No More Free Lunch: Challenges to Mendelian Randomization Due to Sample Selection and Complex Methods.

Abstract

Mendelian randomization (MR) is increasingly used in epidemiological studies to investigate causal relationships. MR depends on 3 fundamental instrumental variable assumptions: relevance, independence, and exclusion restriction. Studies often assume that MR mitigates bias from confounding due to the random allocation of genetic variants at conception. In this perspective, using causal directed acyclic graphs, we discuss several scenarios where biases in MR analyses may arise due to the nature of the data or methods being used. These include (1) collider bias due to the nonrandom selection of participants into study populations used for conducting genome-wide association studies (GWAS), (2) indirect genetic effects arising from population-based GWAS rather than within-family studies, and (3) collider bias due to gene-environment interaction effects on the exposure in nonlinear MR analyses. We provide practical considerations for examining and reducing these biases in MR analyses.