Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies

• Published in: The American journal of clinical nutrition Vol. 103; no. 4; pp. 965 - 978
• Main Authors: Haycock, Philip C, Burgess, Stephen, Wade, Kaitlin H, Bowden, Jack, Relton, Caroline, Davey Smith, George
• Format: Journal Article
• Language: English
• Published: United States American Society for Clinical Nutrition, Inc 01.04.2016; American Society for Nutrition
• Subjects: Causality; Coronary Artery Disease - diagnosis; Coronary Artery Disease - genetics; cost effectiveness; Cost-Benefit Analysis; Databases, Genetic; disease prevention; Epidemiology; experimental design; Genetic Association Studies; genome-wide association study; Genotype; Genotype & phenotype; health policy; Humans; inheritance (genetics); Interleukin-6 - genetics; Interleukin-6 - metabolism; Intervention; Mendelian Randomization Analysis - methods; Polymorphism, Single Nucleotide; Public health; Receptors, Interleukin-6 - genetics; Receptors, Interleukin-6 - metabolism; Reproducibility of Results; Research Design; Risk Factors; Statistical
• ISSN: 0002-9165; 1938-3207; 1938-3207
• DOI: 10.3945/ajcn.115.118216

Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel’s first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genome-wide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy.