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Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data
Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to c...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376220/ https://www.ncbi.nlm.nih.gov/pubmed/34370750 http://dx.doi.org/10.1371/journal.pgen.1009703 |
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author | Barry, Ciarrah Liu, Junxi Richmond, Rebecca Rutter, Martin K. Lawlor, Deborah A. Dudbridge, Frank Bowden, Jack |
author_facet | Barry, Ciarrah Liu, Junxi Richmond, Rebecca Rutter, Martin K. Lawlor, Deborah A. Dudbridge, Frank Bowden, Jack |
author_sort | Barry, Ciarrah |
collection | PubMed |
description | Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias. |
format | Online Article Text |
id | pubmed-8376220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83762202021-08-20 Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data Barry, Ciarrah Liu, Junxi Richmond, Rebecca Rutter, Martin K. Lawlor, Deborah A. Dudbridge, Frank Bowden, Jack PLoS Genet Methods Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias. Public Library of Science 2021-08-09 /pmc/articles/PMC8376220/ /pubmed/34370750 http://dx.doi.org/10.1371/journal.pgen.1009703 Text en © 2021 Barry et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Methods Barry, Ciarrah Liu, Junxi Richmond, Rebecca Rutter, Martin K. Lawlor, Deborah A. Dudbridge, Frank Bowden, Jack Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data |
title | Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data |
title_full | Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data |
title_fullStr | Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data |
title_full_unstemmed | Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data |
title_short | Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data |
title_sort | exploiting collider bias to apply two-sample summary data mendelian randomization methods to one-sample individual level data |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376220/ https://www.ncbi.nlm.nih.gov/pubmed/34370750 http://dx.doi.org/10.1371/journal.pgen.1009703 |
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