Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects.

ABSTRACT:

Motivation

Meta-analysis is essential to combine the results of genome-wide association studies (GWASs). Recent large-scale meta-analyses have combined studies of different ethnicities, environments and even studies of different related phenotypes. These differences between studies can manifest as effect size heterogeneity. We previously developed a modified random effects model (RE2) that can achieve higher power to detect heterogeneous effects than the commonly used fixed effects model (FE). However, RE2 cannot perform meta-analysis of correlated statistics, which are found in recent research designs, and the identified variants often overlap with those found by FE.

Results

Here, we propose RE2C, which increases the power of RE2 in two ways. First, we generalized the likel

SUBMITTER: Lee CH 

PROVIDER: S-EPMC5870848 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature