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Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exp...

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Autores principales: Pingault, Jean-Baptiste, Rijsdijk, Frühling, Schoeler, Tabea, Choi, Shing Wan, Selzam, Saskia, Krapohl, Eva, O’Reilly, Paul F., Dudbridge, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238188/
https://www.ncbi.nlm.nih.gov/pubmed/34115765
http://dx.doi.org/10.1371/journal.pgen.1009590
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author Pingault, Jean-Baptiste
Rijsdijk, Frühling
Schoeler, Tabea
Choi, Shing Wan
Selzam, Saskia
Krapohl, Eva
O’Reilly, Paul F.
Dudbridge, Frank
author_facet Pingault, Jean-Baptiste
Rijsdijk, Frühling
Schoeler, Tabea
Choi, Shing Wan
Selzam, Saskia
Krapohl, Eva
O’Reilly, Paul F.
Dudbridge, Frank
author_sort Pingault, Jean-Baptiste
collection PubMed
description Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes – child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely.
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spelling pubmed-82381882021-07-09 Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations Pingault, Jean-Baptiste Rijsdijk, Frühling Schoeler, Tabea Choi, Shing Wan Selzam, Saskia Krapohl, Eva O’Reilly, Paul F. Dudbridge, Frank PLoS Genet Research Article Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes – child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely. Public Library of Science 2021-06-11 /pmc/articles/PMC8238188/ /pubmed/34115765 http://dx.doi.org/10.1371/journal.pgen.1009590 Text en © 2021 Pingault 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 Research Article
Pingault, Jean-Baptiste
Rijsdijk, Frühling
Schoeler, Tabea
Choi, Shing Wan
Selzam, Saskia
Krapohl, Eva
O’Reilly, Paul F.
Dudbridge, Frank
Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations
title Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations
title_full Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations
title_fullStr Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations
title_full_unstemmed Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations
title_short Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations
title_sort genetic sensitivity analysis: adjusting for genetic confounding in epidemiological associations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238188/
https://www.ncbi.nlm.nih.gov/pubmed/34115765
http://dx.doi.org/10.1371/journal.pgen.1009590
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