Meta-Analysis for Penalized Regression Methods with Multi-Cohort Genome-Wide Association Studies.

ABSTRACT:

Objective

Penalized regression has been successfully applied in genome-wide association studies. While meta-analysis is often conducted to increase power and protect patients' confidentiality, methods for meta-analyzing results of penalized regression in multi-cohort setting are still under development.

Methods

We propose to use a data-splitting method to obtain valid p values (or equivalently, coefficient estimates and standard errors) for meta-analysis across multiple cohorts. We examine two ways of splitting data in multi-cohort setting and propose three methods to conduct meta-analysis based on p values. We compare the three meta-analysis methods to mega-analysis, which consists of pooling individual level data. We also apply our proposed meta-analysis approaches to the

SUBMITTER: Lu C 

PROVIDER: S-EPMC5331116 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature