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Testing gene-environment interactions for rare and/or common variants in sequencing association studies
The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064198/ https://www.ncbi.nlm.nih.gov/pubmed/32155162 http://dx.doi.org/10.1371/journal.pone.0229217 |
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author | Zhao, Zihan Zhang, Jianjun Sha, Qiuying Hao, Han |
author_facet | Zhao, Zihan Zhang, Jianjun Sha, Qiuying Hao, Han |
author_sort | Zhao, Zihan |
collection | PubMed |
description | The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on testing the main effects of common and/or rare genetic variants. There are limited methods developed to test the effects of GE interactions for rare variants only or rare and common variants simultaneously. In this study, we develop novel approaches to test the effects of GE interactions of rare and/or common risk, and/or protective variants in sequencing association studies. We propose two approaches: 1) testing the effects of an optimally weighted combination of GE interactions for rare variants (TOW-GE); 2) testing the effects of a weighted combination of GE interactions for both rare and common variants (variable weight TOW-GE, VW-TOW-GE). Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are GE interactions’ effects for rare risk and/or protective variants; VW-TOW-GE is more powerful when there are GE interactions’ effects for both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to the directions of effects of causal GE interactions. We demonstrate the applications of TOW-GE and VW-TOW-GE using an imputed data from the COPDGene Study. |
format | Online Article Text |
id | pubmed-7064198 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70641982020-03-23 Testing gene-environment interactions for rare and/or common variants in sequencing association studies Zhao, Zihan Zhang, Jianjun Sha, Qiuying Hao, Han PLoS One Research Article The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on testing the main effects of common and/or rare genetic variants. There are limited methods developed to test the effects of GE interactions for rare variants only or rare and common variants simultaneously. In this study, we develop novel approaches to test the effects of GE interactions of rare and/or common risk, and/or protective variants in sequencing association studies. We propose two approaches: 1) testing the effects of an optimally weighted combination of GE interactions for rare variants (TOW-GE); 2) testing the effects of a weighted combination of GE interactions for both rare and common variants (variable weight TOW-GE, VW-TOW-GE). Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are GE interactions’ effects for rare risk and/or protective variants; VW-TOW-GE is more powerful when there are GE interactions’ effects for both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to the directions of effects of causal GE interactions. We demonstrate the applications of TOW-GE and VW-TOW-GE using an imputed data from the COPDGene Study. Public Library of Science 2020-03-10 /pmc/articles/PMC7064198/ /pubmed/32155162 http://dx.doi.org/10.1371/journal.pone.0229217 Text en © 2020 Zhao et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 Zhao, Zihan Zhang, Jianjun Sha, Qiuying Hao, Han Testing gene-environment interactions for rare and/or common variants in sequencing association studies |
title | Testing gene-environment interactions for rare and/or common variants in sequencing association studies |
title_full | Testing gene-environment interactions for rare and/or common variants in sequencing association studies |
title_fullStr | Testing gene-environment interactions for rare and/or common variants in sequencing association studies |
title_full_unstemmed | Testing gene-environment interactions for rare and/or common variants in sequencing association studies |
title_short | Testing gene-environment interactions for rare and/or common variants in sequencing association studies |
title_sort | testing gene-environment interactions for rare and/or common variants in sequencing association studies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064198/ https://www.ncbi.nlm.nih.gov/pubmed/32155162 http://dx.doi.org/10.1371/journal.pone.0229217 |
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