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Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis

Nature Genetics volume 44, pages 483–489 (2012)Cite this article

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Abstract

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.

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Figure 1: Association of polygenic risk scores with common disease case-control status in independent validation datasets.
Figure 2: Posterior probability densities of the number of associated SNPs and the total liability-scale variance explained for the Bayesian analysis of the polygenic analysis results.
Figure 3: Posterior probability distributions of the relative risk and minor allele frequency of the inferred disease-associated SNPs.
Figure 4: Causal variants underlying the rheumatoid arthritis polygenic disease architecture inferred from the GWAS data.

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Acknowledgements

R.M.P. is supported by grants from the US National Institutes of Health (NIH) (R01-AR057108, R01-AR056768, U01-GM092691 and R01-AR059648) and holds a Career Award for Medical Scientists from the Burroughs Wellcome Fund. S.R. is supported by an NIH Career Development Award (K08AR055688-01A1). The Brigham Rheumatoid Arthritis Sequential Study Registry is supported by a grant from Crescendo and Biogen-Idec. The North American Rheumatoid Arthritis Consortium is supported by the NIH (NO1-AR-2-2263 and RO1-AR44422). This research was also supported in part by the Intramural Research Program of the National Institute of Arthritis, Musculoskeletal and Skin Diseases of the NIH and by a Canada Research Chair and grants to K.A.S. from the Canadian Institutes for Health Research (MOP79321 and IIN-84042) and the Ontario Research Fund (RE01061). We acknowledge S. Purcell, A. Price and N. Zaitlen for help with the design and implementation of the study and analysis.

Author information

Author notes

  1. Paul I W de Bakker, Soumya Raychaudhuri and Robert M Plenge: These authors contributed equally to this work.

  2. rplenge@partners.org

Authors and Affiliations

  1. Division of Rheumatology Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Eli A Stahl, Robert Chen, Fina A S Kurreeman, Soumya Raychaudhuri & Robert M Plenge

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

    Eli A Stahl, Ron Do, Robert Chen, Fina A S Kurreeman, Sekar Kathiresan, Paul I W de Bakker, Soumya Raychaudhuri & Robert M Plenge

  3. Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Eli A Stahl, Robert Chen, Fina A S Kurreeman, Paul I W de Bakker, Soumya Raychaudhuri & Robert M Plenge

  4. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA

    Daniel Wegmann

  5. Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands

    Gosia Trynka, Javier Gutierrez-Achury & Cisca Wijmenga

  6. Center for Human Genetic Research and Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Ron Do & Sekar Kathiresan

  7. Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Benjamin F Voight

  8. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA

    Peter Kraft

  9. Institute of Environmental Medicine, Karolinska Institutet Hospital Solna, Stockholm, Sweden

    Henrik J Kallberg & Lars Alfredsson

  10. The Feinstein Institute for Medical Research, North Shore–Long Island Jewish Health System, Manhasset, New York, USA

    Peter K Gregersen

  11. Department of Medicine, University of Toronto, Mount Sinai Hospital and University Health Network, Toronto, Ontario, Canada

    Katherine A Siminovitch

  12. Arthritis Research UK Epidemiology Unit, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK

    Jane Worthington

  13. Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands

    Paul I W de Bakker

  14. Department of Epidemiology, University Medical Center Utrecht, Utrecht, The Netherlands

    Paul I W de Bakker

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  1. Eli A Stahl
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Consortia

Diabetes Genetics Replication and Meta-analysis Consortium

Myocardial Infarction Genetics Consortium

Contributions

Study design: R.M.P., E.A.S., S.R. and P.I.W.d.B. Analysis: E.A.S. (lead), D.W., G.T., J.G.-A., R.D., B.F.V. (primary contributors), R.C., H.J.K. and F.A.S.K. Samples and data: C.W., S.K., B.F.V., the Myocardial Infarction Genetics Consortium, the Diabetes Genetics Replication and Meta-analysis Consortium, J.W., L.A., P.K.G., K.A.S. and R.M.P. Writing: R.M.P., E.A.S. (leads), D.W., P.K. (primary contributors) and all other authors.

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Correspondence to Eli A Stahl or Robert M Plenge.

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A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

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Supplementary Tables 1–4, Supplementary Figures 1–5 and Supplementary Note. (PDF 5277 kb)

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Stahl, E., Wegmann, D., Trynka, G. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet 44, 483–489 (2012). https://doi.org/10.1038/ng.2232

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