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Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis.
Nat. Genet. 44, 483-489 (2012)
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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Publication type Article: Journal article
Document type Scientific Article
Keywords GENOME-WIDE ASSOCIATION; SUSCEPTIBILITY LOCI; CELIAC-DISEASE; GENETIC SUSCEPTIBILITY; MISSING HERITABILITY; HEART-DISEASE; HUMAN HEIGHT; COMMON SNPS; RISK LOCI; VARIANTS
ISSN (print) / ISBN 1061-4036
Journal Nature Genetics
Quellenangaben Volume: 44, Issue: 5, Pages: 483-489
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed