A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework.

ABSTRACT:

Background

There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr).

Results

We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, an

SUBMITTER: LeBlanc M 

PROVIDER: S-EPMC6019513 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature