A robust and efficient method for Mendelian randomization with hundreds of genetic variants
Year: 2020
Type: article
Abstract: Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.
Source: Nature Communications
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,380
Awards: Genetics and causality: towards more accessible and more reliable Mendelian randomization investigations, G1671907826, NIHR Senior Investigator Award, G1953244154, Large-scale integrative genomic studies of high-dimensional traits in cardiovascular diseases (renewal), G3240403866, G3725869576, G3986547339, G4897140007, Genetics and causality: towards more accessible and more reliable Mendelian randomization investigations, Large-scale integrative studies of risk factors in coronary heart disease: from discovery to application (renewal). (Joint funding with MRC), G762219591, G7939076303, G8131818847, G85616738, Large-scale integrative studies of risk factors in coronary heart disease: from discovery to application
OpenAlex ID: W2996249643
