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Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects
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 f...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325677/ https://www.ncbi.nlm.nih.gov/pubmed/25632051 http://dx.doi.org/10.1093/aje/kwu283 |
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author | Burgess, Stephen Thompson, Simon G. |
author_facet | Burgess, Stephen Thompson, Simon G. |
author_sort | Burgess, Stephen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4325677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-43256772015-03-02 Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects Burgess, Stephen Thompson, Simon G. Am J Epidemiol Practice of Epidemiology 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. Oxford University Press 2015-02-15 2015-01-27 /pmc/articles/PMC4325677/ /pubmed/25632051 http://dx.doi.org/10.1093/aje/kwu283 Text en © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Practice of Epidemiology Burgess, Stephen Thompson, Simon G. Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects |
title | Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects |
title_full | Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects |
title_fullStr | Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects |
title_full_unstemmed | Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects |
title_short | Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects |
title_sort | multivariable mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects |
topic | Practice of Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4325677/ https://www.ncbi.nlm.nih.gov/pubmed/25632051 http://dx.doi.org/10.1093/aje/kwu283 |
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