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

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...

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Veröffentlicht in:American journal of epidemiology Jg. 181; H. 4; S. 251 - 260
Hauptverfasser: Burgess, Stephen, Thompson, Simon G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Oxford University Press 15.02.2015
Schlagworte:
Biomarkers - blood
Causality
Cholesterol, HDL - blood
Cholesterol, LDL - blood
Coronary Disease - blood
Coronary Disease - diagnosis
Coronary Disease - epidemiology
Coronary Disease - genetics
Genetic Pleiotropy - genetics
Genetic Variation - genetics
Humans
Mathematical Computing
Mendelian Randomization Analysis - methods
Models, Genetic
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0002-9262
1476-6256
1476-6256
DOI:10.1093/aje/kwu283