DOI: 10.1101/477828Nov 24, 2018Paper

Bayesian meta-analysis across genome-wide association studies of diverse phenotypes

BioRxiv : the Preprint Server for Biology
Holly TrochetChris C. A. Spencer

Abstract

Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared to standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case-Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for, a range of possible true patterns of association across studies in a computationally efficient framework.

Related Concepts

Research Design
Isolation Aspects
Meta Analysis (Statistical Procedure)
Genome-Wide Association Study
Study

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