Inference of gene-environment interaction from heterogeneous case-parent trios
Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845406/ https://www.ncbi.nlm.nih.gov/pubmed/36685810 http://dx.doi.org/10.3389/fgene.2022.1065568 |
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author | Ratnasekera, Pulindu Graham , Jinko McNeney, Brad |
author_facet | Ratnasekera, Pulindu Graham , Jinko McNeney, Brad |
author_sort | Ratnasekera, Pulindu |
collection | PubMed |
description | Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios. |
format | Online Article Text |
id | pubmed-9845406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98454062023-01-19 Inference of gene-environment interaction from heterogeneous case-parent trios Ratnasekera, Pulindu Graham , Jinko McNeney, Brad Front Genet Genetics Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9845406/ /pubmed/36685810 http://dx.doi.org/10.3389/fgene.2022.1065568 Text en Copyright © 2023 Ratnasekera, Graham and McNeney. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Ratnasekera, Pulindu Graham , Jinko McNeney, Brad Inference of gene-environment interaction from heterogeneous case-parent trios |
title | Inference of gene-environment interaction from heterogeneous case-parent trios |
title_full | Inference of gene-environment interaction from heterogeneous case-parent trios |
title_fullStr | Inference of gene-environment interaction from heterogeneous case-parent trios |
title_full_unstemmed | Inference of gene-environment interaction from heterogeneous case-parent trios |
title_short | Inference of gene-environment interaction from heterogeneous case-parent trios |
title_sort | inference of gene-environment interaction from heterogeneous case-parent trios |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845406/ https://www.ncbi.nlm.nih.gov/pubmed/36685810 http://dx.doi.org/10.3389/fgene.2022.1065568 |
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