Genomics of quantitative variation

Nicole Soranzo, leader of the Genomics of quantitative trait variation in humans team, is interested in the use of quantitative intermediate traits to unravel novel mechanisms underlying common, complex diseases such as cardiovascular and metabolic diseases.

[Genome Research Limited]

Background

Cardiovascular disease is the leading cause of death in developed countries, principally resulting from coronary artery disease (CAD) and myocardial infarction (MI). Heritability estimates of CAD (38-54%) reflect the complex pathophysiology of this disease, where different genetic and environmental triggers are likely to contribute to distinct clinical sub-phenotypes, the understanding and definition of which can lead to better treatments. Our research combines large-scale genetic analyses, emerging initiatives in stem cell biology and advances in metabolic phenotyping to characterize key processes underlying predisposition to CAD and MI and to provide new opportunities for experimental treatment.

Research

Our approach

Our group uses large-scale genetic association analyses to identify genetic determinant of quantitative cardiometabolic traits in deeply phenotyped population-based cohorts. More recently, we have begun using massively parallel whole-exome and whole-genome sequencing with the aim to investigate the contribution of low-frequency and rare genetic variants.

Ongoing projects

Common and rare genetic determinants of cardiometabolic traits

Within the cohorts component of the Wellcome Trust-funded UK10K project, we are generating low-coverage whole-genome sequence information for 4,000 individuals from the UK. We will carry out associations of these sequence variants with a suite of key cardiometabolic traits, with the aim to explore the role of rare and low-frequency variants. We will also extend these analyses to genetically-isolated populations from Italy (INGI Val Borbera and Friuli Venezia Giulia. In these two populations, we will use sequencing of individuals at low coverage and imputation of sequenced variants into GWAS datasets to investigate the role of rare and common variants with the same traits investigated in the UK10K sample.

Metabolomic genetics

Large numbers of drugs fail in phases 2 and 3 clinical trials due to lack of efficacy, even though the drugs may be effective in a subset of the tested clinical population. It is hoped that decisions about patient stratification will be enhanced by the application of high throughput technologies based on liquid-chromatography mass-spectrometry (LC-MS)-metabolomics. This project is funded by the drug company Pfizer, and aims to explore the use of metabolomics technology to stratify metabolically a healthy UK population. We have extended the metabolomic phenotyping (Suhre et al Nature 2011), and we will analyze genetic associations in this extended dataset, overlying the information to information of complex trait loci, gene expression patterns and variation in underlying clinical phenotypes to address the usefulness of metabolomics for the stratification of the patient population.

Genetic and epigenetic determinants of hematopoiesis

The hematopoietic system provides a good model system to inform interpretation of association studies owing to

  1. simple phenotypes at the cellular level;
  2. nearly unlimited access to suitable tissue with good ability for in vitro manipulation;
  3. suitable model organisms;
  4. widespread clinical relevance.

Our group has led - together with many collaborators - the discovery of nearly ~100 loci affecting variation in blood cell elements through genome-wide association studies. We have further seeked to combine genetic discoveries to a host of integrative analyses and functional approaches, including protein-protein interaction networks, in vitro differentiation of HSCs towards red cell and platelet precursors, and silencing experiments in model organisms (fly, zebrafish and mouse). Our results to date support the notion that the regulation of the formation and survival of blood cells in healthy individuals is mediated through a host of previously unknown regulators, prevalently active in the late stages of lineage commitment, and affecting blood cell formation in a prevalently lineage-specific manner.

As an extension to this work, we now aim to identify and characterize in greater depth genes implicated in hematopoietic development in the EU FP7-funded BLUEPRINT project, which will generate reference genomes and epigenomes of at least 100 specific blood cell types. Our group will be responsible for the genomic (through whole-genome sequencing) and epigenetic characterization of two cell types in 200 individuals, with the aim characterize the role of human variation on the epigenomic landscape.

Selected Publications

  • New gene functions in megakaryopoiesis and platelet formation.

    Gieger C, Radhakrishnan A, Cvejic A, Tang W, Porcu E, Pistis G, Serbanovic-Canic J, Elling U, Goodall AH, Labrune Y, Lopez LM, Mägi R, Meacham S, Okada Y, Pirastu N, Sorice R, Teumer A, Voss K, Zhang W, Ramirez-Solis R, Bis JC, Ellinghaus D, Gögele M, Hottenga JJ, Langenberg C, Kovacs P, O'Reilly PF, Shin SY, Esko T, Hartiala J, Kanoni S, Murgia F, Parsa A, Stephens J, van der Harst P, Ellen van der Schoot C, Allayee H, Attwood A, Balkau B, Bastardot F, Basu S, Baumeister SE, Biino G, Bomba L, Bonnefond A, Cambien F, Chambers JC, Cucca F, D'Adamo P, Davies G, de Boer RA, de Geus EJ, Döring A, Elliott P, Erdmann J, Evans DM, Falchi M, Feng W, Folsom AR, Frazer IH, Gibson QD, Glazer NL, Hammond C, Hartikainen AL, Heckbert SR, Hengstenberg C, Hersch M, Illig T, Loos RJ, Jolley J, Khaw KT, Kühnel B, Kyrtsonis MC, Lagou V, Lloyd-Jones H, Lumley T, Mangino M, Maschio A, Mateo Leach I, McKnight B, Memari Y, Mitchell BD, Montgomery GW, Nakamura Y, Nauck M, Navis G, Nöthlings U, Nolte IM, Porteous DJ, Pouta A, Pramstaller PP, Pullat J, Ring SM, Rotter JI, Ruggiero D, Ruokonen A, Sala C, Samani NJ, Sambrook J, Schlessinger D, Schreiber S, Schunkert H, Scott J, Smith NL, Snieder H, Starr JM, Stumvoll M, Takahashi A, Tang WH, Taylor K, Tenesa A, Lay Thein S, Tönjes A, Uda M, Ulivi S, van Veldhuisen DJ, Visscher PM, Völker U, Wichmann HE, Wiggins KL, Willemsen G, Yang TP, Hua Zhao J, Zitting P, Bradley JR, Dedoussis GV, Gasparini P, Hazen SL, Metspalu A, Pirastu M, Shuldiner AR, Joost van Pelt L, Zwaginga JJ, Boomsma DI, Deary IJ, Franke A, Froguel P, Ganesh SK, Jarvelin MR, Martin NG, Meisinger C, Psaty BM, Spector TD, Wareham NJ, Akkerman JW, Ciullo M, Deloukas P, Greinacher A, Jupe S, Kamatani N, Khadake J, Kooner JS, Penninger J, Prokopenko I, Stemple D, Toniolo D, Wernisch L, Sanna S, Hicks AA, Rendon A, Ferreira MA, Ouwehand WH and Soranzo N

    Nature 2011;480;7376;201-8

  • Human metabolic individuality in biomedical and pharmaceutical research.

    Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D, Wägele B, Altmaier E, CARDIoGRAM, Deloukas P, Erdmann J, Grundberg E, Hammond CJ, de Angelis MH, Kastenmüller G, Köttgen A, Kronenberg F, Mangino M, Meisinger C, Meitinger T, Mewes HW, Milburn MV, Prehn C, Raffler J, Ried JS, Römisch-Margl W, Samani NJ, Small KS, Wichmann HE, Zhai G, Illig T, Spector TD, Adamski J, Soranzo N and Gieger C

    Nature 2011;477;7362;54-60

  • 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

    Diabetes 2010;59;12;3229-39

  • A genome-wide perspective of genetic variation in human metabolism.

    Illig T, Gieger C, Zhai G, Römisch-Margl W, Wang-Sattler R, Prehn C, Altmaier E, Kastenmüller G, Kato BS, Mewes HW, Meitinger T, de Angelis MH, Kronenberg F, Soranzo N, Wichmann HE, Spector TD, Adamski J and Suhre K

    Nature genetics 2010;42;2;137-41

  • 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

    Nature genetics 2010;42;2;105-16

  • A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium.

    Soranzo N, Spector TD, Mangino M, Kühnel B, Rendon A, Teumer A, Willenborg C, Wright B, Chen L, Li M, Salo P, Voight BF, Burns P, Laskowski RA, Xue Y, Menzel S, Altshuler D, Bradley JR, Bumpstead S, Burnett MS, Devaney J, Döring A, Elosua R, Epstein SE, Erber W, Falchi M, Garner SF, Ghori MJ, Goodall AH, Gwilliam R, Hakonarson HH, Hall AS, Hammond N, Hengstenberg C, Illig T, König IR, Knouff CW, McPherson R, Melander O, Mooser V, Nauck M, Nieminen MS, O'Donnell CJ, Peltonen L, Potter SC, Prokisch H, Rader DJ, Rice CM, Roberts R, Salomaa V, Sambrook J, Schreiber S, Schunkert H, Schwartz SM, Serbanovic-Canic J, Sinisalo J, Siscovick DS, Stark K, Surakka I, Stephens J, Thompson JR, Völker U, Völzke H, Watkins NA, Wells GA, Wichmann HE, Van Heel DA, Tyler-Smith C, Thein SL, Kathiresan S, Perola M, Reilly MP, Stewart AF, Erdmann J, Samani NJ, Meisinger C, Greinacher A, Deloukas P, Ouwehand WH and Gieger C

    Nature genetics 2009;41;11;1182-90

  • Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium.

    Ganesh SK, Zakai NA, van Rooij FJ, Soranzo N, Smith AV, Nalls MA, Chen MH, Kottgen A, Glazer NL, Dehghan A, Kuhnel B, Aspelund T, Yang Q, Tanaka T, Jaffe A, Bis JC, Verwoert GC, Teumer A, Fox CS, Guralnik JM, Ehret GB, Rice K, Felix JF, Rendon A, Eiriksdottir G, Levy D, Patel KV, Boerwinkle E, Rotter JI, Hofman A, Sambrook JG, Hernandez DG, Zheng G, Bandinelli S, Singleton AB, Coresh J, Lumley T, Uitterlinden AG, Vangils JM, Launer LJ, Cupples LA, Oostra BA, Zwaginga JJ, Ouwehand WH, Thein SL, Meisinger C, Deloukas P, Nauck M, Spector TD, Gieger C, Gudnason V, van Duijn CM, Psaty BM, Ferrucci L, Chakravarti A, Greinacher A, O'Donnell CJ, Witteman JC, Furth S, Cushman M, Harris TB and Lin JP

    Nature genetics 2009;41;11;1191-8

  • Large scale association analysis of novel genetic loci for coronary artery disease.

    Coronary Artery Disease Consortium, Samani NJ, Deloukas P, Erdmann J, Hengstenberg C, Kuulasmaa K, McGinnis R, Schunkert H, Soranzo N, Thompson J, Tiret L and Ziegler A

    Arteriosclerosis, thrombosis, and vascular biology 2009;29;5;774-80

  • A novel variant on chromosome 7q22.3 associated with mean platelet volume, counts, and function.

    Soranzo N, Rendon A, Gieger C, Jones CI, Watkins NA, Menzel S, Döring A, Stephens J, Prokisch H, Erber W, Potter SC, Bray SL, Burns P, Jolley J, Falchi M, Kühnel B, Erdmann J, Schunkert H, Samani NJ, Illig T, Garner SF, Rankin A, Meisinger C, Bradley JR, Thein SL, Goodall AH, Spector TD, Deloukas P and Ouwehand WH

    Blood 2009;113;16;3831-7

  • 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

    PLoS genetics 2009;5;4;e1000445

  • A genome-wide association study identifies three loci associated with mean platelet volume.

    Meisinger C, Prokisch H, Gieger C, Soranzo N, Mehta D, Rosskopf D, Lichtner P, Klopp N, Stephens J, Watkins NA, Deloukas P, Greinacher A, Koenig W, Nauck M, Rimmbach C, Völzke H, Peters A, Illig T, Ouwehand WH, Meitinger T, Wichmann HE and Döring A

    American journal of human genetics 2009;84;1;66-71

  • 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

    Nature genetics 2009;41;1;77-81

Team

Team members

Nicole Soranzo
Group Leader
Klaudia Walter
kw8@sanger.ac.ukStaff Scientist

Nicole Soranzo

- Group Leader

Nicole graduated in biological sciences at the University of Milano, Italy, with a dissertation on plant population and evolutionary genetics. She later obtained a PhD in genetics from the University of Dundee, and undertook post-doctoral training in human population and statistical genetics at University College London, conducting applied and methodological work in evolutionary genetics and association studies. In 2005 Nicole joined the pharmacogenomics department at Johnson and Johnson Pharmaceutical Research and Development (Raritan, USA). Since 2007 she has been employed at the Wellcome Trust Sanger Institute, and since 2009 she has led her own team.

Research

Our group uses large-scale genetic association analyses to identify genetic determinant of quantitative cardiometabolic traits in deeply phenotyped population-based cohorts. More recently, we have begun using massively parallel whole-exome and whole-genome sequencing with the aim to investigate the contribution of low-frequency and rare genetic variants.

Ongoing projects focus on 1) Common and rare genetic determinants of cardiometabolic traits 2) Metabolomic genetics 3) Genetic and epigenetic determinants of hematopoiesis

References

  • Seventy-five genetic loci influencing the human red blood cell.

    van der Harst P, Zhang W, Mateo Leach I, Rendon A, Verweij N, Sehmi J, Paul DS, Elling U, Allayee H, Li X, Radhakrishnan A, Tan ST, Voss K, Weichenberger CX, Albers CA, Al-Hussani A, Asselbergs FW, Ciullo M, Danjou F, Dina C, Esko T, Evans DM, Franke L, Gögele M, Hartiala J, Hersch M, Holm H, Hottenga JJ, Kanoni S, Kleber ME, Lagou V, Langenberg C, Lopez LM, Lyytikäinen LP, Melander O, Murgia F, Nolte IM, O'Reilly PF, Padmanabhan S, Parsa A, Pirastu N, Porcu E, Portas L, Prokopenko I, Ried JS, Shin SY, Tang CS, Teumer A, Traglia M, Ulivi S, Westra HJ, Yang J, Zhao JH, Anni F, Abdellaoui A, Attwood A, Balkau B, Bandinelli S, Bastardot F, Benyamin B, Boehm BO, Cookson WO, Das D, de Bakker PI, de Boer RA, de Geus EJ, de Moor MH, Dimitriou M, Domingues FS, Döring A, Engström G, Eyjolfsson GI, Ferrucci L, Fischer K, Galanello R, Garner SF, Genser B, Gibson QD, Girotto G, Gudbjartsson DF, Harris SE, Hartikainen AL, Hastie CE, Hedblad B, Illig T, Jolley J, Kähönen M, Kema IP, Kemp JP, Liang L, Lloyd-Jones H, Loos RJ, Meacham S, Medland SE, Meisinger C, Memari Y, Mihailov E, Miller K, Moffatt MF, Nauck M, Novatchkova M, Nutile T, Olafsson I, Onundarson PT, Parracciani D, Penninx BW, Perseu L, Piga A, Pistis G, Pouta A, Puc U, Raitakari O, Ring SM, Robino A, Ruggiero D, Ruokonen A, Saint-Pierre A, Sala C, Salumets A, Sambrook J, Schepers H, Schmidt CO, Silljé HH, Sladek R, Smit JH, Starr JM, Stephens J, Sulem P, Tanaka T, Thorsteinsdottir U, Tragante V, van Gilst WH, van Pelt LJ, van Veldhuisen DJ, Völker U, Whitfield JB, Willemsen G, Winkelmann BR, Wirnsberger G, Algra A, Cucca F, d'Adamo AP, Danesh J, Deary IJ, Dominiczak AF, Elliott P, Fortina P, Froguel P, Gasparini P, Greinacher A, Hazen SL, Jarvelin MR, Khaw KT, Lehtimäki T, Maerz W, Martin NG, Metspalu A, Mitchell BD, Montgomery GW, Moore C, Navis G, Pirastu M, Pramstaller PP, Ramirez-Solis R, Schadt E, Scott J, Shuldiner AR, Smith GD, Smith JG, Snieder H, Sorice R, Spector TD, Stefansson K, Stumvoll M, Tang WH, Toniolo D, Tönjes A, Visscher PM, Vollenweider P, Wareham NJ, Wolffenbuttel BH, Boomsma DI, Beckmann JS, Dedoussis GV, Deloukas P, Ferreira MA, Sanna S, Uda M, Hicks AA, Penninger JM, Gieger C, Kooner JS, Ouwehand WH, Soranzo N and Chambers JC

    Department of Cardiology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands. p.van.der.harst@umcg.nl

    Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

    Funded by: British Heart Foundation: RG/08/014/24067; Chief Scientist Office: CZB/4/505, ETM/55; Medical Research Council: G0600705, G0700704, G0801056, G1000143, G9815508, MC_U106179471, MC_U106188470; NCATS NIH HHS: UL1 TR000439; NCI NIH HHS: R01 CA165001; NCRR NIH HHS: K12 RR023250, U54 RR020278, UL1 RR025005; NHGRI NIH HHS: T32 HG002536, U01 HG004402; NHLBI NIH HHS: HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C, P01 HL076491, P01 HL098055, P20 HL113452, R01 HL059367, R01 HL086694, R01 HL087641, R01 HL087679, R01 HL088119, R01 HL103866, R01 HL103931, U01 HL072515, U01 HL084756; NIA NIH HHS: N01AG12109, R01 AG018728; NICHD NIH HHS: R01 HD042157; NIDA NIH HHS: HHSN271201100005C; NIDDK NIH HHS: P30 DK072488; NIGMS NIH HHS: R01 GM053275, U01 GM074518; NIMH NIH HHS: R01 MH081802, RL1 MH083268, U24 MH068457; NLM NIH HHS: R01 LM010098; Wellcome Trust: 092731, 097117

    Nature 2012;492;7429;369-75

  • New gene functions in megakaryopoiesis and platelet formation.

    Gieger C, Radhakrishnan A, Cvejic A, Tang W, Porcu E, Pistis G, Serbanovic-Canic J, Elling U, Goodall AH, Labrune Y, Lopez LM, Mägi R, Meacham S, Okada Y, Pirastu N, Sorice R, Teumer A, Voss K, Zhang W, Ramirez-Solis R, Bis JC, Ellinghaus D, Gögele M, Hottenga JJ, Langenberg C, Kovacs P, O'Reilly PF, Shin SY, Esko T, Hartiala J, Kanoni S, Murgia F, Parsa A, Stephens J, van der Harst P, Ellen van der Schoot C, Allayee H, Attwood A, Balkau B, Bastardot F, Basu S, Baumeister SE, Biino G, Bomba L, Bonnefond A, Cambien F, Chambers JC, Cucca F, D'Adamo P, Davies G, de Boer RA, de Geus EJ, Döring A, Elliott P, Erdmann J, Evans DM, Falchi M, Feng W, Folsom AR, Frazer IH, Gibson QD, Glazer NL, Hammond C, Hartikainen AL, Heckbert SR, Hengstenberg C, Hersch M, Illig T, Loos RJ, Jolley J, Khaw KT, Kühnel B, Kyrtsonis MC, Lagou V, Lloyd-Jones H, Lumley T, Mangino M, Maschio A, Mateo Leach I, McKnight B, Memari Y, Mitchell BD, Montgomery GW, Nakamura Y, Nauck M, Navis G, Nöthlings U, Nolte IM, Porteous DJ, Pouta A, Pramstaller PP, Pullat J, Ring SM, Rotter JI, Ruggiero D, Ruokonen A, Sala C, Samani NJ, Sambrook J, Schlessinger D, Schreiber S, Schunkert H, Scott J, Smith NL, Snieder H, Starr JM, Stumvoll M, Takahashi A, Tang WH, Taylor K, Tenesa A, Lay Thein S, Tönjes A, Uda M, Ulivi S, van Veldhuisen DJ, Visscher PM, Völker U, Wichmann HE, Wiggins KL, Willemsen G, Yang TP, Hua Zhao J, Zitting P, Bradley JR, Dedoussis GV, Gasparini P, Hazen SL, Metspalu A, Pirastu M, Shuldiner AR, Joost van Pelt L, Zwaginga JJ, Boomsma DI, Deary IJ, Franke A, Froguel P, Ganesh SK, Jarvelin MR, Martin NG, Meisinger C, Psaty BM, Spector TD, Wareham NJ, Akkerman JW, Ciullo M, Deloukas P, Greinacher A, Jupe S, Kamatani N, Khadake J, Kooner JS, Penninger J, Prokopenko I, Stemple D, Toniolo D, Wernisch L, Sanna S, Hicks AA, Rendon A, Ferreira MA, Ouwehand WH and Soranzo N

    Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr 1, 85764 Neuherberg, Germany. christian.gieger@helmholtz-muenchen.de

    Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.

    Funded by: Biotechnology and Biological Sciences Research Council: BB/F019394/1; British Heart Foundation: RG/09/012/28096; Chief Scientist Office: CZB/4/505, ETM/55; Medical Research Council: G0601966, G0700704, G0700931, G0701120, G0701863, G0801056, G1000143, MC_U105260799, MC_U106179471; NCRR NIH HHS: K12 RR023250, K12 RR023250-05, M01 RR016500-08, U54 RR020278-06, UL1 RR025005, UL1 RR025005-05; NHGRI NIH HHS: P41 HG003751, T32 HG002536; NHLBI NIH HHS: N01 HC055015, N01 HC055016, N01 HC055018, N01 HC055019, N01 HC055020, N01 HC055021, N01 HC055022, N01 HC085079, P01 HL076491, P01 HL076491-09, P01 HL098055, P01 HL098055-03, R01 HL059367, R01 HL059367-11, R01 HL068986, R01 HL068986-06, R01 HL073410-08, R01 HL085251, R01 HL085251-04, R01 HL086694, R01 HL086694-05, R01 HL087641, R01 HL087641-03, R01 HL087679-03, R01 HL088119, R01 HL088119-04, R01 HL103866, R01 HL103866-03, R01 HL105756, U01 HL072515, U01 HL072515-06, U01 HL084756, U01 HL084756-03; NIA NIH HHS: R01 AG018728, R01 AG018728-05S1; NICHD NIH HHS: R01 HD042157-01A1; NIDDK NIH HHS: P30 DK072488, P30 DK072488-08; NIGMS NIH HHS: R01 GM053275, R01 GM053275-14, U01 GM074518, U01 GM074518-04; NIMH NIH HHS: RL1 MH083268, RL1 MH083268-05; Wellcome Trust: 092731, 098051, WT077037/Z/05/Z, WT077047/Z/05/Z, WT082597/Z/07/Z

    Nature 2011;480;7376;201-8

  • Silencing of RhoA nucleotide exchange factor, ARHGEF3, reveals its unexpected role in iron uptake.

    Serbanovic-Canic J, Cvejic A, Soranzo N, Stemple DL, Ouwehand WH and Freson K

    Department of Haematology, University of Cambridge and NHS Blood and Transplant, Cambridge, UK.

    Genomewide association meta-analysis studies have identified > 100 independent genetic loci associated with blood cell indices, including volume and count of platelets and erythrocytes. Although several of these loci encode known regulators of hematopoiesis, the mechanism by which most sequence variants exert their effect on blood cell formation remains elusive. An example is the Rho guanine nucleotide exchange factor, ARHGEF3, which was previously implicated by genomewide association meta-analysis studies in bone cell biology. Here, we report on the unexpected role of ARHGEF3 in regulation of iron uptake and erythroid cell maturation. Although early erythroid differentiation progressed normally, silencing of arhgef3 in Danio rerio resulted in microcytic and hypochromic anemia. This was rescued by intracellular supplementation of iron, showing that arhgef3-depleted erythroid cells are fully capable of hemoglobinization. Disruption of the arhgef3 target, RhoA, also produced severe anemia, which was, again, corrected by iron injection. Moreover, silencing of ARHGEF3 in erythromyeloblastoid cells K562 showed that the uptake of transferrin was severely impaired. Taken together, this is the first study to provide evidence for ARHGEF3 being a regulator of transferrin uptake in erythroid cells, through activation of RHOA.

    Funded by: Wellcome Trust: WT 077037/Z/05/Z, WT077047/Z/05/Z, WT082597/Z/07/Z

    Blood 2011;118;18;4967-76

  • Human metabolic individuality in biomedical and pharmaceutical research.

    Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D, Wägele B, Altmaier E, CARDIoGRAM, Deloukas P, Erdmann J, Grundberg E, Hammond CJ, de Angelis MH, Kastenmüller G, Köttgen A, Kronenberg F, Mangino M, Meisinger C, Meitinger T, Mewes HW, Milburn MV, Prehn C, Raffler J, Ried JS, Römisch-Margl W, Samani NJ, Small KS, Wichmann HE, Zhai G, Illig T, Spector TD, Adamski J, Soranzo N and Gieger C

    Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany. karsten@suhre.fr

    Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

    Funded by: Biotechnology and Biological Sciences Research Council; British Heart Foundation; Canadian Institutes of Health Research: MOP172605, MOP77682, MOP‐82810; Cancer Research UK; Medical Research Council; NHLBI NIH HHS: 1R01HL103931‐01, HL087647, N01‐HC‐55015, N01‐HC‐55016, N01‐HC‐55018, N01‐HC‐55019, N01‐HC‐55020, N01‐HC‐55021, N01‐HC‐55022, P01 HL098055, P01HL076491‐06, P01HL087018, R01 HL087647, R01 HL087676, R01HL089650‐02; NIA NIH HHS: N01‐AG‐12100; NIDDK NIH HHS: R01DK080732; Wellcome Trust: 091746, 091746/Z/10/Z

    Nature 2011;477;7362;54-60

  • Maps of open chromatin guide the functional follow-up of genome-wide association signals: application to hematological traits.

    Paul DS, Nisbet JP, Yang TP, Meacham S, Rendon A, Hautaviita K, Tallila J, White J, Tijssen MR, Sivapalaratnam S, Basart H, Trip MD, Cardiogenics Consortium, MuTHER Consortium, Göttgens B, Soranzo N, Ouwehand WH and Deloukas P

    Wellcome Trust Sanger Institute, Hinxton, United Kingdom. dp5@sanger.ac.uk

    Turning genetic discoveries identified in genome-wide association (GWA) studies into biological mechanisms is an important challenge in human genetics. Many GWA signals map outside exons, suggesting that the associated variants may lie within regulatory regions. We applied the formaldehyde-assisted isolation of regulatory elements (FAIRE) method in a megakaryocytic and an erythroblastoid cell line to map active regulatory elements at known loci associated with hematological quantitative traits, coronary artery disease, and myocardial infarction. We showed that the two cell types exhibit distinct patterns of open chromatin and that cell-specific open chromatin can guide the finding of functional variants. We identified an open chromatin region at chromosome 7q22.3 in megakaryocytes but not erythroblasts, which harbors the common non-coding sequence variant rs342293 known to be associated with platelet volume and function. Resequencing of this open chromatin region in 643 individuals provided strong evidence that rs342293 is the only putative causative variant in this region. We demonstrated that the C- and G-alleles differentially bind the transcription factor EVI1 affecting PIK3CG gene expression in platelets and macrophages. A protein-protein interaction network including up- and down-regulated genes in Pik3cg knockout mice indicated that PIK3CG is associated with gene pathways with an established role in platelet membrane biogenesis and thrombus formation. Thus, rs342293 is the functional common variant at this locus; to the best of our knowledge this is the first such variant to be elucidated among the known platelet quantitative trait loci (QTLs). Our data suggested a molecular mechanism by which a non-coding GWA index SNP modulates platelet phenotype.

    Funded by: British Heart Foundation: RG/09/12/28096; Medical Research Council: G0800784, G0900339, MC_U105260799; Wellcome Trust: 081917/Z/07/Z, 091746/Z/10/Z

    PLoS genetics 2011;7;6;e1002139

  • 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

  • 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

  • A novel variant on chromosome 7q22.3 associated with mean platelet volume, counts, and function.

    Soranzo N, Rendon A, Gieger C, Jones CI, Watkins NA, Menzel S, Döring A, Stephens J, Prokisch H, Erber W, Potter SC, Bray SL, Burns P, Jolley J, Falchi M, Kühnel B, Erdmann J, Schunkert H, Samani NJ, Illig T, Garner SF, Rankin A, Meisinger C, Bradley JR, Thein SL, Goodall AH, Spector TD, Deloukas P and Ouwehand WH

    Human Genetics Department, Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom. ns6@sanger.ac.uk

    Mean platelet volume (MPV) and platelet count (PLT) are highly heritable and tightly regulated traits. We performed a genome-wide association study for MPV and identified one SNP, rs342293, as having highly significant and reproducible association with MPV (per-G allele effect 0.016 +/- 0.001 log fL; P < 1.08 x 10(-24)) and PLT (per-G effect -4.55 +/- 0.80 10(9)/L; P < 7.19 x 10(-8)) in 8586 healthy subjects. Whole-genome expression analysis in the 1-MB region showed a significant association with platelet transcript levels for PIK3CG (n = 35; P = .047). The G allele at rs342293 was also associated with decreased binding of annexin V to platelets activated with collagen-related peptide (n = 84; P = .003). The region 7q22.3 identifies the first QTL influencing platelet volume, counts, and function in healthy subjects. Notably, the association signal maps to a chromosome region implicated in myeloid malignancies, indicating this site as an important regulatory site for hematopoiesis. The identification of loci regulating MPV by this and other studies will increase our insight in the processes of megakaryopoiesis and proplatelet formation, and it may aid the identification of genes that are somatically mutated in essential thrombocytosis.

    Funded by: Medical Research Council: G0000111; Wellcome Trust: 072856, 077011, 079771, 082597, 084183

    Blood 2009;113;16;3831-7

  • 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, R01 HG002651-05; NHLBI NIH HHS: HC-25195, HL-084729, HL-087679, N01 HC025195, N02-HL-6-4278, R01 HL087679-02, U01 HL084729, 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, K23 DK065978-05, K24 DK080140, K24 DK080140-01, K24 DK080140-02, R01 DK029867, R01 DK062370, R01 DK062370-05, R01 DK069922-02, R01 DK072193, R01 DK072193-04, R01 DK078616, R01 DK078616-01A1; NIMH NIH HHS: MH059160, R01 MH059160, R01 MH059160-04; Wellcome Trust: 076113, 077011, 077016, 079557, 083948, 089061, GR069224, GR072960

    Nature genetics 2009;41;1;77-81

Klaudia Walter

kw8@sanger.ac.uk Staff Scientist

Klaudia graduated with a Mag.rer.nat. degree in mathematics and physical education from the University of Vienna, Austria. Later she completed an MSc in statistics at the University of Sheffield in 2003 and a PhD in statistical methods in comparative genomics at the University of Cambridge in 2007.

She joined Matt Hurles' group at the WTSI as a postdoctoral fellow to work mainly on the 1000 Genomes Project from 2007 to 2011.

Research

Since June 2011 she is working as a statistical geneticist in the UK10K project in Nicole Soranzo's group at the WTSI.

References

  • A systematic survey of loss-of-function variants in human protein-coding genes.

    MacArthur DG, Balasubramanian S, Frankish A, Huang N, Morris J, Walter K, Jostins L, Habegger L, Pickrell JK, Montgomery SB, Albers CA, Zhang ZD, Conrad DF, Lunter G, Zheng H, Ayub Q, DePristo MA, Banks E, Hu M, Handsaker RE, Rosenfeld JA, Fromer M, Jin M, Mu XJ, Khurana E, Ye K, Kay M, Saunders GI, Suner MM, Hunt T, Barnes IH, Amid C, Carvalho-Silva DR, Bignell AH, Snow C, Yngvadottir B, Bumpstead S, Cooper DN, Xue Y, Romero IG, 1000 Genomes Project Consortium, Wang J, Li Y, Gibbs RA, McCarroll SA, Dermitzakis ET, Pritchard JK, Barrett JC, Harrow J, Hurles ME, Gerstein MB and Tyler-Smith C

    Wellcome Trust Sanger Institute, Hinxton, UK. macarthur@atgu.mgh.harvard.edu

    Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.

    Funded by: British Heart Foundation: RG/09/012/28096; NHGRI NIH HHS: U54 HG003273; Wellcome Trust: 085532, 090532, 090532/Z/09/Z, 098051

    Science (New York, N.Y.) 2012;335;6070;823-8

  • Mapping copy number variation by population-scale genome sequencing.

    Mills RE, Walter K, Stewart C, Handsaker RE, Chen K, Alkan C, Abyzov A, Yoon SC, Ye K, Cheetham RK, Chinwalla A, Conrad DF, Fu Y, Grubert F, Hajirasouliha I, Hormozdiari F, Iakoucheva LM, Iqbal Z, Kang S, Kidd JM, Konkel MK, Korn J, Khurana E, Kural D, Lam HY, Leng J, Li R, Li Y, Lin CY, Luo R, Mu XJ, Nemesh J, Peckham HE, Rausch T, Scally A, Shi X, Stromberg MP, Stütz AM, Urban AE, Walker JA, Wu J, Zhang Y, Zhang ZD, Batzer MA, Ding L, Marth GT, McVean G, Sebat J, Snyder M, Wang J, Ye K, Eichler EE, Gerstein MB, Hurles ME, Lee C, McCarroll SA, Korbel JO and 1000 Genomes Project

    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

    Funded by: Howard Hughes Medical Institute; Medical Research Council: G0701805; NHGRI NIH HHS: P01 HG004120, P41 HG004221, P41 HG004221-01, P41 HG004221-02, P41 HG004221-03, P41 HG004221-03S1, P41 HG004221-03S2, P41 HG004221-03S3, R01 HG004719, R01 HG004719-01, R01 HG004719-02, R01 HG004719-02S1, R01 HG004719-03, R01 HG004719-04, RC2 HG005552, RC2 HG005552-01, RC2 HG005552-02, U01 HG005209, U01 HG005209-01, U01 HG005209-02, U54 HG003273; NIGMS NIH HHS: R01 GM059290, R01 GM081533, R01 GM081533-01A1, R01 GM081533-02, R01 GM081533-03, R01 GM081533-04, R01 GM59290; NIMH NIH HHS: R01 MH091350; Wellcome Trust: 062023, 077009, 077014, 077192, 085532

    Nature 2011;470;7332;59-65

  • A map of human genome variation from population-scale sequencing.

    1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME and McVean GA

    The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.

    Funded by: British Heart Foundation: RG/09/012/28096; Howard Hughes Medical Institute; Medical Research Council: G0801823, G0801823(89305); NCRR NIH HHS: S10RR025056; NHGRI NIH HHS: 01HG3229, N01HG62088, P01 HG004120, P01HG4120, P41HG2371, P41HG4221, P41HG4222, P50HG2357, R01 HG003229, R01 HG003229-05, R01 HG004719, R01 HG004719-01, R01 HG004719-02, R01 HG004719-02S1, R01 HG004719-03, R01 HG004719-04, R01HG2651, R01HG3698, R01HG4333, R01HG4719, R01HG4960, RC2 HG005552, RC2 HG005552-01, RC2 HG005552-02, RC2HG5552, U01HG5208, U01HG5209, U01HG5210, U01HG5211, U01HG5214, U41HG4568, U54 HG003273, U54HG2750, U54HG2757, U54HG3067, U54HG3079, U54HG3273; NIGMS NIH HHS: R01GM59290, R01GM72861, T32 GM007753; NIMH NIH HHS: 01MH84698; Wellcome Trust: 075491, 077009, 077014, 077192, 081407, 085532, 086084, 089061, 089062, 089088, WT075491/Z/04, WT077009, WT081407/Z/06/Z, WT085532AIA, WT086084/Z/08/Z, WT089088/Z/09/Z

    Nature 2010;467;7319;1061-73

  • Deep short-read sequencing of chromosome 17 from the mouse strains A/J and CAST/Ei identifies significant germline variation and candidate genes that regulate liver triglyceride levels.

    Sudbery I, Stalker J, Simpson JT, Keane T, Rust AG, Hurles ME, Walter K, Lynch D, Teboul L, Brown SD, Li H, Ning Z, Nadeau JH, Croniger CM, Durbin R and Adams DJ

    The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1HH, UK. ims@sanger.ac.uk

    Genome sequences are essential tools for comparative and mutational analyses. Here we present the short read sequence of mouse chromosome 17 from the Mus musculus domesticus derived strain A/J, and the Mus musculus castaneus derived strain CAST/Ei. We describe approaches for the accurate identification of nucleotide and structural variation in the genomes of vertebrate experimental organisms, and show how these techniques can be applied to help prioritize candidate genes within quantitative trait loci.

    Funded by: Cancer Research UK; Medical Research Council: G0800024; Wellcome Trust

    Genome biology 2009;10;10;R112

  • Accurate whole human genome sequencing using reversible terminator chemistry.

    Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR, Boutell JM, Bryant J, Carter RJ, Keira Cheetham R, Cox AJ, Ellis DJ, Flatbush MR, Gormley NA, Humphray SJ, Irving LJ, Karbelashvili MS, Kirk SM, Li H, Liu X, Maisinger KS, Murray LJ, Obradovic B, Ost T, Parkinson ML, Pratt MR, Rasolonjatovo IM, Reed MT, Rigatti R, Rodighiero C, Ross MT, Sabot A, Sankar SV, Scally A, Schroth GP, Smith ME, Smith VP, Spiridou A, Torrance PE, Tzonev SS, Vermaas EH, Walter K, Wu X, Zhang L, Alam MD, Anastasi C, Aniebo IC, Bailey DM, Bancarz IR, Banerjee S, Barbour SG, Baybayan PA, Benoit VA, Benson KF, Bevis C, Black PJ, Boodhun A, Brennan JS, Bridgham JA, Brown RC, Brown AA, Buermann DH, Bundu AA, Burrows JC, Carter NP, Castillo N, Chiara E Catenazzi M, Chang S, Neil Cooley R, Crake NR, Dada OO, Diakoumakos KD, Dominguez-Fernandez B, Earnshaw DJ, Egbujor UC, Elmore DW, Etchin SS, Ewan MR, Fedurco M, Fraser LJ, Fuentes Fajardo KV, Scott Furey W, George D, Gietzen KJ, Goddard CP, Golda GS, Granieri PA, Green DE, Gustafson DL, Hansen NF, Harnish K, Haudenschild CD, Heyer NI, Hims MM, Ho JT, Horgan AM, Hoschler K, Hurwitz S, Ivanov DV, Johnson MQ, James T, Huw Jones TA, Kang GD, Kerelska TH, Kersey AD, Khrebtukova I, Kindwall AP, Kingsbury Z, Kokko-Gonzales PI, Kumar A, Laurent MA, Lawley CT, Lee SE, Lee X, Liao AK, Loch JA, Lok M, Luo S, Mammen RM, Martin JW, McCauley PG, McNitt P, Mehta P, Moon KW, Mullens JW, Newington T, Ning Z, Ling Ng B, Novo SM, O'Neill MJ, Osborne MA, Osnowski A, Ostadan O, Paraschos LL, Pickering L, Pike AC, Pike AC, Chris Pinkard D, Pliskin DP, Podhasky J, Quijano VJ, Raczy C, Rae VH, Rawlings SR, Chiva Rodriguez A, Roe PM, Rogers J, Rogert Bacigalupo MC, Romanov N, Romieu A, Roth RK, Rourke NJ, Ruediger ST, Rusman E, Sanches-Kuiper RM, Schenker MR, Seoane JM, Shaw RJ, Shiver MK, Short SW, Sizto NL, Sluis JP, Smith MA, Ernest Sohna Sohna J, Spence EJ, Stevens K, Sutton N, Szajkowski L, Tregidgo CL, Turcatti G, Vandevondele S, Verhovsky Y, Virk SM, Wakelin S, Walcott GC, Wang J, Worsley GJ, Yan J, Yau L, Zuerlein M, Rogers J, Mullikin JC, Hurles ME, McCooke NJ, West JS, Oaks FL, Lundberg PL, Klenerman D, Durbin R and Smith AJ

    Illumina Cambridge Ltd. (Formerly Solexa Ltd), Chesterford Research Park, Little Chesterford, Nr Saffron Walden, Essex CB10 1XL, UK. dbentley@illumina.com

    DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally used long (400-800 base pair) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intraspecies genetic variation. Here we report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high-quality sequence. We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from >30x average depth of paired 35-base reads. We characterize four million single-nucleotide polymorphisms and four hundred thousand structural variants, many of which were previously unknown. Our approach is effective for accurate, rapid and economical whole-genome re-sequencing and many other biomedical applications.

    Funded by: Biotechnology and Biological Sciences Research Council: B05823, MOL04534; Medical Research Council: G0701805; NHGRI NIH HHS: Z01 HG200330-03; Wellcome Trust

    Nature 2008;456;7218;53-9

  • Parallel evolution of conserved non-coding elements that target a common set of developmental regulatory genes from worms to humans.

    Vavouri T, Walter K, Gilks WR, Lehner B and Elgar G

    Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. tv1@sanger.ac.uk

    Background: The human genome contains thousands of non-coding sequences that are often more conserved between vertebrate species than protein-coding exons. These highly conserved non-coding elements (CNEs) are associated with genes that coordinate development, and have been proposed to act as transcriptional enhancers. Despite their extreme sequence conservation in vertebrates, sequences homologous to CNEs have not been identified in invertebrates.

    Results: Here we report that nematode genomes contain an alternative set of CNEs that share sequence characteristics, but not identity, with their vertebrate counterparts. CNEs thus represent a very unusual class of sequences that are extremely conserved within specific animal lineages yet are highly divergent between lineages. Nematode CNEs are also associated with developmental regulatory genes, and include well-characterized enhancers and transcription factor binding sites, supporting the proposed function of CNEs as cis-regulatory elements. Most remarkably, 40 of 156 human CNE-associated genes with invertebrate orthologs are also associated with CNEs in both worms and flies.

    Conclusion: A core set of genes that regulate development is associated with CNEs across three animal groups (worms, flies and vertebrates). We propose that these CNEs reflect the parallel evolution of alternative enhancers for a common set of developmental regulatory genes in different animal groups. This 're-wiring' of gene regulatory networks containing key developmental coordinators was probably a driving force during the evolution of animal body plans. CNEs may, therefore, represent the genomic traces of these 'hard-wired' core gene regulatory networks that specify the development of each alternative animal body plan.

    Funded by: Medical Research Council: G0401138, MC_U105260799

    Genome biology 2007;8;2;R15

  • Increased human IgE induced by killing Schistosoma mansoni in vivo is associated with pretreatment Th2 cytokine responsiveness to worm antigens.

    Walter K, Fulford AJ, McBeath R, Joseph S, Jones FM, Kariuki HC, Mwatha JK, Kimani G, Kabatereine NB, Vennervald BJ, Ouma JH and Dunne DW

    Division of Microbiology and Parasitology, Department of Pathology, University of Cambridge, Cambridge, United Kingdom. klaudia.walter@mrc-bsu.cam.ac.uk

    In schistosomiasis endemic areas, children are very susceptible to postchemotherapy reinfection, whereas adults are relatively resistant. Different studies have reported that schistosome-specific IL-4 and IL-5 responses, or posttreatment worm-IgE levels, correlate with subsequent low reinfection. Chemotherapy kills i.v. worms providing an in vivo Ag challenge. We measured anti-worm (soluble worm Ag (SWA) and recombinant tegumental Ag (rSm22.6)) and anti-egg (soluble egg Ag) Ab levels in 177 Ugandans (aged 7-50) in a high Schistosoma mansoni transmission area, both before and 7 wk posttreatment, and analyzed these data in relation to whole blood in vitro cytokine responses at the same time points. Soluble egg Ag-Ig levels were unaffected by treatment but worm-IgG1 and -IgG4 increased, whereas worm-IgE increased in many but not all individuals. An increase in worm-IgE was mainly seen in >15-year-olds and, unlike in children, was inversely correlated to pretreatment infection intensities, suggesting this response was associated both with resistance to pretreatment infection, as well as posttreatment reinfection. The increases in SWA-IgE and rSm22.6-IgE positively correlated with pretreatment Th2 cytokines, but not IFN-gamma, induced by SWA. These relationships remained significant after allowing for the confounding effects of pretreatment infection intensity, age, and pretreatment IgE levels, indicating a link between SWA-specific Th2 cytokine responsiveness and subsequent increases in worm-IgE. An exceptionally strong relationship between IL-5 and posttreatment worm-IgE levels in < 15-year-olds suggested that the failure of younger children to respond to in vivo Ag stimulation with increased levels of IgE, is related to their lack of pretreatment SWA Th2 cytokine responsiveness.

    Funded by: Medical Research Council: G7708609; Wellcome Trust

    Journal of immunology (Baltimore, Md. : 1950) 2006;177;8;5490-8

  • Striking nucleotide frequency pattern at the borders of highly conserved vertebrate non-coding sequences.

    Walter K, Abnizova I, Elgar G and Gilks WR

    MRC Rosalind Franklin Centre for Genomics Research, Hinxton, Cambridge CB10 1SB, UK.

    In a recent study, 1373 highly conserved non-coding elements (CNEs) were detected by aligning the human and Takifugu rubripes (Fugu) genomes. The remarkable degree of sequence conservation in CNEs compared with their surroundings suggested comparing the base composition within CNEs with their 5' and 3' flanking regions. The analysis reveals a novel, sharp and distinct signal of nucleotide frequency bias precisely at the border between CNEs and flanking regions.

    Trends in genetics : TIG 2005;21;8;436-40

  • Highly conserved non-coding sequences are associated with vertebrate development.

    Woolfe A, Goodson M, Goode DK, Snell P, McEwen GK, Vavouri T, Smith SF, North P, Callaway H, Kelly K, Walter K, Abnizova I, Gilks W, Edwards YJ, Cooke JE and Elgar G

    Medical Research Council Rosalind Franklin Centre for Genomics Research Hinxton, Cambridge, United Kingdom.

    In addition to protein coding sequence, the human genome contains a significant amount of regulatory DNA, the identification of which is proving somewhat recalcitrant to both in silico and functional methods. An approach that has been used with some success is comparative sequence analysis, whereby equivalent genomic regions from different organisms are compared in order to identify both similarities and differences. In general, similarities in sequence between highly divergent organisms imply functional constraint. We have used a whole-genome comparison between humans and the pufferfish, Fugu rubripes, to identify nearly 1,400 highly conserved non-coding sequences. Given the evolutionary divergence between these species, it is likely that these sequences are found in, and furthermore are essential to, all vertebrates. Most, and possibly all, of these sequences are located in and around genes that act as developmental regulators. Some of these sequences are over 90% identical across more than 500 bases, being more highly conserved than coding sequence between these two species. Despite this, we cannot find any similar sequences in invertebrate genomes. In order to begin to functionally test this set of sequences, we have used a rapid in vivo assay system using zebrafish embryos that allows tissue-specific enhancer activity to be identified. Functional data is presented for highly conserved non-coding sequences associated with four unrelated developmental regulators (SOX21, PAX6, HLXB9, and SHH), in order to demonstrate the suitability of this screen to a wide range of genes and expression patterns. Of 25 sequence elements tested around these four genes, 23 show significant enhancer activity in one or more tissues. We have identified a set of non-coding sequences that are highly conserved throughout vertebrates. They are found in clusters across the human genome, principally around genes that are implicated in the regulation of development, including many transcription factors. These highly conserved non-coding sequences are likely to form part of the genomic circuitry that uniquely defines vertebrate development.

    PLoS biology 2005;3;1;e7

  • Increases in human T helper 2 cytokine responses to Schistosoma mansoni worm and worm-tegument antigens are induced by treatment with praziquantel.

    Joseph S, Jones FM, Walter K, Fulford AJ, Kimani G, Mwatha JK, Kamau T, Kariuki HC, Kazibwe F, Tukahebwa E, Kabatereine NB, Ouma JH, Vennervald BJ and Dunne DW

    Department of Pathology, University of Cambridge, Cambridge, United Kingdom. sj122@cam.ac.uk

    Levels of Schistosoma mansoni-induced interleukin (IL)-4 and IL-5 and posttreatment levels of immunoglobulin E recognizing the parasite's tegument (Teg) correlate with human resistance to subsequent reinfection after treatment. We measured changes in whole-blood cytokine production in response to soluble egg antigen (SEA), soluble worm antigen (SWA), or Teg after treatment with praziquantel (PZQ) in a cohort of 187 individuals living near Lake Albert, Uganda. Levels of SWA-induced IL-4, IL-5, IL-10, and IL-13 increased after treatment with PZQ, and the greatest relative increases were seen in the responses to Teg. Mean levels of Teg-specific IL-5 and IL-10 increased ~10-15-fold, and mean levels of IL-13 increased ~5-fold. Correlations between the changes in cytokines suggested that their production was positively coregulated by tegumentally derived antigens. Levels of SEA-, SWA-, and Teg-induced interferon- gamma were not significantly changed by treatment, and, with the exception of IL-10, which increased slightly, responses to SEA also remained largely unchanged. The changes in cytokines were not strongly influenced by age or intensity of infection and were not accompanied by corresponding increases in the numbers of circulating eosinophils or lymphocytes.

    The Journal of infectious diseases 2004;190;4;835-42

* quick link - http://q.sanger.ac.uk/qtraits