Improved score statistics for meta-analysis in single-variant and gene-level association studies.

ABSTRACT: Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings. We derive improved meta-score-statistics that can accurately approximate the joint-score-statistics with combined individual-level data, for both linear and logistic

SUBMITTER: Yang J 

PROVIDER: S-EPMC5980755 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature