Gene-environment dependencies lead to collider bias in models with polygenic scores

Abstract The application of polygenic scores has transformed our ability to investigate whether and how genetic and environmental factors jointly contribute to the variation of complex traits. Modelling the complex interplay between genes and environment, however, raises serious methodological chall...

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Autores principales:	Evelina T. Akimova, Richard Breen, David M. Brazel, Melinda C. Mills
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/761cc895c9364720913852b6f88955f3
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spelling	oai:doaj.org-article:761cc895c9364720913852b6f88955f32021-12-02T15:37:58ZGene-environment dependencies lead to collider bias in models with polygenic scores10.1038/s41598-021-89020-x2045-2322https://doaj.org/article/761cc895c9364720913852b6f88955f32021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89020-xhttps://doaj.org/toc/2045-2322Abstract The application of polygenic scores has transformed our ability to investigate whether and how genetic and environmental factors jointly contribute to the variation of complex traits. Modelling the complex interplay between genes and environment, however, raises serious methodological challenges. Here we illustrate the largely unrecognised impact of gene-environment dependencies on the identification of the effects of genes and their variation across environments. We show that controlling for heritable covariates in regression models that include polygenic scores as independent variables introduces endogenous selection bias when one or more of these covariates depends on unmeasured factors that also affect the outcome. This results in the problem of conditioning on a collider, which in turn leads to spurious associations and effect sizes. Using graphical and simulation methods we demonstrate that the degree of bias depends on the strength of the gene-covariate correlation and of hidden heterogeneity linking covariates with outcomes, regardless of whether the main analytic focus is mediation, confounding, or gene × covariate (commonly gene × environment) interactions. We offer potential solutions, highlighting the importance of causal inference. We also urge further caution when fitting and interpreting models with polygenic scores and non-exogenous environments or phenotypes and demonstrate how spurious associations are likely to arise, advancing our understanding of such results.Evelina T. AkimovaRichard BreenDavid M. BrazelMelinda C. MillsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Evelina T. Akimova Richard Breen David M. Brazel Melinda C. Mills Gene-environment dependencies lead to collider bias in models with polygenic scores
description	Abstract The application of polygenic scores has transformed our ability to investigate whether and how genetic and environmental factors jointly contribute to the variation of complex traits. Modelling the complex interplay between genes and environment, however, raises serious methodological challenges. Here we illustrate the largely unrecognised impact of gene-environment dependencies on the identification of the effects of genes and their variation across environments. We show that controlling for heritable covariates in regression models that include polygenic scores as independent variables introduces endogenous selection bias when one or more of these covariates depends on unmeasured factors that also affect the outcome. This results in the problem of conditioning on a collider, which in turn leads to spurious associations and effect sizes. Using graphical and simulation methods we demonstrate that the degree of bias depends on the strength of the gene-covariate correlation and of hidden heterogeneity linking covariates with outcomes, regardless of whether the main analytic focus is mediation, confounding, or gene × covariate (commonly gene × environment) interactions. We offer potential solutions, highlighting the importance of causal inference. We also urge further caution when fitting and interpreting models with polygenic scores and non-exogenous environments or phenotypes and demonstrate how spurious associations are likely to arise, advancing our understanding of such results.
format	article
author	Evelina T. Akimova Richard Breen David M. Brazel Melinda C. Mills
author_facet	Evelina T. Akimova Richard Breen David M. Brazel Melinda C. Mills
author_sort	Evelina T. Akimova
title	Gene-environment dependencies lead to collider bias in models with polygenic scores
title_short	Gene-environment dependencies lead to collider bias in models with polygenic scores
title_full	Gene-environment dependencies lead to collider bias in models with polygenic scores
title_fullStr	Gene-environment dependencies lead to collider bias in models with polygenic scores
title_full_unstemmed	Gene-environment dependencies lead to collider bias in models with polygenic scores
title_sort	gene-environment dependencies lead to collider bias in models with polygenic scores
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/761cc895c9364720913852b6f88955f3
work_keys_str_mv	AT evelinatakimova geneenvironmentdependenciesleadtocolliderbiasinmodelswithpolygenicscores AT richardbreen geneenvironmentdependenciesleadtocolliderbiasinmodelswithpolygenicscores AT davidmbrazel geneenvironmentdependenciesleadtocolliderbiasinmodelswithpolygenicscores AT melindacmills geneenvironmentdependenciesleadtocolliderbiasinmodelswithpolygenicscores
_version_	1718386180957929472

Gene-environment dependencies lead to collider bias in models with polygenic scores

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