A Scalable Bayesian Method for Integrating Functional Information in Genome-wide Association Studies.

ABSTRACT: Genome-wide association studies (GWASs) have identified many complex loci. However, most loci reside in noncoding regions and have unknown biological functions. Integrative analysis that incorporates known functional information into GWASs can help elucidate the underlying biological mechanisms and prioritize important functional variants. Hence, we develop a flexible Bayesian variable selection model with efficient computational techniques for such integrative analysis. Different from previous approaches, our method models the effect-size distribution and probability of causality for variants with different annotations and jointly models genome-wide variants to account for linkage disequilibrium (LD), thus prioritizing associations based on the quantification of the annotations and allowi

SUBMITTER: Yang J 

PROVIDER: S-EPMC5590971 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature