Statistical methods for cis-Mendelian randomization with two-sample summary-level data.

ABSTRACT: Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian

SUBMITTER: Gkatzionis A 

PROVIDER: S-EPMC7614127 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature