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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.

journal_name

Nat Genet

journal_title

Nature genetics

authors

Stahl 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 Consortium.,Myocardial Infarction Genetics Consortium.,Kathiresan S,Wijmenga

doi

10.1038/ng.2232

subject

Has Abstract

pub_date

2012-03-25 00:00:00

pages

483-9

issue

5

eissn

1061-4036

issn

1546-1718

pii

ng.2232

journal_volume

44

pub_type

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