 diabetes and obesity is likely to allow more detailed examination of these issues.

      Funded by: Medical Research Council: G0601261; Wellcome Trust: 077016, 079557, 088885, WT077016/Z/05/Z, WT088885/Z/09/Z

      Diabetologia 2009;52;9;1846-51

    • Partial lipodystrophy and insulin resistant diabetes in a patient with a homozygous nonsense mutation in CIDEC.

      Rubio-Cabezas O, Puri V, Murano I, Saudek V, Semple RK, Dash S, Hyden CS, Bottomley W, Vigouroux C, Magré J, Raymond-Barker P, Murgatroyd PR, Chawla A, Skepper JN, Chatterjee VK, Suliman S, Patch AM, Agarwal AK, Garg A, Barroso I, Cinti S, Czech MP, Argente J, O'Rahilly S, Savage DB and LD Screening Consortium

      Department of Endocrinology, Hospital Infantil Universitario Niño Jesús, Madrid, Spain.

      Lipodystrophic syndromes are characterized by adipose tissue deficiency. Although rare, they are of considerable interest as they, like obesity, typically lead to ectopic lipid accumulation, dyslipidaemia and insulin resistant diabetes. In this paper we describe a female patient with partial lipodystrophy (affecting limb, femorogluteal and subcutaneous abdominal fat), white adipocytes with multiloculated lipid droplets and insulin-resistant diabetes, who was found to be homozygous for a premature truncation mutation in the lipid droplet protein cell death-inducing Dffa-like effector C (CIDEC) (E186X). The truncation disrupts the highly conserved CIDE-C domain and the mutant protein is mistargeted and fails to increase the lipid droplet size in transfected cells. In mice, Cidec deficiency also reduces fat mass and induces the formation of white adipocytes with multilocular lipid droplets, but in contrast to our patient, Cidec null mice are protected against diet-induced obesity and insulin resistance. In addition to describing a novel autosomal recessive form of familial partial lipodystrophy, these observations also suggest that CIDEC is required for unilocular lipid droplet formation and optimal energy storage in human fat.

      Funded by: Medical Research Council: G0600414; NIDDK NIH HHS: DK30898, DK32520, DK54387, DK60837, P30 DK032520-25, P30 DK032520-26, R01 DK054387-13, R37 DK030898-23; Wellcome Trust: 077016, 077016/Z/05/Z

      EMBO molecular medicine 2009;1;5;280-7

    • Genetic variation in LIN28B is associated with the timing of puberty.

      Ong KK, Elks CE, Li S, Zhao JH, Luan J, Andersen LB, Bingham SA, Brage S, Smith GD, Ekelund U, Gillson CJ, Glaser B, Golding J, Hardy R, Khaw KT, Kuh D, Luben R, Marcus M, McGeehin MA, Ness AR, Northstone K, Ring SM, Rubin C, Sims MA, Song K, Strachan DP, Vollenweider P, Waeber G, Waterworth DM, Wong A, Deloukas P, Barroso I, Mooser V, Loos RJ and Wareham NJ

      Medical Research Council (MRC) Epidemiology Unit, Addenbrooke's Hospital, Cambridge, UK. ken.ong@mrc-epid.cam.ac.uk

      The timing of puberty is highly variable. We carried out a genome-wide association study for age at menarche in 4,714 women and report an association in LIN28B on chromosome 6 (rs314276, minor allele frequency (MAF) = 0.33, P = 1.5 × 10(-8)). In independent replication studies in 16,373 women, each major allele was associated with 0.12 years earlier menarche (95% CI = 0.08-0.16; P = 2.8 × 10(-10); combined P = 3.6 × 10(-16)). This allele was also associated with earlier breast development in girls (P = 0.001; N = 4,271); earlier voice breaking (P = 0.006, N = 1,026) and more advanced pubic hair development in boys (P = 0.01; N = 4,588); a faster tempo of height growth in girls (P = 0.00008; N = 4,271) and boys (P = 0.03; N = 4,588); and shorter adult height in women (P = 3.6 × 10(-7); N = 17,274) and men (P = 0.006; N = 9,840) in keeping with earlier growth cessation. These studies identify variation in LIN28B, a potent and specific regulator of microRNA processing, as the first genetic determinant regulating the timing of human pubertal growth and development.

      Funded by: Cancer Research UK; Medical Research Council: 73437, G0000934, G0401527, G0401527(74922), G0701863, G9815508, MC_U105630924, MC_U106179471, MC_U106179472, MC_U106179473, MC_U106188470, MC_U123092720, MC_U123092721, U.1061.00.001 (79471), U.1061.00.004(79472); Wellcome Trust: 068049, 068545/Z/02, 076467/Z/05/Z, 077011, 077016, 077016/Z/05/Z, 079996

      Nature genetics 2009;41;6;729-33

    • Genome-wide association study identifies eight loci associated with blood pressure.

      Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, Najjar SS, Zhao JH, Heath SC, Eyheramendy S, Papadakis K, Voight BF, Scott LJ, Zhang F, Farrall M, Tanaka T, Wallace C, Chambers JC, Khaw KT, Nilsson P, van der Harst P, Polidoro S, Grobbee DE, Onland-Moret NC, Bots ML, Wain LV, Elliott KS, Teumer A, Luan J, Lucas G, Kuusisto J, Burton PR, Hadley D, McArdle WL, Wellcome Trust Case Control Consortium, Brown M, Dominiczak A, Newhouse SJ, Samani NJ, Webster J, Zeggini E, Beckmann JS, Bergmann S, Lim N, Song K, Vollenweider P, Waeber G, Waterworth DM, Yuan X, Groop L, Orho-Melander M, Allione A, Di Gregorio A, Guarrera S, Panico S, Ricceri F, Romanazzi V, Sacerdote C, Vineis P, Barroso I, Sandhu MS, Luben RN, Crawford GJ, Jousilahti P, Perola M, Boehnke M, Bonnycastle LL, Collins FS, Jackson AU, Mohlke KL, Stringham HM, Valle TT, Willer CJ, Bergman RN, Morken MA, Döring A, Gieger C, Illig T, Meitinger T, Org E, Pfeufer A, Wichmann HE, Kathiresan S, Marrugat J, O'Donnell CJ, Schwartz SM, Siscovick DS, Subirana I, Freimer NB, Hartikainen AL, McCarthy MI, O'Reilly PF, Peltonen L, Pouta A, de Jong PE, Snieder H, van Gilst WH, Clarke R, Goel A, Hamsten A, Peden JF, Seedorf U, Syvänen AC, Tognoni G, Lakatta EG, Sanna S, Scheet P, Schlessinger D, Scuteri A, Dörr M, Ernst F, Felix SB, Homuth G, Lorbeer R, Reffelmann T, Rettig R, Völker U, Galan P, Gut IG, Hercberg S, Lathrop GM, Zelenika D, Deloukas P, Soranzo N, Williams FM, Zhai G, Salomaa V, Laakso M, Elosua R, Forouhi NG, Völzke H, Uiterwaal CS, van der Schouw YT, Numans ME, Matullo G, Navis G, Berglund G, Bingham SA, Kooner JS, Connell JM, Bandinelli S, Ferrucci L, Watkins H, Spector TD, Tuomilehto J, Altshuler D, Strachan DP, Laan M, Meneton P, Wareham NJ, Uda M, Jarvelin MR, Mooser V, Melander O, Loos RJ, Elliott P, Abecasis GR, Caulfield M and Munroe PB

      Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA. cnewtoncheh@chgr.mgh.harvard.edu

      Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5 million genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N ≤ 71,225 European ancestry, N ≤ 12,889 Indian Asian ancestry) and in silico comparison (CHARGE consortium, N = 29,136). We identified association between systolic or diastolic blood pressure and common variants in eight regions near the CYP17A1 (P = 7 × 10(-24)), CYP1A2 (P = 1 × 10(-23)), FGF5 (P = 1 × 10(-21)), SH2B3 (P = 3 × 10(-18)), MTHFR (P = 2 × 10(-13)), c10orf107 (P = 1 × 10(-9)), ZNF652 (P = 5 × 10(-9)) and PLCD3 (P = 1 × 10(-8)) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease.

      Funded by: British Heart Foundation: FS/05/061/19501, PG02/128, SP/04/002; Cancer Research UK; Chief Scientist Office: CZB/4/540, ETM/137, ETM/75; Medical Research Council: 85374, G0000934, G0400874, G0401527, G0501942, G0600329, G0701863, G0800759, G0801056, G9521010, G9521010D, MC_QA137934, MC_U105630924, MC_U106188470, MC_U137686854; NCRR NIH HHS: U54RR020278; NHGRI NIH HHS: 1Z01HG000024; NHLBI NIH HHS: K23 HL080025-04, K23HL083102, K23HL80025, R01 HL056931, R01 HL056931-02, R01 HL056931-03, R01 HL056931-04, R01HL056931, R01HL087676, R01HL087679; NIA NIH HHS: N01-AG-1-2109, N01AG-821336, N01AG-916413; NICHD NIH HHS: N01-HD-1-3107; NIDA NIH HHS: U54DA021519; NIDDK NIH HHS: DK062370, DK072193, R01 DK029867, R01 DK072193-04, U01DK062418; NIEHS NIH HHS: P30ES007033; NIMH NIH HHS: RL1MH083268; NIMHD NIH HHS: 263MD821336, 263MD916413; PHS HHS: 263-MA-410953; Wellcome Trust: 061858, 068545/Z/02, 070191/Z/03/Z, 076113, 076113/B/04/Z, 077011, 077016, 077016/Z/05/Z, 079557, 079895, 088885, 089061, WT088885/Z/09/Z

      Nature genetics 2009;41;6;666-76

    • Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden.

      Renström F, Payne F, Nordström A, Brito EC, Rolandsson O, Hallmans G, Barroso I, Nordström P, Franks PW and GIANT Consortium

      Department of Public Health and Clinical Medicine, Umeå University Hospital, Umeå, Sweden.

      Recent genome-wide association studies (GWAS) have identified multiple risk loci for common obesity (FTO, MC4R, TMEM18, GNPDA2, SH2B1, KCTD15, MTCH2, NEGR1 and PCSK1). Here we extend those studies by examining associations with adiposity and type 2 diabetes in Swedish adults. The nine single nucleotide polymorphisms (SNPs) were genotyped in 3885 non-diabetic and 1038 diabetic individuals with available measures of height, weight and body mass index (BMI). Adipose mass and distribution were objectively assessed using dual-energy X-ray absorptiometry in a sub-group of non-diabetics (n = 2206). In models with adipose mass traits, BMI or obesity as outcomes, the most strongly associated SNP was FTO rs1121980 (P < 0.001). Five other SNPs (SH2B1 rs7498665, MTCH2 rs4752856, MC4R rs17782313, NEGR1 rs2815752 and GNPDA2 rs10938397) were significantly associated with obesity. To summarize the overall genetic burden, a weighted risk score comprising a subset of SNPs was constructed; those in the top quintile of the score were heavier (+2.6 kg) and had more total (+2.4 kg), gynoid (+191 g) and abdominal (+136 g) adipose tissue than those in the lowest quintile (all P < 0.001). The genetic burden score significantly increased diabetes risk, with those in the highest quintile (n = 193/594 cases/controls) being at 1.55-fold (95% CI 1.21-1.99; P < 0.0001) greater risk of type 2 diabetes than those in the lowest quintile (n = 130/655 cases/controls). In summary, we have statistically replicated six of the previously associated obese-risk loci and our results suggest that the weight-inducing effects of these variants are explained largely by increased adipose accumulation.

      Funded by: Wellcome Trust

      Human molecular genetics 2009;18;8;1489-96

    • Meta-analysis of genome-wide scans for human adult stature identifies novel Loci and associations with measures of skeletal frame size.

      Soranzo N, Rivadeneira F, Chinappen-Horsley U, Malkina I, Richards JB, Hammond N, Stolk L, Nica A, Inouye M, Hofman A, Stephens J, Wheeler E, Arp P, Gwilliam R, Jhamai PM, Potter S, Chaney A, Ghori MJ, Ravindrarajah R, Ermakov S, Estrada K, Pols HA, Williams FM, McArdle WL, van Meurs JB, Loos RJ, Dermitzakis ET, Ahmadi KR, Hart DJ, Ouwehand WH, Wareham NJ, Barroso I, Sandhu MS, Strachan DP, Livshits G, Spector TD, Uitterlinden AG and Deloukas P

      Human Genetics Department, Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom.

      Recent genome-wide (GW) scans have identified several independent loci affecting human stature, but their contribution through the different skeletal components of height is still poorly understood. We carried out a genome-wide scan in 12,611 participants, followed by replication in an additional 7,187 individuals, and identified 17 genomic regions with GW-significant association with height. Of these, two are entirely novel (rs11809207 in CATSPER4, combined P-value = 6.1x10(-8) and rs910316 in TMED10, P-value = 1.4x10(-7)) and two had previously been described with weak statistical support (rs10472828 in NPR3, P-value = 3x10(-7) and rs849141 in JAZF1, P-value = 3.2x10(-11)). One locus (rs1182188 at GNA12) identifies the first height eQTL. We also assessed the contribution of height loci to the upper- (trunk) and lower-body (hip axis and femur) skeletal components of height. We find evidence for several loci associated with trunk length (including rs6570507 in GPR126, P-value = 4x10(-5) and rs6817306 in LCORL, P-value = 4x10(-4)), hip axis length (including rs6830062 at LCORL, P-value = 4.8x10(-4) and rs4911494 at UQCC, P-value = 1.9x10(-4)), and femur length (including rs710841 at PRKG2, P-value = 2.4x10(-5) and rs10946808 at HIST1H1D, P-value = 6.4x10(-6)). Finally, we used conditional analyses to explore a possible differential contribution of the height loci to these different skeletal size measurements. In addition to validating four novel loci controlling adult stature, our study represents the first effort to assess the contribution of genetic loci to three skeletal components of height. Further statistical tests in larger numbers of individuals will be required to verify if the height loci affect height preferentially through these subcomponents of height.

      Funded by: Medical Research Council: G0000934, G0701863, MC_QA137934, MC_U106188470; Wellcome Trust: 068545/Z/02

      PLoS genetics 2009;5;4;e1000445

    • Variants in MTNR1B influence fasting glucose levels.

      Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G, Loos RJ, Manning AK, Jackson AU, Aulchenko Y, Potter SC, Erdos MR, Sanna S, Hottenga JJ, Wheeler E, Kaakinen M, Lyssenko V, Chen WM, Ahmadi K, Beckmann JS, Bergman RN, Bochud M, Bonnycastle LL, Buchanan TA, Cao A, Cervino A, Coin L, Collins FS, Crisponi L, de Geus EJ, Dehghan A, Deloukas P, Doney AS, Elliott P, Freimer N, Gateva V, Herder C, Hofman A, Hughes TE, Hunt S, Illig T, Inouye M, Isomaa B, Johnson T, Kong A, Krestyaninova M, Kuusisto J, Laakso M, Lim N, Lindblad U, Lindgren CM, McCann OT, Mohlke KL, Morris AD, Naitza S, Orrù M, Palmer CN, Pouta A, Randall J, Rathmann W, Saramies J, Scheet P, Scott LJ, Scuteri A, Sharp S, Sijbrands E, Smit JH, Song K, Steinthorsdottir V, Stringham HM, Tuomi T, Tuomilehto J, Uitterlinden AG, Voight BF, Waterworth D, Wichmann HE, Willemsen G, Witteman JC, Yuan X, Zhao JH, Zeggini E, Schlessinger D, Sandhu M, Boomsma DI, Uda M, Spector TD, Penninx BW, Altshuler D, Vollenweider P, Jarvelin MR, Lakatta E, Waeber G, Fox CS, Peltonen L, Groop LC, Mooser V, Cupples LA, Thorsteinsdottir U, Boehnke M, Barroso I, Van Duijn C, Dupuis J, Watanabe RM, Stefansson K, McCarthy MI, Wareham NJ, Meigs JB and Abecasis GR

      [1] Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK. [2] Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. [3] These authors contributed equally to this work.

      To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 x 10(-50)) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 x 10(-15)). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 x 10(-7)) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 x 10(-57)) and GCK (rs4607517, P = 1.0 x 10(-25)) loci.

      Funded by: Medical Research Council: G0000649, G016121, G0500539, G0601261, G0701863, MC_U106179471, MC_U106188470; NCRR NIH HHS: RR-163736; NHGRI NIH HHS: HG-02651, R01 HG002651-05; NHLBI NIH HHS: HC-25195, HL-084729, HL-087679, N01 HC025195, N02-HL-6-4278, R01 HL087679-02, U01 HL084729-03; NIDA NIH HHS: DA-021519, U54 DA021519, U54 DA021519-04; NIDDK NIH HHS: DK-062370, DK-065978, DK-072193, DK-078616, DK-080140, DK069922, K23 DK065978-05, K24 DK080140, K24 DK080140-01, K24 DK080140-02, K24 DK080140-05, R01 DK029867, R01 DK062370, R01 DK062370-05, R01 DK069922-02, R01 DK072193-04, R01 DK078616-01A1; NIMH NIH HHS: MH059160, R01 MH059160-04; Wellcome Trust: 076113, 077011, 077016, 079557, 083948, 089061, GR069224, GR072960

      Nature genetics 2009;41;1;77-81

    • Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

      Lindgren CM, Heid IM, Randall JC, Lamina C, Steinthorsdottir V, Qi L, Speliotes EK, Thorleifsson G, Willer CJ, Herrera BM, Jackson AU, Lim N, Scheet P, Soranzo N, Amin N, Aulchenko YS, Chambers JC, Drong A, Luan J, Lyon HN, Rivadeneira F, Sanna S, Timpson NJ, Zillikens MC, Zhao JH, Almgren P, Bandinelli S, Bennett AJ, Bergman RN, Bonnycastle LL, Bumpstead SJ, Chanock SJ, Cherkas L, Chines P, Coin L, Cooper C, Crawford G, Doering A, Dominiczak A, Doney AS, Ebrahim S, Elliott P, Erdos MR, Estrada K, Ferrucci L, Fischer G, Forouhi NG, Gieger C, Grallert H, Groves CJ, Grundy S, Guiducci C, Hadley D, Hamsten A, Havulinna AS, Hofman A, Holle R, Holloway JW, Illig T, Isomaa B, Jacobs LC, Jameson K, Jousilahti P, Karpe F, Kuusisto J, Laitinen J, Lathrop GM, Lawlor DA, Mangino M, McArdle WL, Meitinger T, Morken MA, Morris AP, Munroe P, Narisu N, Nordström A, Nordström P, Oostra BA, Palmer CN, Payne F, Peden JF, Prokopenko I, Renström F, Ruokonen A, Salomaa V, Sandhu MS, Scott LJ, Scuteri A, Silander K, Song K, Yuan X, Stringham HM, Swift AJ, Tuomi T, Uda M, Vollenweider P, Waeber G, Wallace C, Walters GB, Weedon MN, Wellcome Trust Case Control Consortium, Witteman JC, Zhang C, Zhang W, Caulfield MJ, Collins FS, Davey Smith G, Day IN, Franks PW, Hattersley AT, Hu FB, Jarvelin MR, Kong A, Kooner JS, Laakso M, Lakatta E, Mooser V, Morris AD, Peltonen L, Samani NJ, Spector TD, Strachan DP, Tanaka T, Tuomilehto J, Uitterlinden AG, van Duijn CM, Wareham NJ, Hugh Watkins, Procardis Consortia, Waterworth DM, Boehnke M, Deloukas P, Groop L, Hunter DJ, Thorsteinsdottir U, Schlessinger D, Wichmann HE, Frayling TM, Abecasis GR*, Hirschhorn JN*, Loos RJ*, Stefansson K*, Mohlke KL*, Barroso I*, McCarthy MI* and Giant Consortium

      Wellcome Trust Centre for Human Genetics, University of Oxford, , Oxford, United Kingdom.

      To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

      Funded by: Biotechnology and Biological Sciences Research Council; British Heart Foundation; Medical Research Council: 0600705, G0000649, G0000934, G0500539, G0601261, G9521010D; NHLBI NIH HHS: HL084729, HL087679; NIDDK NIH HHS: DK062370, DK067288, DK07191, DK072193, DK075787, DK079466, DK080145, F32 DK079466-01, K23 DK080145-01, R01 DK029867, R01 DK072193-01, R01 DK072193-02, R01 DK072193-03, R01 DK072193-04; PHS HHS: G02651; Wellcome Trust: 064890, 068545/Z/02, 081682, 086596/Z/08/Z, GR069224, GR072960, GR076113

      PLoS genetics 2009;5;6;e1000508

      (* Equal communicating author)

      • Common genetic variation in the melatonin receptor 1B gene (MTNR1B) is associated with decreased early-phase insulin response.

        Langenberg C, Pascoe L, Mari A, Tura A, Laakso M, Frayling TM, Barroso I, Loos RJ, Wareham NJ, Walker M and RISC Consortium

        MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK. claudia.langenberg@mrc-epid.cam.ac.uk

        We investigated whether variation in MTNR1B, which was recently identified as a common genetic determinant of fasting glucose levels in healthy, diabetes-free individuals, is associated with measures of beta cell function and whole-body insulin sensitivity.

        Methods: We studied 1,276 healthy individuals of European ancestry at 19 centres of the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study. Whole-body insulin sensitivity was assessed by euglycaemic-hyperinsulinaemic clamp and indices of beta cell function were derived from a 75 g oral glucose tolerance test (including 30 min insulin response and glucose sensitivity). We studied rs10830963 in MTNR1B using additive genetic models, adjusting for age, sex and recruitment centre.

        Results: The minor (G) allele of rs10830963 in MTNR1B (frequency 0.30 in HapMap Centre d'Etude du Polymorphisme [Utah residents with northern and western European ancestry] [CEU]; 0.29 in RISC participants) was associated with higher levels of fasting plasma glucose (standardised beta [95% CI] 0.17 [0.085, 0.25] per G allele, p = 5.8 x 10(-5)), consistent with recent observations. In addition, the G-allele was significantly associated with lower early insulin response (-0.19 [-0.28, -0.10], p = 1.7 x 10(-5)), as well as with decreased beta cell glucose sensitivity (-0.11 [-0.20, -0.027], p = 0.010). No associations were observed with clamp-assessed insulin sensitivity (p = 0.15) or different measures of body size (p > 0.7 for all).

        Genetic variation in MTNR1B is associated with defective early insulin response and decreased beta cell glucose sensitivity, which may contribute to the higher glucose levels of non-diabetic individuals carrying the minor G allele of rs10830963 in MTNR1B.

        Funded by: Biotechnology and Biological Sciences Research Council; Medical Research Council: G0701863, MC_U106188470; Wellcome Trust: 077016, 077016/Z/05/Z

        Diabetologia 2009;52;8;1537-42

      • A truncation mutation in TBC1D4 in a family with acanthosis nigricans and postprandial hyperinsulinemia.

        Dash S, Sano H, Rochford JJ, Semple RK, Yeo G, Hyden CS, Soos MA, Clark J, Rodin A, Langenberg C, Druet C, Fawcett KA, Tung YC, Wareham NJ, Barroso I, Lienhard GE, O'Rahilly S and Savage DB

        Departments of Medicine and Clinical Biochemistry, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.

        Tre-2, BUB2, CDC16, 1 domain family member 4 (TBC1D4) (AS160) is a Rab-GTPase activating protein implicated in insulin-stimulated glucose transporter 4 (GLUT4) translocation in adipocytes and myotubes. To determine whether loss-of-function mutations in TBC1D4 might impair GLUT4 translocation and cause insulin resistance in humans, we screened the coding regions of this gene in 156 severely insulin-resistant patients. A female presenting at age 11 years with acanthosis nigricans and extreme postprandial hyperinsulinemia was heterozygous for a premature stop mutation (R363X) in TBC1D4. After demonstrating reduced expression of wild-type TBC1D4 protein and expression of the truncated protein in lymphocytes from the proband, we further characterized the biological effects of the truncated protein in 3T3L1 adipocytes. Prematurely truncated TBC1D4 protein tended to increase basal cell membrane GLUT4 levels (P = 0.053) and significantly reduced insulin-stimulated GLUT4 cell membrane translocation (P < 0.05). When coexpressed with wild-type TBC1D4, the truncated protein dimerized with full-length TBC1D4, suggesting that the heterozygous truncated variant might interfere with its wild-type counterpart in a dominant negative fashion. Two overweight family members with the mutation also manifested normal fasting glucose and insulin levels but disproportionately elevated insulin levels following an oral glucose challenge. This family provides unique genetic evidence of TBC1D4 involvement in human insulin action.

        Funded by: British Heart Foundation; Medical Research Council: G0600414; NCI NIH HHS: P30 CA023108; NIDDK NIH HHS: DK25336, R01 DK025336; Wellcome Trust

        Proceedings of the National Academy of Sciences of the United States of America 2009;106;23;9350-5

      • IRS2 variants and syndromes of severe insulin resistance.

        Bottomley WE, Soos MA, Adams C, Guran T, Howlett TA, Mackie A, Miell J, Monson JP, Temple R, Tenenbaum-Rakover Y, Tymms J, Savage DB, Semple RK, O'Rahilly S and Barroso I

        Funded by: Wellcome Trust: 077016, 077016/Z/05/Z, 078986, 078986/Z/06/Z, 080952, 080952/Z/06/Z

        Diabetologia 2009;52;6;1208-11

2008 Publications

  • The Gly482Ser genotype at the PPARGC1A gene and elevated blood pressure: a meta-analysis involving 13,949 individuals.

    Vimaleswaran KS, Luan J, Andersen G, Muller YL, Wheeler E, Brito EC, O'Rahilly S, Pedersen O, Baier LJ, Knowler WC, Barroso I, Wareham NJ, Loos RJ and Franks PW

    Department of Public Health & Clinical Medicine, Umeå University Hospital, Umeå, Sweden.

    The protein encoded by the PPARGC1A gene is expressed at high levels in metabolically active tissues and is involved in the control of oxidative stress via reactive oxygen species detoxification. Several recent reports suggest that the PPARGC1A Gly482Ser (rs8192678) missense polymorphism may relate inversely with blood pressure. We used conventional meta-analysis methods to assess the association between Gly482Ser and systolic (SBP) or diastolic blood pressures (DBP) or hypertension in 13,949 individuals from 17 studies, of which 6,042 were previously unpublished observations. The studies comprised cohorts of white European, Asian, and American Indian adults, and adolescents from South America. Stratified analyses were conducted to control for population stratification. Pooled genotype frequencies were 0.47 (Gly482Gly), 0.42 (Gly482Ser), and 0.11 (Ser482Ser). We found no evidence of association between Gly482Ser and SBP [Gly482Gly: mean = 131.0 mmHg, 95% confidence interval (CI) = 130.5-131.5 mmHg; Gly482Ser mean = 133.1 mmHg, 95% CI = 132.6-133.6 mmHg; Ser482Ser: mean = 133.5 mmHg, 95% CI = 132.5-134.5 mmHg; P = 0.409] or DBP (Gly482Gly: mean = 80.3 mmHg, 95% CI = 80.0-80.6 mmHg; Gly482Ser mean = 81.5 mmHg, 95% CI = 81.2-81.8 mmHg; Ser482Ser: mean = 82.1 mmHg, 95% CI = 81.5-82.7 mmHg; P = 0.651). Contrary to previous reports, we did not observe significant effect modification by sex (SBP, P = 0.966; DBP, P = 0.715). We were also unable to confirm the previously reported association between the Ser482 allele and hypertension [odds ratio: 0.97, 95% CI = 0.87-1.08, P = 0.585]. These results were materially unchanged when analyses were focused on whites only. However, statistical evidence of gene-age interaction was apparent for DBP [Gly482Gly: 73.5 (72.8, 74.2), Gly482Ser: 77.0 (76.2, 77.8), Ser482Ser: 79.1 (77.4, 80.9), P = 4.20 x 10(-12)] and SBP [Gly482Gly: 121.4 (120.4, 122.5), Gly482Ser: 125.9 (124.6, 127.1), Ser482Ser: 129.2 (126.5, 131.9), P = 7.20 x 10(-12)] in individuals <50 yr (n = 2,511); these genetic effects were absent in those older than 50 yr (n = 5,088) (SBP, P = 0.41; DBP, P = 0.51). Our findings suggest that the PPARGC1A Ser482 allele may be associated with higher blood pressure, but this is only apparent in younger adults.

    Funded by: Medical Research Council: MC_U106179471, MC_U106188470; Wellcome Trust: 077016

    Journal of applied physiology (Bethesda, Md. : 1985) 2008;105;4;1352-8

  • Genome-wide association analysis identifies 20 loci that influence adult height.

    Weedon MN, Lango H, Lindgren CM, Wallace C, Evans DM, Mangino M, Freathy RM, Perry JR, Stevens S, Hall AS, Samani NJ, Shields B, Prokopenko I, Farrall M, Dominiczak A, Diabetes Genetics Initiative, Wellcome Trust Case Control Consortium, Johnson T, Bergmann S, Beckmann JS, Vollenweider P, Waterworth DM, Mooser V, Palmer CN, Morris AD, Ouwehand WH, Cambridge GEM Consortium, Zhao JH, Li S, Loos RJ, Barroso I, Deloukas P, Sandhu MS, Wheeler E, Soranzo N, Inouye M, Wareham NJ, Caulfield M, Munroe PB, Hattersley AT, McCarthy MI and Frayling TM

    Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road, Exeter EX1 2LU, UK.

    Adult height is a model polygenic trait, but there has been limited success in identifying the genes underlying its normal variation. To identify genetic variants influencing adult human height, we used genome-wide association data from 13,665 individuals and genotyped 39 variants in an additional 16,482 samples. We identified 20 variants associated with adult height (P < 5 x 10(-7), with 10 reaching P < 1 x 10(-10)). Combined, the 20 SNPs explain approximately 3% of height variation, with a approximately 5 cm difference between the 6.2% of people with 17 or fewer 'tall' alleles compared to the 5.5% with 27 or more 'tall' alleles. The loci we identified implicate genes in Hedgehog signaling (IHH, HHIP, PTCH1), extracellular matrix (EFEMP1, ADAMTSL3, ACAN) and cancer (CDK6, HMGA2, DLEU7) pathways, and provide new insights into human growth and developmental processes. Finally, our results provide insights into the genetic architecture of a classic quantitative trait.

    Funded by: British Heart Foundation: FS/05/061/19501, PG/02/128/14470, PG02/128; Medical Research Council: G0600705, G0701863, G9521010, G9521010(63660), G9521010D, MC_U106188470; Wellcome Trust: 076113, 077011, 077016

    Nature genetics 2008;40;5;575-83

  • Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes.

    Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, de Bakker PI, Abecasis GR, Almgren P, Andersen G, Ardlie K, Boström KB, Bergman RN, Bonnycastle LL, Borch-Johnsen K, Burtt NP, Chen H, Chines PS, Daly MJ, Deodhar P, Ding CJ, Doney AS, Duren WL, Elliott KS, Erdos MR, Frayling TM, Freathy RM, Gianniny L, Grallert H, Grarup N, Groves CJ, Guiducci C, Hansen T, Herder C, Hitman GA, Hughes TE, Isomaa B, Jackson AU, Jørgensen T, Kong A, Kubalanza K, Kuruvilla FG, Kuusisto J, Langenberg C, Lango H, Lauritzen T, Li Y, Lindgren CM, Lyssenko V, Marvelle AF, Meisinger C, Midthjell K, Mohlke KL, Morken MA, Morris AD, Narisu N, Nilsson P, Owen KR, Palmer CN, Payne F, Perry JR, Pettersen E, Platou C, Prokopenko I, Qi L, Qin L, Rayner NW, Rees M, Roix JJ, Sandbaek A, Shields B, Sjögren M, Steinthorsdottir V, Stringham HM, Swift AJ, Thorleifsson G, Thorsteinsdottir U, Timpson NJ, Tuomi T, Tuomilehto J, Walker M, Watanabe RM, Weedon MN, Willer CJ, Wellcome Trust Case Control Consortium, Illig T, Hveem K, Hu FB, Laakso M, Stefansson K, Pedersen O, Wareham NJ, Barroso I, Hattersley AT, Collins FS, Groop L, McCarthy MI, Boehnke M and Altshuler D

    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.

    Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and approximately 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 x 10(-14)), CDC123-CAMK1D (P = 1.2 x 10(-10)), TSPAN8-LGR5 (P = 1.1 x 10(-9)), THADA (P = 1.1 x 10(-9)), ADAMTS9 (P = 1.2 x 10(-8)) and NOTCH2 (P = 4.1 x 10(-8)) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.

    Funded by: Department of Health: DHCS/07/07/008; Medical Research Council: G0000649, G0600705, G0601261, MC_U106179471; NCI NIH HHS: CA87969; NHGRI NIH HHS: HG002651, N01-HG-65403, U01 HG004171, U01 HG004399; NHLBI NIH HHS: HL084729, T32 HL007627; NIDA NIH HHS: U54 DA021519; NIDDK NIH HHS: DK062370, DK072193, DK58845, P30 DK040561-12, R01 DK029867, R01 DK072193; Wellcome Trust: 072960, 076113, 077016, 079557, GR072960

    Nature genetics 2008;40;5;638-45

  • Whole genome-amplified DNA: insights and imputation.

    Teo YY, Inouye M, Small KS, Fry AE, Potter SC, Dunstan SJ, Seielstad M, Barroso I, Wareham NJ, Rockett KA, Kwiatkowski DP and Deloukas P

    Funded by: Medical Research Council: G0600230, G19/9, MC_U106179471; Wellcome Trust: 077011, 077016

    Nature methods 2008;5;4;279-80

  • Habitual energy expenditure modifies the association between NOS3 gene polymorphisms and blood pressure.

    Vimaleswaran KS, Franks PW, Barroso I, Brage S, Ekelund U, Wareham NJ and Loos RJ

    Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK. vks22@medschl.cam.ac.uk

    Background: The endothelial nitric-oxide synthase (NOS3) gene encodes the enzyme (eNOS) that synthesizes the molecule nitric oxide, which facilitates endothelium-dependent vasodilation in response to physical activity. Thus, energy expenditure may modify the association between the genetic variation at NOS3 and blood pressure.

    Methods: To test this hypothesis, we genotyped 11 NOS3 polymorphisms, capturing all common variations, in 726 men and women from the Medical Research Council (MRC) Ely Study (age (mean +/- s.d.): 55 +/- 10 years, body mass index: 26.4 +/- 4.1 kg/m(2)). Habitual/non-resting energy expenditure (NREE) was assessed via individually calibrated heart rate monitoring over 4 days.

    Results: The intronic variant, IVS25+15 [G-->A], was significantly associated with blood pressure; GG homozygotes had significantly lower levels of diastolic blood pressure (DBP) (-2.8 mm Hg; P = 0.016) and systolic blood pressure (SBP) (-1.9 mm Hg; P = 0.018) than A-allele carriers. The interaction between NREE and IVS25+15 was also significant for both DBP (P = 0.006) and SBP (P = 0.026), in such a way that the effect of the GG-genotype on blood pressure was stronger in individuals with higher NREE (DBP: -4.9 mm Hg, P = 0.02. SBP: -3.8 mm Hg, P= 0.03 for the third tertile). Similar results were observed when the outcome was dichotomously defined as hypertension.

    Conclusions: In summary, the NOS3 IVS25+15 is directly associated with blood pressure and hypertension in white Europeans. However, the associations are most evident in the individuals with the highest NREE. These results need further replication and have to be ideally tested in a trial before being informative for targeted disease prevention. Eventually, the selection of individuals for lifestyle intervention programs could be guided by knowledge of genotype.

    Funded by: Medical Research Council: MC_U106179471, MC_U106179473, MC_U106188470, U.1061.00.001 (79471), U.1061.00.005(79473); Wellcome Trust: 077016, 087636

    American journal of hypertension 2008;21;3;297-302

  • LDL-cholesterol concentrations: a genome-wide association study.

    Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K, Zhao JH, Song K, Yuan X, Johnson T, Ashford S, Inouye M, Luben R, Sims M, Hadley D, McArdle W, Barter P, Kesäniemi YA, Mahley RW, McPherson R, Grundy SM, Wellcome Trust Case Control Consortium, Bingham SA, Khaw KT, Loos RJ, Waeber G, Barroso I, Strachan DP, Deloukas P, Vollenweider P, Wareham NJ and Mooser V

    Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK. manj.sandhu@srl.cam.ac.uk

    Background: LDL cholesterol has a causal role in the development of cardiovascular disease. Improved understanding of the biological mechanisms that underlie the metabolism and regulation of LDL cholesterol might help to identify novel therapeutic targets. We therefore did a genome-wide association study of LDL-cholesterol concentrations.

    Methods: We used genome-wide association data from up to 11,685 participants with measures of circulating LDL-cholesterol concentrations across five studies, including data for 293 461 autosomal single nucleotide polymorphisms (SNPs) with a minor allele frequency of 5% or more that passed our quality control criteria. We also used data from a second genome-wide array in up to 4337 participants from three of these five studies, with data for 290,140 SNPs. We did replication studies in two independent populations consisting of up to 4979 participants. Statistical approaches, including meta-analysis and linkage disequilibrium plots, were used to refine association signals; we analysed pooled data from all seven populations to determine the effect of each SNP on variations in circulating LDL-cholesterol concentrations.

    Findings: In our initial scan, we found two SNPs (rs599839 [p=1.7x10(-15)] and rs4970834 [p=3.0x10(-11)]) that showed genome-wide statistical association with LDL cholesterol at chromosomal locus 1p13.3. The second genome screen found a third statistically associated SNP at the same locus (rs646776 [p=4.3x10(-9)]). Meta-analysis of data from all studies showed an association of SNPs rs599839 (combined p=1.2x10(-33)) and rs646776 (p=4.8x10(-20)) with LDL-cholesterol concentrations. SNPs rs599839 and rs646776 both explained around 1% of the variation in circulating LDL-cholesterol concentrations and were associated with about 15% of an SD change in LDL cholesterol per allele, assuming an SD of 1 mmol/L.

    Interpretation: We found evidence for a novel locus for LDL cholesterol on chromosome 1p13.3. These results potentially provide insight into the biological mechanisms that underlie the regulation of LDL cholesterol and might help in the discovery of novel therapeutic targets for cardiovascular disease.

    Funded by: Medical Research Council: G0000934, G0701863, MC_QA137934, MC_U105630924, MC_U106188470; Wellcome Trust: 068545/Z/02

    Lancet 2008;371;9611;483-91

  • Common variants near MC4R are associated with fat mass, weight and risk of obesity.

    Loos RJ, Lindgren CM, Wheeler E, Zhao JH, Prokopenko I, Inouye M, Freathy RM, Attwood AP, Beckmann JS, Berndt SI, Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, Bergmann S, Bennett AJ, Bingham SA, Bochud M, Brown M, Cauchi S, Connell JM, Cooper C, Smith GD, Day I, Dina C, De S, Dermitzakis ET, Doney AS, Elliott KS, Elliott P, Evans DM, Farooqi IS, Froguel P, Ghori J, Groves CJ, Gwilliam R, Hadley D, Hall AS, Hattersley AT, Hebebrand J, Heid IM, KOR A, Herrera B, Hinney A, Hunt SE, Jarvelin MR, Johnson T, Jolley JD, Karpe F, Keniry A, Khaw KT, Luben RN, Mangino M, Marchini J, McArdle WL, McGinnis R, Meyre D, Munroe PB, Morris AD, Ness AR, Neville MJ, Nica AC, Ong KK, O'Rahilly S, Owen KR, Palmer CN, Papadakis K, Potter S, Pouta A, Qi L, Nurses' Health Study, Randall JC, Rayner NW, Ring SM, Sandhu MS, Scherag A, Sims MA, Song K, Soranzo N, Speliotes EK, Diabetes Genetics Initiative, Syddall HE, Teichmann SA, Timpson NJ, Tobias JH, Uda M, SardiNIA Study, Vogel CI, Wallace C, Waterworth DM, Weedon MN, Wellcome Trust Case Control Consortium, Willer CJ, FUSION, Wraight, Yuan X, Zeggini E, Hirschhorn JN, Strachan DP, Ouwehand WH, Caulfield MJ, Samani NJ, Frayling TM, Vollenweider P, Waeber G, Mooser V, Deloukas P, McCarthy MI*, Wareham NJ* and Barroso I*

    MRC Epidemiology Unit, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.

    To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.

    Funded by: British Heart Foundation; Cancer Research UK; Medical Research Council: G0000934(68341), G0601261(80227), G9521010(63660); NIDDK NIH HHS: F32 DK079466-01, K23 DK080145-01, P30 DK040561-13, R01 DK072193-01, R01 DK072193-02, R01 DK072193-03; Wellcome Trust: 068545, 076113, 077016, 079557, 084713

    Nature genetics 2008;40;6;768-75

    (* Equal communicating author)

    • Evaluating the role of LPIN1 variation in insulin resistance, body weight, and human lipodystrophy in U.K. Populations.

      Fawcett KA, Grimsey N, Loos RJ, Wheeler E, Daly A, Soos M, Semple R, Syddall H, Cooper C, Siniossoglou S, O'Rahilly S, Wareham NJ and Barroso I

      Metabolic Disease Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, U.K.

      Objective: Loss of lipin 1 activity causes lipodystrophy and insulin resistance in the fld mouse, and LPIN1 expression and common genetic variation were recently suggested to influence adiposity and insulin sensitivity in humans. We aimed to conduct a comprehensive association study to clarify the influence of common LPIN1 variation on adiposity and insulin sensitivity in U.K. populations and to examine the role of LPIN1 mutations in insulin resistance syndromes.

      Twenty-two single nucleotide polymorphisms tagging common LPIN1 variation were genotyped in Medical Research Council (MRC) Ely (n = 1,709) and Hertfordshire (n = 2,901) population-based cohorts. LPIN1 exons, exon/intron boundaries, and 3' untranslated region were sequenced in 158 patients with idiopathic severe insulin resistance (including 23 lipodystrophic patients) and 48 control subjects.

      Results: We found no association between LPIN1 single nucleotide polymorphisms and fasting insulin but report a nominal association between rs13412852 and BMI (P = 0.042) in a meta-analysis of 8,504 samples from in-house and publicly available studies. Three rare nonsynonymous variants (A353T, R552K, and G582R) were detected in severely insulin-resistant patients. However, these did not cosegregate with disease in affected families, and Lipin1 protein expression and phosphorylation in patients with variants were indistinguishable from those in control subjects.

      Conclusions: Our data do not support a major effect of common LPIN1 variation on metabolic traits and suggest that mutations in LPIN1 are not a common cause of lipodystrophy in humans. The nominal associations with BMI and other metabolic traits in U.K. cohorts require replication in larger cohorts.

      Funded by: Medical Research Council: G0000934, G0000934(68341), G0701446, MC_U106188470, MC_U147574221, MC_U147585824, MC_UP_A620_1014, U.1475.00.002.00001.01 (85824), U.1475.00.004.00002.01(74221); Wellcome Trust: 068545, 068545/Z/02, 077016, 078986, 078986/Z/06/Z, 080952, 080952/Z/06/Z

      Diabetes 2008;57;9;2527-33

    • Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations.

      Franks PW, Rolandsson O, Debenham SL, Fawcett KA, Payne F, Dina C, Froguel P, Mohlke KL, Willer C, Olsson T, Wareham NJ, Hallmans G, Barroso I and Sandhu MS

      Department of Public Health and Clinical Medicine, Umeå University Hospital, Umeå, Sweden. paul.franks@medicin.umu.se

      Mutations at the gene encoding wolframin (WFS1) cause Wolfram syndrome, a rare neurological condition. Associations between single nucleotide polymorphisms (SNPs) at WFS1 and type 2 diabetes have recently been reported. Thus, our aim was to replicate those associations in a northern Swedish case-control study of type 2 diabetes. We also performed a meta-analysis of published and previously unpublished data from Sweden, Finland and France, to obtain updated summary effect estimates.

      Methods: Four WFS1 SNPs (rs10010131, rs6446482, rs752854 and rs734312 [H611R]) were genotyped in a type 2 diabetes case-control study (n = 1,296/1,412) of Swedish adults. Logistic regression was used to assess the association between each WFS1 SNP and type 2 diabetes, following adjustment for age, sex and BMI. We then performed a meta-analysis of 11 studies of type 2 diabetes, comprising up to 14,139 patients and 16,109 controls, to obtain a summary effect estimate for the WFS1 variants.

      Results: In the northern Swedish study, the minor allele at rs752854 was associated with reduced type 2 diabetes risk [odds ratio (OR) 0.85, 95% CI 0.75-0.96, p=0.010]. Borderline statistical associations were observed for the remaining SNPs. The meta-analysis of the four independent replication studies for SNP rs10010131 and correlated variants showed evidence for statistical association (OR 0.87, 95% CI 0.82-0.93, p=4.5 x 10(-5)). In an updated meta-analysis of all 11 studies, strong evidence of statistical association was also observed (OR 0.89, 95% CI 0.86-0.92; p=4.9 x 10(-11)).

      In this study of WFS1 variants and type 2 diabetes risk, we have replicated the previously reported associations between SNPs at this locus and the risk of type 2 diabetes.

      Funded by: Medical Research Council: MC_U106179471; NIDDK NIH HHS: DK62370, DK72193, R01 DK072193-03; Wellcome Trust: 077016

      Diabetologia 2008;51;3;458-63

    • Testing of diabetes-associated WFS1 polymorphisms in the Diabetes Prevention Program.

      Florez JC, Jablonski KA, McAteer J, Sandhu MS, Wareham NJ, Barroso I, Franks PW, Altshuler D, Knowler WC and Diabetes Prevention Program Research Group

      Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA. jcflorez@partners.org

      Wolfram syndrome (diabetes insipidus, diabetes mellitus, optic atrophy and deafness) is caused by mutations in the WFS1 gene. Recently, single nucleotide polymorphisms (SNPs) in WFS1 have been reproducibly associated with type 2 diabetes. We therefore examined the effects of these variants on diabetes incidence and response to interventions in the Diabetes Prevention Program (DPP), in which a lifestyle intervention or metformin treatment was compared with placebo.

      Methods: We genotyped the WFS1 SNPs rs10010131, rs752854 and rs734312 (H611R) in 3,548 DPP participants and performed Cox regression analysis using genotype, intervention and their interactions as predictors of diabetes incidence. We also evaluated the effect of these SNPs on insulin resistance and beta cell function at 1 year.

      Results: Although none of the three SNPs was associated with diabetes incidence in the overall cohort, white homozygotes for the previously reported protective alleles appeared less likely to develop diabetes in the lifestyle arm. Examination of the publicly available Diabetes Genetics Initiative genome-wide association dataset revealed that rs10012946, which is in strong linkage disequilibrium with the three WFS1 SNPs (r(2)=0.88-1.0), was associated with type 2 diabetes (allelic odds ratio 0.85, 95% CI 0.75-0.97, p=0.026). In the DPP, we noted a trend towards increased insulin secretion in carriers of the protective variants, although for most SNPs this was seen as compensatory for the diminished insulin sensitivity.

      The previously reported protective effect of select WFS1 alleles may be magnified by a lifestyle intervention. These variants appear to confer an improvement in beta cell function.

      Funded by: Medical Research Council: MC_U106179471; NIDDK NIH HHS: K23 DK65978-04, R01 DK072041-02, U01 DK048489, U01 DK048489-06

      Diabetologia 2008;51;3;451-7

2007 Publications

  • The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase.

    Gerken T, Girard CA, Tung YC, Webby CJ, Saudek V, Hewitson KS, Yeo GS, McDonough MA, Cunliffe S, McNeill LA, Galvanovskis J, Rorsman P, Robins P, Prieur X, Coll AP, Ma M, Jovanovic Z, Farooqi IS, Sedgwick B, Barroso I, Lindahl T, Ponting CP, Ashcroft FM, O'Rahilly S and Schofield CJ

    Chemistry Research Laboratory and Oxford Centre for Integrative Systems Biology, University of Oxford, 12 Mansfield Road, Oxford, Oxon OX1 3TA, UK.

    Variants in the FTO (fat mass and obesity associated) gene are associated with increased body mass index in humans. Here, we show by bioinformatics analysis that FTO shares sequence motifs with Fe(II)- and 2-oxoglutarate-dependent oxygenases. We find that recombinant murine Fto catalyzes the Fe(II)- and 2OG-dependent demethylation of 3-methylthymine in single-stranded DNA, with concomitant production of succinate, formaldehyde, and carbon dioxide. Consistent with a potential role in nucleic acid demethylation, Fto localizes to the nucleus in transfected cells. Studies of wild-type mice indicate that Fto messenger RNA (mRNA) is most abundant in the brain, particularly in hypothalamic nuclei governing energy balance, and that Fto mRNA levels in the arcuate nucleus are regulated by feeding and fasting. Studies can now be directed toward determining the physiologically relevant FTO substrate and how nucleic acid methylation status is linked to increased fat mass.

    Funded by: Medical Research Council: G108/617, G9824984, MC_U137761446; NIGMS NIH HHS: U54 GM064346; Wellcome Trust: 068086, 077016

    Science (New York, N.Y.) 2007;318;5855;1469-72

  • The V103I polymorphism of the MC4R gene and obesity: population based studies and meta-analysis of 29 563 individuals.

    Young EH, Wareham NJ, Farooqi S, Hinney A, Hebebrand J, Scherag A, O'rahilly S, Barroso I and Sandhu MS

    MRC Epidemiology Unit, Strangeways Research Laboratory, Cambridge, UK. elizabeth.young@mrc-epid.cam.ac.uk

    Background: Previous studies have suggested that a variant in the melanocortin-4 receptor (MC4R) gene is important in protecting against common obesity. Larger studies are needed, however, to confirm this relation.

    Methods: We assessed the association between the V103I polymorphism in the MC4R gene and obesity in three UK population based cohort studies, totalling 8304 individuals. We also did a meta-analysis of relevant studies, involving 10 975 cases and 18 588 controls, to place our findings in context.

    Finding: In an analysis of all studies, individuals carrying the isoleucine allele had an 18% (95% confidence interval 4-30%, P=0.015) lower risk of obesity compared with non-carriers. There was no heterogeneity among studies and no apparent publication bias.

    Interpretation: This study confirms that the V103I polymorphism protects against human obesity at a population level. As such it provides proof of principle that specific gene variants may, at least in part, explain susceptibility and resistance to common forms of human obesity. A better understanding of the mechanisms underlying this association will help determine whether changes in MC4R activity have therapeutic potential.

    Funded by: Medical Research Council: G0100103, G9824984, MC_U106179471, MC_U106188470; Wellcome Trust: 068086, 077016

    International journal of obesity (2005) 2007;31;9;1437-41

  • Common variants in WFS1 confer risk of type 2 diabetes.

    Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL, Daly A, Lango H, Frayling TM, Neumann RJ, Sherva R, Blech I, Pharoah PD, Palmer CN, Kimber C, Tavendale R, Morris AD, McCarthy MI, Walker M, Hitman G, Glaser B, Permutt MA, Hattersley AT, Wareham NJ and Barroso I

    UK Medical Research Council (MRC) Epidemiology Unit, Strangeways Research Laboratory, Cambridge CB1 8RN, UK. manj.sandhu@srl.cam.ac.uk

    We studied genes involved in pancreatic beta cell function and survival, identifying associations between SNPs in WFS1 and diabetes risk in UK populations that we replicated in an Ashkenazi population and in additional UK studies. In a pooled analysis comprising 9,533 cases and 11,389 controls, SNPs in WFS1 were strongly associated with diabetes risk. Rare mutations in WFS1 cause Wolfram syndrome; using a gene-centric approach, we show that variation in WFS1 also predisposes to common type 2 diabetes.

    Funded by: Medical Research Council: G0500070, MC_U106179471; Wellcome Trust: 068545/z/02, 077016

    Nature genetics 2007;39;8;951-3

  • TCF7L2 polymorphisms modulate proinsulin levels and beta-cell function in a British Europid population.

    Loos RJ, Franks PW, Francis RW, Barroso I, Gribble FM, Savage DB, Ong KK, O'Rahilly S and Wareham NJ

    Medical Research Council Epidemiology Unit, Strangeways Research Laboratory, Cambridge, UK. ruth.loos@mrc-epid.cam.ac.uk

    Rapidly accumulating evidence shows that common T-cell transcription factor (TCF)7L2 polymorphisms confer risk of type 2 diabetes through unknown mechanisms. We examined the association between four TCF7L2 single nucleotide polymorphisms (SNPs), including rs7903146, and measures of insulin sensitivity and insulin secretion in 1,697 Europid men and women of the population-based MRC (Medical Research Council)-Ely study. The T-(minor) allele of rs7903146 was strongly and positively associated with fasting proinsulin (P = 4.55 x 10(-9)) and 32,33 split proinsulin (P = 1.72 x 10(-4)) relative to total insulin levels; i.e., differences between T/T and C/C homozygotes amounted to 21.9 and 18.4% respectively. Notably, the insulin-to-glucose ratio (IGR) at 30-min oral glucose tolerance test (OGTT), a frequently used surrogate of first-phase insulin secretion, was not associated with the TCF7L2 SNP (P > 0.7). However, the insulin response (IGR) at 60-min OGTT was significantly lower in T-allele carriers (P = 3.5 x 10(-3)). The T-allele was also associated with higher A1C concentrations (P = 1.2 x 10(-2)) and reduced beta-cell function, assessed by homeostasis model assessment of beta-cell function (P = 2.8 x 10(-2)). Similar results were obtained for the other TCF7L2 SNPs. Of note, both major genes involved in proinsulin processing (PC1, PC2) contain TCF-binding sites in their promoters. Our findings suggest that the TCF7L2 risk allele may predispose to type 2 diabetes by impairing beta-cell proinsulin processing. The risk allele increases proinsulin levels and diminishes the 60-min but not 30-min insulin response during OGTT. The strong association between the TCF7L2 risk allele and fasting proinsulin but not insulin levels is notable, as, in this unselected and largely normoglycemic population, external influences on beta-cell stress are unlikely to be major factors influencing the efficiency of proinsulin processing.

    Funded by: Medical Research Council: G9824984, MC_U106179471, MC_U106179472, MC_U106188470; Wellcome Trust: 071187, 077016

    Diabetes 2007;56;7;1943-7

  • A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity.

    Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD, Hattersley AT and McCarthy MI

    Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road, Exeter, UK.

    Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.

    Funded by: Medical Research Council: G0000934, G0500070, G0600705, G9815508, MC_U106179471, MC_U106188470; NIA NIH HHS: Z99 AG999999; Wellcome Trust: 079557

    Science (New York, N.Y.) 2007;316;5826;889-94

  • Adiponectin receptor genes: mutation screening in syndromes of insulin resistance and association studies for type 2 diabetes and metabolic traits in UK populations.

    Collins SC, Luan J, Thompson AJ, Daly A, Semple RK, O'Rahilly S, Wareham NJ and Barroso I

    Metabolic Disease Group, The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK.

    Adiponectin is an adipokine with insulin-sensitising and anti-atherogenic properties. Several reports suggest that genetic variants in the adiponectin gene are associated with circulating levels of adiponectin, insulin sensitivity and type 2 diabetes risk. Recently two receptors for adiponectin have been cloned. Genetic studies have yielded conflicting results on the role of these genes and type 2 diabetes predisposition. In this study we aimed to evaluate the potential role of genetic variation in these genes in syndromes of severe insulin resistance, type 2 diabetes and in related metabolic traits in UK Europid populations.

    Exons and splice junctions of the adiponectin receptor 1 and 2 genes (ADIPOR1; ADIPOR2) were sequenced in patients from our severe insulin resistance cohort (n=129). Subsequently, 24 polymorphisms were tested for association with type 2 diabetes in population-based type 2 diabetes case-control studies (n=2,127) and with quantitative traits in a population-based longitudinal study (n=1,721).

    Results: No missense or nonsense mutations in ADIPOR1 and ADIPOR2 were detected in the cohort of patients with severe insulin resistance. None of the 24 polymorphisms (allele frequency 2.3-48.3%) tested was associated with type 2 diabetes in the case-control study. Similarly, none of the polymorphisms was associated with fasting plasma insulin, fasting and 2-h post-load plasma glucose, 30-min insulin increment or BMI.

    Genetic variation in ADIPOR1 and ADIPOR2 is not a major cause of extreme insulin resistance in humans, nor does it contribute in a significant manner to type 2 diabetes risk and related traits in UK Europid populations.

    Funded by: Medical Research Council: MC_U106179471; Wellcome Trust

    Diabetologia 2007;50;3;555-62

  • Analysis of genetic variation in Akt2/PKB-beta in severe insulin resistance, lipodystrophy, type 2 diabetes, and related metabolic phenotypes.

    Tan K, Kimber WA, Luan J, Soos MA, Semple RK, Wareham NJ, O'Rahilly S and Barroso I

    Metabolic Disease Group, Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, U.K.

    We previously reported a family in which a heterozygous missense mutation in Akt2 led to a dominantly inherited syndrome of insulin-resistant diabetes and partial lipodystrophy. To determine whether genetic variation in AKT2 plays a broader role in human metabolic disease, we sequenced the entire coding region and splice junctions of AKT2 in 94 unrelated patients with severe insulin resistance, 35 of whom had partial lipodystrophy. Two rare missense mutations (R208K and R467W) were identified in single individuals. However, insulin-stimulated kinase activities of these variants were indistinguishable from wild type. In two large case-control studies (total number of participants 2,200), 0 of 11 common single nucleotide polymorphism (SNPs) in AKT2 showed significant association with type 2 diabetes. In a quantitative trait study of 1,721 extensively phenotyped individuals from the U.K., no association was found with any relevant intermediate metabolic trait. In summary, although heterozygous loss-of- function mutations in AKT2 can cause a syndrome of severe insulin resistance and lipodystrophy in humans, such mutations are uncommon causes of these syndromes. Furthermore, genetic variation in and around the AKT2 locus is unlikely to contribute significantly to the risk of type 2 diabetes or related intermediate metabolic traits in U.K. populations.

    Funded by: Medical Research Council: MC_U106179471; Wellcome Trust: 077016

    Diabetes 2007;56;3;714-9

  • Lamin A/C polymorphisms, type 2 diabetes, and the metabolic syndrome: case-control and quantitative trait studies.

    Mesa JL, Loos RJ, Franks PW, Ong KK, Luan J, O'Rahilly S, Wareham NJ and Barroso I

    Medical Research Center Epidemiology Unit, Cambridge, U.K.

    Mutations in the LMNA gene, encoding the nuclear envelope protein lamin A/C, are responsible for a number of distinct disease entities including Dunnigan-type familial partial lipodystrophy. Dunningan-type lipodystrophy is characterized by loss of subcutaneous adipose tissue, insulin resistance, dyslipidemia, and type 2 diabetes and shares many of the features of the metabolic syndrome. Furthermore, several genome-wide linkage scans for type 2 diabetes have found evidence of linkage at chromosome 1q21.2, the region that harbors the LMNA gene. Therefore, LMNA is a biological and positional candidate for type 2 diabetes susceptibility. Previous studies have reported association between a common LMNA variant (1908C>T; rs4641) and adverse metabolic traits in ethnically diverse populations from Asia and North America. In the present study, we characterized the common variation across the LMNA gene (including rs4641) and tested for association with type 2 diabetes in two large case-control studies (n = 2,052) and with features of the metabolic syndrome in a separate cohort study (n = 1,572). Despite our study being sufficiently powered to detect effects similar and even smaller in magnitude than those previously reported, none of the LMNA single nucleotide polymorphisms were statistically significantly associated with type 2 diabetes or the metabolic syndrome. Thus, it appears unlikely that variation at LMNA substantially increases the risk of type 2 diabetes or related traits in U.K. Europids.

    Funded by: Medical Research Council: MC_U106179471, MC_U106179472, MC_U106188470; Wellcome Trust: 077016

    Diabetes 2007;56;3;884-9

  • PPARGC1A coding variation may initiate impaired NEFA clearance during glucose challenge.

    Franks PW, Ekelund U, Brage S, Luan J, Schafer AJ, O'Rahilly S, Barroso I and Wareham NJ

    Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden. paul.franks@medicin.umu.se

    The peroxisome proliferator-activated receptor gamma coactivator 1-alpha protein, encoded by the PPARGC1A gene, transcriptionally activates a complex pathway of lipid and glucose metabolism and is expressed primarily in tissues of high metabolic activity such as liver, heart and exercising oxidative skeletal muscle fibre. Ppargc1a-null mice develop systemic dyslipidaemia and hepatic steatosis. In humans, NEFAs downregulate PPARGC1A expression in skeletal muscle. Furthermore, a common non-synonymous coding variant at PPARGC1A (Gly482Ser, rs8192678) is associated with decreased PPARGC1A mRNA levels and increased type 2 diabetes risk.

    In a population-based sample of 691 healthy middle-aged Europids we assessed whether Gly482Ser is associated with levels of NEFA when fasting and in response to an oral glucose challenge. We also assessed the potential effect-modifying role of adipose tissue mass on these phenotypes.

    Results: After adjustment for age, sex, fat mass and fat-free mass, the Ser482 allele associated with higher NEFA at 30 min and 2 h and with NEFA AUC (all values p<or=0.02). Furthermore, suggestive evidence of interaction between fat mass and Gly482Ser was observed for fasting NEFA (p=0.059). After stratification by level of obesity, genotype associations were observed in the obese for fasting NEFA (p=0.028) and NEFA at 30 min (p=0.013) and 2 h (p=0.002), and with NEFA AUC (p=0.005), but no significant associations were observed in lean individuals (all values p>0.6).

    Our observations indicate that NEFA clearance is blunted following a glucose load in carriers of the PPARCG1A Ser482 allele. This association is augmented by obesity.

    Funded by: Medical Research Council: MC_U106179471, MC_U106179473; Wellcome Trust: 077016

    Diabetologia 2007;50;3;569-73

  • Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor.

    Farooqi IS, Wangensteen T, Collins S, Kimber W, Matarese G, Keogh JM, Lank E, Bottomley B, Lopez-Fernandez J, Ferraz-Amaro I, Dattani MT, Ercan O, Myhre AG, Retterstol L, Stanhope R, Edge JA, McKenzie S, Lessan N, Ghodsi M, De Rosa V, Perna F, Fontana S, Barroso I, Undlien DE and O'Rahilly S

    Cambridge Institute for Medical Research, University Department of Clinical Biochemistry, Addenbrooke's Hospital, Cambridge, United Kingdom. isf20@cam.ac.uk

    Background: A single family has been described in which obesity results from a mutation in the leptin-receptor gene (LEPR), but the prevalence of such mutations in severe, early-onset obesity has not been systematically examined.

    Methods: We sequenced LEPR in 300 subjects with hyperphagia and severe early-onset obesity, including 90 probands from consanguineous families, and investigated the extent to which mutations cosegregated with obesity and affected receptor function. We evaluated metabolic, endocrine, and immune function in probands and affected relatives.

    Results: Of the 300 subjects, 8 (3%) had nonsense or missense LEPR mutations--7 were homozygotes, and 1 was a compound heterozygote. All missense mutations resulted in impaired receptor signaling. Affected subjects were characterized by hyperphagia, severe obesity, alterations in immune function, and delayed puberty due to hypogonadotropic hypogonadism. Serum leptin levels were within the range predicted by the elevated fat mass in these subjects. Their clinical features were less severe than those of subjects with congenital leptin deficiency.

    Conclusions: The prevalence of pathogenic LEPR mutations in a cohort of subjects with severe, early-onset obesity was 3%. Circulating levels of leptin were not disproportionately elevated, suggesting that serum leptin cannot be used as a marker for leptin-receptor deficiency. Congenital leptin-receptor deficiency should be considered in the differential diagnosis in any child with hyperphagia and severe obesity in the absence of developmental delay or dysmorphism.

    Funded by: Medical Research Council: G0502115; Telethon: GJT04008; Wellcome Trust: 067457, 068086, 077016

    The New England journal of medicine 2007;356;3;237-47

  • Comment on "A common genetic variant is associated with adult and childhood obesity".

    Loos RJ, Barroso I, O'rahilly S and Wareham NJ

    Medical Research Council Epidemiology Unit, Cambridge, UK. ruth.loos@mrc-epid.cam.ac.uk

    Herbert et al. (Reports, 14 April 2006, p. 279) found that the rs7566605 genetic variant, located upstream of the INSIG2 gene, was consistently associated with increased body mass index. However, we found no evidence of association between rs7566605 and body mass index in two large ethnically homogeneous population-based cohorts. On the contrary, an opposite tendency was observed.

    Funded by: Medical Research Council: G9824984, MC_U106179471, MC_U106188470; Wellcome Trust: 077016

    Science (New York, N.Y.) 2007;315;5809;187; author reply 187

2006 Publications

  • PARL Leu262Val is not associated with fasting insulin levels in UK populations.

    Fawcett KA, Wareham NJ, Luan J, Syddall H, Cooper C, O'Rahilly S, Day IN, Sandhu MS and Barroso I

    Metabolic Disease Group, Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.

    PARL, the gene encoding presenilins-associated rhomboid-like protein, maps to chromosome 3q27 within a quantitative trait locus that influences components of the metabolic syndrome. Recently, an amino acid substitution (Leu262Val, rs3732581) in PARL was associated with fasting plasma insulin levels in a US white population (N=1031). This variant was also found to modify the positive association between age and fasting insulin. The aim of this study was to test whether these findings could be replicated in two UK population-based cohorts.

    Methods: Participants from the Medical Research Council Ely and Hertfordshire cohort studies were genotyped for this variant using a SNaPshot primer extension assay and Taqman assay respectively. Full phenotypic and genotypic data were available for 3,666 study participants.

    Results: Based on a dominant model, we found no association between the Leu262Val polymorphism and fasting insulin levels (p=0.79) or BMI (p=0.98). We did not observe the previously reported interaction between age and genotype on fasting insulin (p=0.14).

    Despite having greater statistical power, our data do not support the previously reported association between PARL Leu262Val and fasting plasma insulin levels, a measure of insulin resistance. Our findings indicate that this variant is unlikely to be an important contributor to insulin resistance in UK populations.

    Funded by: Medical Research Council: MC_U106179471, MC_U147585824, MC_UP_A620_1014, U.1061.00.001 (79471); Wellcome Trust: 077016

    Diabetologia 2006;49;11;2649-52

  • Polymorphisms in the gene encoding sterol regulatory element-binding factor-1c are associated with type 2 diabetes.

    Harding AH, Loos RJ, Luan J, O'Rahilly S, Wareham NJ and Barroso I

    MRC Epidemiology Unit, Cambridge, UK.

    The sterol regulatory element-binding factor (SREBF)-1c is a transcription factor involved in the regulation of lipid and glucose metabolism. We have previously found evidence that a common SREBF1c single-nucleotide polymorphism (SNP), located between exons 18c and 19c, is associated with an increased risk of type 2 diabetes. The present study aimed to replicate our previously reported association in a larger case-control study and to examine an additional five SREBF1c SNPs for their association with diabetes risk and plasma glucose concentrations.

    Methods: We genotyped six SREBF1c SNPs in two case-control studies (n=1,938) and in a large cohort study (n=1,721) and tested for association with type 2 diabetes and with plasma glucose concentrations (fasting and 120-min post-glucose load), respectively.

    Results: In the case-control studies, carriers of the minor allele of the previously reported SNP (rs11868035) had a significantly increased diabetes risk (odds ratio [OR]=1.20 [95% CI 1.04-1.38], p=0.015). Also, three other SNPs (rs2236513, rs6502618 and rs1889018), located in the 5' region, were significantly associated with diabetes risk (OR > or =1.21, p< or =0.006). Furthermore, two SNPs (rs2236513 and rs1889018) in the 5' region were weakly (p<0.09) associated with plasma glucose concentrations in the cohort study. Rare homozygotes had increased (p< or =0.05) 120-min post-load glucose concentrations compared with carriers of the wild-type allele. Haplotype analyses showed significant (p=0.04) association with diabetes risk and confirmed the single SNP analyses.

    In summary, we replicated our previous finding and found evidence for SNPs in the 5' region of the SREBF1c gene to be associated with the risk of type 2 diabetes and plasma glucose concentration.

    Funded by: Medical Research Council: MC_U106179471, MC_U106188470; Wellcome Trust: 077016

    Diabetologia 2006;49;11;2642-8

  • Non-DNA binding, dominant-negative, human PPARgamma mutations cause lipodystrophic insulin resistance.

    Agostini M, Schoenmakers E, Mitchell C, Szatmari I, Savage D, Smith A, Rajanayagam O, Semple R, Luan J, Bath L, Zalin A, Labib M, Kumar S, Simpson H, Blom D, Marais D, Schwabe J, Barroso I, Trembath R, Wareham N, Nagy L, Gurnell M, O'Rahilly S and Chatterjee K

    Department of Medicine, University of Cambridge, United Kingdom.

    PPARgamma is essential for adipogenesis and metabolic homeostasis. We describe mutations in the DNA and ligand binding domains of human PPARgamma in lipodystrophic, severe insulin resistance. These receptor mutants lack DNA binding and transcriptional activity but can translocate to the nucleus, interact with PPARgamma coactivators and inhibit coexpressed wild-type receptor. Expression of PPARgamma target genes is markedly attenuated in mutation-containing versus receptor haploinsufficent primary cells, indicating that such dominant-negative inhibition operates in vivo. Our observations suggest that these mutants restrict wild-type PPARgamma action via a non-DNA binding, transcriptional interference mechanism, which may involve sequestration of functionally limiting coactivators.

    Funded by: Wellcome Trust: 080237

    Cell metabolism 2006;4;4;303-11

  • Meta-analysis of the Gly482Ser variant in PPARGC1A in type 2 diabetes and related phenotypes.

    Barroso I, Luan J, Sandhu MS, Franks PW, Crowley V, Schafer AJ, O'Rahilly S and Wareham NJ

    The Wellcome Trust Sanger Institute, Metabolic Disease Group, The Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. ib1@sanger.ac.uk

    Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PPARGC1A) is a transcriptional co-activator with a central role in energy expenditure and glucose metabolism. Several studies have suggested that the common PPARGC1A polymorphism Gly482Ser may be associated with risk of type 2 diabetes, with conflicting results. To clarify the role of Gly482Ser in type 2 diabetes and related human metabolic phenotypes we genotyped this polymorphism in a case-control study and performed a meta-analysis of relevant published data.

    Gly482Ser was genotyped in a type 2 diabetes case-control study (N=1,096) using MassArray technology. A literature search revealed publications that examined Gly482Ser for association with type 2 diabetes and related metabolic phenotypes. Meta-analysis of the current study and relevant published data was undertaken.

    Results: In the pooled meta-analysis, including data from this study and seven published reports (3,718 cases, 4,818 controls), there was evidence of between-study heterogeneity (p<0.1). In the fixed-effects meta-analysis, the pooled odds ratio for risk of type 2 diabetes per Ser482 allele was 1.07 (95% CI 1.00-1.15, p=0.044). Elimination of one of the studies from the meta-analysis gave a summary odds ratio of 1.11 (95% CI 1.04-1.20, p=0.004), with no between-study heterogeneity (p=0.475). For quantitative metabolic traits in normoglycaemic subjects, we also found significant between-study heterogeneity. However, no significant association was observed between Gly482Ser and BMI, fasting glucose or fasting insulin.

    This meta-analysis of data from the current and published studies supports a modest role for the Gly482Ser PPARGC1A variant in type 2 diabetes risk.

    Funded by: Wellcome Trust

    Diabetologia 2006;49;3;501-5

2005 Publications

  • PPARGC1A genotype (Gly482Ser) predicts exceptional endurance capacity in European men.

    Lucia A, Gómez-Gallego F, Barroso I, Rabadán M, Bandrés F, San Juan AF, Chicharro JL, Ekelund U, Brage S, Earnest CP, Wareham NJ and Franks PW

    European University of Madrid, Spain.

    Animal and human data indicate a role for the peroxisome proliferator-activated receptor-gamma coactivator 1alpha (PPARGC1A) gene product in the development of maximal oxygen uptake (V(O2 max)), a determinant of endurance capacity, diabetes, and early death. We tested the hypothesis that the frequency of the minor Ser482 allele at the PPARGC1A locus is lower in World-class Spanish male endurance athletes (cases) [n = 104; mean (SD) age: 26.8 (3.8) yr] than in unfit United Kingdom (UK) Caucasian male controls [n = 100; mean (SD) age: 49.3 (8.1) yr]. In cases and controls, the Gly482Ser genotype met Hardy-Weinberg expectations (P > 0.05 in both groups tested separately). Cases had significantly higher V(O2 max) [73.4 (5.7) vs. 29.4 ml x kg(-1) x min(-1) (3.8); P < 0.0001] and were leaner [body mass index: 20.6 (1.5) vs. 27.6 kg/m2 (3.9); P < 0.0001] than controls. In unadjusted chi2 analyses, the frequency of the minor Ser482 allele was significantly lower in cases than in controls (29.1 vs. 40.0%; P = 0.01). To assess the possibility that genetic stratification could confound these observations, we also compared Gly482Ser genotype frequencies in Spanish (n = 164) and UK Caucasian men (n = 381) who were unselected for their level of fitness. In these analyses, Ser482 allele frequencies were very similar (36.9% in Spanish vs. 37.5% in UK Caucasians, P = 0.83), suggesting that confounding by genetic stratification is unlikely to explain the association between Gly482Ser genotype and endurance capacity. In summary, our data indicate a role for the Gly482Ser genotype in determining aerobic fitness. This finding has relevance from the perspective of physical performance, but it may also be informative for the targeted prevention of diseases associated with low fitness such as Type 2 diabetes.

    Funded by: Wellcome Trust

    Journal of applied physiology (Bethesda, Md. : 1985) 2005;99;1;344-8

  • Differentiating campomelic dysplasia from Cumming syndrome.

    Watiker V, Lachman RS, Wilcox WR, Barroso I, Schafer AJ and Scherer G

    Funded by: NICHD NIH HHS: 5P01 HD 22657

    American journal of medical genetics. Part A 2005;135;1;110-2

2004 Publications

  • A family with severe insulin resistance and diabetes due to a mutation in AKT2.

    George S, Rochford JJ, Wolfrum C, Gray SL, Schinner S, Wilson JC, Soos MA, Murgatroyd PR, Williams RM, Acerini CL, Dunger DB, Barford D, Umpleby AM, Wareham NJ, Davies HA, Schafer AJ, Stoffel M, O'Rahilly S and Barroso I

    Department of Clinical Biochemistry, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge CB2 2QQ, UK.

    Inherited defects in signaling pathways downstream of the insulin receptor have long been suggested to contribute to human type 2 diabetes mellitus. Here we describe a mutation in the gene encoding the protein kinase AKT2/PKBbeta in a family that shows autosomal dominant inheritance of severe insulin resistance and diabetes mellitus. Expression of the mutant kinase in cultured cells disrupted insulin signaling to metabolic end points and inhibited the function of coexpressed, wild-type AKT. These findings demonstrate the central importance of AKT signaling to insulin sensitivity in humans.

    Funded by: Wellcome Trust: 078986

    Science (New York, N.Y.) 2004;304;5675;1325-8

  • Familial partial lipodystrophy associated with compound heterozygosity for novel mutations in the LMNA gene.

    Savage DB, Soos MA, Powlson A, O'Rahilly S, McFarlane I, Halsall DJ, Barroso I, Thomas EL, Bell JD, Scobie I, Belchetz PE, Kelly WF and Schafer AJ

    Diabetologia 2004;47;4;753-6

  • Genetic variants in human sterol regulatory element binding protein-1c in syndromes of severe insulin resistance and type 2 diabetes.

    Laudes M, Barroso I, Luan J, Soos MA, Yeo G, Meirhaeghe A, Logie L, Vidal-Puig A, Schafer AJ, Wareham NJ and O'Rahilly S

    Department of Medicine and Clinical Biochemistry, University of Cambridge/Addenbrooke's Hospital, Cambridge, U.K.

    The transcription factor sterol regulatory element binding protein (SREBP)-1c is intimately involved in the regulation of lipid and glucose metabolism. To investigate whether mutations in this gene might contribute to insulin resistance, we screened the exons encoding the aminoterminal transcriptional activation domain in a cohort of 85 unrelated human subjects with severe insulin resistance. Two missense mutations (P87L and P416A) were found in single affected patients but not in 47 control subjects. However, these variants were indistinguishable from the wild-type in their ability to bind DNA or to transactivate an SREBP-1 responsive promoter construct. We also identified a common intronic single nucleotide polymorphism (C/T) located between exon 18c and 19c. In a case-control study of 517 U.K. Caucasian case subjects and 517 age- and sex-matched control subjects, the T-allele at this locus was significantly associated with type 2 diabetes in men (odds ratio = 1.42 [1.11-1.82], P = 0.005) but not women. In a separate population-based study of 1,100 Caucasians, carriers of the T-allele showed significantly higher levels of total and LDL cholesterol (P < 0.05) compared with wild-type individuals. In summary, we have conducted the first study of the SREBP-1c gene as a candidate for human insulin resistance. Although the rare mutations identified were functionally silent in the assays used, we obtained some evidence, which requires conformation in other populations, that a common variant in the SREBP-1c gene might influence diabetes risk and plasma cholesterol level.

    Diabetes 2004;53;3;842-6

2003 Publications

  • PGC-1alpha genotype modifies the association of volitional energy expenditure with [OV0312]O2max.

    Franks PW, Barroso I, Luan J, Ekelund U, Crowley VE, Brage S, Sandhu MS, Jakes RW, Middelberg RP, Harding AH, Schafer AJ, O'Rahilly S and Wareham NJ

    Institute of Public Health, University of Cambridge, United Kingdom.

    Unlabelled: Sedentary lifestyles are increasingly common and result in low cardiorespiratory fitness ([OV0312]O2max), a well-established predictor of early mortality and coronary heart disease (CHD). Adaptation in [OV0312]O2max after exercise training varies considerably between people. Because there are hereditary components to fitness, it is likely that genetic factors explain some of this variability. PPARGC1 (PGC-1alpha) coactivates genes involved in energy transduction and mitochondrial biogenesis. Transgenic mouse data demonstrate that overexpression of PGC-1alpha mRNA increases endurance capacity by transformation of nonoxidative to oxidative skeletal muscle tissue. Other murine studies demonstrate that exercise increases PGC-1alpha mRNA expression.

    Purpose: To explore whether a candidate polymorphism in the PGC-1alpha gene modifies the association between physical activity energy expenditure (PAEE) and predicted [OV0312]O2max ([OV0312]O2max.pred).

    Method: We examined whether the Gly482Ser polymorphism of PGC-1alpha modified the relationship between objectively measured PAEE and [OV0312]O2max.pred in a population-based sample of 599 healthy middle-aged people. PAEE was assessed using individual calibration with 4 d of heart rate monitoring. [OV0312]O2max.pred was measured during a submaximal exercise stress test on a bicycle ergometer.

    Results: Homozygosity at the Ser482 allele was found in 12.7% of the cohort, whereas 38.9% and 48.4% carried the Gly482Gly and Gly482Ser genotypes, respectively. The association between PAEE and [OV0312]O2max.pred (mL x kg(-1) x min(-1)) was strongest in people homozygous for the Ser482 allele (beta = 12.03; P < 0.00001) compared with carriers of the Gly allele (beta = 5.61; P < 0.00001). In a recessive model for the Ser482 allele, the interaction between PAEE and genotype on [OV0312]O2max.pred (L x min(-1)) was highly significant (P = 0.009).

    Conclusion: Our results indicate that Ser482 homozygotes may be most capable of improving cardiorespiratory fitness when physically active, and that Gly482Ser may explain some of the between-person variance previously reported in cardiorespiratory adaptation after exercise training.

    Medicine and science in sports and exercise 2003;35;12;1998-2004

  • Candidate gene association study in type 2 diabetes indicates a role for genes involved in beta-cell function as well as insulin action.

    Barroso I*, Luan J, Middelberg RP, Harding AH, Franks PW, Jakes RW, Clayton D, Schafer AJ*, O'Rahilly S* and Wareham NJ*

    Incyte, Palo Alto, California, USA. ib1@sanger.ac.uk

    Type 2 diabetes is an increasingly common, serious metabolic disorder with a substantial inherited component. It is characterised by defects in both insulin secretion and action. Progress in identification of specific genetic variants predisposing to the disease has been limited. To complement ongoing positional cloning efforts, we have undertaken a large-scale candidate gene association study. We examined 152 SNPs in 71 candidate genes for association with diabetes status and related phenotypes in 2,134 Caucasians in a case-control study and an independent quantitative trait (QT) cohort in the United Kingdom. Polymorphisms in five of 15 genes (33%) encoding molecules known to primarily influence pancreatic beta-cell function-ABCC8 (sulphonylurea receptor), KCNJ11 (KIR6.2), SLC2A2 (GLUT2), HNF4A (HNF4alpha), and INS (insulin)-significantly altered disease risk, and in three genes, the risk allele, haplotype, or both had a biologically consistent effect on a relevant physiological trait in the QT study. We examined 35 genes predicted to have their major influence on insulin action, and three (9%)-INSR, PIK3R1, and SOS1-showed significant associations with diabetes. These results confirm the genetic complexity of Type 2 diabetes and provide evidence that common variants in genes influencing pancreatic beta-cell function may make a significant contribution to the inherited component of this disease. This study additionally demonstrates that the systematic examination of panels of biological candidate genes in large, well-characterised populations can be an effective complement to positional cloning approaches. The absence of large single-gene effects and the detection of multiple small effects accentuate the need for the study of larger populations in order to reliably identify the size of effect we now expect for complex diseases.

    PLoS biology 2003;1;1;E20

    (* Equal communicating author)

2002 Publications

  • Genetic variants of insulin receptor substrate-1 (IRS-1) in syndromes of severe insulin resistance. Functional analysis of Ala513Pro and Gly1158Glu IRS-1.

    Berger D, Barroso I, Soos M, Yeo G, Schafer AJ, O'Rahilly S and Whitehead JP

    University of Cambridge, Department of Medicine and Clinical Biochemistry, Addenbrooke's Hospital, Cambridge, UK.

    Aims: To define further the role of IRS-1 mutations in human syndromes of severe insulin resistance.

    Methods: The IRS-1 gene was scanned for mutations in 83 unrelated affected subjects and 47 unaffected individuals using fluorescent single-strand conformation polymorphism (fSSCP) analysis. A novel heterozygous mutation, Gly1158Glu, was found in one affected subject. Four and two subjects were heterozygous for the previously reported variants Gly972Arg and Ala513Pro, respectively. The previously identified variant Gly819Arg was found in one affected and one unaffected subject. While Gly972Arg has been described to alter the signalling properties of IRS-1, no functional studies of Ala513Pro or Gly1158Glu have been reported.

    Results: Chinese hamster ovary (CHO) cells stably over-expressing the insulin receptor were transiently transfected with vectors expressing either wild-type, Glu1158 or Pro513 IRS-1. A modest increase in insulin-stimulated tyrosine phosphorylation of Glu1158 IRS-1 was observed. However, this did not result in any significant change in the association of Grb2 or the p85 alpha subunit of PI3-kinase or of PI3-kinase activity. In parallel studies, the Pro513 IRS-1 variant was indistinguishable from wild-type IRS-1.

    Conclusions: While subtle effects of these variants cannot be excluded in this system, it is unlikely that these variants are responsible for the extreme insulin resistance seen in the subjects harbouring them. Although IRS proteins play a central role in insulin signalling, functionally significant mutations in the IRS-1 gene are a rare cause of human syndromes of severe insulin resistance.

    Diabetic medicine : a journal of the British Diabetic Association 2002;19;10;804-9

  • Digenic inheritance of severe insulin resistance in a human pedigree.

    Savage DB*, Agostini M*, Barroso I*, Gurnell M*, Luan J*, Meirhaeghe A, Harding AH, Ihrke G, Rajanayagam O, Soos MA, George S, Berger D, Thomas EL, Bell JD, Meeran K, Ross RJ, Vidal-Puig A, Wareham NJ, O'Rahilly S, Chatterjee VK and Schafer AJ

    Department of Clinical Biochemistry, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge CB2 2QQ, UK.

    Impaired insulin action is a key feature of type 2 diabetes and is also found, to a more extreme degree, in familial syndromes of insulin resistance. Although inherited susceptibility to insulin resistance may involve the interplay of several genetic loci, no clear examples of interactions among genes have yet been reported. Here we describe a family in which five individuals with severe insulin resistance, but no unaffected family members, were doubly [corrected] heterozygous with respect to frameshift/premature stop mutations in two unlinked genes, PPARG and PPP1R3A these encode peroxisome proliferator activated receptor gamma, which is highly expressed in adipocytes, and protein phosphatase 1, regulatory subunit 3, the muscle-specific regulatory subunit of protein phosphatase 1, which are centrally involved in the regulation of carbohydrate and lipid metabolism, respectively. That mutant molecules primarily involved in either carbohydrate or lipid metabolism can combine to produce a phenotype of extreme insulin resistance provides a model of interactions among genes that may underlie common human metabolic disorders such as type 2 diabetes.

    Nature genetics 2002;31;4;379-84

    (* Equal contributing author)

2001 Publications

  • Compound effects of point mutations causing campomelic dysplasia/autosomal sex reversal upon SOX9 structure, nuclear transport, DNA binding, and transcriptional activation.

    Preiss S, Argentaro A, Clayton A, John A, Jans DA, Ogata T, Nagai T, Barroso I, Schafer AJ and Harley VR

    Department of Genetics, University of Melbourne, Howard Florey Institute, University of Melbourne, Parkville 3052, Australia.

    Human mutations in the transcription factor SOX9 cause campomelic dysplasia/autosomal sex reversal. Here we identify and characterize two novel heterozygous mutations, F154L and A158T, that substitute conserved "hydrophobic core" amino acids of the high mobility group domain at positions thought to stabilize SOX9 conformation. Circular dichroism studies indicated that both mutations disrupt alpha-helicity within their high mobility group domain, whereas tertiary structure is essentially maintained as judged by fluorescence spectroscopy. In cultured cells, strictly nuclear localization was observed for wild type SOX9 and the F154L mutant; however, the A158T mutant showed a 2-fold reduction in nuclear import efficiency. Importin-beta was demonstrated to be the nuclear transport receptor recognized by SOX9, with both mutant proteins binding importin-beta with wild type affinity. Whereas DNA bending was unaffected, DNA binding was drastically reduced in both mutants (to 5% of wild type activity in F154L, 17% in A158T). Despite this large effect, transcriptional activation in cultured cells was only reduced to 26% in F154L and 62% in A158T of wild type activity, suggesting that a small loss of SOX9 transactivation activity could be sufficient to disrupt proper regulation of target genes during bone and testis formation. Thus, clinically relevant mutations of SOX9 affect protein structure leading to compound effects of reduced nuclear import and reduced DNA binding, the net effect being loss of transcriptional activation.

    The Journal of biological chemistry 2001;276;30;27864-72

1999 Publications

  • Dominant negative mutations in human PPARgamma associated with severe insulin resistance, diabetes mellitus and hypertension.

    Barroso I*, Gurnell M*, Crowley VE*, Agostini M, Schwabe JW, Soos MA, Maslen GL, Williams TD, Lewis H, Schafer AJ, Chatterjee VK and O'Rahilly S

    Incyte Europe Ltd, Cambridge, UK.

    Thiazolidinediones are a new class of antidiabetic agent that improve insulin sensitivity and reduce plasma glucose and blood pressure in subjects with type 2 diabetes. Although these agents can bind and activate an orphan nuclear receptor, peroxisome proliferator-activated receptor gamma (PPARgamma), there is no direct evidence to conclusively implicate this receptor in the regulation of mammalian glucose homeostasis. Here we report two different heterozygous mutations in the ligand-binding domain of PPARgamma in three subjects with severe insulin resistance. In the PPARgamma crystal structure, the mutations destabilize helix 12 which mediates transactivation. Consistent with this, both receptor mutants are markedly transcriptionally impaired and, moreover, are able to inhibit the action of coexpressed wild-type PPARgamma in a dominant negative manner. In addition to insulin resistance, all three subjects developed type 2 diabetes mellitus and hypertension at an unusually early age. Our findings represent the first germline loss-of-function mutations in PPARgamma and provide compelling genetic evidence that this receptor is important in the control of insulin sensitivity, glucose homeostasis and blood pressure in man.

    Funded by: Wellcome Trust

    Nature 1999;402;6764;880-3

    (* Equal contributing author)

1997 Publications

  • Mapping the multiple self-healing squamous epithelioma (MSSE) gene and investigation of xeroderma pigmentosum group A (XPA) and PATCHED (PTCH) as candidate genes.

    Richards FM, Goudie DR, Cooper WN, Jene Q, Barroso I, Wicking C, Wainwright BJ and Ferguson-Smith MA

    Cambridge University Dept. of Pathology, UK.

    The MSSE gene predisposes to the development of multiple invasive but self-healing skin tumours (multiple self-healing squamous epitheliomata, MSSE). MSSE (previously named ESS1) was mapped to chromosome 9q by linkage analysis; haplotype analysis in families then suggested a common founder mutation and indicated that the gene lies in the interval D9S1-D9S29 (9q22-q31). Squamous cell carcinomata also develop as one of the complications of xeroderma pigmentosum, and one of the xeroderma pigmentosum genes (XPA) maps within the MSSE interval. We have investigated the hypothesis that a novel dominant mutation in XPA is responsible for MSSE. We screened the entire coding region, 3' untranslated region (UTR) and 5'UTR of XPA for germline mutations in MSSE families by single-stranded conformation polymorphism analysis and by direct DNA sequencing. No mutations were detected but a novel intragenic polymorphism was identified in the 5'UTR of XPA, in both MSSE-affected and unrelated normal individuals. This XPA polymorphism and nine new polymorphic markers that map in the MSSE region were typed in eleven MSSE families; XPA was excluded as the MSSE gene and the most likely location of MSSE was reduced to the interval between D9S197 and (D9S287, D9S1809). The Patched (PTCH) gene, which is mutated in naevoid basal cell carcinoma syndrome (NBCCS or Gorlin syndrome) lies in this interval and all MSSE families have been shown to share a common haplotype at three novel intragenic PTCH polymorphisms. Although no mutation has been detected in MSSE families, PTCH has not been excluded as the MSSE gene.

    Human genetics 1997;101;3;317-22

Reviews, invited commentaries and book chapters

  • The emerging use of zebrafish to model metabolic disease.

    Seth A, Stemple DL and Barroso I

    Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.

    The zebrafish research community is celebrating! The zebrafish genome has recently been sequenced, the Zebrafish Mutation Project (launched by the Wellcome Trust Sanger Institute) has published the results of its first large-scale ethylnitrosourea (ENU) mutagenesis screen, and a host of new techniques, such as the genome editing technologies TALEN and CRISPR-Cas, are enabling specific mutations to be created in model organisms and investigated in vivo. The zebrafish truly seems to be coming of age. These powerful resources invoke the question of whether zebrafish can be increasingly used to model human disease, particularly common, chronic diseases of metabolism such as obesity and type 2 diabetes. In recent years, there has been considerable success, mainly from genomic approaches, in identifying genetic variants that are associated with these conditions in humans; however, mechanistic insights into the role of implicated disease loci are lacking. In this Review, we highlight some of the advantages and disadvantages of zebrafish to address the organism's utility as a model system for human metabolic diseases.

    Funded by: Medical Research Council; Wellcome Trust: WT098051

    Disease models & mechanisms 2013;6;5;1080-8

  • Genomics: ENCODE explained.

    Ecker JR, Bickmore WA, Barroso I, Pritchard JK, Gilad Y and Segal E

    Howard Hughes Medical Institute and the Salk Institute for Biological Studies, La Jolla, California 92037, USA. ecker@salk.edu

    Nature 2012;489;7414;52-5

  • Genome-wide association studies and type 2 diabetes.

    Wheeler E and Barroso I

    Wellcome Trust Sanger Institute, Cambridge, UK.

    In recent years, the search for genetic determinants of type 2 diabetes (T2D) has changed dramatically. Although linkage and small-scale candidate gene studies were highly successful in the identification of genes, which, when mutated, caused monogenic forms of T2D, they were largely unsuccessful when applied to the more common forms of the disease. To date, these approaches have only identified two loci (PPARG, KCNJ11) robustly implicated in T2D susceptibility. The ability to perform large-scale association analysis, including genome-wide association studies (GWAS) in many thousands of samples from different populations, and subsequently, the shift to form large international collaborations to perform meta-analyses across many studies has taken the number of independent loci showing genome-wide significant associations with T2D to 44. This number includes six loci identified initially through the analysis of quantitative glycaemic phenotypes, illustrating the usefulness of this approach both to identify new disease genes and gain insight into the mechanisms leading to disease. Combined, these loci still only account for ∼10% of the observed familial clustering in Europeans, leaving much of the variance unexplained. In this review, we will describe what GWAS have taught us about the genetic basis of T2D and discuss possible next steps to uncover the remaining heritability.

    Funded by: Wellcome Trust: 077016/Z/05/Z

    Briefings in functional genomics 2011;10;2;52-60

  • The genetics of obesity: FTO leads the way.

    Fawcett KA and Barroso I

    Metabolic Disease Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK.

    In 2007, an association of single nucleotide polymorphisms (SNPs) in the fat mass and obesity-associated (FTO) gene region with body mass index (BMI) and risk of obesity was identified in multiple populations, making FTO the first locus unequivocally associated with adiposity. At the time, FTO was a gene of unknown function and it was not known whether these SNPs exerted their effect on adiposity by affecting FTO or neighboring genes. Therefore, this breakthrough association inspired a wealth of in silico, in vitro, and in vivo analyses in model organisms and humans to improve knowledge of FTO function. These studies suggested that FTO plays a role in controlling feeding behavior and energy expenditure. Here, we review the approaches taken that provide a blueprint for the study of other obesity-associated genes in the hope that this strategy will result in increased understanding of the biological mechanisms underlying body weight regulation.

    Funded by: Wellcome Trust: 077016/Z/05/Z

    Trends in genetics : TIG 2010;26;6;266-74

  • Mendelian randomisation studies of type 2 diabetes: future prospects.

    Sandhu MS, Debenham SL, Barroso I and Loos RJ

    MRC Epidemiology Unit, Strangeways Research Laboratory, Cambridge, UK. manj.sandhu@srl.cam.ac.uk

    Funded by: Medical Research Council: MC_U106179471, MC_U106188470; Wellcome Trust: 077016

    Diabetologia 2008;51;2;211-3

  • Complex disease: pleiotropic gene effects in obesity and type 2 diabetes.

    Barroso I

    European journal of human genetics : EJHG 2005;13;12;1243-4

  • Genetics of Type 2 diabetes.

    Barroso I

    Metabolic Disease Group, The Wellcome Trust Sanger Institute, Cambridge, UK. ib1@sanger.ac.uk

    Type 2 diabetes (T2D) has become a health-care problem worldwide, with the rise in disease prevalence being all the more worrying as it not only affects the developed world but also developing nations with fewer resources to cope with yet another major disease burden. Furthermore, the problem is no longer restricted to the ageing population, as young adults and children are also being diagnosed with T2D. In recent years, there has been a surge in the number of genetic studies of T2D in attempts to identify some of the underlying risk factors. In this review, I highlight the main genes known to cause uncommon monogenic forms of diabetes (e.g. maturity-onset diabetes of the young--MODY--and insulin resistance syndromes), as well as describe some of the main approaches used to identify genes involved in the more common forms of T2D that result from the interaction between environmental risk factors and predisposing genotypes. Linkage and candidate gene studies have been highly successful in the identification of genes that cause the monogenic variants of diabetes and, although progress in the more common forms of T2D has been slow, a number of genes have now been reproducibly associated with T2D risk in multiple studies. These are discussed, as well as the main implications that the diabetes gene discoveries will have in diabetes treatment and prevention.

    Diabetic medicine : a journal of the British Diabetic Association 2005;22;5;517-35

  • Genetic factors in type 2 diabetes: the end of the beginning?

    O'Rahilly S, Barroso I and Wareham NJ

    University of Cambridge, Department of Clinical Biochemistry, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK. so104@medschl.cam.ac.uk

    The intensive search for genetic variants that predispose to type 2 diabetes was launched with optimism, but progress has been slower than was hoped. Even so, major advances have been made in the understanding of monogenic forms of the disease which together represent a substantial health burden, and a few common gene variants that influence susceptibility have now been unequivocally identified. Armed with a better understanding of the tools needed to detect such genes, it seems inevitable that the rate of progress will increase and the relevance of genetic information to the diagnosis, treatment, and prevention of diabetes will become increasingly tangible.

    Science (New York, N.Y.) 2005;307;5708;370-3

  • The Genetics of Type 2 Diabetes

    Stevenson C, Barroso I and Wareham NJ

    Nutritional Genomics (R Bigelius-Flohe and H-G Joost. Wiley-VCH. ISBN-3527312943) 2006 Chapter 13 pg 223 - 265

Team

Team members

Jennifer Asimit
ja11@sanger.ac.ukPostdoctoral Fellow - Statistical Geneticist
Allan Daly
Senior Bioinformatician
Christopher Franklin
cf8@sanger.ac.ukPostdoctoral Fellow - Statistical Genetics
Audrey Hendricks
UK10K Postdoctoral Fellow
Gaelle Marenne
gm10@sanger.ac.ukPostdoctoral Fellow
Felicity Payne
Staff Scientist
Rachel Watson
Postdoctoral Fellow
Eleanor Wheeler
ew2@sanger.ac.ukSenior Staff Scientist

Jennifer Asimit

ja11@sanger.ac.uk Postdoctoral Fellow - Statistical Geneticist

July 2013-present: MRC Methodology Research Postdoctoral Fellow, Metabolic Disease Group, Wellcome Trust Sanger Institute, UK 2010 - 2013: Postdoctoral fellow in statistical genetics, Wellcome Trust Sanger Institute, UK 2006 - 2009: Postdoctoral fellow in statistical genetics - Samuel Lunenfeld Research Institute, University of Toronto, Canada 2007 PhD in Statistics (Analysis of Point Processes with Applications to Reaction Time Experiments) – University of Western Ontario, Canada 2002 MSc in Statistics – University of Western Ontario, Canada 2001 BSc (double major) in Mathematics & statistics, University of Winnipeg, Canada

Research

The current focus of my research is methodology development for the joint analysis of epidemiologically linked traits, such as obesity and osteoarthritis.

References

  • Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases.

    Mägi R, Asimit JL, Day-Williams AG, Zeggini E and Morris AP

    Estonian Genome Centre, University of Tartu, Tartu, Estonia.

    Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome-wide genotype data up to high-density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re-sequencing experiments. By application of this approach to genome-wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome-wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.

    Funded by: Wellcome Trust: 090532, 098017

    Genetic epidemiology 2012

  • An evaluation of different meta-analysis approaches in the presence of allelic heterogeneity.

    Asimit J, Day-Williams A, Zgaga L, Rudan I, Boraska V and Zeggini E

    Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.

    Meta-analysis has proven a useful tool in genetic association studies. Allelic heterogeneity can arise from ethnic background differences across populations being meta-analyzed (for example, in search of common frequency variants through genome-wide association studies), and through the presence of multiple low frequency and rare associated variants in the same functional unit of interest (for example, within a gene or a regulatory region). The latter challenge will be increasingly relevant in whole-genome and whole-exome sequencing studies investigating association with complex traits. Here, we evaluate the performance of different approaches to meta-analysis in the presence of allelic heterogeneity. We simulate allelic heterogeneity scenarios in three populations and examine the performance of current approaches to the analysis of these data. We show that current approaches can detect only a small fraction of common frequency causal variants. We also find that for low-frequency variants with large effects (odds ratios 2-3), single-point tests have high power, but also high false-positive rates. P-value based meta-analysis of summary results from allele-matching locus-wide tests outperforms collapsing approaches. We conclude that current strategies for the combination of genetic association data in the presence of allelic heterogeneity are insufficiently powered.

    Funded by: Wellcome Trust: 098051

    European journal of human genetics : EJHG 2012;20;6;709-12

  • A combined functional annotation score for non-synonymous variants.

    Lopes MC, Joyce C, Ritchie GR, John SL, Cunningham F, Asimit J and Zeggini E

    Wellcome Trust Sanger Institute, Hinxton, Hinxton, UK. ml10@sanger.ac.uk

    Aims: Next-generation sequencing has opened the possibility of large-scale sequence-based disease association studies. A major challenge in interpreting whole-exome data is predicting which of the discovered variants are deleterious or neutral. To address this question in silico, we have developed a score called Combined Annotation scoRing toOL (CAROL), which combines information from 2 bioinformatics tools: PolyPhen-2 and SIFT, in order to improve the prediction of the effect of non-synonymous coding variants.

    Methods: We used a weighted Z method that combines the probabilistic scores of PolyPhen-2 and SIFT. We defined 2 dataset pairs to train and test CAROL using information from the dbSNP: 'HGMD-PUBLIC' and 1000 Genomes Project databases. The training pair comprises a total of 980 positive control (disease-causing) and 4,845 negative control (non-disease-causing) variants. The test pair consists of 1,959 positive and 9,691 negative controls.

    Results: CAROL has higher predictive power and accuracy for the effect of non-synonymous variants than each individual annotation tool (PolyPhen-2 and SIFT) and benefits from higher coverage.

    Conclusion: The combination of annotation tools can help improve automated prediction of whole-genome/exome non-synonymous variant functional consequences.

    Funded by: Wellcome Trust: 095908, 098051, WT088885/Z/09/Z

    Human heredity 2012;73;1;47-51

  • ARIEL and AMELIA: testing for an accumulation of rare variants using next-generation sequencing data.

    Asimit JL, Day-Williams AG, Morris AP and Zeggini E

    Wellcome Trust Sanger Institute, Hinxton, UK.

    Objectives: There is increasing evidence that rare variants play a role in some complex traits, but their analysis is not straightforward. Locus-based tests become necessary due to low power in rare variant single-point association analyses. In addition, variant quality scores are available for sequencing data, but are rarely taken into account. Here, we propose two locus-based methods that incorporate variant quality scores: a regression-based collapsing approach and an allele-matching method.

    Methods: Using simulated sequencing data we compare 4 locus-based tests of trait association under different scenarios of data quality. We test two collapsing-based approaches and two allele-matching-based approaches, taking into account variant quality scores and ignoring variant quality scores. We implement the collapsing and allele-matching approaches accounting for variant quality in the freely available ARIEL and AMELIA software.

    Results: The incorporation of variant quality scores in locus-based association tests has power advantages over weighting each variant equally. The allele-matching methods are robust to the presence of both protective and risk variants in a locus, while collapsing methods exhibit a dramatic loss of power in this scenario.

    Conclusions: The incorporation of variant quality scores should be a standard protocol when performing locus-based association analysis on sequencing data. The ARIEL and AMELIA software implement collapsing and allele-matching locus association analysis methods, respectively, that allow the incorporation of variant quality scores.

    Funded by: Wellcome Trust: 088885, 090532, 098051

    Human heredity 2012;73;2;84-94

  • Imputation of rare variants in next-generation association studies.

    Asimit JL and Zeggini E

    Wellcome Trust Sanger Institute, Hinxton, UK. ja11@sanger.ac.uk

    The role of rare variants has become a focus in the search for association with complex traits. Imputation is a powerful and cost-efficient tool to access variants that have not been directly typed, but there are several challenges when imputing rare variants, most notably reference panel selection. Extensions to rare variant association tests to incorporate genotype uncertainty from imputation are discussed, as well as the use of imputed low-frequency and rare variants in the study of population isolates.

    Funded by: Wellcome Trust: 098051

    Human heredity 2012;74;3-4;196-204

  • Defining the power limits of genome-wide association scan meta-analyses.

    Chapman K, Ferreira T, Morris A, Asimit J and Zeggini E

    Wellcome Trust Centre for Human Genetics, Roosevelt Drive, University of Oxford, Oxford, United Kingdom.

    Large-scale meta-analyses of genome-wide association scans (GWAS) have been successful in discovering common risk variants with modest and small effects. The detection of lower frequency signals will undoubtedly require concerted efforts of at least similar scale. We investigate the sample size-dictated power limits of GWAS meta-analyses, in the presence and absence of modest levels of heterogeneity and across a range of different allelic architectures. We find that data combination through large-scale collaboration is vital in the quest for complex trait susceptibility loci, but that effect size heterogeneity across meta-analyzed studies drawn from similar populations does not appear to have a profound effect on sample size requirements.

    Funded by: Wellcome Trust: 088885, 090532, WT079557MA, WT081682/Z/06/Z, WT088885/Z/09/Z

    Genetic epidemiology 2011;35;8;781-9

  • Regression models, scan statistics and reappearance probabilities to detect regions of association between gene expression and copy number.

    Asimit JL, Andrulis IL and Bull SB

    Samuel Lunenfeld Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada.

    Early studies of breast cancer microarray data used linear models to quantify the relationship between measures of gene expression (GE) and copy number (CN) obtained from tumour samples. Motivated by a study of women with axillary node-negative breast cancer, we propose a regression-based scan statistic to identify within-chromosome clusters of genetic probes that exhibit association between GE and CN, while accounting for tumour characteristics known to be prognostic for clinical outcome. As a measure of the association between GE and CN, for each genetic probe available from a microarray we regress GE on CN, and include subject-specific covariates. In the development of the scan statistic, the within-chromosome spatial distribution of the subset of probes with a statistically significant association is approximated by a Poisson process. By incorporating the distance between the probe positions, the scan statistic accounts for the spatial nature of CN alterations. Regions identified as clusters of significant associations are hypothesized to harbour genes involved in breast cancer progression. Using simulations, we examine the sensitivity of the method to certain factors, and to address issues of repeatability, we consider reappearance probabilities for each probe within detected regions and assess the utility of a quantity estimated by bootstrap sample frequencies. Applications of the proposed method to joint analysis of GE and CN in breast tumours, with and without an informative covariate, and comparisons with alternative methods suggest that inclusion of covariates and the use of a regional test statistic can serve to refine regions for further investigation including the analysis of their association with outcome.

    Funded by: Canadian Institutes of Health Research: MSS-55118

    Statistics in medicine 2011;30;10;1157-78

  • An evaluation of power to detect low-frequency variant associations using allele-matching tests that account for uncertainty.

    Zeggini E and Asimit JL

    Wellcome Trust Sanger Institute, Hinxton, CB10 1HH, UK. Eleftheria@sanger.ac.uk

    There is growing interest in the role of rare variants in multifactorial disease etiology, and increasing evidence that rare variants are associated with complex traits. Single SNP tests are underpowered in rare variant association analyses, so locus-based tests must be used. Quality scores at both the SNP and genotype level are available for sequencing data and they are rarely accounted for. A locus-based method that has high power in the presence of rare variants is extended to incorporate such quality scores as weights, and its power is compared with the original method via a simulation study. Preliminary results suggest that taking uncertainty into account does not improve the power.

    Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2011;100-5

  • Rare variant association analysis methods for complex traits.

    Asimit J and Zeggini E

    Wellcome Trust Sanger Institute, Hinxton CB10 1SA, United Kingdom.

    There has been increasing interest in rare variants and their association with disease, and several rare variant-disease associations have already been detected. The usual association tests for common variants are underpowered for detecting variants of lower frequency, so alternative approaches are required. In addition to reviewing the association analysis methods for rare variants, we discuss the limitations of genome-wide association studies in identifying rare variants and the problems that arise in the imputation of rare variants.

    Funded by: Wellcome Trust: WT088885/Z/09/Z

    Annual review of genetics 2010;44;293-308

  • Testing for rare variant associations in complex diseases.

    Asimit J and Zeggini E

    Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK. ja11@sanger.ac.uk.

    The study of rare variants holds the promise of accounting for some of the missing heritability in complex traits. Next-generation sequencing technologies enable probing of variation across the full spectrum of allele frequencies. Multiple methods for the analysis of rare variants have been proposed and, recently, Ionita-Laza et al. have presented an approach with the theoretical capacity to detect risk and protective variants. The identification of rare risk variants could have major implications in understanding complex disease etiopathogenesis.

    Genome medicine 2009;1;11;24

Allan Daly

- Senior Bioinformatician

Allan obtained a B.Sc(Hons) degree in Molecular Biology from the University of Glasgow in 1986. He spent 17 years working in Plant Biotechnology at Syngenta. During which time he progressed from research in the laboratory through to managing a research project before moving into bioinformatics. Allan was the lead bioinformatician for Syngenta in a collaboration with the John Innes Centre, Norwich from 2000 until 2002.

In 2003 Allan joined the Metabolic disease group at the Sanger Institute as a bioinformatician.

Research

In the Metabolic disease group Allan is responsible for the informatics and data handling needs of the group. He investigates new developments in bioinformatics that may improve the groups research and acts as the contact between the core informatics groups at the institute and the other members of the metabolic disease group. In addition he provides informatic support to the work of the group and our collaborators.

Christopher Franklin

cf8@sanger.ac.uk Postdoctoral Fellow - Statistical Genetics

March 2012-present: Postdoctoral Fellow, Metabolic disease group, Wellcome Trust Sanger Institute, UK

2009-2012: Postdoctoral research assistant: WTCCC3 project, Wellcome Trust Sanger Institute

2006-2009: PhD – Centre for population health sciences, University of Edinburgh Linkage and association mapping for quantitative phenotypes in isolated populations

2005-2006: MSc – Institute for evolutionary biology, University of Edinburgh Quantitative genetics and genome analysis

2001-2005: BSc – Biology with honours in genetics – University of Edinburgh

Research

My current research involves analysis of high density SNP array data and next generation sequence data to identify variants with roles in common complex or rare monogenic disorders.

References

  • Using ancestry-informative markers to identify fine structure across 15 populations of European origin.

    Huckins LM, Boraska V, Franklin CS, Floyd JA, Southam L, GCAN, WTCCC3, Sullivan PF, Bulik CM, Collier DA, Tyler-Smith C, Zeggini E, Tachmazidou I, GCAN and WTCCC3

    The Wellcome Trust Sanger Institute (WTSI), Hinxton, UK.

    The Wellcome Trust Case Control Consortium 3 anorexia nervosa genome-wide association scan includes 2907 cases from 15 different populations of European origin genotyped on the Illumina 670K chip. We compared methods for identifying population stratification, and suggest list of markers that may help to counter this problem. It is usual to identify population structure in such studies using only common variants with minor allele frequency (MAF) >5%; we find that this may result in highly informative SNPs being discarded, and suggest that instead all SNPs with MAF >1% may be used. We established informative axes of variation identified via principal component analysis and highlight important features of the genetic structure of diverse European-descent populations, some studied for the first time at this scale. Finally, we investigated the substructure within each of these 15 populations and identified SNPs that help capture hidden stratification. This work can provide information regarding the designing and interpretation of association results in the International Consortia.

    Funded by: NIA NIH HHS: U19 AG023122; Wellcome Trust: 090532

    European journal of human genetics : EJHG 2014;22;10;1190-200

  • A genome-wide association study of anorexia nervosa.

    Boraska V, Franklin CS, Floyd JA, Thornton LM, Huckins LM, Southam L, Rayner NW, Tachmazidou I, Klump KL, Treasure J, Lewis CM, Schmidt U, Tozzi F, Kiezebrink K, Hebebrand J, Gorwood P, Adan RA, Kas MJ, Favaro A, Santonastaso P, Fernández-Aranda F, Gratacos M, Rybakowski F, Dmitrzak-Weglarz M, Kaprio J, Keski-Rahkonen A, Raevuori A, Van Furth EF, Slof-Op 't Landt MC, Hudson JI, Reichborn-Kjennerud T, Knudsen GP, Monteleone P, Kaplan AS, Karwautz A, Hakonarson H, Berrettini WH, Guo Y, Li D, Schork NJ, Komaki G, Ando T, Inoko H, Esko T, Fischer K, Männik K, Metspalu A, Baker JH, Cone RD, Dackor J, Desocio JE, Hilliard CE, O'Toole JK, Pantel J, Szatkiewicz JP, Taico C, Zerwas S, Trace SE, Davis OS, Helder S, Bühren K, Burghardt R, de Zwaan M, Egberts K, Ehrlich S, Herpertz-Dahlmann B, Herzog W, Imgart H, Scherag A, Scherag S, Zipfel S, Boni C, Ramoz N, Versini A, Brandys MK, Danner UN, de Kovel C, Hendriks J, Koeleman BP, Ophoff RA, Strengman E, van Elburg AA, Bruson A, Clementi M, Degortes D, Forzan M, Tenconi E, Docampo E, Escaramís G, Jiménez-Murcia S, Lissowska J, Rajewski A, Szeszenia-Dabrowska N, Slopien A, Hauser J, Karhunen L, Meulenbelt I, Slagboom PE, Tortorella A, Maj M, Dedoussis G, Dikeos D, Gonidakis F, Tziouvas K, Tsitsika A, Papezova H, Slachtova L, Martaskova D, Kennedy JL, Levitan RD, Yilmaz Z, Huemer J, Koubek D, Merl E, Wagner G, Lichtenstein P, Breen G, Cohen-Woods S, Farmer A, McGuffin P, Cichon S, Giegling I, Herms S, Rujescu D, Schreiber S, Wichmann HE, Dina C, Sladek R, Gambaro G, Soranzo N, Julia A, Marsal S, Rabionet R, Gaborieau V, Dick DM, Palotie A, Ripatti S, Widén E, Andreassen OA, Espeseth T, Lundervold A, Reinvang I, Steen VM, Le Hellard S, Mattingsdal M, Ntalla I, Bencko V, Foretova L, Janout V, Navratilova M, Gallinger S, Pinto D, Scherer SW, Aschauer H, Carlberg L, Schosser A, Alfredsson L, Ding B, Klareskog L, Padyukov L, Courtet P, Guillaume S, Jaussent I, Finan C, Kalsi G, Roberts M, Logan DW, Peltonen L, Ritchie GR, Barrett JC, The Wellcome Trust Case Control Consortium 3, Estivill X, Hinney A, Sullivan PF, Collier DA, Zeggini E and Bulik CM

    1] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK [2] University of Split School of Medicine, Split, Croatia.

    Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome-wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2907 cases with AN from 14 countries (15 sites) and 14 860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery data sets. Seventy-six (72 independent) single nucleotide polymorphisms were taken forward for in silico (two data sets) or de novo (13 data sets) replication genotyping in 2677 independent AN cases and 8629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication data sets comprised 5551 AN cases and 21 080 controls. AN subtype analyses (1606 AN restricting; 1445 AN binge-purge) were performed. No findings reached genome-wide significance. Two intronic variants were suggestively associated: rs9839776 (P=3.01 × 10(-7)) in SOX2OT and rs17030795 (P=5.84 × 10(-6)) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (P=5.76 × 10(-)(6)) between CUL3 and FAM124B and rs1886797 (P=8.05 × 10(-)(6)) near SPATA13. Comparing discovery with replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (P=4 × 10(-6)), strongly suggesting that true findings exist but our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field.Molecular Psychiatry advance online publication, 11 February 2014; doi:10.1038/mp.2013.187.

    Funded by: Wellcome Trust: 090532

    Molecular psychiatry 2014

  • Genome-wide association study to identify common variants associated with brachial circumference: a meta-analysis of 14 cohorts.

    Boraska V, Day-Williams A, Franklin CS, Elliott KS, Panoutsopoulou K, Tachmazidou I, Albrecht E, Bandinelli S, Beilin LJ, Bochud M, Cadby G, Ernst F, Evans DM, Hayward C, Hicks AA, Huffman J, Huth C, James AL, Klopp N, Kolcic I, Kutalik Z, Lawlor DA, Musk AW, Pehlic M, Pennell CE, Perry JR, Peters A, Polasek O, St Pourcain B, Ring SM, Salvi E, Schipf S, Staessen JA, Teumer A, Timpson N, Vitart V, Warrington NM, Yaghootkar H, Zemunik T, Zgaga L, An P, Anttila V, Borecki IB, Holmen J, Ntalla I, Palotie A, Pietiläinen KH, Wedenoja J, Winsvold BS, Dedoussis GV, Kaprio J, Province MA, Zwart JA, Burnier M, Campbell H, Cusi D, Smith GD, Frayling TM, Gieger C, Palmer LJ, Pramstaller PP, Rudan I, Völzke H, Wichmann HE, Wright AF and Zeggini E

    Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom. vb2@sanger.ac.uk

    Brachial circumference (BC), also known as upper arm or mid arm circumference, can be used as an indicator of muscle mass and fat tissue, which are distributed differently in men and women. Analysis of anthropometric measures of peripheral fat distribution such as BC could help in understanding the complex pathophysiology behind overweight and obesity. The purpose of this study is to identify genetic variants associated with BC through a large-scale genome-wide association scan (GWAS) meta-analysis. We used fixed-effects meta-analysis to synthesise summary results across 14 GWAS discovery and 4 replication cohorts comprising overall 22,376 individuals (12,031 women and 10,345 men) of European ancestry. Individual analyses were carried out for men, women, and combined across sexes using linear regression and an additive genetic model: adjusted for age and adjusted for age and BMI. We prioritised signals for follow-up in two-stages. We did not detect any signals reaching genome-wide significance. The FTO rs9939609 SNP showed nominal evidence for association (p<0.05) in the age-adjusted strata for men and across both sexes. In this first GWAS meta-analysis for BC to date, we have not identified any genome-wide significant signals and do not observe robust association of previously established obesity loci with BC. Large-scale collaborations will be necessary to achieve higher power to detect loci underlying BC.

    Funded by: Canadian Institutes of Health Research: MOP-82893; Medical Research Council: G0800582, G9815508, MC_PC_U127561128, MC_U127561128; NHLBI NIH HHS: R01-HL-087700, R01-HL-088215; NIA NIH HHS: N01-AG-5-0002, N1-AG-1-1, N1-AG-1-2111; NIDDK NIH HHS: R01-DK-075681, R01-DK-8925601; NIMHD NIH HHS: 263 MD 821336, 263 MD 9164; Wellcome Trust: 092731, 098051, WT089062, WT092731

    PloS one 2012;7;3;e31369

  • Genome-wide association study identifies 12 new susceptibility loci for primary biliary cirrhosis.

    Mells GF, Floyd JA, Morley KI, Cordell HJ, Franklin CS, Shin SY, Heneghan MA, Neuberger JM, Donaldson PT, Day DB, Ducker SJ, Muriithi AW, Wheater EF, Hammond CJ, Dawwas MF, UK PBC Consortium, Wellcome Trust Case Control Consortium 3, Jones DE, Peltonen L, Alexander GJ, Sandford RN and Anderson CA

    Academic Department of Medical Genetics, Cambridge University, Cambridge, UK; Department of Hepatology, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, Cambridge, UK.

    In addition to the HLA locus, six genetic risk factors for primary biliary cirrhosis (PBC) have been identified in recent genome-wide association studies (GWAS). To identify additional loci, we carried out a GWAS using 1,840 cases from the UK PBC Consortium and 5,163 UK population controls as part of the Wellcome Trust Case Control Consortium 3 (WTCCC3). We followed up 28 loci in an additional UK cohort of 620 PBC cases and 2,514 population controls. We identified 12 new susceptibility loci (at a genome-wide significance level of P < 5 × 10⁻⁸) and replicated all previously associated loci. We identified three further new loci in a meta-analysis of data from our study and previously published GWAS results. New candidate genes include STAT4, DENND1B, CD80, IL7R, CXCR5, TNFRSF1A, CLEC16A and NFKB1. This study has considerably expanded our knowledge of the genetic architecture of PBC.

    Funded by: Medical Research Council: G0500020, G0800460, G0802068; PHS HHS: 1R01LEY018246; Wellcome Trust: 085925/Z/08/Z, 091745, WT090355/B/09/Z, WT09355A/09/Z, WT91745/Z/10/Z

    Nature genetics 2011;43;4;329-32

  • The TCF7L2 diabetes risk variant is associated with HbA₁(C) levels: a genome-wide association meta-analysis.

    Franklin CS, Aulchenko YS, Huffman JE, Vitart V, Hayward C, Polašek O, Knott S, Zgaga L, Zemunik T, Rudan I, Campbell H, Wright AF, Wild SH and Wilson JF

    Centre for Population Health Sciences, University of Edinburgh, Scotland.

    Genome-wide association (GWA) studies have identified around 20 common genetic variants influencing the risk of type 2 diabetes (T2D). Likewise, a number of variants have been associated with diabetes-related quantitative glycaemic traits, but to date the overlap between these genes and variants has been low. The majority of genetic studies have focused on fasting plasma glucose levels; however, this measure is highly variable. We have conducted a GWA meta-analysis of glycated haemoglobin (HbA₁(C) ) levels within three healthy nondiabetic populations. This phenotype provides an estimate of mean glucose levels over 2-3 months and is a more stable predictor of future diabetes risk. Participants were from three isolated populations: the Orkney Isles in the north of Scotland, the Dalmatian islands of Vis, and Korčula in Croatia (total of 1782 nondiabetic subjects). Association was tested in each population and results combined by meta-analysis. The strongest association was with the TCF7L2 gene (rs7903146, P= 1.48 × 10⁻⁷). This is also the strongest common genetic risk factor for T2D but it has not been identified in previous genome-wide studies of glycated haemoglobin.

    Funded by: Chief Scientist Office: CZB/4/710; Medical Research Council: MC_PC_U127561128, MC_U127561128

    Annals of human genetics 2010;74;6;471-8

  • Genomic runs of homozygosity record population history and consanguinity.

    Kirin M, McQuillan R, Franklin CS, Campbell H, McKeigue PM and Wilson JF

    Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom.

    The human genome is characterised by many runs of homozygous genotypes, where identical haplotypes were inherited from each parent. The length of each run is determined partly by the number of generations since the common ancestor: offspring of cousin marriages have long runs of homozygosity (ROH), while the numerous shorter tracts relate to shared ancestry tens and hundreds of generations ago. Human populations have experienced a wide range of demographic histories and hold diverse cultural attitudes to consanguinity. In a global population dataset, genome-wide analysis of long and shorter ROH allows categorisation of the mainly indigenous populations sampled here into four major groups in which the majority of the population are inferred to have: (a) recent parental relatedness (south and west Asians); (b) shared parental ancestry arising hundreds to thousands of years ago through long term isolation and restricted effective population size (N(e)), but little recent inbreeding (Oceanians); (c) both ancient and recent parental relatedness (Native Americans); and (d) only the background level of shared ancestry relating to continental N(e) (predominantly urban Europeans and East Asians; lowest of all in sub-Saharan African agriculturalists), and the occasional cryptically inbred individual. Moreover, individuals can be positioned along axes representing this demographic historic space. Long runs of homozygosity are therefore a globally widespread and under-appreciated characteristic of our genomes, which record past consanguinity and population isolation and provide a distinctive record of the demographic history of an individual's ancestors. Individual ROH measures will also allow quantification of the disease risk arising from polygenic recessive effects.

    PloS one 2010;5;11;e13996

Audrey Hendricks

- UK10K Postdoctoral Fellow

In 2002 I earned two BAs from the University of Colorado in Economics and Music. Between 2005-2011 I pursued a PhD in Biostatistics at Boston University. At BU, I received a NIH training grant that funded me through rotations in clinical trials, ethical conduct, bioinformatics, and statistical genetics. I then worked on a variety of projects (PD, HD, mouse, brain, Atrial Fibrillation, etc.) and statistical genetics issues (GWAS, gene-gene interaction, cryptic relatedness, and gene-region summary methods, etc.). During this time I also consulted as a statistical geneticist for the Framingham Heart Study and taught several courses in statistics.

Research

In 2011 I joined the Wellcome Trust Sanger Institute as a postdoctoral research fellow for the obesity arm of the UK10K project. I work directly with exome sequencing data to find genes that are associated to and cause obesity. I also work with the statistical genetics group and UK10K team to parse out general statistical problems that arise when analyzing exome and whole genome sequencing data.

References

  • Genomewide linkage study of modifiers of LRRK2-related Parkinson's disease.

    Latourelle JC, Hendricks AE, Pankratz N, Wilk JB, Halter C, Nichols WC, Gusella JF, Destefano AL, Myers RH, Foroud T and PSG-Progeni GenePD Investigators, Coordinators, and Molecular Genetic Laboratories

    Boston University School of Medicine, Boston, Massachusetts, USA. jlatoure@bu.edu

    Mutations in the leucine-rich repeat kinase 2 gene, located at 12q12, are the most common known genetic causes of Parkinson's disease. Studies of leucine-rich repeat kinase 2 mutation carriers have shown incomplete and age-dependent penetrance, and previous studies have suggested that inherited susceptibility factors may modify the penetrance of leucine-rich repeat kinase 2 mutations. Genomewide linkage to age of onset of leucine-rich repeat kinase 2-related Parkinson's disease was evaluated in a sample of 113 leucine-rich repeat kinase 2 mutation carriers from 64 families using single-nucleotide polymorphism data from the Illumina HumanCNV370 genotyping array. Association between onset age and single-nucleotide polymorphisms under suggestive linkage peaks was also evaluated. The top logarithmic odds score for onset age (logarithmic odds score = 2.43) was in the chromosome 1q32.1 region. Moderate linkage to onset was also identified at 16q12.1 (logarithmic odds score = 1.58). Examination of single-nucleotide polymorphism association to Parkinson's disease onset under the linkage peaks revealed no statistically significant single-nucleotide polymorphism associations. The 2 novel genomic regions identified may harbor modifiers of leucine-rich repeat kinase 2-related Parkinson's disease onset age or penetrance, and further study of these regions may provide important insight into leucine-rich repeat kinase 2-related Parkinson's disease.

    Funded by: NIGMS NIH HHS: T32 GM074905; NINDS NIH HHS: R01 NS036711, R01 NS036711-09, R01 NS037167-10S2, R01 NS076843-02, R01 NS37167; PHS HHS: HHSN268200782096C; Telethon: GTB07001

    Movement disorders : official journal of the Movement Disorder Society 2011;26;11;2039-44

  • Somatic expansion of the Huntington's disease CAG repeat in the brain is associated with an earlier age of disease onset.

    Swami M, Hendricks AE, Gillis T, Massood T, Mysore J, Myers RH and Wheeler VC

    Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA.

    The age of onset of Huntington's disease (HD) is determined primarily by the length of the HD CAG repeat mutation, but is also influenced by other modifying factors. Delineating these modifiers is a critical step towards developing validated therapeutic targets in HD patients. The HD CAG repeat is somatically unstable, undergoing progressive length increases over time, particularly in brain regions that are the targets of neurodegeneration. Here, we have explored the hypothesis that somatic instability of the HD CAG repeat is itself a modifier of disease. Using small-pool PCR, we quantified somatic instability in the cortex region of the brain from a cohort of HD individuals exhibiting phenotypic extremes of young and old disease onset as predicted by the length of their constitutive HD CAG repeat lengths. After accounting for constitutive repeat length, somatic instability was found to be a significant predictor of onset age, with larger repeat length gains associated with earlier disease onset. These data are consistent with the hypothesis that somatic HD CAG repeat length expansions in target tissues contribute to the HD pathogenic process, and support pursuing factors that modify somatic instability as viable therapeutic targets.

    Funded by: NIGMS NIH HHS: T32 GM074905; NINDS NIH HHS: NS049206, P50 NS16367-28, R01 NS049206

    Human molecular genetics 2009;18;16;3039-47

  • Estimating the probability of de novo HD cases from transmissions of expanded penetrant CAG alleles in the Huntington disease gene from male carriers of high normal alleles (27-35 CAG).

    Hendricks AE, Latourelle JC, Lunetta KL, Cupples LA, Wheeler V, MacDonald ME, Gusella JF and Myers RH

    Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA.

    Huntington disease (HD) is a dominantly transmitted neurodegenerative disorder that arises from expansion of a CAG trinucleotide repeat on chromosome 4p16.3. CAG repeat allele lengths are defined as fully penetrant at >or=40, reduced penetrance at 36-39, high normal at 27-35, and normal at <or=26. Fathers, but not mothers, with high normal alleles are at risk of transmitting potentially penetrant HD alleles (>or=36) to offspring. We estimated the conditional probability of an offspring inheriting an expanded penetrant allele given a father with a high normal allele by applying probability definitions and rules to estimates of HD incidence, paternal birth rate, frequency of de novo HD, and frequency of high normal alleles in the general population. The estimated probability that a male high normal allele carrier will have an offspring with an expanded penetrant allele ranges from 1/6,241 to 1/951. These estimates may be useful in genetic counseling for male high normal allele carriers.

    Funded by: NCRR NIH HHS: 1S10RR163736-01A1; NIGMS NIH HHS: T32 GM074905; NINDS NIH HHS: NS049206, P50 NS016367, P50 NS016367-280014, P50 NS016367-290016, P50 NS16367, R01 NS049206, R01 NS049206-04

    American journal of medical genetics. Part A 2009;149A;7;1375-81

  • Intergenerational and striatal CAG repeat instability in Huntington's disease knock-in mice involve different DNA repair genes.

    Dragileva E, Hendricks A, Teed A, Gillis T, Lopez ET, Friedberg EC, Kucherlapati R, Edelmann W, Lunetta KL, MacDonald ME and Wheeler VC

    Molecular Neurogenetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston MA 02114, USA.

    Modifying the length of the Huntington's disease (HD) CAG repeat, the major determinant of age of disease onset, is an attractive therapeutic approach. To explore this we are investigating mechanisms of intergenerational and somatic HD CAG repeat instability. Here, we have crossed HD CAG knock-in mice onto backgrounds deficient in mismatch repair genes, Msh3 and Msh6, to discern the effects on CAG repeat size and disease pathogenesis. We find that different mechanisms predominate in inherited and somatic instability, with Msh6 protecting against intergenerational contractions and Msh3 required both for increasing CAG length and for enhancing an early disease phenotype in striatum. Therefore, attempts to decrease inherited repeat size may entail a full understanding of Msh6 complexes, while attempts to block the age-dependent increases in CAG size in striatal neurons and to slow the disease process will require a full elucidation of Msh3 complexes and their function in CAG repeat instability.

    Funded by: NINDS NIH HHS: NS049206, NS16367, NS532167, P50 NS016367, P50 NS016367-28, R01 NS049206, R01 NS049206-03

    Neurobiology of disease 2009;33;1;37-47

  • Genome-wide association and linkage analysis of quantitative traits: comparison of likelihood-ratio test and conditional score statistic.

    Hendricks AE, Zhu Y and Dupuis J

    Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Talbot Building, Boston, Massachusetts 02118 USA. baera@bu.edu.

    Over the past decade, genetic analysis has shifted from linkage studies, which identify broad regions containing putative trait loci, to genome-wide association studies, which detect the association of a marker with a specific phenotype. Because linkage and association analysis provide complementary information, developing a method to combine these analyses may increase the power to detect a true association. In this paper we compare a linkage score and association score test as well as a newly proposed combination of these two scores with traditional linkage and association methods.

    Funded by: NIGMS NIH HHS: R01 GM031575, R01 GM031575-28, T32 GM074905

    BMC proceedings 2009;3 Suppl 7;S100

  • Incorporating biological knowledge in the search for gene x gene interaction in genome-wide association studies.

    Manning AK, Ngwa JS, Hendricks AE, Liu CT, Johnson AD, Dupuis J and Cupples LA

    School of Public Health, Boston University, 715 Albany Street, Boston, Massachusetts 02118, USA. amanning@bu.edu.

    We sought to find significant gene x gene interaction in a genome-wide association analysis of rheumatoid arthritis (RA) by performing pair-wise tests of interaction among collections of single-nucleotide polymorphisms (SNPs) obtained by one of two methods. The first method involved screening the results of the genome-wide association analysis for main effects p-values < 1 x 10-4. The second method used biological databases such as the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes to define gene collections that each contained one of four genes with known associations with RA: PTPN22, STAT4, TRAF1, and C5. We used a permutation approach to determine whether any of these SNP sets had empirical enrichment of significant interaction effects. We found that the SNP set obtained by the first method was significantly enriched with significant interaction effects (empirical p = 0.003). Additionally, we found that the "protein complex assembly" collection of genes from the Gene Ontology collection containing the TRAF1 gene was significantly enriched with interaction effects with p-values < 1 x 10-8 (empirical p = 0.012).

    Funded by: NIGMS NIH HHS: R01 GM031575, R01 GM031575-28, T32 GM074905

    BMC proceedings 2009;3 Suppl 7;S81

  • Replication of association between ELAVL4 and Parkinson disease: the GenePD study.

    DeStefano AL, Latourelle J, Lew MF, Suchowersky O, Klein C, Golbe LI, Mark MH, Growdon JH, Wooten GF, Watts R, Guttman M, Racette BA, Perlmutter JS, Marlor L, Shill HA, Singer C, Goldwurm S, Pezzoli G, Saint-Hilaire MH, Hendricks AE, Gower A, Williamson S, Nagle MW, Wilk JB, Massood T, Huskey KW, Baker KB, Itin I, Litvan I, Nicholson G, Corbett A, Nance M, Drasby E, Isaacson S, Burn DJ, Chinnery PF, Pramstaller PP, Al-Hinti J, Moller AT, Ostergaard K, Sherman SJ, Roxburgh R, Snow B, Slevin JT, Cambi F, Gusella JF and Myers RH

    Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Crosstown Center, 3rd floor, Boston, MA 02118, USA. adestef@bu.edu

    Genetic variants in embryonic lethal, abnormal vision, Drosophila-like 4 (ELAVL4) have been reported to be associated with onset age of Parkinson disease (PD) or risk for PD affection in Caucasian populations. In the current study we genotyped three single nucleotide polymorphisms in ELAVL4 in a Caucasian study sample consisting of 712 PD patients and 312 unrelated controls from the GenePD study. The minor allele of rs967582 was associated with increased risk of PD (odds ratio = 1.46, nominal P value = 0.011) in the GenePD population. The minor allele of rs967582 was also the risk allele for PD affection or earlier onset age in the previously studied populations. This replication of association with rs967582 in a third cohort further implicates ELAVL4 as a PD susceptibility gene.

    Funded by: NIGMS NIH HHS: T32 GM074905; NINDS NIH HHS: R01 NS036711, R01 NS036711-09, R01 NS041509, R01 NS041509-09, R01 NS050425, R01 NS050425-05, R01 NS058714, R01 NS058714-03, R01 NS36711-05; Telethon: GTB07001

    Human genetics 2008;124;1;95-9

  • Haplotypes and gene expression implicate the MAPT region for Parkinson disease: the GenePD Study.

    Tobin JE, Latourelle JC, Lew MF, Klein C, Suchowersky O, Shill HA, Golbe LI, Mark MH, Growdon JH, Wooten GF, Racette BA, Perlmutter JS, Watts R, Guttman M, Baker KB, Goldwurm S, Pezzoli G, Singer C, Saint-Hilaire MH, Hendricks AE, Williamson S, Nagle MW, Wilk JB, Massood T, Laramie JM, DeStefano AL, Litvan I, Nicholson G, Corbett A, Isaacson S, Burn DJ, Chinnery PF, Pramstaller PP, Sherman S, Al-hinti J, Drasby E, Nance M, Moller AT, Ostergaard K, Roxburgh R, Snow B, Slevin JT, Cambi F, Gusella JF and Myers RH

    Department of Anatomy, Physiology, and Genetics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA. jetobinphd@gmail.com

    Background: Microtubule-associated protein tau (MAPT) has been associated with several neurodegenerative disorders including forms of parkinsonism and Parkinson disease (PD). We evaluated the association of the MAPT region with PD in a large cohort of familial PD cases recruited by the GenePD Study. In addition, postmortem brain samples from patients with PD and neurologically normal controls were used to evaluate whether the expression of the 3-repeat and 4-repeat isoforms of MAPT, and neighboring genes Saitohin (STH) and KIAA1267, are altered in PD cerebellum.

    Methods: Twenty-one single-nucleotide polymorphisms (SNPs) in the region of MAPT on chromosome 17q21 were genotyped in the GenePD Study. Single SNPs and haplotypes, including the H1 haplotype, were evaluated for association to PD. Relative quantification of gene expression was performed using real-time RT-PCR.

    Results: After adjusting for multiple comparisons, SNP rs1800547 was significantly associated with PD affection. While the H1 haplotype was associated with a significantly increased risk for PD, a novel H1 subhaplotype was identified that predicted a greater increased risk for PD. The expression of 4-repeat MAPT, STH, and KIAA1267 was significantly increased in PD brains relative to controls. No difference in expression was observed for 3-repeat MAPT.

    Conclusions: This study supports a role for MAPT in the pathogenesis of familial and idiopathic Parkinson disease (PD). Interestingly, the results of the gene expression studies suggest that other genes in the vicinity of MAPT, specifically STH and KIAA1267, may also have a role in PD and suggest complex effects for the genes in this region on PD risk.

    Funded by: NHLBI NIH HHS: N01-HC 25195; NIA NIH HHS: 5-T32-AG00277-05, 5R01-AG 16495, 5R01-AG08122, P30 AG13846; NIGMS NIH HHS: T32 GM074905; NIMH NIH HHS: R24 MH 068855; NINDS NIH HHS: 2R01-NS17950, R01 NS036711, R01 NS041509, R01 NS041509-08A2, R01 NS041509-09, R01 NS050425, R01 NS050425-05, R01 NS058714, R01 NS058714-02, R01 NS058714-03, R01 NS36711-05; Telethon: GTF04007

    Neurology 2008;71;1;28-34

  • The Gly2019Ser mutation in LRRK2 is not fully penetrant in familial Parkinson's disease: the GenePD study.

    Latourelle JC, Sun M, Lew MF, Suchowersky O, Klein C, Golbe LI, Mark MH, Growdon JH, Wooten GF, Watts RL, Guttman M, Racette BA, Perlmutter JS, Ahmed A, Shill HA, Singer C, Goldwurm S, Pezzoli G, Zini M, Saint-Hilaire MH, Hendricks AE, Williamson S, Nagle MW, Wilk JB, Massood T, Huskey KW, Laramie JM, DeStefano AL, Baker KB, Itin I, Litvan I, Nicholson G, Corbett A, Nance M, Drasby E, Isaacson S, Burn DJ, Chinnery PF, Pramstaller PP, Al-hinti J, Moller AT, Ostergaard K, Sherman SJ, Roxburgh R, Snow B, Slevin JT, Cambi F, Gusella JF and Myers RH

    Department of Neurology, Boston University School of Medicine, Boston University, Boston, MA, USA. jlatoure@bu.edu

    Background: We report age-dependent penetrance estimates for leucine-rich repeat kinase 2 (LRRK2)-related Parkinson's disease (PD) in a large sample of familial PD. The most frequently seen LRRK2 mutation, Gly2019Ser (G2019S), is associated with approximately 5 to 6% of familial PD cases and 1 to 2% of idiopathic cases, making it the most common known genetic cause of PD. Studies of the penetrance of LRRK2 mutations have produced a wide range of estimates, possibly due to differences in study design and recruitment, including in particular differences between samples of familial PD versus sporadic PD.

    Methods: A sample, including 903 affected and 58 unaffected members from 509 families ascertained for having two or more PD-affected members, 126 randomly ascertained PD patients and 197 controls, was screened for five different LRRK2 mutations. Penetrance was estimated in families of LRRK2 carriers with consideration of the inherent bias towards increased penetrance in a familial sample.

    Results: Thirty-one out of 509 families with multiple cases of PD (6.1%) were found to have 58 LRRK2 mutation carriers (6.4%). Twenty-nine of the 31 families had G2019S mutations while two had R1441C mutations. No mutations were identified among controls or unaffected relatives of PD cases. Nine PD-affected relatives of G2019S carriers did not carry the LRRK2 mutation themselves. At the maximum observed age range of 90 to 94 years, the unbiased estimated penetrance was 67% for G2019S families, compared with a baseline PD risk of 17% seen in the non-LRRK2-related PD families.

    Conclusion: Lifetime penetrance of LRRK2 estimated in the unascertained relatives of multiplex PD families is greater than that reported in studies of sporadically ascertained LRRK2 cases, suggesting that inherited susceptibility factors may modify the penetrance of LRRK2 mutations. In addition, the presence of nine PD phenocopies in the LRRK2 families suggests that these susceptibility factors may also increase the risk of non-LRRK2-related PD. No differences in penetrance were found between men and women, suggesting that the factors that influence penetrance for LRRK2 carriers are independent of the factors which increase PD prevalence in men.

    Funded by: NIGMS NIH HHS: T32 GM074905; NINDS NIH HHS: R01 NS041509, R01 NS041509-08A2, R01 NS041509-09, R01 NS050425, R01 NS050425-05, R01 NS058714, R01 NS058714-03, R01 NS36711-09; Telethon: GTF04007

    BMC medicine 2008;6;32

Gaelle Marenne

gm10@sanger.ac.uk Postdoctoral Fellow

March 2013 - present: Postdoctoral Fellow, Metabolic disease group, Wellcome Trust Sanger Institute, UK

2008 – 2012: PhD in Statistical Genetics (Statistical methods to combine SNP and CNV information in genome-wide association study: an application to bladder cancer), Spanish National Cancer Research Center (CNIO), University Autónoma of Madrid, Spain; French National Institute of Health and Medical Research (Inserm-UMRS946), University Paris-Sud, France

2006 – 2007: MSc in Statistical Genetics (Robustness of multiple comparison procedures to the change of tested markers set in whole-genome association studies), Inserm-UMRS535, University Paris-Sud 11, France

2003 – 2006: BSc in Bio-statistics, Paris Institute of Statistics (ISUP), University UPMC, France

Research

As a postdoctoral fellow in the Metabolic Disease Group I perform statistical analyses on data from whole-exome sequencing studies, including copy number variants, to identify genes or genomic regions associated with severe early onset obesity.

References

  • Advantage of using allele-specific copy numbers when testing for association in regions with common copy number variants.

    Marenne G, Chanock SJ, Malats N and Génin E

    Inserm UMR-S946, Univ. Paris Diderot, Institut Universitaire d'Hématologie, Paris, France ; Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.

    Copy number variants (CNV) can be called from SNP-arrays; however, few studies have attempted to combine both CNV and SNP calls to test for association with complex diseases. Even when SNPs are located within CNVs, two separate association analyses are necessary, to compare the distribution of bi-allelic genotypes in cases and controls (referred to as SNP-only strategy) and the number of copies of a region (referred to as CNV-only strategy). However, when disease susceptibility is actually associated with allele specific copy-number states, the two strategies may not yield comparable results, raising a series of questions about the optimal analytical approach. We performed simulations of the performance of association testing under different scenarios that varied genotype frequencies and inheritance models. We show that the SNP-only strategy lacks power under most scenarios when the SNP is located within a CNV; frequently it is excluded from analysis as it does not pass quality control metrics either because of an increased rate of missing calls or a departure from fitness for Hardy-Weinberg proportion. The CNV-only strategy also lacks power because the association testing depends on the allele which copy number varies. The combined strategy performs well in most of the scenarios. Hence, we advocate the use of this combined strategy when testing for association with SNPs located within CNVs.

    PloS one 2013;8;9;e75350

  • Genome-wide CNV analysis replicates the association between GSTM1 deletion and bladder cancer: a support for using continuous measurement from SNP-array data.

    Marenne G, Real FX, Rothman N, Rodríguez-Santiago B, Pérez-Jurado L, Kogevinas M, García-Closas M, Silverman DT, Chanock SJ, Génin E and Malats N

    Spanish National Cancer Research Center (CNIO), Madrid, E-28029, Spain.

    Background: Structural variations such as copy number variants (CNV) influence the expression of different phenotypic traits. Algorithms to identify CNVs through SNP-array platforms are available. The ability to evaluate well-characterized CNVs such as GSTM1 (1p13.3) deletion provides an important opportunity to assess their performance.

    Results: 773 cases and 759 controls from the SBC/EPICURO Study were genotyped in the GSTM1 region using TaqMan, Multiplex Ligation-dependent Probe Amplification (MLPA), and Illumina Infinium 1 M SNP-array platforms. CNV callings provided by TaqMan and MLPA were highly concordant and replicated the association between GSTM1 and bladder cancer. This was not the case when CNVs were called using Illumina 1 M data through available algorithms since no deletion was detected across the study samples. In contrast, when the Log R Ratio (LRR) was used as a continuous measure for the 5 probes contained in this locus, we were able to detect their association with bladder cancer using simple regression models or more sophisticated methods such as the ones implemented in the CNVtools package.

    Conclusions: This study highlights an important limitation in the CNV calling from SNP-array data in regions of common aberrations and suggests that there may be added advantage for using LRR as a continuous measure in association tests rather than relying on calling algorithms.

    BMC genomics 2012;13;326

  • Assessment of copy number variation using the Illumina Infinium 1M SNP-array: a comparison of methodological approaches in the Spanish Bladder Cancer/EPICURO study.

    Marenne G, Rodríguez-Santiago B, Closas MG, Pérez-Jurado L, Rothman N, Rico D, Pita G, Pisano DG, Kogevinas M, Silverman DT, Valencia A, Real FX, Chanock SJ, Génin E and Malats N

    Centro Nacional de Investigaciones Oncológicas (CNIO) Madrid, Spain.

    High-throughput single nucleotide polymorphism (SNP)-array technologies allow to investigate copy number variants (CNVs) in genome-wide scans and specific calling algorithms have been developed to determine CNV location and copy number. We report the results of a reliability analysis comparing data from 96 pairs of samples processed with CNVpartition, PennCNV, and QuantiSNP for Infinium Illumina Human 1Million probe chip data. We also performed a validity assessment with multiplex ligation-dependent probe amplification (MLPA) as a reference standard. The number of CNVs per individual varied according to the calling algorithm. Higher numbers of CNVs were detected in saliva than in blood DNA samples regardless of the algorithm used. All algorithms presented low agreement with mean Kappa Index (KI) <66. PennCNV was the most reliable algorithm (KI(w=) 98.96) when assessing the number of copies. The agreement observed in detecting CNV was higher in blood than in saliva samples. When comparing to MLPA, all algorithms identified poorly known copy aberrations (sensitivity = 0.19-0.28). In contrast, specificity was very high (0.97-0.99). Once a CNV was detected, the number of copies was truly assessed (sensitivity >0.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genome-wide scans. Further refinement is required to assess CNVs as risk factors in complex diseases.

    Funded by: NCI NIH HHS: Z01 CP010121-13

    Human mutation 2011;32;2;240-8

  • Impaired performance of FDR-based strategies in whole-genome association studies when SNPs are excluded prior to the analysis.

    Marenne G, Dalmasso C, Perdry H, Génin E and Broët P

    Inserm, UMR-S0535, Villejuif, France.

    With recent advances in genomewide microarray technologies, whole-genome association (WGA) studies have aimed at identifying susceptibility genes for complex human diseases using hundreds of thousands of single nucleotide polymorphisms (SNPs) genotyped at the same time. In this context and to take into account multiple testing, false discovery rate (FDR)-based strategies are now used frequently. However, a critical aspect of these strAtegies is that they are applied to a collection or a family of hypotheses and, thus, critically depend on these precise hypotheses. We investigated how modifying the family of hypotheses to be tested affected the performance of FDR-based procedures in WGA studies. We showed that FDR-based procedures performed more poorly when excluding SNPs with high prior probability of being associated. Results of simulation studies mimicking WGA studies according to three scenarios are reported, and show the extent to which SNPs elimination (family contraction) prior to the analysis impairs the performance of FDR-based procedures. To illustrate this situation, we used the data from a recent WGA study on type-1 diabetes (Clayton et al. [2005] Nat. Genet. 37:1243-1246) and report the results obtained when excluding or not SNPs located inside the human leukocyte antigen region. Based on our findings, excluding markers with high prior probability of being associated cannot be recommended for the analysis of WGA data with FDR-based strategies.

    Genetic epidemiology 2009;33;1;45-53

Felicity Payne

- Staff Scientist

2005 - present: Metabolic Disease Group, Wellcome Trust Sanger Institute, Hinxton, UK

2000 - 2005: Research Scientist, Diabetes and Inflammation Laboratory, Cambridge University, UK

2000: Laboratory Assistant, The Technology Partnership Plc, Melbourn, UK

1999: BSc (Hons) Biochemistry with Medical Biochemistry, University of Bristol, UK

Research

I am principally involved in the analysis of whole exome sequence data, looking for variants potentially causative of severe insulin resistance and other metabolic disorders.

References

  • Mosaic overgrowth with fibroadipose hyperplasia is caused by somatic activating mutations in PIK3CA.

    Lindhurst MJ, Parker VE, Payne F, Sapp JC, Rudge S, Harris J, Witkowski AM, Zhang Q, Groeneveld MP, Scott CE, Daly A, Huson SM, Tosi LL, Cunningham ML, Darling TN, Geer J, Gucev Z, Sutton VR, Tziotzios C, Dixon AK, Helliwell T, O'Rahilly S, Savage DB, Wakelam MJ, Barroso I, Biesecker LG and Semple RK

    The National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA.

    The phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway is critical for cellular growth and metabolism. Correspondingly, loss of function of PTEN, a negative regulator of PI3K, or activating mutations in AKT1, AKT2 or AKT3 have been found in distinct disorders featuring overgrowth or hypoglycemia. We performed exome sequencing of DNA from unaffected and affected cells from an individual with an unclassified syndrome of congenital progressive segmental overgrowth of fibrous and adipose tissue and bone and identified the cancer-associated mutation encoding p.His1047Leu in PIK3CA, the gene that encodes the p110α catalytic subunit of PI3K, only in affected cells. Sequencing of PIK3CA in ten additional individuals with overlapping syndromes identified either the p.His1047Leu alteration or a second cancer-associated alteration, p.His1047Arg, in nine cases. Affected dermal fibroblasts showed enhanced basal and epidermal growth factor (EGF)-stimulated phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) generation and concomitant activation of downstream signaling relative to their unaffected counterparts. Our findings characterize a distinct overgrowth syndrome, biochemically demonstrate activation of PI3K signaling and thereby identify a rational therapeutic target.

    Funded by: Biotechnology and Biological Sciences Research Council; Medical Research Council; NCATS NIH HHS: UL1 TR000423; Wellcome Trust: 077016, 078986, 078986/Z/06/Z, 080952, 091551, 091551/Z/10/Z, 095515, 097721, 097721/Z/11/Z, 098051/Z/05/Z, 80952/Z/06/Z

    Nature genetics 2012;44;8;928-33

  • An activating mutation of AKT2 and human hypoglycemia.

    Hussain K, Challis B, Rocha N, Payne F, Minic M, Thompson A, Daly A, Scott C, Harris J, Smillie BJ, Savage DB, Ramaswami U, De Lonlay P, O'Rahilly S, Barroso I and Semple RK

    Clinical and Molecular Genetics Unit, Developmental Endocrinology Research Group, Institute of Child Health, University College London, London WC1N 1EH, UK.

    Pathological fasting hypoglycemia in humans is usually explained by excessive circulating insulin or insulin-like molecules or by inborn errors of metabolism impairing liver glucose production. We studied three unrelated children with unexplained, recurrent, and severe fasting hypoglycemia and asymmetrical growth. All were found to carry the same de novo mutation, p.Glu17Lys, in the serine/threonine kinase AKT2, in two cases as heterozygotes and in one case in mosaic form. In heterologous cells, the mutant AKT2 was constitutively recruited to the plasma membrane, leading to insulin-independent activation of downstream signaling. Thus, systemic metabolic disease can result from constitutive, cell-autonomous activation of signaling pathways normally controlled by insulin.

    Funded by: Medical Research Council: G0502115; Wellcome Trust: 077016, 077016/Z/05/Z, 078986, 078986/Z/06/Z, 080952, 080952/Z/06/Z, 091551, 091551/Z/10/Z, 095515

    Science (New York, N.Y.) 2011;334;6055;474

  • Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

    Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Bengtsson Boström K, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney AS, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PR, Jørgensen T, Kao WH, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JR, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CN, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann HE, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI, MAGIC investigators and GIANT Consortium

    Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.

    By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.

    Funded by: Chief Scientist Office: CZB/4/710; Department of Health: DHCS/07/07/008; Medical Research Council: G0601261, G0700222, G0700222(81696), G0701863, MC_U106179471, MC_U106179474, MC_U127592696; NCRR NIH HHS: UL1RR025005; NHGRI NIH HHS: 1 Z01 HG000024, U01HG004171, U01HG004399, U01HG004402; NHLBI NIH HHS: 1K99HL094535-01A1, N01-HC-25195, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N02-HL-6-4278, R01HL086694, R01HL087641, R01HL59367; NIAMS NIH HHS: 1K08AR055688, K08 AR055688, K08 AR055688-03; NIDA NIH HHS: U54 DA021519; NIDDK NIH HHS: DK062370, DK069922, DK072193, DK073490, DK078616, DK58845, K23-DK65978, K24-DK080140, R01 DK029867, R01 DK072193; PHS HHS: HHSN268200625226C; Wellcome Trust: 064890, 072960, 075491, 076113, 077016, 079557, 081682, 083270, 086596, 088885

    Nature genetics 2010;42;7;579-89

  • Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden.

    Renström F, Payne F, Nordström A, Brito EC, Rolandsson O, Hallmans G, Barroso I, Nordström P, Franks PW and GIANT Consortium

    Department of Public Health and Clinical Medicine, Umeå University Hospital, Umeå, Sweden.

    Recent genome-wide association studies (GWAS) have identified multiple risk loci for common obesity (FTO, MC4R, TMEM18, GNPDA2, SH2B1, KCTD15, MTCH2, NEGR1 and PCSK1). Here we extend those studies by examining associations with adiposity and type 2 diabetes in Swedish adults. The nine single nucleotide polymorphisms (SNPs) were genotyped in 3885 non-diabetic and 1038 diabetic individuals with available measures of height, weight and body mass index (BMI). Adipose mass and distribution were objectively assessed using dual-energy X-ray absorptiometry in a sub-group of non-diabetics (n = 2206). In models with adipose mass traits, BMI or obesity as outcomes, the most strongly associated SNP was FTO rs1121980 (P < 0.001). Five other SNPs (SH2B1 rs7498665, MTCH2 rs4752856, MC4R rs17782313, NEGR1 rs2815752 and GNPDA2 rs10938397) were significantly associated with obesity. To summarize the overall genetic burden, a weighted risk score comprising a subset of SNPs was constructed; those in the top quintile of the score were heavier (+2.6 kg) and had more total (+2.4 kg), gynoid (+191 g) and abdominal (+136 g) adipose tissue than those in the lowest quintile (all P < 0.001). The genetic burden score significantly increased diabetes risk, with those in the highest quintile (n = 193/594 cases/controls) being at 1.55-fold (95% CI 1.21-1.99; P < 0.0001) greater risk of type 2 diabetes than those in the lowest quintile (n = 130/655 cases/controls). In summary, we have statistically replicated six of the previously associated obese-risk loci and our results suggest that the weight-inducing effects of these variants are explained largely by increased adipose accumulation.

    Funded by: Wellcome Trust

    Human molecular genetics 2009;18;8;1489-96

  • Interaction analysis of the CBLB and CTLA4 genes in type 1 diabetes.

    Payne F, Cooper JD, Walker NM, Lam AC, Smink LJ, Nutland S, Stevens HE, Hutchings J and Todd JA

    Juvenile Diabetes Research Foundation/Wellcome Trust, Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Addenbrooke's Hospital, Cambridge, UK.

    Gene-gene interaction analyses have been suggested as a potential strategy to help identify common disease susceptibility genes. Recently, evidence of a statistical interaction between polymorphisms in two negative immunoregulatory genes, CBLB and CTLA4, has been reported in type 1 diabetes (T1D). This study, in 480 Danish families, reported an association between T1D and a synonymous coding SNP in exon 12 of the CBLB gene (rs3772534 G>A; minor allele frequency, MAF=0.24; derived relative risk, RR for G allele=1.78; P=0.046). Furthermore, evidence of a statistical interaction with the known T1D susceptibility-associated CTLA4 polymorphism rs3087243 (laboratory name CT60, G>A) was reported (P<0.0001), such that the CBLB SNP rs3772534 G allele was overtransmitted to offspring with the CTLA4 rs3087243 G/G genotype. We have, therefore, attempted to obtain additional support for this finding in both large family and case-control collections. In a primary analysis, no evidence for an association of the CBLB SNP rs3772534 with disease was found in either sample set (2162 parent-child trios, P=0.33; 3453 cases and 3655 controls, P=0.69). In the case-only statistical interaction analysis between rs3772534 and rs3087243, there was also no support for an effect (1994 T1D affected offspring, and 3215 cases, P=0.92). These data highlight the need for large, well-characterized populations, offering the possibility of obtaining additional support for initial observations owing to the low prior probability of identifying reproducible evidence of gene-gene interactions in the analysis of common disease-associated variants in human populations.

    Funded by: Medical Research Council: G0000934; Wellcome Trust

    Journal of leukocyte biology 2007;81;3;581-3

  • No evidence for association of the TATA-box binding protein glutamine repeat sequence or the flanking chromosome 6q27 region with type 1 diabetes.

    Payne F, Smyth DJ, Pask R, Cooper JD, Masters J, Wang WY, Godfrey LM, Bowden G, Szeszko J, Smink LJ, Lam AC, Burren O, Walker NM, Nutland S, Rance H, Undlien DE, Rønningen KS, Guja C, Ionescu-Tîrgovişte C, Todd JA and Twells RC

    Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Hills Road, Cambridge, UK.

    Susceptibility to the autoimmune disease type 1 diabetes has been linked to human chromosome 6q27 and, moreover, recently associated with one of the genes in the region, TATA box-binding protein (TBP). Using a much larger sample of T1D families than those studied by others, and by extensive re-sequencing of nine other genes in the proximity, in which we identified 279 polymorphisms, 83 of which were genotyped in up to 725 T1D multiplex and simplex families, we obtained no evidence for association of the TBP CAG/CAA (glutamine) microsatellite repeat sequence with disease, or for nine other genes, PDCD2, PSMB1, KIAA1838, DLL1, dJ894D12.4, FLJ25454, FLJ13162, FLJ11152, PHF10 and CCR6. This study also provides an exon-based tag single nucleotide polymorphism map for these 10 genes that can be used for analysis of other diseases.

    Funded by: Wellcome Trust

    Biochemical and biophysical research communications 2005;331;2;435-41

  • Haplotype tag single nucleotide polymorphism analysis of the human orthologues of the rat type 1 diabetes genes Ian4 (Lyp/Iddm1) and Cblb.

    Payne F, Smyth DJ, Pask R, Barratt BJ, Cooper JD, Twells RC, Walker NM, Lam AC, Smink LJ, Nutland S, Rance HE and Todd JA

    Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke's Hospital, Cambridge, U.K.

    The diabetes-prone BioBreeding (BB) and Komeda diabetes-prone (KDP) rats are both spontaneous animal models of human autoimmune, T-cell-associated type 1 diabetes. Both resemble the human disease, and consequently, susceptibility genes for diabetes found in these two strains can be considered as potential candidate genes in humans. Recently, a frameshift deletion in Ian4, a member of the immune-associated nucleotide (Ian)-related gene family, has been shown to map to BB rat Iddm1. In the KDP rat, a nonsense mutation in the T-cell regulatory gene, Cblb, has been described as a major susceptibility locus. Following a strategy of examining the human orthologues of susceptibility genes identified in animal models for association with type 1 diabetes, we identified single nucleotide polymorphisms (SNPs) from each gene by resequencing PCR product from at least 32 type 1 diabetic patients. Haplotype tag SNPs (htSNPs) were selected and genotyped in 754 affected sib-pair families from the U.K. and U.S. Evaluation of disease association by a multilocus transmission/disequilibrium test (TDT) gave a P value of 0.484 for IAN4L1 and 0.692 for CBLB, suggesting that neither gene influences susceptibility to common alleles of human type 1 diabetes in these populations.

    Diabetes 2004;53;2;505-9

  • Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease.

    Ueda H, Howson JM, Esposito L, Heward J, Snook H, Chamberlain G, Rainbow DB, Hunter KM, Smith AN, Di Genova G, Herr MH, Dahlman I, Payne F, Smyth D, Lowe C, Twells RC, Howlett S, Healy B, Nutland S, Rance HE, Everett V, Smink LJ, Lam AC, Cordell HJ, Walker NM, Bordin C, Hulme J, Motzo C, Cucca F, Hess JF, Metzker ML, Rogers J, Gregory S, Allahabadia A, Nithiyananthan R, Tuomilehto-Wolf E, Tuomilehto J, Bingley P, Gillespie KM, Undlien DE, Rønningen KS, Guja C, Ionescu-Tîrgovişte C, Savage DA, Maxwell AP, Carson DJ, Patterson CC, Franklyn JA, Clayton DG, Peterson LB, Wicker LS, Todd JA and Gough SC

    Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Cambridge, CB2 2XY, UK.

    Genes and mechanisms involved in common complex diseases, such as the autoimmune disorders that affect approximately 5% of the population, remain obscure. Here we identify polymorphisms of the cytotoxic T lymphocyte antigen 4 gene (CTLA4)--which encodes a vital negative regulatory molecule of the immune system--as candidates for primary determinants of risk of the common autoimmune disorders Graves' disease, autoimmune hypothyroidism and type 1 diabetes. In humans, disease susceptibility was mapped to a non-coding 6.1 kb 3' region of CTLA4, the common allelic variation of which was correlated with lower messenger RNA levels of the soluble alternative splice form of CTLA4. In the mouse model of type 1 diabetes, susceptibility was also associated with variation in CTLA-4 gene splicing with reduced production of a splice form encoding a molecule lacking the CD80/CD86 ligand-binding domain. Genetic mapping of variants conferring a small disease risk can identify pathways in complex disorders, as exemplified by our discovery of inherited, quantitative alterations of CTLA4 contributing to autoimmune tissue destruction.

    Nature 2003;423;6939;506-11

  • Haplotype tagging for the identification of common disease genes.

    Johnson GC, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RC, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SC, Clayton DG and Todd JA

    JDRF/WT Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/Medical Research Council Building, Hills Road, Cambridge, UK.

    Genome-wide linkage disequilibrium (LD) mapping of common disease genes could be more powerful than linkage analysis if the appropriate density of polymorphic markers were known and if the genotyping effort and cost of producing such an LD map could be reduced. Although different metrics that measure the extent of LD have been evaluated, even the most recent studies have not placed significant emphasis on the most informative and cost-effective method of LD mapping-that based on haplotypes. We have scanned 135 kb of DNA from nine genes, genotyped 122 single-nucleotide polymorphisms (SNPs; approximately 184,000 genotypes) and determined the common haplotypes in a minimum of 384 European individuals for each gene. Here we show how knowledge of the common haplotypes and the SNPs that tag them can be used to (i) explain the often complex patterns of LD between adjacent markers, (ii) reduce genotyping significantly (in this case from 122 to 34 SNPs), (iii) scan the common variation of a gene sensitively and comprehensively and (iv) provide key fine-mapping data within regions of strong LD. Our results also indicate that, at least for the genes studied here, the current version of dbSNP would have been of limited utility for LD mapping because many common haplotypes could not be defined. A directed re-sequencing effort of the approximately 10% of the genome in or near genes in the major ethnic groups would aid the systematic evaluation of the common variant model of common disease.

    Nature genetics 2001;29;2;233-7

Rachel Watson

- Postdoctoral Fellow

November 2012 – present: Postdoctoral Fellow, Metabolic disease group, Wellcome Trust Sanger Institute

2008-2012: PhD – Babraham Institute, University of Cambridge (The role of Akt2 in skeletal muscle)

2005-2008: BA (Hons) Natural Sciences, University of Cambridge

Research

A number of candidate genes have been implicated in human metabolic disease through both genome-wide association studies and the analysis of whole-exome sequence data. I am working to provide functional data relating to these candidate genes and to investigate their role in metabolic disease.

Eleanor Wheeler

ew2@sanger.ac.uk Senior Staff Scientist

January 2014-present: Senior Staff Scientist, Metabolic Disease Group, Wellcome Trust Sanger Institute, UK

June 2006-January 2014: Staff Scientist, Metabolic Disease Group, Wellcome Trust Sanger Institute, UK

2002-2006: PhD in Statistical Genetics – Cambridge Institute for Medical Research, University of Cambridge (Genetic statistical methods for analysis of immune response quantitative trait data with application to the Belém Family Study)

2001-2002: MPhil in Statistical Science, University of Cambridge

1998-2001: BSc. (Hons) Mathematics, University College London

Research

I use large-scale data sets, including SNP array data and next generation sequence data to identify genes involved in metabolic diseases, and related quantitative phenotypes. I am actively involved in the international consortia MAGIC and GIANT, working on large-scale meta-analyses for glucose and insulin-related traits and anthropometric phenotypes.

References

  • Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture.

    Berndt SI, Gustafsson S, Mägi R, Ganna A, Wheeler E, Feitosa MF, Justice AE, Monda KL, Croteau-Chonka DC, Day FR, Esko T, Fall T, Ferreira T, Gentilini D, Jackson AU, Luan J, Randall JC, Vedantam S, Willer CJ, Winkler TW, Wood AR, Workalemahu T, Hu YJ, Lee SH, Liang L, Lin DY, Min JL, Neale BM, Thorleifsson G, Yang J, Albrecht E, Amin N, Bragg-Gresham JL, Cadby G, den Heijer M, Eklund N, Fischer K, Goel A, Hottenga JJ, Huffman JE, Jarick I, Johansson Å, Johnson T, Kanoni S, Kleber ME, König IR, Kristiansson K, Kutalik Z, Lamina C, Lecoeur C, Li G, Mangino M, McArdle WL, Medina-Gomez C, Müller-Nurasyid M, Ngwa JS, Nolte IM, Paternoster L, Pechlivanis S, Perola M, Peters MJ, Preuss M, Rose LM, Shi J, Shungin D, Smith AV, Strawbridge RJ, Surakka I, Teumer A, Trip MD, Tyrer J, Van Vliet-Ostaptchouk JV, Vandenput L, Waite LL, Zhao JH, Absher D, Asselbergs FW, Atalay M, Attwood AP, Balmforth AJ, Basart H, Beilby J, Bonnycastle LL, Brambilla P, Bruinenberg M, Campbell H, Chasman DI, Chines PS, Collins FS, Connell JM, Cookson WO, de Faire U, de Vegt F, Dei M, Dimitriou M, Edkins S, Estrada K, Evans DM, Farrall M, Ferrario MM, Ferrières J, Franke L, Frau F, Gejman PV, Grallert H, Grönberg H, Gudnason V, Hall AS, Hall P, Hartikainen AL, Hayward C, Heard-Costa NL, Heath AC, Hebebrand J, Homuth G, Hu FB, Hunt SE, Hyppönen E, Iribarren C, Jacobs KB, Jansson JO, Jula A, Kähönen M, Kathiresan S, Kee F, Khaw KT, Kivimäki M, Koenig W, Kraja AT, Kumari M, Kuulasmaa K, Kuusisto J, Laitinen JH, Lakka TA, Langenberg C, Launer LJ, Lind L, Lindström J, Liu J, Liuzzi A, Lokki ML, Lorentzon M, Madden PA, Magnusson PK, Manunta P, Marek D, März W, Mateo Leach I, McKnight B, Medland SE, Mihailov E, Milani L, Montgomery GW, Mooser V, Mühleisen TW, Munroe PB, Musk AW, Narisu N, Navis G, Nicholson G, Nohr EA, Ong KK, Oostra BA, Palmer CN, Palotie A, Peden JF, Pedersen N, Peters A, Polasek O, Pouta A, Pramstaller PP, Prokopenko I, Pütter C, Radhakrishnan A, Raitakari O, Rendon A, Rivadeneira F, Rudan I, Saaristo TE, Sambrook JG, Sanders AR, Sanna S, Saramies J, Schipf S, Schreiber S, Schunkert H, Shin SY, Signorini S, Sinisalo J, Skrobek B, Soranzo N, Stančáková A, Stark K, Stephens JC, Stirrups K, Stolk RP, Stumvoll M, Swift AJ, Theodoraki EV, Thorand B, Tregouet DA, Tremoli E, Van der Klauw MM, van Meurs JB, Vermeulen SH, Viikari J, Virtamo J, Vitart V, Waeber G, Wang Z, Widén E, Wild SH, Willemsen G, Winkelmann BR, Witteman JC, Wolffenbuttel BH, Wong A, Wright AF, Zillikens MC, Amouyel P, Boehm BO, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Cupples LA, Cusi D, Dedoussis GV, Erdmann J, Eriksson JG, Franks PW, Froguel P, Gieger C, Gyllensten U, Hamsten A, Harris TB, Hengstenberg C, Hicks AA, Hingorani A, Hinney A, Hofman A, Hovingh KG, Hveem K, Illig T, Jarvelin MR, Jöckel KH, Keinanen-Kiukaanniemi SM, Kiemeney LA, Kuh D, Laakso M, Lehtimäki T, Levinson DF, Martin NG, Metspalu A, Morris AD, Nieminen MS, Njølstad I, Ohlsson C, Oldehinkel AJ, Ouwehand WH, Palmer LJ, Penninx B, Power C, Province MA, Psaty BM, Qi L, Rauramaa R, Ridker PM, Ripatti S, Salomaa V, Samani NJ, Snieder H, Sørensen TI, Spector TD, Stefansson K, Tönjes A, Tuomilehto J, Uitterlinden AG, Uusitupa M, van der Harst P, Vollenweider P, Wallaschofski H, Wareham NJ, Watkins H, Wichmann HE, Wilson JF, Abecasis GR, Assimes TL, Barroso I, Boehnke M, Borecki IB, Deloukas P, Fox CS, Frayling T, Groop LC, Haritunian T, Heid IM, Hunter D, Kaplan RC, Karpe F, Moffatt MF, Mohlke KL, O'Connell JR, Pawitan Y, Schadt EE, Schlessinger D, Steinthorsdottir V, Strachan DP, Thorsteinsdottir U, van Duijn CM, Visscher PM, Di Blasio AM, Hirschhorn JN, Lindgren CM, Morris AP, Meyre D, Scherag A, McCarthy MI, Speliotes EK, North KE, Loos RJ and Ingelsson E

    US Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA.

    Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

    Funded by: British Heart Foundation: PG/11/63/29011; Cancer Research UK; Chief Scientist Office: CZB/4/710; Medical Research Council: G0600237, G0601261, G1000143, G9521010, MC_PC_U127561128, MC_U105260558, MC_U106179471, MC_U106179472, MC_U106188470, MC_U123092720; NHLBI NIH HHS: R01 HL105756; NIAAA NIH HHS: K05 AA017688; NIDDK NIH HHS: R01 DK072193, R01 DK075787; NIGMS NIH HHS: T32 GM074905; Wellcome Trust: 090532, 097117, 098017

    Nature genetics 2013;45;5;501-12

  • Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity.

    Wheeler E, Huang N, Bochukova EG, Keogh JM, Lindsay S, Garg S, Henning E, Blackburn H, Loos RJ, Wareham NJ, O'Rahilly S, Hurles ME, Barroso I and Farooqi IS

    Wellcome Trust Sanger Institute, Cambridge, UK.

    Common and rare variants associated with body mass index (BMI) and obesity account for <5% of the variance in BMI. We performed SNP and copy number variation (CNV) association analyses in 1,509 children with obesity at the extreme tail (>3 s.d. from the mean) of the BMI distribution and 5,380 controls. Evaluation of 29 SNPs (P < 1 × 10(-5)) in an additional 971 severely obese children and 1,990 controls identified 4 new loci associated with severe obesity (LEPR, PRKCH, PACS1 and RMST). A previously reported 43-kb deletion at the NEGR1 locus was significantly associated with severe obesity (P = 6.6 × 10(-7)). However, this signal was entirely driven by a flanking 8-kb deletion; absence of this deletion increased risk for obesity (P = 6.1 × 10(-11)). We found a significant burden of rare, single CNVs in severely obese cases (P < 0.0001). Integrative gene network pathway analysis of rare deletions indicated enrichment of genes affecting G protein-coupled receptors (GPCRs) involved in the neuronal regulation of energy homeostasis.

    Funded by: Cancer Research UK; Medical Research Council: G0900554, G9824984, MC_U106179471, MC_U106188470; NIDA NIH HHS: R25 DA027995; Wellcome Trust: 084713, 098497, WT098051

    Nature genetics 2013;45;5;513-7

  • Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

    Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J, Mägi R, Strawbridge RJ, Rehnberg E, Gustafsson S, Kanoni S, Rasmussen-Torvik LJ, Yengo L, Lecoeur C, Shungin D, Sanna S, Sidore C, Johnson PC, Jukema JW, Johnson T, Mahajan A, Verweij N, Thorleifsson G, Hottenga JJ, Shah S, Smith AV, Sennblad B, Gieger C, Salo P, Perola M, Timpson NJ, Evans DM, Pourcain BS, Wu Y, Andrews JS, Hui J, Bielak LF, Zhao W, Horikoshi M, Navarro P, Isaacs A, O'Connell JR, Stirrups K, Vitart V, Hayward C, Esko T, Mihailov E, Fraser RM, Fall T, Voight BF, Raychaudhuri S, Chen H, Lindgren CM, Morris AP, Rayner NW, Robertson N, Rybin D, Liu CT, Beckmann JS, Willems SM, Chines PS, Jackson AU, Kang HM, Stringham HM, Song K, Tanaka T, Peden JF, Goel A, Hicks AA, An P, Müller-Nurasyid M, Franco-Cereceda A, Folkersen L, Marullo L, Jansen H, Oldehinkel AJ, Bruinenberg M, Pankow JS, North KE, Forouhi NG, Loos RJ, Edkins S, Varga TV, Hallmans G, Oksa H, Antonella M, Nagaraja R, Trompet S, Ford I, Bakker SJ, Kong A, Kumari M, Gigante B, Herder C, Munroe PB, Caulfield M, Antti J, Mangino M, Small K, Miljkovic I, Liu Y, Atalay M, Kiess W, James AL, Rivadeneira F, Uitterlinden AG, Palmer CN, Doney AS, Willemsen G, Smit JH, Campbell S, Polasek O, Bonnycastle LL, Hercberg S, Dimitriou M, Bolton JL, Fowkes GR, Kovacs P, Lindström J, Zemunik T, Bandinelli S, Wild SH, Basart HV, Rathmann W, Grallert H, DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, Maerz W, Kleber ME, Boehm BO, Peters A, Pramstaller PP, Province MA, Borecki IB, Hastie ND, Rudan I, Campbell H, Watkins H, Farrall M, Stumvoll M, Ferrucci L, Waterworth DM, Bergman RN, Collins FS, Tuomilehto J, Watanabe RM, de Geus EJ, Penninx BW, Hofman A, Oostra BA, Psaty BM, Vollenweider P, Wilson JF, Wright AF, Hovingh GK, Metspalu A, Uusitupa M, Magnusson PK, Kyvik KO, Kaprio J, Price JF, Dedoussis GV, Deloukas P, Meneton P, Lind L, Boehnke M, Shuldiner AR, van Duijn CM, Morris AD, Toenjes A, Peyser PA, Beilby JP, Körner A, Kuusisto J, Laakso M, Bornstein SR, Schwarz PE, Lakka TA, Rauramaa R, Adair LS, Smith GD, Spector TD, Illig T, de Faire U, Hamsten A, Gudnason V, Kivimaki M, Hingorani A, Keinanen-Kiukaanniemi SM, Saaristo TE, Boomsma DI, Stefansson K, van der Harst P, Dupuis J, Pedersen NL, Sattar N, Harris TB, Cucca F, Ripatti S, Salomaa V, Mohlke KL, Balkau B, Froguel P, Pouta A, Jarvelin MR, Wareham NJ, Bouatia-Naji N, McCarthy MI, Franks PW, Meigs JB, Teslovich TM, Florez JC, Langenberg C, Ingelsson E, Prokopenko I and Barroso I

    Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.

    Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

    Funded by: AHRQ HHS: HS06516; Biotechnology and Biological Sciences Research Council: G20234; British Heart Foundation: PG/07/133/24260, RG/07/008/23674; Chief Scientist Office: CZB/4/672, CZB/4/710; Department of Health; FIC NIH HHS: TW05596; Medical Research Council: 74882, 85374, G0500539, G0600705, G0701863, G0800582, G0902037, G19/35, G8802774, G9521010, MC_PC_U127561128, MC_U106179471, MC_U127561128, MC_U127592696, MC_UP_A100_1003, U.1061.00.001 (79471); NCATS NIH HHS: UL1 TR000130; NCRR NIH HHS: M01 RR 16500, M01-RR00425, RR20649, UL1RR025005; NHGRI NIH HHS: 1Z01-HG000024, N01-HG-65403, U01HG004402; NHLBI NIH HHS: 5R01HL087679-02, HL075366, HL080295, HL085144, HL087652, HL087660, HL100245, HL105756, N01 HC-15103, N01 HC-55222, N01-HC-25195, N01-HC-35129, N01-HC-45133, N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, N01-HC-75150, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, N01-HC-85239, N02-HL-6-4278, R01 HL088119, R01-HL-087700, R01-HL-088215, R01HL086694, R01HL087641, R01HL59367, U01 HL072515-06, U01 HL84756; NIA NIH HHS: 1R01AG032098-01A1, AG-023629, AG-027058, AG-15928, AG-20098, AG028555, AG04563, AG08724, AG08861, AG10175, AG13196, N01-AG-1-2100, N01-AG-1-2109, N01AG62101, N01AG62103, N01AG62106, R01 AG18728, T32 AG000219; NIAMS NIH HHS: K08AR055688; NICHD NIH HHS: R24 HD050924; NIDDK NIH HHS: DK063491, DK078150, DK56350, K24 DK080140, P30 DK72488, P60DK79637, R01 DK072193, R01 DK078150, R01 DK078616, R01 DK54261, R01-DK-075681, R01-DK-8925601, R01-DK062370, R01-DK072193; NIEHS NIH HHS: ES10126, P30 ES010126; NIGMS NIH HHS: U01 GM074518; NIMH NIH HHS: 1RL1MH083268-01, 5R01MH63706:02, U24 MH068457-06; NLM NIH HHS: LM010098; PHS HHS: HHSN268200625226C, HHSN268200782096C, R01D0042157-01A; Wellcome Trust: 075491/Z/04, 076467, 081682, 083948, 090532, 092731, 098017, 098051, GR069224

    Nature genetics 2012;44;9;991-1005

  • The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits.

    Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, Burtt NP, Fuchsberger C, Li Y, Erdmann J, Frayling TM, Heid IM, Jackson AU, Johnson T, Kilpeläinen TO, Lindgren CM, Morris AP, Prokopenko I, Randall JC, Saxena R, Soranzo N, Speliotes EK, Teslovich TM, Wheeler E, Maguire J, Parkin M, Potter S, Rayner NW, Robertson N, Stirrups K, Winckler W, Sanna S, Mulas A, Nagaraja R, Cucca F, Barroso I, Deloukas P, Loos RJ, Kathiresan S, Munroe PB, Newton-Cheh C, Pfeufer A, Samani NJ, Schunkert H, Hirschhorn JN, Altshuler D, McCarthy MI, Abecasis GR and Boehnke M

    Medical Population Genetics, The Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

    Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the "Metabochip," a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.

    Funded by: British Heart Foundation; Medical Research Council: MC_U106188470; NHGRI NIH HHS: HG000376, HG005214, HG005581, R01 HG000376; NIA NIH HHS: N01-AG-1-2109; NIDDK NIH HHS: DK062370; Wellcome Trust: 064890, 081682, 090532, 098051

    PLoS genetics 2012;8;8;e1002793

  • Genome-wide association studies and type 2 diabetes.

    Wheeler E and Barroso I

    Wellcome Trust Sanger Institute, Cambridge, UK.

    In recent years, the search for genetic determinants of type 2 diabetes (T2D) has changed dramatically. Although linkage and small-scale candidate gene studies were highly successful in the identification of genes, which, when mutated, caused monogenic forms of T2D, they were largely unsuccessful when applied to the more common forms of the disease. To date, these approaches have only identified two loci (PPARG, KCNJ11) robustly implicated in T2D susceptibility. The ability to perform large-scale association analysis, including genome-wide association studies (GWAS) in many thousands of samples from different populations, and subsequently, the shift to form large international collaborations to perform meta-analyses across many studies has taken the number of independent loci showing genome-wide significant associations with T2D to 44. This number includes six loci identified initially through the analysis of quantitative glycaemic phenotypes, illustrating the usefulness of this approach both to identify new disease genes and gain insight into the mechanisms leading to disease. Combined, these loci still only account for ∼10% of the observed familial clustering in Europeans, leaving much of the variance unexplained. In this review, we will describe what GWAS have taught us about the genetic basis of T2D and discuss possible next steps to uncover the remaining heritability.

    Funded by: Wellcome Trust: 077016/Z/05/Z

    Briefings in functional genomics 2011;10;2;52-60

  • Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

    Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia-Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL, Stewart AF, Voight BF, Willenborg C, Wright B, Altshuler D, Arking D, Balkau B, Barnes D, Boerwinkle E, Böhm B, Bonnefond A, Bonnycastle LL, Boomsma DI, Bornstein SR, Böttcher Y, Bumpstead S, Burnett-Miller MS, Campbell H, Cao A, Chambers J, Clark R, Collins FS, Coresh J, de Geus EJ, Dei M, Deloukas P, Döring A, Egan JM, Elosua R, Ferrucci L, Forouhi N, Fox CS, Franklin C, Franzosi MG, Gallina S, Goel A, Graessler J, Grallert H, Greinacher A, Hadley D, Hall A, Hamsten A, Hayward C, Heath S, Herder C, Homuth G, Hottenga JJ, Hunter-Merrill R, Illig T, Jackson AU, Jula A, Kleber M, Knouff CW, Kong A, Kooner J, Köttgen A, Kovacs P, Krohn K, Kühnel B, Kuusisto J, Laakso M, Lathrop M, Lecoeur C, Li M, Li M, Loos RJ, Luan J, Lyssenko V, Mägi R, Magnusson PK, Mälarstig A, Mangino M, Martínez-Larrad MT, März W, McArdle WL, McPherson R, Meisinger C, Meitinger T, Melander O, Mohlke KL, Mooser VE, Morken MA, Narisu N, Nathan DM, Nauck M, O'Donnell C, Oexle K, Olla N, Pankow JS, Payne F, Peden JF, Pedersen NL, Peltonen L, Perola M, Polasek O, Porcu E, Rader DJ, Rathmann W, Ripatti S, Rocheleau G, Roden M, Rudan I, Salomaa V, Saxena R, Schlessinger D, Schunkert H, Schwarz P, Seedorf U, Selvin E, Serrano-Ríos M, Shrader P, Silveira A, Siscovick D, Song K, Spector TD, Stefansson K, Steinthorsdottir V, Strachan DP, Strawbridge R, Stumvoll M, Surakka I, Swift AJ, Tanaka T, Teumer A, Thorleifsson G, Thorsteinsdottir U, Tönjes A, Usala G, Vitart V, Völzke H, Wallaschofski H, Waterworth DM, Watkins H, Wichmann HE, Wild SH, Willemsen G, Williams GH, Wilson JF, Winkelmann J, Wright AF, WTCCC, Zabena C, Zhao JH, Epstein SE, Erdmann J, Hakonarson HH, Kathiresan S, Khaw KT, Roberts R, Samani NJ, Fleming MD, Sladek R, Abecasis G, Boehnke M, Froguel P, Groop L, McCarthy MI, Kao WH, Florez JC, Uda M, Wareham NJ, Barroso I and Meigs JB

    Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.

    Objective: Glycated hemoglobin (HbA₁(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA₁(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA₁(c) levels.

    We studied associations with HbA₁(c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA₁(c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.

    Results: Ten loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10⁻²⁶), HFE (rs1800562/P = 2.6 × 10⁻²⁰), TMPRSS6 (rs855791/P = 2.7 × 10⁻¹⁴), ANK1 (rs4737009/P = 6.1 × 10⁻¹²), SPTA1 (rs2779116/P = 2.8 × 10⁻⁹) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10⁻⁹), and four known HbA₁(c) loci: HK1 (rs16926246/P = 3.1 × 10⁻⁵⁴), MTNR1B (rs1387153/P = 4.0 × 10⁻¹¹), GCK (rs1799884/P = 1.5 × 10⁻²⁰) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10⁻¹⁸). We show that associations with HbA₁(c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA₁(c)) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA₁(c).

    Conclusions: GWAS identified 10 genetic loci reproducibly associated with HbA₁(c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA₁(c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA₁(c).

    Funded by: Chief Scientist Office: CZB/4/710; Medical Research Council: G0401527, G0701863, MC_QA137934, MC_U106179471, MC_U106188470, MC_U127561128, MC_UP_A100_1003; NIDDK NIH HHS: R01 DK072193

    Diabetes 2010;59;12;3229-39

  • Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.

    Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Lango Allen H, Lindgren CM, Luan J, Mägi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segrè AV, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpeläinen TO, Yang J, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti-Proença C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L, Connell J, Day IN, den Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grässler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jørgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, König IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaløy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimäki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN, Ludwig B, MAGIC, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O'Donnell CJ, O'Rahilly S, Ong KK, Oostra B, Paré G, Parker AN, Perola M, Pichler I, Pietiläinen KH, Platou CG, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstråle M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich TM, Thompson JR, Thomson B, Tönjes A, Tuomi T, van Meurs JB, van Ommen GJ, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CI, Voight BF, Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL, Kähönen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J, Froguel P, Grönberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Procardis Consortium, Wilson JF, Wright AF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E and Loos RJ

    Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.

    Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

    Funded by: British Heart Foundation; Cancer Research UK; Chief Scientist Office: CZB/4/710; Department of Health; Medical Research Council: G0000934, G0401527, G0501184, G0600705, G0601261, G0701863, G0801056, G0900554, G9521010, G9824984, MC_QA137934, MC_U106179471, MC_U106179472, MC_U106188470, MC_U127561128, MC_U137686854; NCI NIH HHS: CA047988, CA49449, CA50385, CA65725, CA67262, CA87969, U01-CA098233; NCRR NIH HHS: M01-RR00425, U54-RR020278, UL1-RR025005; NHGRI NIH HHS: HG002651, N01-HG-65403, T32 HG000040, T32 HG000040-17, T32-HG00040, U01-HG004399, U01-HG004402, Z01-HG000024; NHLBI NIH HHS: HL084729, HL71981, K99-HL094535, N01-HC15103, N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC55018, N01-HC55019, N01-HC55020, N01-HC55022, N01-HC55222, N01-HC75150, N01-HC85079, N01-HC85080, N01-HC85081, N01-HC85082, N01-HC85083, N01-HC85084, N01-HC85085, N01-HC85086, N01-N01HC-55021, N02-HL64278, R01 HL071981, R01 HL087647, R01-HL086694, R01-HL087641, R01-HL087647, R01-HL087652, R01-HL087676, R01-HL087679, R01-HL087700, R01-HL088119, R01-HL59367, U01 HL054527, U01-HL080295, U01-HL084756, U01-HL72515; NIA NIH HHS: N01-AG12100, N01-AG12109, R01-AG031890; NIAAA NIH HHS: AA014041, AA07535, AA10248, AA13320, AA13321, AA13326, K05 AA017688; NIAMS NIH HHS: K08 AR055688, K08 AR055688-03, K08 AR055688-04; NIDA NIH HHS: DA12854, R01 DA012854; NIDDK NIH HHS: DK062370, DK063491, DK072193, DK46200, DK58845, F32 DK079466, F32 DK079466-01, K23 DK080145, K23 DK080145-01, K23-DK080145, P30-DK072488, R01 DK072193, R01 DK072193-05, R01-DK073490, R01-DK075787, R01DK068336, R01DK075681, U01 DK062370, U01 DK062370-08, U01-DK062418; NIGMS NIH HHS: T32 GM074905, U01-GM074518; NIMH NIH HHS: MH084698, R01-MH59160, R01-MH59565, R01-MH59566, R01-MH59571, R01-MH59586, R01-MH59587, R01-MH59588, R01-MH60870, R01-MH60879, R01-MH61675, R01-MH63706, R01-MH67257, R01-MH79469, R01-MH79470, R01-MH81800, RL1-MH083268; PHS HHS: 263-MA-410953; Wellcome Trust: 064890, 068545, 072960, 075491, 076113, 077016, 079557, 079895, 081682, 083270, 085301, 086596

    Nature genetics 2010;42;11;937-48

  • New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

    Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Mägi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JR, Egan JM, Lajunen T, Grarup N, Sparsø T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proença C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day IN, de Geus EJ, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S, Hallmans G, Hammond N, Han X, Hartikainen AL, Hassanali N, Hayward C, Heath SC, Hercberg S, Herder C, Hicks AA, Hillman DR, Hingorani AD, Hofman A, Hui J, Hung J, Isomaa B, Johnson PR, Jørgensen T, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Lyssenko V, Mahley R, Mangino M, Manning AK, Martínez-Larrad MT, McAteer JB, McCulloch LJ, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, Narisu N, Neville MJ, Oostra BA, Orrù M, Pakyz R, Palmer CN, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, Seedorf U, Sharp SJ, Shields B, Sigurethsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Thorand B, Tichet J, Tönjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, DIAGRAM Consortium, GIANT Consortium, Global BPgen Consortium, Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Hattersley AT, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Ríos M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WH, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF, Anders Hamsten on behalf of Procardis Consortium, MAGIC investigators, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC and Barroso I

    Department of Biostatistics, Boston University School of Public Health, Massachusetts, USA.

    Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

    Funded by: Chief Scientist Office: CZB/4/710; Medical Research Council: G0600705, G0601261, G0700222, G0700222(81696), G0701863, G0801056, G19/35, MC_U106179471, MC_U106188470, MC_U127561128, MC_U127592696, MC_U137686854, MC_UP_A620_1014, MC_UP_A620_1015; NIDDK NIH HHS: K24 DK080140, P30 DK040561, P30 DK040561-14, P30 DK072488, R01 DK029867, R01 DK072193, R01 DK078616, R01 DK078616-01A1; The Dunhill Medical Trust: R69/0208; Wellcome Trust: 064890, 077011, 077016, 081682, 088885, 089061, 091746

    Nature genetics 2010;42;2;105-16

  • Population-specific risk of type 2 diabetes conferred by HNF4A P2 promoter variants: a lesson for replication studies.

    Barroso I, Luan J, Wheeler E, Whittaker P, Wasson J, Zeggini E, Weedon MN, Hunt S, Venkatesh R, Frayling TM, Delgado M, Neuman RJ, Zhao J, Sherva R, Glaser B, Walker M, Hitman G, McCarthy MI, Hattersley AT, Permutt MA, Wareham NJ and Deloukas P

    Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ib1@sanger.ac.uk

    Objective: Single nucleotide polymorphisms (SNPs) in the P2 promoter region of HNF4A were originally shown to be associated with predisposition for type 2 diabetes in Finnish, Ashkenazi, and, more recently, Scandinavian populations, but they generated conflicting results in additional populations. We aimed to investigate whether data from a large-scale mapping approach would replicate this association in nov