Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects

• Published in: American journal of epidemiology Vol. 181; no. 4; pp. 251 - 260
• Main Authors: Burgess, Stephen, Thompson, Simon G.
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 15.02.2015; Oxford Publishing Limited (England)
• Subjects: Biomarkers - blood; Cardiovascular disease; Cardiovascular diseases; Causality; Cholesterol; Cholesterol, HDL - blood; Cholesterol, LDL - blood; Coronary Disease - blood; Coronary Disease - diagnosis; Coronary Disease - epidemiology; Coronary Disease - genetics; Epidemiology; Genetic Pleiotropy - genetics; Genetic variance; Genetic Variation - genetics; Genetics; Genotype & phenotype; Humans; Lipids; Low density lipoprotein; Mathematical Computing; Mendelian Randomization Analysis - methods; Models, Genetic; Practice of Epidemiology; Predictive Value of Tests; Risk Factors; Sensitivity and Specificity; Triglycerides - blood; United Kingdom - epidemiology; lipid fractions; epidemiologic methods; instrumental variables; Mendelian randomization; causal inference; pleiotropy
• ISSN: 0002-9262; 1476-6256; 1476-6256
• DOI: 10.1093/aje/kwu283

Abstract A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This “multivariable Mendelian randomization” approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.