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Nature genetics DOI:10.1038/ng.2232

Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis.

Publication TypeJournal Article
Year of Publication2012
AuthorsStahl, EA, Wegmann, D, Trynka, G, Gutierrez-Achury, J, Do, R, Voight, BF, Kraft, P, Chen, R, Kallberg, HJ, Kurreeman, FA, Diabetes Genetics Replication and Meta-analysis, C, Myocardial Infarction Genetics, C, Kathiresan, S, Wijmenga, C, Gregersen, PK, Alfredsson, L, Siminovitch, KA, Worthington, J, de Bakker, PI, Raychaudhuri, S, Plenge, RM
JournalNature genetics
Date Published2012/03/25

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.