New gene functions in megakaryopoiesis and platelet formation

doi:10.1038/nature10659

Nature. Author manuscript; available in PMC 2012 April 24.

Published in final edited form as:

Nature. 2011 November 30; 480(7376): 201–208.

doi: 10.1038/nature10659

PMCID: PMC3335296

NIHMSID: NIHMS362305

New gene functions in megakaryopoiesis and platelet formation

Christian Gieger,¹^* Aparna Radhakrishnan,^2,³^* Ana Cvejic,^2,³^* Weihong Tang,⁴^* Eleonora Porcu,⁵^* Giorgio Pistis,⁶^* Jovana Serbanovic-Canic,^2,³^* Ulrich Elling,⁷ Alison H. Goodall,^8,⁹ Yann Labrune,¹⁰ Lorna M. Lopez,^11,¹² Reedik Mägi,^13,²⁸ Stuart Meacham,^2,³ Yukinori Okada,^14,¹⁵ Nicola Pirastu,^16,¹⁰² Rossella Sorice,¹⁷ Alexander Teumer,¹⁸ Katrin Voss,² Weihua Zhang,¹⁹ Ramiro Ramirez-Solis,³ Joshua C. Bis,²⁰ David Ellinghaus,²¹ Martin Gögele,²² Jouke-Jan Hottenga,²³ Claudia Langenberg,²⁴ Peter Kovacs,²⁵ Paul F. O’Reilly,²⁶ So-Youn Shin,^3,²⁷ Tõnu Esko,^28,^29,³⁰ Jaana Hartiala,^31,³² Stavroula Kanoni,^3,³³ Federico Murgia,³⁴ Afshin Parsa,³⁵ Jonathan Stephens,² Pim van der Harst,³⁶ C. Ellen van der Schoot,³⁷ Hooman Allayee,^31,³² Antony Attwood,^2,³ Beverley Balkau,^38,³⁹ François Bastardot,⁴⁰ Saonli Basu,⁴¹ Sebastian E. Baumeister,⁴² Ginevra Biino,^34,^43,⁴⁴ Lorenzo Bomba,⁴⁵ Amélie Bonnefond,¹⁰ François Cambien,⁴⁶ John C. Chambers,¹⁹ Francesco Cucca,⁵ Pio D’Adamo,^16,¹⁰² Gail Davies,¹² Rudolf A. de Boer,³⁶ Eco J. C. de Geus,²³ Angela Döring,^47,⁴⁸ Paul Elliott,^19,⁴⁹ Jeanette Erdmann,^50,¹⁰¹ David M. Evans,⁵¹ Mario Falchi,⁵² Wei Feng,⁴¹ Aaron R. Folsom,⁴ Ian H. Frazer,⁵³ Quince D. Gibson,³⁵ Nicole L. Glazer,⁵⁴ Chris Hammond,²⁷ Anna-Liisa Hartikainen,⁵⁵ Susan R. Heckbert,^56,⁵⁷ Christian Hengstenberg,⁵⁸ Micha Hersch,⁵⁹ Thomas Illig,⁶⁰ Ruth J. F. Loos,²⁴ Jennifer Jolley,² Kay Tee Khaw,^61,⁶² Brigitte Kühnel,¹ Marie-Christine Kyrtsonis,⁶³ Vasiliki Lagou,⁶⁴ Heather Lloyd-Jones,² Thomas Lumley,⁶⁵ Massimo Mangino,²⁷ Andrea Maschio,⁵ Irene Mateo Leach,³⁶ Barbara McKnight,⁶⁵ Yasin Memari,^3,²⁷ Braxton D. Mitchell,³⁵ Grant W. Montgomery,⁶⁶ Yusuke Nakamura,⁶⁷ Matthias Nauck,⁶⁸ Gerjan Navis,⁶⁹ Ute Nöthlings,^70,⁷¹ Ilja M. Nolte,⁷² David J. Porteous,^11,⁷³ Anneli Pouta,^55,⁷⁴ Peter P. Pramstaller,²² Janne Pullat,²⁸ Susan M. Ring,⁵¹ Jerome I. Rotter,⁷⁵ Daniela Ruggiero,¹⁷ Aimo Ruokonen,⁷⁶ Cinzia Sala,⁶ Nilesh J. Samani,^8,⁹ Jennifer Sambrook,^2,⁷⁷ David Schlessinger,⁷⁸ Stefan Schreiber,²¹ Heribert Schunkert,^50,¹⁰¹ James Scott,⁷⁹ Nicholas L. Smith,^56,^57,⁸⁰ Harold Snieder,⁷² John M. Starr,^11,⁸¹ Michael Stumvoll,⁸² Atsushi Takahashi,¹⁴ W. H. Wilson Tang,^83,⁸⁴ Kent Taylor,⁷⁵ Albert Tenesa,^85,⁸⁶ Swee Lay Thein,⁸⁷ Anke Tönjes,⁸² Manuela Uda,⁵ Sheila Ulivi,¹⁶ Dirk J. van Veldhuisen,³⁶ Peter M. Visscher,^11,⁶⁶ Uwe Völker,¹⁸ H.-Erich Wichmann,^47,⁸⁸ Kerri L. Wiggins,²⁰ Gonneke Willemsen,²³ Tsun-Po Yang,³ Jing Hua Zhao,²⁴ Paavo Zitting,⁸⁹ John R. Bradley,⁷⁷ George V. Dedoussis,³³ Paolo Gasparini,^16,¹⁰² Stanley L. Hazen,⁸³ Andres Metspalu,^28,^29,³⁰ Mario Pirastu,^34,⁴³ Alan R. Shuldiner,^35,⁹⁰ L. Joost van Pelt,⁹¹ Jaap-Jan Zwaginga,⁹² Dorret I. Boomsma,²³ Ian J. Deary,^11,¹² Andre Franke,²¹ Philippe Froguel,^10,⁵² Santhi K. Ganesh,⁹³ Marjo-Riitta Jarvelin,^26,^94,⁹⁵ Nicholas G. Martin,⁶⁶ Christa Meisinger,⁴⁸ Bruce M. Psaty,^57,⁹⁶ Timothy D. Spector,²⁷ Nicholas J. Wareham,²⁴ Jan-Willem N. Akkerman,⁹⁷ Marina Ciullo,¹⁷ Panos Deloukas,³ Andreas Greinacher,⁹⁸ Steve Jupe,⁹⁹ Naoyuki Kamatani,¹⁴ Jyoti Khadake,⁹⁹ Jaspal S. Kooner,⁷⁹ Josef Penninger,⁷ Inga Prokopenko,⁶⁴ Derek Stemple,³ Daniela Toniolo,^6,⁴⁴ Lorenz Wernisch,¹⁰⁰ Serena Sanna,⁵^* Andrew A. Hicks,²²^* Augusto Rendon,^2,¹⁰⁰^* Manuel A. Ferreira,⁶⁶^* Willem H. Ouwehand,^2,^3,⁷⁷^* and Nicole Soranzo³^*

¹Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr.1, 85764 Neuherberg, Germany

²Department of Haematology, University of Cambridge & NHS Blood and Transplant, Cambridge CB2 0PT, UK

³Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH/1SA, UK

⁴Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota 55455-0354, USA

⁵Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy

⁶San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milano, Italy

⁷IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, 1030 Vienna, Austria

⁸Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK

⁹Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester LE3 9QP, UK

¹⁰CNRS-UMR-8199, Institut de Biologie de Lille, 1 Rue du Professor Calmette, BP245, 59019 Lille Cedex, France

¹¹Centre for Cognitive Ageing and Cognitive Epidemiology, 7 George Square, The University of Edinburgh, Edinburgh EH8 9JZ, UK.

¹²Department of Psychology, The University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK

¹³Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK

¹⁴Laboratory for Statistical Analysis, RIKEN Center for Genomic Medicine, Institute ofPhysical and Chemical Research,1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

¹⁵Department of Allergy and Rheumatology, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan

¹⁶Institute for Maternal and Child Health — IRCCS ‘Burlo Garofolo’ — via dell’Istria 65/1, 34137 Trieste, Italy

¹⁷Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, Via P. Castellino 111, 8031 Naples, Italy

¹⁸Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany

¹⁹Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St Mary’s Campus, London W2 1PG, UK

²⁰Department of Medicine, University of Washington, 1959 Northeast Pacific Street, Seattle, Washington 98195-0001, USA

²¹Institute of Clinical Molecular Biology, Christian-Albrechts-University, 24105 Kiel, Germany

²²Centre for Biomedicine, EuropeanAcademy Bozen/Bolzano, Viale Druso 1, 39100 Bolzano, Italy

²³Department of Biological Psychology, VU University Amsterdam, De Boelelaan 1105, Van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands

²⁴MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Box 285, Cambridge CB2 0QQ, UK

²⁵Interdisciplinary Centre for Clinical Research, University of Leipzig, Inselstr. 22, 04103 Leipzig, Germany

²⁶Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK

²⁷Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK

²⁸Estonian Genome Center, University of Tartu, Tiigi 61b, 50410 Tartu, Estonia

²⁹Estonian Biocenter, Riia 23b, 51010, Tartu, Estonia

³⁰Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia

³¹Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9075, USA

³²Institute for Genetic Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9075, USA

³³Department of Nutrition-Dietetics, Harokopio University, Athens 17671, Greece

³⁴Institute of Population Genetics, National Council of Research, 07100 Sassari, Italy

³⁵University of Maryland School of Medicine, 655 West Baltimore Street, Baltimore, Maryland 21201-1559, USA

³⁶Department of Cardiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands

³⁷Department of Experimental Immunohaematology, Sanquin Research, Plesmanlaan 125, 1066 CX Amsterdam, The Netherlands and Landsteiner Laboratory, Academic Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands

³⁸Inserm, CESP Centre for research in Epidemiology and Population Health, U1018Villejuif, 94807France

³⁹University Paris Sud 11,UMRS 1018, Villejuif, 94807 France

⁴⁰Service de Médecine Interne, CHUV, 1011 Lausanne, Switzerland

⁴¹Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, mmc 303, 420 Delaware St SE, Minneapolis, Minnesota 55455, USA

⁴²Institute for Community Medicine, Ernst-Moritz-Arndt-University Greifswald, 17475 Greifswald, Germany

⁴³Shardna Life Sciences, Loc. Piscinamanna 09010 Pula (CA), Italy

⁴⁴Institute of Molecular Genetics-CNR Via Abbiategrasso 207, 27100 Pavia, Italy

⁴⁵Istituto di Zootecnica, Universita’ Cattolica del Sacro Cuore sede Piacenza, via Emilia Parmense 84, 29122 Piacenza, Italy

⁴⁶INSERM UMRS 937, Pierre and Marie Curie University (UPMC, Paris 6) and Medical School, 91 Bd de l’Hôpital, 75013 Paris, France.

⁴⁷Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr.1, 85764 Neuherberg, Germany

⁴⁸Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr.1, 85764 Neuherberg, Germany

⁴⁹MRC-HPA Centre for Environment and Health, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, UK

⁵⁰Universität zu Lübeck, Medizinische Klinik 2, Lübeck Germany

⁵¹MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK

⁵²Department of Genomics of Common Disease, School of Public Health, Imperial College London, The Commonwealth Building, The Hammersmith Hospital, Du Care Road, London W12 ONN, UK

⁵³The University of Queensland Diamantina Institute, Princess Alexandra Hospital, Brisbane, Queensland 4102, Australia.

⁵⁴Department of Medicine, Boston University, Boston, Massachusetts 02118, USA.

⁵⁵Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, Aapistie 7, Oulu, 90220 Finland

⁵⁶Department of Epidemiology, University of Washington, Seattle, Washington 98195-7236, USA

⁵⁷Group Health Research Institute, Group Health Cooperative, Seattle, Washington 98101-1448, USA

⁵⁸Klinik und Poliklinik für Innere Medizin II, Universitätsklinikum Regensburg, 93053 Regensburg, Germany

⁵⁹Department of Medical Genetics, University of Lausanne, and Swiss Institute of Bioinformatics, Lausanne, Switzerland

⁶⁰Unit for Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr.1, 85764 Neuherberg, Germany

⁶¹Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK

⁶²Clinical Gerontology Unit, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 2QQ, UK

⁶³First Department of Propedeutic Internal Medicine and Hematology Department, National and Kapodistrian University of Athens Medical School, Laikon General Hospital,Athens 11727, Greece

⁶⁴Oxford Centre for Diabetes,Endocrinologyand Metabolism, University of Oxford, Oxford OX3 7LJ, UK

⁶⁵Department of Biostatistics, University of Washington, Seattle, Washington 98195-7232, USA

⁶⁶Genetic Epidemiology, Queensland Institute of Medical Research, The Bancroft Centre, 300 Herston Road, Herston 4006, Queensland, Australia

⁶⁷Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan

⁶⁸Institute of Clinical Chemistry and Laboratory Medicine, Ernst-Moritz-Arndt-University Greifswald, 17475 Greifswald, Germany

⁶⁹Department of Internal Medicine, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands

⁷⁰Section for Epidemiology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, 24105 Kiel, Germany

⁷¹Popgen Biobank, University Clinic Schleswig-Holstein, 24105 Kiel, Germany

⁷²Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands

⁷³Medical Genetics Section, University of Edinburgh, Molecular Medicine Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK

⁷⁴National Institute for Health and Welfare, Aapistie 1, Oulu 90220, Finland

⁷⁵Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA

⁷⁶Institute of Diagnostics, Clinical Chemistry, University of Oulu, Box 5000Y, University of Oulu, Oulu 90014, Finland

⁷⁷NIHR Cambridge Biomedical Research Centre, Cambridge CB2 0QQ, UK

⁷⁸Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland 21224-6825, USA

⁷⁹Faculty of Medicine, National Heart and Lung Institute, Cardiovascular Science, Hammersmith Campus, Hammersmith Hospital, Imperial College London, London W12 0HS, UK

⁸⁰Seattle Epidemiologic Research and Information Center of the Department of Veterans Affairs Office of Research and Development, Seattle, Washington 98108, USA

⁸¹Geriatric Medicine Unit, The University of Edinburgh, Royal Victoria, Craigleith Road, Edinburgh EH4 2DN, UK

⁸²Department of Medicine, University of Leipzig, Liebigstr. 18, 04103 Leipzig, Germany

⁸³Center for Cardiovascular Diagnostics and Prevention, Department of Cell Biology and Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio 44195, USA

⁸⁴Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA

⁸⁵Institute of Genetics and Molecular Medicine, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK

⁸⁶The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin Midlothian EH25 9RG, UK

⁸⁷Department of Molecular Haematology, King’s College London, 125 Coldharbour Lane, London SE5 9NU, UK

⁸⁸Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität and Klinikum Grosshadern, Marchioninistr. 15, 81377 Munich, Germany

⁸⁹Department of Physiatrics, Lapland Central Hospital, Rovaniemi, 96101, Finland

⁹⁰Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland 21208, USA.

⁹¹Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, 9713 GZ, Groningen, The Netherlands

⁹²Sanquin Research Leiden, Jon J. van Rood Center for Clinical Transfusion research, and the Department of Immuno-hematology and Blood Transfusion, Leiden University Medical Center, 2333 Leiden, The Netherlands

⁹³Division of Cardiovascular Medicine, University of Michigan Health System, Ann Arbor, Michigan, USA

⁹⁴Institute of Health Sciences, University of Oulu, Aapistie 7, Oulu, 90220 Finland

⁹⁵National Institute for Health and Welfare, Biocenter Oulu, University of Oulu, Aapistie 7, Oulu, 90220 Finland

⁹⁶Department of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, Washington 98101-1448, USA

⁹⁷Department of Clinical Chemistry and Haematology, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands

⁹⁸Institute for Immunology and Transfusion Medicine, Ernst-Moritz-Arndt-University Greifswald, 17475 Greifswald, Germany

⁹⁹Proteomics Services, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

¹⁰⁰MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

¹⁰¹Nordic Center of Cardiovascular Research, Lubeck, Germany

¹⁰²University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy.

Correspondence and requests for materials should be addressed to C.G. (Email: christian.gieger/at/helmholtz-muenchen.de), W.H.O. (who1000@cam.ac.uk) or N.S. (Email: ns6/at/sanger.ac.uk).

^*These authors contributed equally to this work.

The publisher's final edited version of this article is available at Nature

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Abstract

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.

To discover novel genetic determinants of megakaryopoiesis and platelet formation, we performed meta-analyses of GWAS for mean platelet volume (MPV) and platelet count (PLT). Our analyses included 18,600 (13 studies, MPV) and 48,666 (23 studies, PLT) individuals of European descent, respectively, and up to ~2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs)¹. Briefly, we tested within each study (Supplementary Table 1) the associations of MPV and PLT with each SNP using an additive model; we then combined these study-specific test statistics in a fixed-effects meta-analysis. To reduce the risk of spurious associations, we applied common stringent quality control filters and the genomic control method² to the meta-analysis, which shows no evidence for residual inflation of summary statistics (Supplementary Fig. 1).

A total of 52 genomic loci reaching statistical significance at the genome-wide adjusted threshold of P ≤ 5 × 10⁻⁸ were discovered in this stage 1 analysis; 55 additional loci reached suggestive association (5 × 10⁻⁸ < P ≤ 5 × 10⁻⁶). We tested one SNP per locus in a stage 2 analysis that included in silico and de novo replication data in up to 18,838 individuals from 12 additional studies, confirming 15 additional loci (Supplementary Table 2). One further independent locus (TRIM58) associated with PLT was identified through detection of secondary association signals. Overall, 68 independent genomic regions were associated with PLT and MPV with P ≤ 5 × 10⁻⁸, of which 52 are new and 16 were described previously in Europeans^3-6 (Table 1). Of the 68 loci, 43 and 25 loci were associated significantly with PLT and MPV, respectively; 16 of them reached genome-wide significance with both traits (Supplementary Fig. 2). This partial overlap reflects the negative correlation of both traits (gender-adjusted r = −0.49, Fig. 1a) that results from the tight control of platelet mass (PLT × MPV)⁷. The association of some loci with both PLT and MPV may reflect this negative correlation between the two traits or independent pleiotropic effects of a locus on megakaryopoiesis and platelet formation. The different statistical power at the two traits and small effect sizes at many loci reduce our power to discriminate among loci controlling MPV and PLT through analysis of platelet mass. Their testing will require the collection and analysis of PLT and MPV in large independent homogeneous cohorts. Some loci, however, have a clear-cut effect. For instance, BAK1 affects PLT specifically, compatible with its role in apoptosis and platelet lifespan.

Table 1

Summary of loci associated with platelet count and mean platelet volume in Europeans

Figure 1

Protein-protein interaction network and gene transcription patterns

We further tested the association of the 68 loci in 7,949 (MPV) and 8,295 (PLT) samples of south Asian and 14,697 (PLT) samples of Japanese⁸ origin. We detected substantial overlap of association signals, with effect size and direction highly concordant with findings in Europeans (Supplementary Fig. 3 and Supplementary Table 3). In the south Asian sample, 15 of the 68 (22.1%) loci were significant after adjustment for multiple testing (P ≤ 7 × 10⁻⁴). In the Japanese sample, 13 of 55 (23.6%) PLT loci showed significance. Moreover, 73 of 84 (87%, South Asians) and 45 of 55 (82%, Japanese) SNPs showed associations with effect estimates directionally consistent with Europeans. Such concordance is highly unlikely to be due to chance (P = 2.3 × 10⁻¹² and P = 2.1 × 10⁻⁶), and provides independent validation of the locus discovery in Europeans.

The 68 loci cumulatively explain 4.8% of the phenotypic variance in PLT and 9.9% in MPV, accounting respectively for average increases of 2.57 × 10⁹ l⁻¹ PLT and 0.10 fl MPV per copy of allele. These levels of explained variance are in accordance with other GWAS of complex quantitative traits⁹. Our results indicate that many other common variants of similar or lower effect size, rare variants as well as structural variants may also contribute to the variation of both platelet traits. We used the method of ref. ¹⁰ to estimate the number of additional PLT- and MPV-associated loci having effect sizes comparable to those observed in our analysis. The method (with caveats discussed in the Supplementary Information) predicted that 137 and 81 such loci exist for PLT and MPV respectively, accounting for 9.7% and 18.3% of the total phenotypic variance.

Gene-prioritization strategies

Evidence from recent, highly powered meta-analyses suggests that the association peaks are enriched for genes controlling key underlying biological pathways^11,12. In our case, a large proportion of the association signals (46 out of 68) had the most significant SNP in stage 1 (‘sentinel SNP’) mapping to within a gene-coding region, including several key regulators of haemostasis (ITGA2B, F2R, GP1BA), megakaryopoiesis (THPO, MEF2C) and platelet lifespan (BAK1). Through an unbiased analysis of our GWAS results, we estimated that PLT-associated SNPs are significantly more likely to map to gene regions than expected by chance (P < 0.05, Supplementary Fig. 4), suggesting that we may prioritize the search of additional yet unknown genes controlling these processes in the associated regions. To define a univocal rule to study the enrichment of functional relationships in associated genes, we made the choice to focus on a set of 54 ‘core’ genes selected as either containing the sentinel SNP or mapping to within 10 kb from an intergenic sentinel SNP (Table 2). This selection strategy is designed to obtain unbiased hypotheses producing interpretable biological inference for genes near the association signals, but has reduced sensitivity for genes that map further from the sentinel SNP. For instance VWF, a key regulator of haemostasis, maps to 55 kb from the sentinel SNP (Supplementary Fig. 3 and Supplementary Table 4) and is therefore not considered as a core gene. We further note that this selection strategy does not imply knowledge of the location of causative variants, which is currently incomplete. A detailed SNP survey showed that at 15 loci the sentinel SNPs either encoded, or were in high linkage disequilibrium (LD, r² ≥ 0.8) with, a non-synonymous variant (Supplementary Table 5); another 11 either matched or were in high linkage disequilibrium with SNPs associated with expression levels of core genes (or cis-eQTLs, Supplementary Table 6), indicating that other loci may exert their effect through regulation of gene expression¹³. The validation of suggestive causative effects, as well as the identification of more complex interactions involving other genomic loci (trans eQTLs), will require a more comprehensive discovery in appropriately powered genomic data sets.

Table 2

Summary of functional evidence for core genes

As a first effort to characterize biological connectivity among the core genes, we applied canonical pathway analyses (see http://www.ingenuity.com), detecting a highly significant over-representation of core genes in relevant biological functions such as haematological disease, cancer and cell cycle (Supplementary Table 7). Encouraged by these results, we extended this effort to construct a comprehensive network of protein-protein interactions incorporating the core genes. This effort integrated information from public databases (principally Reactome and IntAct) with careful manual revision of published evidence and high-throughput gene expression data. The resulting network, which includes 633 nodes and 827 edges, showed extensive connectivity between the proteins encoded by the core genes with an established functional role in megakaryopoiesis and platelet formation and those encoded by genes hitherto unknown to be implicated in these processes (Fig. 1b).

Transcriptional patterns of core genes

We next considered whether this connectivity was also reflected in the regulation of core gene transcription, and whether expression patterns were unique to megakaryocytes. Despite high levels of correlation in gene expression between different blood cell types (median 5 0.8; median absolute deviation = 0.1)¹⁴, we found that core genes tend to have significantly greater expression in megakaryocytes than in the other blood cells (P = 7.5 × 10⁻⁵, Supplementary Fig. 5a). This observation is compatible with the notion that ultimate steps in blood cell lineage specification are accompanied, or driven, by the emergence of increasing numbers of lineage-specific transcripts. To explore this assumption, we used genome-wide expression arrays to determine changes in global transcript levels during in vitro differentiation of umbilical-cord blood-derived haematopoietic stem cells to precursors of blood cells. We considered five different time points and two cell types, erythroblasts (the precursors of red blood cells) and megakaryocytes. Notwithstanding high levels of correlation of gene expression between erythroblasts and megakaryocytes¹⁴, core gene transcripts showed a significant increase over time in megakaryocytes (P = 1.5 × 10⁻⁶) but not in erythroblasts (P = 0.77, Fig. 1c, d; see also Supplementary Fig. 5b). Taken together, these patterns of core gene expression are consistent with a different regulation of their transcription in megakaryocytes versus erythroblasts, and with their centrality in megakaryopoiesis and platelet formation. This hypothesis is also consistent with the observation that only 5 of the 68 sentinel SNPs exert a significant effect on erythrocyte parameters (HBS1L-MYB, RCL1, SH2B3, TRIM58 and TMCC2, Supplementary Table 8).

Gene silencing in model organisms

To assess whether core genes are indeed implicated in haematopoiesis, we interrogated the function of 15 genes using gene silencing in D. rerio and D. melanogaster, and supported empirical data with published evidence on knockout models in M. musculus (Table 2 and Supplementary Table 4). In D. rerio, we applied morpholino constructs to silence the expression of six genes (Fig. 2 and Supplementary Fig. 6) selected to have >50% homology with the human counterpart and no previous evidence of involvement in haematopoiesis. Silencing of four genes in D. rerio (arhgef3, ak3, rnf145, jmjd1c) resulted in the ablation of both primitive erythropoiesis and thrombocyte formation. Silencing of tpma, the orthologue of TPM1 that is transcribed in megakaryocytes but not in other blood cells, abolished the formation of thrombocytes but not of erythrocytes. Silencing of ehd3 did not yield a haematopoietic phenotype. We also screened D. melanogaster RNA interference (RNAi) knockdown lines for quantitative alterations in the two most prevalent classes of blood elements: plasmatocytes and crystal cells. The repertoire of blood cells in D. melanogaster, consisting of about 95% plasmatocytes and 5% crystal cells, is less varied than in vertebrates. Transcription factors and signalling pathways regulating haematopoiesis have, however, been conserved throughout evolution¹⁵, making the RNAi knockdown studies a relevant first step towards a better understanding of the putative role of these GWAS genes in haematopoiesis. Four core-gene D. melanogaster lines (shibire (DNM), ush (ZFPM2), rpn9 (PSMD13), Brf (BRF1)), as well as five others (sun (ATP5E), CG3704 (XAB1), Su(var)205 (CBX5), dve (SATB1) and RpL6 (RPL6)), displayed highly reproducible differences in the numbers of these two cell types (Table 2 and Supplementary Table 4). Despite widespread differences between mammalian and insect haematopoietic lineages¹⁶, our findings from D. melanogaster provide new and supporting examples of functional conservation in the control of blood cell formation in invertebrates and vertebrates^17-19.

Figure 2

Functional assessment of novel loci in D. rerio

New gene and functional discoveries

The data from studies in D. rerio by us and in M. musculus by others (see Supplementary Table 4) provided proof-of-concept evidence that our prioritization strategy is appropriate for selecting novel genes controlling thrombopoiesis and megakaryopoiesis, respectively. More detailed insights and additional implicated genes will be revealed through the systematic silencing of all genes in the associated regions. For instance, RNAi knockdown of dve in D. melanogaster reduces plasmatocyte numbers and increases the number of crystal cells, thus providing supporting evidence that its non-core genehuman homologue SATB1 should be prioritized in functional studies. However, the results of the knockdown study in D. rerio do not clarify at which hierarchical positions in thrombopoiesis and erythropoiesis the genes exert their effect, requiring further assessment in conditional knockout models in M. musculus with lineage-specific regulation of gene transcription. Nevertheless, our results have already allowed novel insights into the genetic control of these processes. Signalling cascades initiated by thrombopoietin (THPO) and its receptor cMPL via the JAK2/STAT3/5A signalling pathway are key regulatory steps initiating changes in gene expression responsible for driving forward megakaryocyte differentiation²⁰. Our study highlights several additional signalling proteins implicating potentially important novel regulatory routes. For instance, two genes encoding guanine nucleotide exchange factors (DOCK8 and ARHGEF3) were identified. Mendelian mutations of the former are causative of the hyper-IgE syndrome, but its effect on platelets had not yet been identified. The silencing of the latter gene in D. rerio resulted in a profound haematopoietic phenotype characterized by a complete ablation of both primitive erythropoiesis and thrombocyte formation, demonstrating its novel regulatory role in myeloid differentiation. In a parallel and in-depth study we demonstrated its novel role in the regulation of iron uptake and erythroid cell maturation²¹. A second class of genes also known to critically control early and late events of megakaryopoiesis are transcription factors. For instance, MYB silencing by microRNA 150 determines the definitive commitment of the megakaryocyte–erythroblast precursor to the megakaryocytic lineage¹⁵. A further 10 core genes identified in this study are implicated in the regulation of transcription. Among these, we have demonstrated here that silencing of rnf145 and jmjd1c in D. rerio severely affects both lineages.

In conclusion, this highly powered study describes a catalogue of known and novel genes associated with key haematopoietic processes in humans, providing an additional example of GWAS leading to biological discoveries. We further showed that for a large proportion of these known and new genes, functional support is achieved from model organisms and by overlap with genes implicated in inherited Mendelian disorders and in human cancers because of acquired mutations. In-depth functional studies and comparative analyses will be necessary to characterize the precise mechanisms by which these new genes and variants affect haematopoiesis, megakaryopoiesis and platelet formation. Furthermore, we provide extensive new resources, most notably a freely accessible knowledge base embedded in the novel protein-protein interaction network, with information about the identified platelet genes being implicated in Mendelian disorders and results from gene-silencing studies in model organisms. We anticipate that these resources will help to advance megakaryopoiesis research, to address key questions in blood stem-cell biology and to propose new targets for the treatment of haematological disorders. Finally, MPV has been associated with the risk of myocardial infarction^22,23. The contribution of the new loci to the aetiology of acute myocardial infarction events will require assessment in a prospective setting.

METHODS SUMMARY

A summary of the methods can be found in Supplementary Information and includes detailed information on: study populations; blood biochemistry measurements; genotyping methods and quality control filters; genome-wide association and meta-analysis methods; gene prioritization strategies for functional assessment and network construction; protein-protein interaction network; in vitro differentiation of blood cells; experimental data sets and analytical methods for gene expression analysis; zebrafish morpholino knockdown generation; assessment of other model organism resources.

Supplementary Material

Supplemental Info

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Acknowledgements

Reactome is supported by grants from the US National Institutes of Health (P41 HG003751), EU grant LSHG-CT-2005-518254 ‘ENFIN’, and the EBI Industry Programme. A.C. and D.L.S. are supported by the Wellcome Trust grants WT 082597/Z/07/Z and WT 077037/Z/05/Z and WT 077047/Z/05/Z, respectively. J.S.C. and K.V. are supported by the European Union NetSim training fellowship scheme (number 215820). A. Radhakrishnan, A.A., H.L.J., J.J., J.S. and W.H.O. are funded by the National Institute for Health Research, UK. Augusto Rendon is funded by programme grant RG/09/012/28096 from the British Heart Foundation. J.M.P. is supported by an Advanced ERC grant. IntAct is funded by the EC under SLING, grant agreement no. 226073 (Integrating Activity) within Research Infrastructures of the FP7, under PSIMEX, contract no. FP7-HEALTH-2007-223411. C.G. received support by the European Community’s Seventh Framework Programme (FP7/2007-2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413. N.S. is supported by the Wellcome Trust (Grant Number 098051). Acknowledgements by individual participating studies are provided in Supplementary Information.

Footnotes

Supplementary Information is linked to the online version of the paper at www.nature.com/nature.

Author Information Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing financial interests. Readers are welcome to comment on the online version of this article at www.nature.com/nature.

Author Contributions Study design group: C.G., S. Sanna, A.A.H., A. Rendon, M.A.F., W.H.O., N.S.; manuscript writing group: C.G., A. Radhakrishnan, S. Sanna, A.A.H., A. Rendon, M.A.F., W.H.O., N.S.; data preparation, meta-analysis and secondary analysis group: A. Radhakrishnan, B.K., W.T., E.P., G.P., R.M., M.A.F., C.G., N.S.; bioinformatics analyses, pathway analyses and protein-protein interaction network group: S.M., J.-W.N.A., S.J., J.K., Y.M., L.B., A. Rendon, W.H.O.; transcript profiling methods and data group: K.V., A. Rendon, L.W., A.H.G., T.-P.Y., F. Cambien, J.E., C. Hengstenberg, N.J.S., H. Schunkert, P.D., W.H.O.; M. musculus models: R.R.-S.; D. rerio knockdown models: A.C., J.S.-C., D. Stemple, W.H.O.; D. melanogaster knockdown models: U.E., J. Penninger and A.A.H. All other author contributions and roles are listed in Supplementary Information.

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