Analytical genomics of complex traits

The Analytical genomics of complex traits group, led by Eleftheria Zeggini, aims to help identify the genetic determinants of complex human traits by using next-generation association studies to detect novel disease loci.

The overarching goal of our research is to elucidate the aetiopathological underpinnings of complex human disease. We carry out large-scale studies to investigate the genetic architecture of complex traits, with a primary focus on cardiometabolic and musculoskeletal phenotypes. In doing so, we identify and address statistical genetics challenges by designing, evaluating and proposing analytical strategies.

[Genome Research Limited]

Background

Advances in high-throughput genotyping and sequencing, coupled with the availability of large sample sets and a better understanding of human genome sequence variation, have made next-generation genetic studies feasible. It is widely accepted that, in the area of complex trait association studies, technology is in danger of outstripping our capacity to analyse and interpret the results obtained. The Applied Statistical Genetics group conducts next-generation association studies for complex phenotypes, such as type 2 diabetes, obesity and related metabolic traits, and develops appropriate robust methodologies to analyse and interpret the data where necessary.

We aim to:

  • identify complex disease loci by carrying out well-powered association studies; and
  • develop, extend and make publicly available analytical tools to achieve this

Team members

  • Graham Ritchie
  • Kalliope Panoutsopoulou
  • Laura Huckins
  • Arthur Gilly
  • Chris Finan
  • Will Rayner
  • Loz Southam
  • Ioanna Tachmazidou
  • Konstantinos Hatzikotoulas
  • Angela Matchan
  • Jennifer Pararajasingham
  • Audrey Hendricks
  • Louise Atkin
  • Eleftheria Zeggini
  • Our group is supported by Cerise Bradford and Danielle Walker (Research Administrator)

Research

Our group conducts next generation genetic association studies in order to identify complex disease loci, and establishes robust analytical strategies to achieve this. We study the role of common, low frequency and rare sequence variants using different approaches. For example, we explore powerful ways to make use of the genetic homogeneity that characterises population isolates in order to identify low frequency variant associations. We are also conducting studies to enhance our understanding of the allelic architecture and genetic heterogeneity attributes that underlie populations of African descent, for complex trait signal fine mapping and de novo discovery.

Representative list of ongoing projects:

Next-generation association studies

We are centrally involved in whole-genome and whole-exome sequencing studies for complex traits, including obesity, type 2 diabetes, and multiple cardiometabolic, anthropometric and related traits (UK10K, 500 exomes, and HELIC studies).

Population isolates

Isolates benefit from population genetics characteristics that can be advantageous in enhancing power to identify low frequency and rare variant associations. We are actively putting together well-characterised sample collections from two population isolates in Greece (HELIC study). All individuals have extensive phenotypic data across a variety of cardiometabolic and anthropometric traits of interest.

Trans-ethnic studies

The majority of genetic studies to date have focused on populations of European descent. African populations are more genetically heterogeneous and have lower levels of linkage disequilibrium. To facilitate GWAS design in African populations, we are undertaking a pilot genotyping project through which we aim to characterise patterns of genetic variation and haplotype structure across different African ethnic groups. We are also involved in trans-ethnic fine-mapping studies for type 2 diabetes (GDC), combining data across case-control studies in individuals with European, East Asian and African descent.

Methodological work

Our primary methodological focus is on developing methods for the analysis of low frequency and rare variants (for example, QuTie, CCRaVAT, ARIEL and AMELIA software). We also work on building a functional annotation meta-predictor, which can be incorporated within whole-exome and whole-genome data association analyses to increase power.

GWAS

We lead on GWAS (primarily carried out in outbred European populations) for a variety of complex phenotypes, including anorexia nervosa (WTCCC3), eating disorder-related traits, autosomal differences between males and females, brachial circumference, type 2 diabetes (DIAGRAM), juvenile idiopathic arthritis (INCHARGE), osteoarthritis and related endophenotypes (arcOGEN, TreatOA), osteolysis, and hair colour.

Collaborations

We are part of a wide collaborative network and are actively involved in several national and international consortia, including:

  • 1000 Genomes Project
  • 10001 Dalmatians
  • arcOGEN
  • DIAGRAM: Diabetes Genetics Replication and Meta-analysis consortium
  • EGG: Early Growth Genetics consortium
  • ENDGAME: Enhancing Development of Genome-wide Association Methods
  • ENGAGE
  • GCAN: Genetics Consortium for Anorexia Nervosa
  • GIANT: Genome-wide International ANThropometrics consortium
  • GlobalBPGen: Global Blood Pressure Genetics consortium
  • GDC: Global Diabetes Consortium
  • GOMAP: Genetic Overlap between Metabolic And Psychiatric diseases
  • HELIC: HELlenic Isolated Cohorts
  • INCHARGE: INternational CHildhood ARthritis GEnetics consortium
  • International T2D 1q Consortium
  • MAGIC: Meta-analysis of Glucose and Insulin traits Consortium
  • MANOLIS: Minoan Isolates
  • QTGEN: QT interval GENetics consortium
  • TreatOA
  • UK10K
  • UK Exome Chip consortium
  • UKRAG: UK Rheumatoid Arthritis Genetics consortium
  • UKT2DGC: UK Type 2 Diabetes Genetics Consortium
  • WTCCC: Wellcome Trust Case Control Consortium
  • WTCCC+
  • WTCCC2
  • WTCCC3

Selected Publications

  • A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates.

    Tachmazidou I, Dedoussis G, Southam L, Farmaki AE, Ritchie GR, Xifara DK, Matchan A, Hatzikotoulas K, Rayner NW, Chen Y, Pollin TI, O'Connell JR, Yerges-Armstrong LM, Kiagiadaki C, Panoutsopoulou K, Schwartzentruber J, Moutsianas L, UK10K consortium, Tsafantakis E, Tyler-Smith C, McVean G, Xue Y and Zeggini E

    Nature communications 2013;4;2872

  • 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

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

  • Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genome-wide association study.

    arcOGEN Consortium, arcOGEN Collaborators, Zeggini E, Panoutsopoulou K, Southam L, Rayner NW, Day-Williams AG, Lopes MC, Boraska V, Esko T, Evangelou E, Hoffman A, Houwing-Duistermaat JJ, Ingvarsson T, Jonsdottir I, Jonnson H, Kerkhof HJ, Kloppenburg M, Bos SD, Mangino M, Metrustry S, Slagboom PE, Thorleifsson G, Raine EV, Ratnayake M, Ricketts M, Beazley C, Blackburn H, Bumpstead S, Elliott KS, Hunt SE, Potter SC, Shin SY, Yadav VK, Zhai G, Sherburn K, Dixon K, Arden E, Aslam N, Battley PK, Carluke I, Doherty S, Gordon A, Joseph J, Keen R, Koller NC, Mitchell S, O'Neill F, Paling E, Reed MR, Rivadeneira F, Swift D, Walker K, Watkins B, Wheeler M, Birrell F, Ioannidis JP, Meulenbelt I, Metspalu A, Rai A, Salter D, Stefansson K, Stykarsdottir U, Uitterlinden AG, van Meurs JB, Chapman K, Deloukas P, Ollier WE, Wallis GA, Arden N, Carr A, Doherty M, McCaskie A, Willkinson JM, Ralston SH, Valdes AM, Spector TD and Loughlin J

    Lancet 2012;380;9844;815-23

  • A variant in MCF2L is associated with osteoarthritis.

    Day-Williams AG, Southam L, Panoutsopoulou K, Rayner NW, Esko T, Estrada K, Helgadottir HT, Hofman A, Ingvarsson T, Jonsson H, Keis A, Kerkhof HJ, Thorleifsson G, Arden NK, Carr A, Chapman K, Deloukas P, Loughlin J, McCaskie A, Ollier WE, Ralston SH, Spector TD, Wallis GA, Wilkinson JM, Aslam N, Birell F, Carluke I, Joseph J, Rai A, Reed M, Walker K, arcOGEN Consortium, Doherty SA, Jonsdottir I, Maciewicz RA, Muir KR, Metspalu A, Rivadeneira F, Stefansson K, Styrkarsdottir U, Uitterlinden AG, van Meurs JB, Zhang W, Valdes AM, Doherty M and Zeggini E

    American journal of human genetics 2011;89;3;446-50

  • Next-generation association studies for complex traits.

    Zeggini E

    Nature genetics 2011;43;4;287-8

  • Rare variant association analysis methods for complex traits.

    Asimit J and Zeggini E

    Annual review of genetics 2010;44;293-308

  • The effect of genome-wide association scan quality control on imputation outcome for common variants.

    Southam L, Panoutsopoulou K, Rayner NW, Chapman K, Durrant C, Ferreira T, Arden N, Carr A, Deloukas P, Doherty M, Loughlin J, McCaskie A, Ollier WE, Ralston S, Spector TD, Valdes AM, Wallis GA, Wilkinson JM, arcOGEN consortium, Marchini J and Zeggini E

    European journal of human genetics : EJHG 2011;19;5;610-4

  • The effect of next-generation sequencing technology on complex trait research.

    Day-Williams AG and Zeggini E

    European journal of clinical investigation 2011;41;5;561-7

  • Synthetic associations in the context of genome-wide association scan signals.

    Orozco G, Barrett JC and Zeggini E

    Human molecular genetics 2010;19;R2;R137-44

  • 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

    Nature genetics 2010;42;7;579-89

  • An evaluation of statistical approaches to rare variant analysis in genetic association studies.

    Morris AP and Zeggini E

    Genetic epidemiology 2010;34;2;188-93

  • Rare variant association analysis methods for complex traits.

    Asimit J and Zeggini E

    Annual review of genetics 2010;44;293-308

  • 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

    Nature genetics 2008;40;5;638-45

Team

Team members

Chris Finan
Postdoctoral Fellow
Arthur Gilly
Statistical Geneticist
Laura Huckins
PhD Student
Angela Matchan
am26@sanger.ac.ukSenior Bioinformatician
Kalliope Panoutsopoulou
Career Development Fellow
Loz Southam
Senior Staff Scientist
Ioanna Tachmazidou
it3@sanger.ac.ukStatistical Geneticist

Chris Finan

- Postdoctoral Fellow

I graduated from the University of Wales, Aberystwyth in 1998 with a BSc in genetics and biochemistry. I stayed in Wales to complete a PhD investigating resuscitation promoting factors in the bacterium Streptomyces coelicolor. Following my PhD, I had a brief stint in industry before joining Brighton and Sussex Medical School to map genes involved in cytokine responses. I left Brighton in 2011 and joined UCL where I developed a system to use genetic data to inform drug development pipelines. I joined Sanger in 2013 to research the genetic basis of a neglected tropic disease called Podoconiosis.

Research

My principal role at the Sanger is to carry out a genome wide association study in a disease called Podoconiosis. Podoconiosis is a non-filarial elephantiasis of the lower leg that is triggered by exposure to volcanic soils. However, it also has a genetic component and previous studies implicated the HLA region in the pathogenesis of the disease.

I am also involved in whole genome sequencing of samples for the Genome Diversity in Africa project.

References

  • 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, Wellcome Trust Case Control Consortium 3, Estivill X, Hinney A, Sullivan PF, Collier DA, Zeggini E, Bulik CM and Wellcome Trust Case Control Consortium 3

    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.

    Funded by: Wellcome Trust: 090532

    Molecular psychiatry 2014;19;10;1085-94

  • Genetic variants at chromosome 9p21 and risk of first versus subsequent coronary heart disease events: a systematic review and meta-analysis.

    Patel RS, Asselbergs FW, Quyyumi AA, Palmer TM, Finan CI, Tragante V, Deanfield J, Hemingway H, Hingorani AD and Holmes MV

    Department of Epidemiology and Public Health, University College London, London, United Kingdom; Department of Cardiology, The Heart Hospital, University College London NHS Trust, London, United Kingdom; Genetic Epidemiology Group, Department of Epidemiology and Public Health, Institute of Cardiovascular Science, University College London, London, United Kingdom. Electronic address: riyaz.patel@ucl.ac.uk.

    Objectives: The purpose of this analysis was to compare the association between variants at the chromosome 9p21 locus (Ch9p21) and risk of first versus subsequent coronary heart disease (CHD) events through systematic review and meta-analysis.

    Background: Ch9p21 is a recognized risk factor for a first CHD event. However, its association with risk of subsequent events in patients with established CHD is less clear.

    Methods: We searched PubMed and EMBASE for prospective studies reporting association of Ch9p21 with incident CHD events and extracted information on cohort type (individuals without prior CHD or individuals with established CHD) and effect estimates for risk of events.

    Results: We identified 31 cohorts reporting on 193,372 individuals. Among the 16 cohorts of individuals without prior CHD (n = 168,209), there were 15,664 first CHD events. Ch9p21 was associated with a pooled hazard ratio (HR) of a first event of 1.19 (95% confidence interval: 1.17 to 1.22) per risk allele. In individuals with established CHD (n = 25,163), there were 4,436 subsequent events providing >99% and 91% power to detect a per-allele HR of 1.19 or 1.10, respectively. The pooled HR for subsequent events was 1.01 (95% confidence interval: 0.97 to 1.06) per risk allele. There was strong evidence of heterogeneity between the effect estimates for first and subsequent events (p value for heterogeneity = 5.6 × 10(-11)). We found no evidence for biases to account for these findings.

    Conclusions: Ch9p21 shows differential association with risk of first versus subsequent CHD events. This has implications for genetic risk prediction in patients with established CHD and for mechanistic understanding of how Ch9p21 influences risk of CHD.

    Journal of the American College of Cardiology 2014;63;21;2234-45

  • Mendelian randomization of blood lipids for coronary heart disease.

    Holmes MV, Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP, Dale CE, Padmanabhan S, Finan C, Swerdlow DI, Tragante V, van Iperen EP, Sivapalaratnam S, Shah S, Elbers CC, Shah T, Engmann J, Giambartolomei C, White J, Zabaneh D, Sofat R, McLachlan S, on behalf of the UCLEB consortium, Doevendans PA, Balmforth AJ, Hall AS, North KE, Almoguera B, Hoogeveen RC, Cushman M, Fornage M, Patel SR, Redline S, Siscovick DS, Tsai MY, Karczewski KJ, Hofker MH, Verschuren WM, Bots ML, van der Schouw YT, Melander O, Dominiczak AF, Morris R, Ben-Shlomo Y, Price J, Kumari M, Baumert J, Peters A, Thorand B, Koenig W, Gaunt TR, Humphries SE, Clarke R, Watkins H, Farrall M, Wilson JG, Rich SS, de Bakker PI, Lange LA, Davey Smith G, Reiner AP, Talmud PJ, Kivimäki M, Lawlor DA, Dudbridge F, Samani NJ, Keating BJ, Hingorani AD and Casas JP

    Genetic Epidemiology Group, Institute of Cardiovacular Science, Faculty of Population Healh Sciences, University College London, 1-19 Torrington Place, London WC1E 6BT, UK.

    Aims: To investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization.

    We developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10(-6)); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75).

    Conclusion: The genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain.

    Funded by: Chief Scientist Office: CZB/4/672; Medical Research Council: MR/K013351/1; Wellcome Trust: 090532

    European heart journal 2014

  • Population genomics of cardiometabolic traits: design of the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium.

    Shah T, Engmann J, Dale C, Shah S, White J, Giambartolomei C, McLachlan S, Zabaneh D, Cavadino A, Finan C, Wong A, Amuzu A, Ong K, Gaunt T, Holmes MV, Warren H, Swerdlow DI, Davies TL, Drenos F, Cooper J, Sofat R, Caulfield M, Ebrahim S, Lawlor DA, Talmud PJ, Humphries SE, Power C, Hypponen E, Richards M, Hardy R, Kuh D, Wareham N, Langenberg C, Ben-Shlomo Y, Day IN, Whincup P, Morris R, Strachan MW, Price J, Kumari M, Kivimaki M, Plagnol V, Dudbridge F, Whittaker JC, Casas JP, Hingorani AD and UCLEB Consortium

    Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, United Kingdom.

    Substantial advances have been made in identifying common genetic variants influencing cardiometabolic traits and disease outcomes through genome wide association studies. Nevertheless, gaps in knowledge remain and new questions have arisen regarding the population relevance, mechanisms, and applications for healthcare. Using a new high-resolution custom single nucleotide polymorphism (SNP) array (Metabochip) incorporating dense coverage of genomic regions linked to cardiometabolic disease, the University College-London School-Edinburgh-Bristol (UCLEB) consortium of highly-phenotyped population-based prospective studies, aims to: (1) fine map functionally relevant SNPs; (2) precisely estimate individual absolute and population attributable risks based on individual SNPs and their combination; (3) investigate mechanisms leading to altered risk factor profiles and CVD events; and (4) use Mendelian randomisation to undertake studies of the causal role in CVD of a range of cardiovascular biomarkers to inform public health policy and help develop new preventative therapies.

    Funded by: AHRQ HHS: HS06516; British Heart Foundation: PG/11/63/29011, RG/04/003, RG/07/008/23674, RG/08/008/25291, RG/08/013/25942, RG/10/12/28456, RG/97006, RG/98002; Chief Scientist Office: CZB/4/672, CZQ/1/38, K/OPR/2/2/D320; Medical Research Council: G0000934, G0500877, G0600237, G0801414, G0802432, G0902037, G1000718, G9521010, MC_U106179472, MC_U123092720, MC_U123092721; NHLBI NIH HHS: 5R01 HL036310, HL33014; NIA NIH HHS: 5R01 AG13196, AG1764406S1; Wellcome Trust: 057762, 068545/Z/02

    PloS one 2013;8;8;e71345

  • HLA class II locus and susceptibility to podoconiosis.

    Tekola Ayele F, Adeyemo A, Finan C, Hailu E, Sinnott P, Burlinson ND, Aseffa A, Rotimi CN, Newport MJ and Davey G

    Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892-5635, USA. ayeleft@mail.nih.gov

    Background: Podoconiosis is a tropical lymphedema resulting from long-term barefoot exposure to red-clay soil derived from volcanic rock. The World Health Organization recently designated it as a neglected tropical disease. Podoconiosis develops in only a subgroup of exposed people, and studies have shown familial clustering with high heritability (63%).

    Methods: We conducted a genomewide association study of 194 case patients and 203 controls from southern Ethiopia. Findings were validated by means of family-based association testing in 202 family trios and HLA typing in 94 case patients and 94 controls.

    Results: We found a genomewide significant association of podoconiosis with the single-nucleotide polymorphism (SNP) rs17612858, located 5.8 kb from the HLA-DQA1 locus (in the allelic model: odds ratio, 2.44; 95% confidence interval [CI], 1.82 to 3.26; P=1.42×10(-9); and in the additive model: odds ratio, 2.19; 95% CI, 1.66 to 2.90; P=3.44×10(-8)), and suggestive associations (P<1.0×10(-5)) with seven other SNPs in or near HLA-DQB1, HLA-DQA1, and HLA-DRB1. We confirmed these associations using family-based association testing. HLA typing showed the alleles HLA-DRB1*0701 (odds ratio, 2.00), DQA1*0201 (odds ratio, 1.91), and DQB1*0202 (odds ratio, 1.79) and the HLA-DRB1*0701-DQB1*0202 haplotype (odds ratio, 1.92) were risk variants for podoconiosis.

    Conclusions: Association between variants in HLA class II loci with podoconiosis (a noncommunicable disease) suggests that the condition may be a T-cell-mediated inflammatory disease and is a model for gene-environment interactions that may be relevant to other complex genetic disorders. (Funded by the Wellcome Trust and others.).

    Funded by: NHGRI NIH HHS: Z99 HG999999; Wellcome Trust: 079791

    The New England journal of medicine 2012;366;13;1200-8

  • Prediction of HLA class II alleles using SNPs in an African population.

    Tekola Ayele F, Ayele FT, Hailu E, Finan C, Aseffa A, Davey G, Newport MJ, Rotimi CN and Adeyemo A

    Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America. ayeleft@mail.nih.gov

    Background: Despite the importance of the human leukocyte antigen (HLA) gene locus in research and clinical practice, direct HLA typing is laborious and expensive. Furthermore, the analysis requires specialized software and expertise which are unavailable in most developing country settings. Recently, in silico methods have been developed for predicting HLA alleles using single nucleotide polymorphisms (SNPs). However, the utility of these methods in African populations has not been systematically evaluated.

    In the present study, we investigate prediction of HLA class II (HLA-DRB1 and HLA-DQB1) alleles using SNPs in the Wolaita population, southern Ethiopia. The subjects comprised 297 Ethiopians with genome-wide SNP data, of whom 188 had also been HLA typed and were used for training and testing the model. The 109 subjects with SNP data alone were used for empirical prediction using the multi-allelic gene prediction method. We evaluated accuracy of the prediction, agreement between predicted and HLA typed alleles, and discriminative ability of the prediction probability supplied by the model. We found that the model predicted intermediate (two-digit) resolution for HLA-DRB1 and HLA-DQB1 alleles at accuracy levels of 96% and 87%, respectively. All measures of performance showed high accuracy and reliability for prediction. The distribution of the majority of HLA alleles in the study was similar to that previously reported for the Oromo and Amhara ethnic groups from Ethiopia.

    We demonstrate that HLA class II alleles can be predicted from SNP genotype data with a high level of accuracy at intermediate (two-digit) resolution in an African population. This finding offers new opportunities for HLA studies of disease epidemiology and population genetics in developing countries.

    Funded by: Wellcome Trust: 079791

    PloS one 2012;7;6;e40206

  • Genome-wide association studies and susceptibility to infectious diseases.

    Newport MJ and Finan C

    Infectious Diseases and Global Health at Brighton and Sussex Medical School, UK. m.j.newport@bsms.ac.uk

    Progress in genomics and the associated technological, statistical and bioinformatics advances have facilitated the successful implementation of genome-wide association studies (GWAS) towards understanding the genetic basis of common diseases. Infectious diseases contribute significantly to the global burden of disease and there is robust epidemiological evidence that host genetic factors are important determinants of the outcome of interactions between host and pathogen. Indeed, infectious diseases have exerted profound selective pressure on human evolution. However, the application of GWAS to infectious diseases has been relatively limited compared with non-communicable diseases. Here we review GWAS findings for important infectious diseases, including malaria, tuberculosis and HIV. We highlight some of the pitfalls recognized more generally for GWAS, as well as issues specific to infection, including the role of the pathogen which also has a genome. We also discuss the challenges encountered when studying African populations which are genetically more ancient and more diverse that other populations and disproportionately bear the main global burden of serious infectious diseases.

    Funded by: Wellcome Trust

    Briefings in functional genomics 2011;10;2;98-107

  • Natural variation in immune responses to neonatal Mycobacterium bovis Bacillus Calmette-Guerin (BCG) Vaccination in a Cohort of Gambian infants.

    Finan C, Ota MO, Marchant A and Newport MJ

    Department of Medicine, Brighton and Sussex Medical School, Falmer, Sussex, United Kingdom.

    Background: There is a need for new vaccines for tuberculosis (TB) that protect against adult pulmonary disease in regions where BCG is not effective. However, BCG could remain integral to TB control programmes because neonatal BCG protects against disseminated forms of childhood TB and many new vaccines rely on BCG to prime immunity or are recombinant strains of BCG. Interferon-gamma (IFN-gamma) is required for immunity to mycobacteria and used as a marker of immunity when new vaccines are tested. Although BCG is widely given to neonates IFN-gamma responses to BCG in this age group are poorly described. Characterisation of IFN-gamma responses to BCG is required for interpretation of vaccine immunogenicity study data where BCG is part of the vaccination strategy.

    236 healthy Gambian babies were vaccinated with M. bovis BCG at birth. IFN-gamma, interleukin (IL)-5 and IL-13 responses to purified protein derivative (PPD), killed Mycobacterium tuberculosis (KMTB), M. tuberculosis short term culture filtrate (STCF) and M. bovis BCG antigen 85 complex (Ag85) were measured in a whole blood assay two months after vaccination. Cytokine responses varied up to 10 log-fold within this population. The majority of infants (89-98% depending on the antigen) made IFN-gamma responses and there was significant correlation between IFN-gamma responses to the different mycobacterial antigens (Spearman's coefficient ranged from 0.340 to 0.675, p = 10(-6)-10(-22)). IL-13 and IL-5 responses were generally low and there were more non-responders (33-75%) for these cytokines. Nonetheless, significant correlations were observed for IL-13 and IL-5 responses to different mycobacterial antigens

    Cytokine responses to mycobacterial antigens in BCG-vaccinated infants are heterogeneous and there is significant inter-individual variation. Further studies in large populations of infants are required to identify the factors that determine variation in IFN-gamma responses.

    Funded by: Medical Research Council: MC_U190071468; Wellcome Trust: 061147

    PloS one 2008;3;10;e3485

Arthur Gilly

- Statistical Geneticist

I graduated from the Grenoble INP - ENSIMAG School of Engineering with a BSc in Engineering and a MSc in Applied Mathematics. First employed in the financial sector, where I dealt with pricing models of complex derivatives, I turned to bioinformatics in 2012 with a position at the CEA/Genoscope in Evry, France.

I joined the Sanger Institute in 2013.

Research

My research revolves around the statistical analysis of high-throughput, high-dimensional biological data. Within the group, my work mainly focuses on sequence data and the ways it can illuminate our understanding of the aetiology of complex traits.

References

  • Using population isolates in genetic association studies.

    Hatzikotoulas K, Gilly A and Zeggini E

    The use of genetically isolated populations can empower next-generation association studies. In this review, we discuss the advantages of this approach and review study design and analytical considerations of genetic association studies focusing on isolates. We cite successful examples of using population isolates in association studies and outline potential ways forward.

    Briefings in functional genomics 2014;13;5;371-7

  • Mapping the epigenetic basis of complex traits.

    Cortijo S, Wardenaar R, Colomé-Tatché M, Gilly A, Etcheverry M, Labadie K, Caillieux E, Hospital F, Aury JM, Wincker P, Roudier F, Jansen RC, Colot V and Johannes F

    Institut de Biologie de l'Ecole Normale Supérieure, Centre National de la Recherche Scientifique (CNRS), UMR 8197, Institut National de la Santé et de la Recherche Médicale (INSERM) U 1024, Paris F-75005, France.

    Quantifying the impact of heritable epigenetic variation on complex traits is an emerging challenge in population genetics. Here, we analyze a population of isogenic Arabidopsis lines that segregate experimentally induced DNA methylation changes at hundreds of regions across the genome. We demonstrate that several of these differentially methylated regions (DMRs) act as bona fide epigenetic quantitative trait loci (QTL(epi)), accounting for 60 to 90% of the heritability for two complex traits, flowering time and primary root length. These QTL(epi) are reproducible and can be subjected to artificial selection. Many of the experimentally induced DMRs are also variable in natural populations of this species and may thus provide an epigenetic basis for Darwinian evolution independently of DNA sequence changes.

    Science (New York, N.Y.) 2014;343;6175;1145-8

Laura Huckins

- PhD Student

I graduated from Imperial College London in 2011 with an MEng in Biomedical Engineering. My studies covered a broad range of electrical and computational approaches, applied to biological data or clinical challenges. My masters project at Imperial focused on computational neuroscience, and integrated knowledge of programming and statistical theory with investigations into the complex wiring and electrical processes of the cerebral cortex.

Research

My research at the Sanger Institute focuses on Psychiatric Genetics, with a specific focus on the genetics and epigenetics underlying Anorexia Nervosa (AN). I plan to perform a comprehensive study of candidate genes associated with AN, and with diseases showing significant co-morbidity. This study comprises a range of staticstical analyses on next-generation sequencing results, and analysis of mechanistic function in a mouse knock-out model. This project should reveal new genes associated with eating disorders, and provide a functional assessment of the mechanisms and pathways involved.

References

  • Olfaction and olfactory-mediated behaviour in psychiatric disease models.

    Huckins LM, Logan DW and Sánchez-Andrade G

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

    Rats and mice are the most widely used species for modelling psychiatric disease. Assessment of these rodent models typically involves the analysis of aberrant behaviour with behavioural interactions often being manipulated to generate the model. Rodents rely heavily on their excellent sense of smell and almost all their social interactions have a strong olfactory component. Therefore, experimental paradigms that exploit these olfactory-mediated behaviours are among the most robust available and are highly prevalent in psychiatric disease research. These include tests of aggression and maternal instinct, foraging, olfactory memory and habituation and the establishment of social hierarchies. An appreciation of the way that rodents regulate these behaviours in an ethological context can assist experimenters to generate better data from their models and to avoid common pitfalls. We describe some of the more commonly used behavioural paradigms from a rodent olfactory perspective and discuss their application in existing models of psychiatric disease. We introduce the four olfactory subsystems that integrate to mediate the behavioural responses and the types of sensory cue that promote them and discuss their control and practical implementation to improve experimental outcomes. In addition, because smell is critical for normal behaviour in rodents and yet olfactory dysfunction is often associated with neuropsychiatric disease, we introduce some tests for olfactory function that can be applied to rodent models of psychiatric disorders as part of behavioural analysis.

    Cell and tissue research 2013;354;1;69-80

Angela Matchan

am26@sanger.ac.uk Senior Bioinformatician

BSc Biology (specialising in genetics), The University of Sheffield 1999

MSc Applied Bioinformatics, The University of Cranfield 2008

Prior to joining Sanger I worked as an IT Consultant and software programmer mainly in the financial services sector. I moved in to Bioinformatics in 2010 as a microarray (gene expression and miRNA) and sequencing data (NGS) analyst for a private company in Oxfordshire.

Research

I am a Senior Bioinformatician working primarily on the HELIC project but I also provide informatics and data management support to the Zeggini Group, on a number of GWAS and sequencing studies in the area of complex disease research.

References

  • A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates.

    Tachmazidou I, Dedoussis G, Southam L, Farmaki AE, Ritchie GR, Xifara DK, Matchan A, Hatzikotoulas K, Rayner NW, Chen Y, Pollin TI, O'Connell JR, Yerges-Armstrong LM, Kiagiadaki C, Panoutsopoulou K, Schwartzentruber J, Moutsianas L, UK10K consortium, Tsafantakis E, Tyler-Smith C, McVean G, Xue Y and Zeggini E

    Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.

    Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies, but the generalizability of such findings remains unknown. Here we genotype 1,267 individuals from a Greek population isolate on the Illumina HumanExome Beadchip, in search of functional coding variants associated with lipids traits. We find genome-wide significant evidence for association between R19X, a functional variant in APOC3, with increased high-density lipoprotein and decreased triglycerides levels. Approximately 3.8% of individuals are heterozygous for this cardioprotective variant, which was previously thought to be private to the Amish founder population. R19X is rare (<0.05% frequency) in outbred European populations. The increased frequency of R19X enables discovery of this lipid traits signal at genome-wide significance in a small sample size. This work exemplifies the value of isolated populations in successfully detecting transferable rare variant associations of high medical relevance.

    Funded by: NHLBI NIH HHS: K01 HL116770, R01 HL104193, U01 HL072515, U01 HL105198; NIDDK NIH HHS: P30 DK072488; Wellcome Trust: 090532, 098051, WT091310

    Nature communications 2013;4;2872

Kalliope Panoutsopoulou

- Career Development Fellow

2008 - 2010 Postdoctoral Research Fellow, Wellcome Trust Centre for Human Genetics, Oxford, UK & Wellcome Trust Sanger Institute, Hinxton, UK

2002 - 2005 Research Associate, UMIST, Manchester, UK

2002 PhD in Genetics, University of Manchester, UK

1998 BSc (Hons) in Biochemistry and Applied Molecular Biology, UMIST, Manchester, UK

Research

We are part of the arcOGEN Consortium, a UK-wide collaboration and have carried out the largest genome-wide association scan of osteoarthritis. Our research has led to the identification of several novel loci implicated in the disease.

References

  • Examining the overlap between genome-wide rare variant association signals and linkage peaks in rheumatoid arthritis.

    Eyre S, Ke X, Lawrence R, Bowes J, Panoutsopoulou K, Barton A, Thomson W, Worthington J and Zeggini E

    University of Manchester, Manchester, UK.

    Objective: With the exception of the major histocompatibility complex (MHC) and STAT4, no other rheumatoid arthritis (RA) linkage peak has been successfully fine-mapped to date. This apparent failure to identify association under peaks of linkage could be ascribed to the examination of common variation, when linkage is likely to be driven by rare variants. The purpose of this study was to investigate the overlap between genome-wide rare variant RA association signals observed in the Wellcome Trust Case Control Consortium (WTCCC) study and 11 replicating RA linkage peaks, defined as regions with evidence for linkage in >1 study.

    Methods: The WTCCC data set contained 40,482 variants with minor allele frequency of ≤0.05 in 1,860 RA patients and 2,938 controls. Genotypes of all rare variants within a given gene region were collapsed into a single locus and a global P value was calculated per gene.

    Results: The distribution of rare variant signals (association P≤10(-5)) was found to differ significantly between regions with and without linkage evidence (P=2×10(-17) by Fisher's exact test). No significant difference was observed after data from the MHC region were removed or when the effect of the HLA-DRB1 locus was accounted for.

    Conclusion: The results suggest that rare variant association signals are significantly overrepresented under linkage peaks in RA, but the effect is driven by the MHC. This is the first study to examine the overlap between linkage peaks and rare variant association signals genome-wide in a complex disease.

    Funded by: Arthritis Research UK: 17552, 18030; Wellcome Trust: 076113, 079557MA, 088885, WT088885/Z/09/Z

    Arthritis and rheumatism 2011;63;6;1522-6

  • The effect of genome-wide association scan quality control on imputation outcome for common variants.

    Southam L, Panoutsopoulou K, Rayner NW, Chapman K, Durrant C, Ferreira T, Arden N, Carr A, Deloukas P, Doherty M, Loughlin J, McCaskie A, Ollier WE, Ralston S, Spector TD, Valdes AM, Wallis GA, Wilkinson JM, arcOGEN consortium, Marchini J and Zeggini E

    Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

    Imputation is an extremely valuable tool in conducting and synthesising genome-wide association studies (GWASs). Directly typed SNP quality control (QC) is thought to affect imputation quality. It is, therefore, common practise to use quality-controlled (QCed) data as an input for imputing genotypes. This study aims to determine the effect of commonly applied QC steps on imputation outcomes. We performed several iterations of imputing SNPs across chromosome 22 in a dataset consisting of 3177 samples with Illumina 610 k (Illumina, San Diego, CA, USA) GWAS data, applying different QC steps each time. The imputed genotypes were compared with the directly typed genotypes. In addition, we investigated the correlation between alternatively QCed data. We also applied a series of post-imputation QC steps balancing elimination of poorly imputed SNPs and information loss. We found that the difference between the unQCed data and the fully QCed data on imputation outcome was minimal. Our study shows that imputation of common variants is generally very accurate and robust to GWAS QC, which is not a major factor affecting imputation outcome. A minority of common-frequency SNPs with particular properties cannot be accurately imputed regardless of QC stringency. These findings may not generalise to the imputation of low frequency and rare variants.

    Funded by: Arthritis Research UK: 18030; Medical Research Council: G0100594, G0901461; Wellcome Trust: 079557, 088885, 090532, WT079557MA, WT088885/Z/09/Z

    European journal of human genetics : EJHG 2011;19;5;610-4

  • Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study.

    Panoutsopoulou K, Southam L, Elliott KS, Wrayner N, Zhai G, Beazley C, Thorleifsson G, Arden NK, Carr A, Chapman K, Deloukas P, Doherty M, McCaskie A, Ollier WE, Ralston SH, Spector TD, Valdes AM, Wallis GA, Wilkinson JM, Arden E, Battley K, Blackburn H, Blanco FJ, Bumpstead S, Cupples LA, Day-Williams AG, Dixon K, Doherty SA, Esko T, Evangelou E, Felson D, Gomez-Reino JJ, Gonzalez A, Gordon A, Gwilliam R, Halldorsson BV, Hauksson VB, Hofman A, Hunt SE, Ioannidis JP, Ingvarsson T, Jonsdottir I, Jonsson H, Keen R, Kerkhof HJ, Kloppenburg MG, Koller N, Lakenberg N, Lane NE, Lee AT, Metspalu A, Meulenbelt I, Nevitt MC, O'Neill F, Parimi N, Potter SC, Rego-Perez I, Riancho JA, Sherburn K, Slagboom PE, Stefansson K, Styrkarsdottir U, Sumillera M, Swift D, Thorsteinsdottir U, Tsezou A, Uitterlinden AG, van Meurs JB, Watkins B, Wheeler M, Mitchell S, Zhu Y, Zmuda JM, arcOGEN Consortium, Zeggini E and Loughlin J

    Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.

    Objectives: The genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis.

    Methods: The authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44,449 individuals), and de novo in 14 534 independent samples, all of European descent.

    Results: None of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects.

    Conclusions: Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.

    Funded by: Arthritis Research UK: 17489; Medical Research Council: G0901461; NIAMS NIH HHS: K24 AR048841, R01 AR052000

    Annals of the rheumatic diseases 2011;70;5;864-7

  • Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22.

    Evangelou E, Valdes AM, Kerkhof HJ, Styrkarsdottir U, Zhu Y, Meulenbelt I, Lories RJ, Karassa FB, Tylzanowski P, Bos SD, arcOGEN Consortium, Akune T, Arden NK, Carr A, Chapman K, Cupples LA, Dai J, Deloukas P, Doherty M, Doherty S, Engstrom G, Gonzalez A, Halldorsson BV, Hammond CL, Hart DJ, Helgadottir H, Hofman A, Ikegawa S, Ingvarsson T, Jiang Q, Jonsson H, Kaprio J, Kawaguchi H, Kisand K, Kloppenburg M, Kujala UM, Lohmander LS, Loughlin J, Luyten FP, Mabuchi A, McCaskie A, Nakajima M, Nilsson PM, Nishida N, Ollier WE, Panoutsopoulou K, van de Putte T, Ralston SH, Rivadeneira F, Saarela J, Schulte-Merker S, Shi D, Slagboom PE, Sudo A, Tamm A, Tamm A, Thorleifsson G, Thorsteinsdottir U, Tsezou A, Wallis GA, Wilkinson JM, Yoshimura N, Zeggini E, Zhai G, Zhang F, Jonsdottir I, Uitterlinden AG, Felson DT, van Meurs JB, Stefansson K, Ioannidis JP, Spector TD and Translation Research in Europe Applied Technologies for Osteoarthritis (TreatOA)

    Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.

    Objectives: Osteoarthritis (OA) is the most prevalent form of arthritis and accounts for substantial morbidity and disability, particularly in older people. It is characterised by changes in joint structure, including degeneration of the articular cartilage, and its aetiology is multifactorial with a strong postulated genetic component.

    Methods: A meta-analysis was performed of four genome-wide association (GWA) studies of 2371 cases of knee OA and 35 909 controls in Caucasian populations. Replication of the top hits was attempted with data from 10 additional replication datasets.

    Results: With a cumulative sample size of 6709 cases and 44 439 controls, one genome-wide significant locus was identified on chromosome 7q22 for knee OA (rs4730250, p=9.2 × 10⁻⁹), thereby confirming its role as a susceptibility locus for OA.

    Conclusion: The associated signal is located within a large (500 kb) linkage disequilibrium block that contains six genes: PRKAR2B (protein kinase, cAMP-dependent, regulatory, type II, β), HPB1 (HMG-box transcription factor 1), COG5 (component of oligomeric golgi complex 5), GPR22 (G protein-coupled receptor 22), DUS4L (dihydrouridine synthase 4-like) and BCAP29 (B cell receptor-associated protein 29). Gene expression analyses of the (six) genes in primary cells derived from different joint tissues confirmed expression of all the genes in the joint environment.

    Funded by: Arthritis Research UK: 17489, 18030; Medical Research Council: G0000934, G0100594, G0901461; Wellcome Trust: 068545, 083948, 088785, WT079557MA, WT088885/Z/09/Z

    Annals of the rheumatic diseases 2011;70;2;349-55

  • Rare variation at the TNFAIP3 locus and susceptibility to rheumatoid arthritis.

    Bowes J, Lawrence R, Eyre S, Panoutsopoulou K, Orozco G, Elliott KS, Ke X, Morris AP, UKRAG, Thomson W, Worthington J, Barton A and Zeggini E

    Arthritis Research UK, Epidemiology Unit, University of Manchester, Manchester, UK.

    Genome-wide association studies (GWAS) conducted using commercial single nucleotide polymorphisms (SNP) arrays have proven to be a powerful tool for the detection of common disease susceptibility variants. However, their utility for the detection of lower frequency variants is yet to be practically investigated. Here we describe the application of a rare variant collapsing method to a large genome-wide SNP dataset, the Wellcome Trust Case Control Consortium rheumatoid arthritis (RA) GWAS. We partitioned the data into gene-centric bins and collapsed genotypes of low frequency variants (defined here as MAF ≤ 0.05) into a single count coupled with univariate analysis. We then prioritized gene regions for further investigation in an independent cohort of 3,355 cases and 2,427 controls based on rare variant signal p value and prior evidence to support involvement in RA. A total of 14,536 gene bins were investigated in the primary analysis and signals mapping to the TNFAIP3 and chr17q24 loci were selected for further investigation. We detected replicating association to low frequency variants in the TNFAIP3 gene (combined p = 6.6 × 10(-6)). Even though rare variants are not well-represented and can be difficult to genotype in GWAS, our study supports the application of low frequency variant collapsing methods to genome-wide SNP datasets as a means of exploiting data that are routinely ignored.

    Funded by: Arthritis Research UK: 17552, 18475; Wellcome Trust: 064890, 081682

    Human genetics 2010;128;6;627-33

  • Finding common susceptibility variants for complex disease: past, present and future.

    Panoutsopoulou K and Zeggini E

    Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.

    The identification of complex disease susceptibility loci has been accelerated considerably by advances in high-throughput genotyping technologies, improved insight into correlation patterns of common variants and the availability of large-scale sample sets. Linkage scans and small-scale candidate gene studies have now given way to genome-wide association scans. In this review, we summarize insights gained from the past, highlight practical issues relating to the design and analysis of current state-of-the-art GWA studies and look into future trends in the field of human complex trait genetics.

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

    Briefings in functional genomics & proteomics 2009;8;5;345-52

Loz Southam

- Senior Staff Scientist

2001 - 2010 Research Assistant, University of Oxford, Botnar Research Centre, Oxford, UK

2000 - 2001 Research Assistant, University of Oxford, Institute of Molecular Medicine, Oxford, UK

1999 - 2000 Scientific Officer, Medical Research Council, Harwell, Didcot, Oxfordshire, UK

1996 - 1999 Research Assistant, University of Oxford, Wellcome Trust Centre for Human Genetics, Oxford, UK

1995 - 1996 Medical Laboratory Scientific Officer 1, University of Oxford, Wellcome Trust Centre for Human Genetics, Oxford, UK

1992 - 1995 Bsc (Hons) First class in Biomedical Science, Bradford University, Bradford, UK

Research

I form part of the arcOGEN Consortium analysis team aiming to identifying susceptibility loci that give rise to osteoarthritis. I am also involved in the UK10K and 500 exomes studies.

References

  • The effect of genome-wide association scan quality control on imputation outcome for common variants.

    Southam L, Panoutsopoulou K, Rayner NW, Chapman K, Durrant C, Ferreira T, Arden N, Carr A, Deloukas P, Doherty M, Loughlin J, McCaskie A, Ollier WE, Ralston S, Spector TD, Valdes AM, Wallis GA, Wilkinson JM, arcOGEN consortium, Marchini J and Zeggini E

    Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

    Imputation is an extremely valuable tool in conducting and synthesising genome-wide association studies (GWASs). Directly typed SNP quality control (QC) is thought to affect imputation quality. It is, therefore, common practise to use quality-controlled (QCed) data as an input for imputing genotypes. This study aims to determine the effect of commonly applied QC steps on imputation outcomes. We performed several iterations of imputing SNPs across chromosome 22 in a dataset consisting of 3177 samples with Illumina 610 k (Illumina, San Diego, CA, USA) GWAS data, applying different QC steps each time. The imputed genotypes were compared with the directly typed genotypes. In addition, we investigated the correlation between alternatively QCed data. We also applied a series of post-imputation QC steps balancing elimination of poorly imputed SNPs and information loss. We found that the difference between the unQCed data and the fully QCed data on imputation outcome was minimal. Our study shows that imputation of common variants is generally very accurate and robust to GWAS QC, which is not a major factor affecting imputation outcome. A minority of common-frequency SNPs with particular properties cannot be accurately imputed regardless of QC stringency. These findings may not generalise to the imputation of low frequency and rare variants.

    Funded by: Arthritis Research UK: 18030; Medical Research Council: G0100594, G0901461; Wellcome Trust: 079557, 088885, 090532, WT079557MA, WT088885/Z/09/Z

    European journal of human genetics : EJHG 2011;19;5;610-4

  • Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study.

    Panoutsopoulou K, Southam L, Elliott KS, Wrayner N, Zhai G, Beazley C, Thorleifsson G, Arden NK, Carr A, Chapman K, Deloukas P, Doherty M, McCaskie A, Ollier WE, Ralston SH, Spector TD, Valdes AM, Wallis GA, Wilkinson JM, Arden E, Battley K, Blackburn H, Blanco FJ, Bumpstead S, Cupples LA, Day-Williams AG, Dixon K, Doherty SA, Esko T, Evangelou E, Felson D, Gomez-Reino JJ, Gonzalez A, Gordon A, Gwilliam R, Halldorsson BV, Hauksson VB, Hofman A, Hunt SE, Ioannidis JP, Ingvarsson T, Jonsdottir I, Jonsson H, Keen R, Kerkhof HJ, Kloppenburg MG, Koller N, Lakenberg N, Lane NE, Lee AT, Metspalu A, Meulenbelt I, Nevitt MC, O'Neill F, Parimi N, Potter SC, Rego-Perez I, Riancho JA, Sherburn K, Slagboom PE, Stefansson K, Styrkarsdottir U, Sumillera M, Swift D, Thorsteinsdottir U, Tsezou A, Uitterlinden AG, van Meurs JB, Watkins B, Wheeler M, Mitchell S, Zhu Y, Zmuda JM, arcOGEN Consortium, Zeggini E and Loughlin J

    Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.

    Objectives: The genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis.

    Methods: The authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44,449 individuals), and de novo in 14 534 independent samples, all of European descent.

    Results: None of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects.

    Conclusions: Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.

    Funded by: Arthritis Research UK: 17489; Medical Research Council: G0901461; NIAMS NIH HHS: K24 AR048841, R01 AR052000

    Annals of the rheumatic diseases 2011;70;5;864-7

  • Identification and characterization of novel parathyroid-specific transcription factor Glial Cells Missing Homolog B (GCMB) mutations in eight families with autosomal recessive hypoparathyroidism.

    Bowl MR, Mirczuk SM, Grigorieva IV, Piret SE, Cranston T, Southam L, Allgrove J, Bahl S, Brain C, Loughlin J, Mughal Z, Ryan F, Shaw N, Thakker YV, Tiosano D, Nesbit MA and Thakker RV

    Academic Endocrine Unit, Nuffield Department of Clinical Medicine, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford OX3 7LJ, UK.

    GCMB is a member of the small transcription factor family GCM (glial cells missing), which are important regulators of development, present in vertebrates and some invertebrates. In man, GCMB encodes a 506 amino acid parathyroid gland-specific protein, mutations of which have been reported to cause both autosomal dominant and autosomal recessive hypoparathyroidism. We ascertained 18 affected individuals from 12 families with autosomal recessive hypoparathyroidism and have investigated them for GCMB abnormalities. Four different homozygous germline mutations were identified in eight families that originate from the Indian Subcontinent. These consisted of a novel nonsense mutation R39X; a missense mutation, R47L in two families; a novel missense mutation, R110W; and a novel frameshifting deletion, I298fsX307 in four families. Haplotype analysis, using polymorphic microsatellites from chromosome 6p23-24, revealed that R47L and I298fsX307 mutations arose either as ancient founders, or recurrent de novo mutations. Functional studies including: subcellular localization studies, EMSAs and luciferase-reporter assays, were undertaken and these demonstrated that: the R39X mutant failed to localize to the nucleus; the R47L and R110W mutants both lost DNA-binding ability; and the I298fsX307 mutant had reduced transactivational ability. In order to gain further insights, we undertook 3D-modeling of the GCMB DNA-binding domain, which revealed that the R110 residue is likely important for the structural integrity of helix 2, which forms part of the GCMB/DNA binding interface. Thus, our results, which expand the spectrum of hypoparathyroidism-associated GCMB mutations, help elucidate the molecular mechanisms underlying DNA-binding and transactivation that are required for this parathyroid-specific transcription factor.

    Funded by: Medical Research Council: G9825289

    Human molecular genetics 2010;19;10;2028-38

  • 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

  • Functional analysis of the osteoarthritis susceptibility-associated GDF5 regulatory polymorphism.

    Egli RJ, Southam L, Wilkins JM, Lorenzen I, Pombo-Suarez M, Gonzalez A, Carr A, Chapman K and Loughlin J

    RMS Foundation, Bettlach, Switzerland.

    Objective: Single-nucleotide polymorphism (SNP) rs143383 (T to C) in the 5'-untranslated region (5'-UTR) of GDF5 has recently been reported to be associated with osteoarthritis (OA) susceptibility, with lower expression of the risk-associated T allele observed in vitro and in vivo. The in vivo studies were performed on cartilage tissue from OA patients. The present study was undertaken to expand the analysis of the effect of this SNP on GDF5 allelic expression to more joint tissue types, to investigate for cis and trans factors that interact with the SNP, and to examine novel cis-acting GDF5 regulatory polymorphisms.

    Methods: Tissue samples were collected from OA patients undergoing joint replacement of the hip or knee. Nucleic acid was extracted, and, using rs143383 and an assay that discriminates and quantifies allelic expression, the relative amount of GDF5 expression from the T and C alleles was measured. Additional common variants in the GDF5 transcript sequence were interrogated as potential regulatory elements using allelic expression and luciferase reporter assays, and electrophoretic mobility shift assays were used to search for trans factors binding to rs143383.

    Results: We observed a consistent allelic expression imbalance of GDF5 in all tissues tested, implying that the functional effect mediated by rs143383 on GDF5 expression is joint-wide. We identified a second polymorphism, located in the 3'-UTR of GDF5, that influenced allelic expression of the gene independent of rs143383. Finally, we observed differential binding of deformed epidermal autoregulatory factor 1 (DEAF-1) to the 2 alleles of rs143383.

    Conclusion: These findings show that the OA susceptibility mediated by polymorphism in GDF5 is not restricted to cartilage, emphasizing the need to consider the disease as involving the whole joint. The existence of an additional cis-acting regulatory polymorphism highlights the complexity of the regulation of expression of this important OA susceptibility locus. DEAF-1 is a trans-acting factor that merits further investigation as a potential tool for modulating GDF5 expression.

    Funded by: Arthritis Research UK; NIAMS NIH HHS: K24 AR048841, P50 AR060752, P50 AR063043, R01 AR052000

    Arthritis and rheumatism 2009;60;7;2055-64

  • Association of a functional microsatellite within intron 1 of the BMP5 gene with susceptibility to osteoarthritis.

    Wilkins JM, Southam L, Mustafa Z, Chapman K and Loughlin J

    University of Oxford, Institute of Musculoskeletal Sciences, Botnar Research Centre, Nuffield Orthopaedic Centre, Oxford, OX3 7LD, UK. james_wilkins@hms.harvard.edu

    Background: In a previous study carried out by our group, the genotyping of 36 microsatellite markers from within a narrow interval of chromosome 6p12.3-q13 generated evidence for linkage and for association to female hip osteoarthritis (OA), with the most compelling association found for a marker within intron 1 of the bone morphogenetic protein 5 gene (BMP5). In this study, we aimed to further categorize the association of variants within intron 1 of BMP5 with OA through an expanded genetic association study of the intron and subsequent functional analysis of associated polymorphisms.

    Methods: We genotyped 18 common polymorphisms including 8 microsatellites and 9 single nucleotide polymorphisms (SNPs) and 1 insertion/deletion (INDEL) from within highly conserved regions between human and mouse within intron 1 of BMP5. These markers were then tested for association to OA by a two-stage approach in which the polymorphisms were initially genotyped in a case-control cohort comprising 361 individuals with associated polymorphisms (P < or = 0.05) then genotyped in a second case-control cohort comprising 1185 individuals.

    Results: Two BMP5 intron 1 polymorphisms demonstrated association in the combined case-control cohort of 1546 individuals (765 cases and 781 controls): microsatellite D6S1276 (P = 0.018) and SNP rs921126 (P = 0.013). Functional analyses in osteoblastic, chondrocytic, and adipocytic cell lines indicated that allelic variants of D6S1276 have significant effects on the transcriptional activity of the BMP5 promoter in vitro.

    Conclusion: Variability in gene expression of BMP5 may be an important contributor to OA genetic susceptibility.

    Funded by: Arthritis Research UK: 16239

    BMC medical genetics 2009;10;141

  • Genome-wide association scan identifies a prostaglandin-endoperoxide synthase 2 variant involved in risk of knee osteoarthritis.

    Valdes AM, Loughlin J, Timms KM, van Meurs JJ, Southam L, Wilson SG, Doherty S, Lories RJ, Luyten FP, Gutin A, Abkevich V, Ge D, Hofman A, Uitterlinden AG, Hart DJ, Zhang F, Zhai G, Egli RJ, Doherty M, Lanchbury J and Spector TD

    Twin Research Unit, St. Thomas' Hospital Campus, Kings College London School of Medicine, London SE1 7EH, UK. ana.valdes@kcl.ac.uk

    Osteoarthritis (OA), the most prevalent form of arthritis in the elderly, is characterized by the degradation of articular cartilage and has a strong genetic component. Our aim was to identify genetic variants involved in risk of knee OA in women. A pooled genome-wide association scan with the Illumina550 Duo array was performed in 255 controls and 387 cases. Twenty-eight variants with p < 1 x 10(-5) were estimated to have probabilities of being false positives <or=0.5 and were genotyped individually in the original samples and in replication cohorts from the UK and the U.S. (599 and 272 cases, 1530 and 258 controls, respectively). The top seven associations were subsequently tested in samples from the Netherlands (306 cases and 584 controls). rs4140564 on chromosome 1 mapping 5' to both the PTGS2 and PLA2G4A genes was associated with risk of knee OA in all the cohorts studied (overall odds ratio OR(mh) = 1.55 95% C.I. 1.30-1.85, p < 6.9 x 10(-7)). Differential allelic expression analysis of PTGS2 with mRNA extracted from the cartilage of joint-replacement surgery OA patients revealed a significant difference in allelic expression (p < 1.0 x 10(-6)). These results suggest the existence of cis-acting regulatory polymorphisms that are in, or near to, PTGS2 and in modest linkage disequilibrium with rs4140564. Our results and previous studies on the role of the cyclooxygenase 2 enzyme encoded by PTGS2 underscore the importance of this signaling pathway in the pathogenesis of knee OA.

    Funded by: Arthritis Research UK: 17489; Wellcome Trust

    American journal of human genetics 2008;82;6;1231-40

  • A meta-analysis of European and Asian cohorts reveals a global role of a functional SNP in the 5' UTR of GDF5 with osteoarthritis susceptibility.

    Chapman K, Takahashi A, Meulenbelt I, Watson C, Rodriguez-Lopez J, Egli R, Tsezou A, Malizos KN, Kloppenburg M, Shi D, Southam L, van der Breggen R, Donn R, Qin J, Doherty M, Slagboom PE, Wallis G, Kamatani N, Jiang Q, Gonzalez A, Loughlin J and Ikegawa S

    Institute of Musculoskeletal Sciences, Botnar Research Centre, Nuffield Orthopaedic Centre, University of Oxford, Oxford, UK.

    We have performed a meta-analysis combining data for more than 11,000 individuals. It provides compelling evidence for a positive association between a functional single-nucleotide polymorphism (SNP) in the 5'-UTR of GDF5 (+104T/C; rs143383) and osteoarthritis (OA) in European and Asian populations. This SNP has recently been reported to be associated with OA in Japanese and Han Chinese populations. Attempts to replicate this association in European samples have been inconclusive, as no association was found in the case-control cohorts from the UK, Spain and Greece when studied individually. However, the pooled data of UK and Spain found an association of the T-allele with an odds ratio (OR) of 1.10. Although the European studies had adequate power to replicate the original findings from the Japanese cohort (OR = 1.79), these results suggest that the role of the GDF5 polymorphism may not be as strong in Europeans. To clarify whether the European studies were hampered by insufficient power, we combined new data from the UK and the Netherlands with the three published studies of Europe and Asia. The results provide strong evidence of a positive association of the GDF5 SNP with knee OA for Europeans as well as for Asians. The combined association for both ethnic groups is highly significant for the allele frequency model (P = 0.0004, OR = 1.21) and the dominant model (P < 0.0001, OR = 1.48). These findings represent the first highly significant evidence for a risk factor for the development of OA which affects two highly diverse ethnic groups.

    Human molecular genetics 2008;17;10;1497-504

Ioanna Tachmazidou

it3@sanger.ac.uk Statistical Geneticist

I graduated from the Aristotle University of Thessaloniki in Greece in 2002 with a BSc in Mathematics and completed an MSc in Statistics at University College London in 2003. I then undertook a 4-years PhD in Bioinformatics at Imperial College London, in the first year of which I completed an MSc in Bioinformatics. I earned my PhD in Statistical Genetics under the supervision of Dr Maria De Iorio in 2008. Subsequently, I took up a Career Development Fellowship at the Biostatistics Unit of the MRC in Cambridge. I joined the Applied Statistical Genetics group as a staff scientist in 2011.

Research

My research interests are on all aspects of statistical genetics, and in particular in the genetic etiology of common disease. My current work is primarily focused on developing statistical methodology and software for the discovery of disease susceptibility loci. My research interests include: fine-scale mapping, design and analysis of large scale association studies, next generation sequence data and rare variants analysis, multivariate analysis of sequence data, Bayesian survival analysis, Bayesian inference and model selection.

I am involved in a number of international consortia, including the 500 exomes, UK10K, HELIC, and the INCHARGE projects.

References

  • Bayesian semiparametric meta-analysis for genetic association studies.

    De Iorio M, Newcombe PJ, Tachmazidou I, Verzilli CJ and Whittaker JC

    Department of Epidemiology and Biostatistics, Imperial College, London, United Kingdom. m.deiorio@imperial.ac.uk

    We present a Bayesian semiparametric model for the meta-analysis of candidate gene studies with a binary outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping genetic markers in the same genetic region. Meta-analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequilibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta-analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian semiparametric model which models the observed genotype group frequencies conditional to the case/control status and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach allows borrowing of strength across studies and across markers. The analysis is based on a mixture of Dirichlet processes model as the underlying semiparametric model. Full posterior inference is performed through Markov chain Monte Carlo algorithms. The approach is demonstrated on simulated and real data.

    Funded by: Medical Research Council: G0600580

    Genetic epidemiology 2011;35;5;333-40

  • Application of the optimal discovery procedure to genetic case-control studies: comparison with p values and asymptotic Bayes factors.

    Tachmazidou I, De Iorio M and Dudbridge F

    Medical Research Council, Biostatistics Unit, Institute of Public Health, Cambridge, UK. ioanna.tachmazidou@mrc-bsu.cam.ac.uk

    Objectives: The power of genetic association studies is limited by stringent levels of statistical significance. To improve power, Bayes factors (BFs) have been suggested as an alternative measure to the p value, and Storey recently introduced an optimal discovery procedure (ODP) for multiple testing. We aimed to adapt the ODP to genetic case-control studies and to compare its power to p values and asymptotic BFs (ABFs).

    Methods: We propose estimators of the ODP based on prospective and retrospective likelihoods. We performed simulations based on independent common SNPs and on sequence data including rare variants. Effects of causal SNPs were simulated under various distributions of effect size.

    Results: The true ODP is never outperformed, but the estimated ODP has similar power to p values and ABFs. For common SNPs the ODP offers power advantages only in extreme scenarios. However, for rare variants the ODP and ABF detect more associations at low false-positive rates than do p values.

    Conclusions: The ODP can provide higher power than p values for genetic case-control studies of common variants. However, as the ABF has similar power to the ODP and is computed more rapidly, it is our currently preferred method.

    Funded by: Medical Research Council: MC_U105292688, WBSU.1052.00.012.00001.01

    Human heredity 2011;71;1;37-49

  • Bayesian variable selection for survival regression in genetics.

    Tachmazidou I, Johnson MR and De Iorio M

    Medical Research Council, Biostatistics Unit, Cambridge, United Kingdom. ioanna.tachmazidou@mrc-bsu.cam.ac.uk

    Variable selection in regression with very big numbers of variables is challenging both in terms of model specification and computation. We focus on genetic studies in the field of survival, and we present a Bayesian-inspired penalized maximum likelihood approach appropriate for high-dimensional problems. In particular, we employ a simple, efficient algorithm that seeks maximum a posteriori (MAP) estimates of regression coefficients. The latter are assigned a Laplace prior with a sharp mode at zero, and non-zero posterior mode estimates correspond to significant single nucleotide polymorphisms (SNPs). Using the Laplace prior reflects a prior belief that only a small proportion of the SNPs significantly influence the response. The method is fast and can handle datasets arising from imputation or resequencing. We demonstrate the localization performance, power and false-positive rates of our method in large simulation studies of dense-SNP datasets and sequence data, and we compare the performance of our method to the univariate Cox regression and to a recently proposed stochastic search approach. In general, we find that our approach improves localization and power slightly, while the biggest advantage is in false-positive counts and computing times. We also apply our method to a real prospective study, and we observe potential association between candidate ABC transporter genes and epilepsy treatment outcomes.

    Funded by: Medical Research Council: MC_U105292688; Wellcome Trust

    Genetic epidemiology 2010;34;7;689-701

  • Bayesian survival analysis in genetic association studies.

    Tachmazidou I, Andrew T, Verzilli CJ, Johnson MR and De Iorio M

    Department of Epidemiology and Public Health, Imperial College, London, UK. ioanna.tachmazidou@imperial.ac.uk

    Motivation: Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. There are several existing methods in the literature for performing this kind of analysis for case-control studies, but less work has been done for prospective cohort studies. We present a Bayesian method for linking markers to censored survival outcome by clustering haplotypes using gene trees. Coalescent-based approaches are promising for LD mapping, as the coalescent offers a good approximation to the evolutionary history of mutations.

    Results: We compare the performance of the proposed method in simulation studies to the univariate Cox regression and to dimension reduction methods, and we observe that it performs similarly in localizing the causal site, while offering a clear advantage in terms of false positive associations. Moreover, it offers computational advantages. Applying our method to a real prospective study, we observe potential association between candidate ABC transporter genes and epilepsy treatment outcomes.

    Availability: R codes are available upon request.

    Supplementary data are available at Bioinformatics online.

    Funded by: Wellcome Trust

    Bioinformatics (Oxford, England) 2008;24;18;2030-6

  • Genetic association mapping via evolution-based clustering of haplotypes.

    Tachmazidou I, Verzilli CJ and De Iorio M

    Department of Epidemiology and Public Health, Imperial College London, United Kingdom. ioanna.tachmazidou03@ic.ac.uk

    Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype-haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.

    Funded by: Wellcome Trust

    PLoS genetics 2007;3;7;e111

Group leader

Ele's photo Professor Eleftheria Zeggini
Ele's profile

Software

  • AMELIA - allele matching empirical locus-specific integrated association test
  • ARIEL - accumulation of rare variants integrated and extended locus-specific test
  • CAROL - a combined functional annotation score of non-synonymous coding variants
  • CCRaVAT - rare variant case-control analysis tool
  • GLIDERS - HapMap based long-range LD search engine
  • GGSD - open-source, web-based and relational database driven data management software for large-scale genetic studies
  • GWAVA - A functional annotation tool for non-coding sequence variation
  • KATE - a program that analyses the effects of low frequency and rare variants on quantitative traits within a chromosomal region
  • QuTie - rare variant quantitative trait analysis tool
* quick link - http://q.sanger.ac.uk/appstgen