Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors.

ABSTRACT: Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome as

SUBMITTER: Burgess S 

PROVIDER: S-EPMC4516908 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature