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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8238188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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