TY - JOUR
T1 - Genetic sensitivity analysis
T2 - Adjusting for genetic confounding in epidemiological associations
AU - Pingault, Jean Baptiste
AU - Rijsdijk, Fruhling
AU - Schoeler, Tabea
AU - Choi, Shing Wan
AU - Selzam, Saskia
AU - Krapohl, Eva
AU - O'Reilly, Paul F.
AU - Dudbridge, Frank
N1 - Publisher Copyright:
© 2021 Pingault et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/6/11
Y1 - 2021/6/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85108157239&partnerID=8YFLogxK
U2 - 10.1371/journal.pgen.1009590
DO - 10.1371/journal.pgen.1009590
M3 - Article
C2 - 34115765
AN - SCOPUS:85108157239
SN - 1553-7390
VL - 17
JO - PLoS Genetics
JF - PLoS Genetics
IS - 6
M1 - e1009590
ER -