Mendelian randomization analysis with multiple genetic variants using summarized data.

ABSTRACT: Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene-gene interactions, linkage disequilibrium, and 'weak instruments' on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized dat

SUBMITTER: Burgess S 

PROVIDER: S-EPMC4377079 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature