Nature Communications · 2020 · 705 citations · 43 references
Mendelian randomization uses genetic variants to infer causal relationships, but its reliability depends on the validity of those variants as instrumental variables. The authors introduce a contamination mixture method for MR that accommodates two modalities. The method first clusters variants with similar causal estimates to capture distinct mechanisms, then performs robust and efficient MR even when some IVs are invalid. Compared to other robust methods, it achieves the lowest mean‑squared error and identifies 11 variants linking HDL, triglycerides, and coronary heart disease risk through a shared platelet‑aggregation mechanism.
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.
43
Marie Verbanck, Chia‐Yen Chen, Benjamin M. Neale et al. · Nature Genetics · 2018 · 8.8K citations · Full text
Linkage Disequilibrium, Epidemiology, Genotype-phenotype Association +13