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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
ISSN1061-4036
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.

URLhttp://dx.doi.org/10.1038/ng.2232
DOI10.1038/ng.2232
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/22446960?dopt=Abstract