Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis

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.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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.

References

  1. Wellcome Trust Case Control Consortium. et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  2. Stahl, E.A. et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat. Genet. 42, 508–514 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Park, J.H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570–575 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Maher, B. Personal genomes: The case of the missing heritability. Nature 456, 18–21 (2008).

    CAS  Article  PubMed  Google Scholar 

  9. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Purcell, S.M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    CAS  PubMed  Google Scholar 

  11. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Bush, W.S. et al. Evidence for polygenic susceptibility to multiple sclerosis—the shape of things to come. Am. J. Hum. Genet. 86, 621–625 (2010).

    Article  PubMed  Google Scholar 

  13. Eijgelsheim, M. et al. Genome-wide association analysis identifies multiple loci related to resting heart rate. Hum. Mol. Genet. 19, 3885–3894 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Lee, S.H., Wray, N.R., Goddard, M.E. & Visscher, P.M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Painter, J.N. et al. Genome-wide association study identifies a locus at 7p15.2 associated with endometriosis. Nat. Genet. 43, 51–54 (2011).

    CAS  Article  PubMed  Google Scholar 

  16. Do, C.B. et al. Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease. PLoS Genet. 7, e1002141 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Chen, R. Fine mapping the TAGAP locus in rheumatoid arthritis. Genes Immun. 12, 314–318 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Dubois, P.C. et al. Multiple common variants for celiac disease influencing immune gene expression. Nat. Genet. 42, 295–302 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Kathiresan, S. et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat. Genet. 41, 334–341 (2009); erratum 41, 762 (2009).

    CAS  Article  PubMed  Google Scholar 

  21. Wellcome Case Control Consortium. et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  22. Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Nagelkerke, N.J.D. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692 (1991).

    Article  Google Scholar 

  24. Leuenberger, C. & Wegmann, D. Bayesian computation and model selection without likelihoods. Genetics 184, 243–252 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wegmann, D., Leuenberger, C., Neuenschwander, S. & Excoffier, L. ABCtoolbox: a versatile toolkit for approximate Bayesian computations. BMC Bioinformatics 11, 116 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. MacGregor, A.J. et al. Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins. Arthritis Rheum. 43, 30–37 (2000).

    CAS  Article  PubMed  Google Scholar 

  28. van der Woude, D. et al. Quantitative heritability of anti-citrullinated protein antibody–positive and anti-citrullinated protein antibody–negative rheumatoid arthritis. Arthritis Rheum. 60, 916–923 (2009).

    Article  PubMed  Google Scholar 

  29. Raychaudhuri, S. Recent advances in the genetics of rheumatoid arthritis. Curr. Opin. Rheumatol. 22, 109–118 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Wheeler, E. & Barroso, I. Genome-wide association studies and type 2 diabetes. Brief. Funct. Genomics 10, 52–60 (2011).

    CAS  Article  PubMed  Google Scholar 

  32. Nisticò, L. et al. Concordance, disease progression, and heritability of coeliac disease in Italian twins. Gut 55, 803–808 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Marenberg, M.E., Risch, N., Berkman, L.F., Floderus, B. & de Faire, U. Genetic susceptibility to death from coronary heart disease in a study of twins. N. Engl. J. Med. 330, 1041–1046 (1994).

    CAS  Article  PubMed  Google Scholar 

  34. Nora, J.J., Lortscher, R.H., Spangler, R.D., Nora, A.H. & Kimberling, W.J. Genetic-epidemiologic study of early-onset ischemic heart disease. Circulation 61, 503–508 (1980).

    CAS  Article  PubMed  Google Scholar 

  35. Almgren, P. et al. Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study. Diabetologia 54, 2811–2819 (2011).

    CAS  Article  PubMed  Google Scholar 

  36. Poulsen, P., Kyvik, K.O., Vaag, A. & Beck-Nielsen, H. Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study. Diabetologia 42, 139–145 (1999).

    CAS  Article  PubMed  Google Scholar 

  37. van Heel, D.A. et al. A genome-wide association study for celiac disease identifies risk variants in the region harboring IL2 and IL21. Nat. Genet. 39, 827–829 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Dickson, S.P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D.B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wray, N.R., Purcell, S.M. & Visscher, P.M. Synthetic associations created by rare variants do not explain most GWAS results. PLoS Biol. 9, e1000579 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 1000 Genomes Project Consortium. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010); erratum 473, 544 (2011).

  41. Spencer, C.C., Su, Z., Donnelly, P. & Marchini, J. Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet. 5, e1000477 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wang, K. et al. Interpretation of association signals and identification of causal variants from genome-wide association studies. Am. J. Hum. Genet. 86, 730–742 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Orozco, G., Barrett, J.C. & Zeggini, E. Synthetic associations in the context of genome-wide association scan signals. Hum. Mol. Genet. 19, R137–R144 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Park, L. Identifying disease polymorphisms from case-control genetic association data. Genetica 138, 1147–1159 (2010).

    CAS  Article  PubMed  Google Scholar 

  45. Spencer, C., Hechter, E., Vukcevic, D. & Donnelly, P. Quantifying the underestimation of relative risks from genome-wide association studies. PLoS Genet. 7, e1001337 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Fisher, R. The correlation between relatives on the supposition of Mendelian inheritance. Phil. Trans. R. Soc. Edinb. 52, 399–433 (1918).

    Article  Google Scholar 

  47. Norton, B. & Pearson, E.S. A note on the background to, and refereeing of, R. A. Fisher's 1918 paper 'On the correlation between relatives on the supposition of Mendelian inheritance'. Notes Rec. R. Soc. Lond. 31, 151–162 (1976).

    CAS  Article  PubMed  Google Scholar 

  48. Stephens, M. & Balding, D.J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 10, 681–690 (2009).

    CAS  Article  PubMed  Google Scholar 

  49. Eleftherohorinou, H. et al. Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. PLoS ONE 4, e8068 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Cornelis, M.C. et al. Joint effects of common genetic variants on the risk for type 2 diabetes in U.S. men and women of European ancestry. Ann. Intern. Med. 150, 541–550 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wei, Z. et al. From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes. PLoS Genet. 5, e1000678 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Pritchard, J.K., Pickrell, J.K. & Coop, G. The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation. Curr. Biol. 20, R208–R215 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. Pritchard, J.K. & Di Rienzo, A. Adaptation—not by sweeps alone. Nat. Rev. Genet. 11, 665–667 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Rossin, E.J. Proteins encoded in genomic regions associated to immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Hu, X. et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89, 496–506 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. Freudenberg, J. et al. Locus category based analysis of a large genome-wide association study of rheumatoid arthritis. Hum. Mol. Genet. 19, 3863–3872 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Falconer, D. & Mackay, T. Introduction to Quantitative Genetics. 4th edn (Longman, 1996).

Download references

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

Authors and Affiliations

Authors

Consortia

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.

Corresponding authors

Correspondence to Eli A Stahl or Robert M Plenge.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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

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

Supplementary information

Supplementary Text and Figures

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

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.2232

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing