A Bayesian method to incorporate hundreds of functional characteristics with association evidence to improve variant prioritization.

ABSTRACT: The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals ("hits") to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL), and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and

SUBMITTER: Gagliano SA 

PROVIDER: S-EPMC4028284 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature