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...

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Main Authors: Zihan Zhao, Jianjun Zhang, Qiuying Sha, Han Hao
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0229217
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spelling doaj-eefef02c42864718bf9b2437e2ae54912021-04-27T04:30:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01153e022921710.1371/journal.pone.0229217Testing gene-environment interactions for rare and/or common variants in sequencing association studies.Zihan ZhaoJianjun ZhangQiuying ShaHan HaoThe 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.https://doi.org/10.1371/journal.pone.0229217
collection DOAJ
language English
format Article
sources DOAJ
author Zihan Zhao
Jianjun Zhang
Qiuying Sha
Han Hao
spellingShingle Zihan Zhao
Jianjun Zhang
Qiuying Sha
Han Hao
Testing gene-environment interactions for rare and/or common variants in sequencing association studies.
PLoS ONE
author_facet Zihan Zhao
Jianjun Zhang
Qiuying Sha
Han Hao
author_sort Zihan Zhao
title 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_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_sort testing gene-environment interactions for rare and/or common variants in sequencing association studies.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2020-01-01
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.
url https://doi.org/10.1371/journal.pone.0229217
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