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

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 with 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.

journal_name

Genet Epidemiol

journal_title

Genetic epidemiology

authors

Trochet H,Pirinen M,Band G,Jostins L,McVean G,Spencer CCA

doi

10.1002/gepi.22202

subject

Has Abstract

pub_date

2019-07-01 00:00:00

pages

532-547

issue

5

eissn

0741-0395

issn

1098-2272

journal_volume

43

pub_type

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