JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.

ABSTRACT: Recently, large scale genome-wide association study (GWAS) meta-analyses have boosted the number of known signals for some traits into the tens and hundreds. Typically, however, variants are only analysed one-at-a-time. This complicates the ability of fine-mapping to identify a small set of SNPs for further functional follow-up. We describe a new and scalable algorithm, joint analysis of marginal summary statistics (JAM), for the re-analysis of published marginal summary statistics under joint multi-SNP models. The correlation is accounted for according to estimates from a reference dataset, and models and SNPs that best explain the complete joint pattern of marginal effects are highlighted via an integrated Bayesian penalized regression framework. We provide both enumerated and Reversible

SUBMITTER: Newcombe PJ 

PROVIDER: S-EPMC4817278 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature