Statistical methods for cis-Mendelian randomization with two-sample summary-level data.
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
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 variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.
Description
Keywords
Journal Title
Conference Name
Journal ISSN
1098-2272
Volume Title
Publisher
Publisher DOI
Rights
Sponsorship
Medical Research Council (MC_UU_00002/7)
British Heart Foundation (None)
British Heart Foundation (CH/12/2/29428)
British Heart Foundation (RG/18/13/33946)