A flexible Bayesian model for studying gene-environment interaction.
An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2012-01-01
|
| Series: | PLoS Genetics |
| Online Access: | http://europepmc.org/articles/PMC3266891?pdf=render |
| _version_ | 1811230547888832512 |
|---|---|
| author | Kai Yu Sholom Wacholder William Wheeler Zhaoming Wang Neil Caporaso Maria Teresa Landi Faming Liang |
| author_facet | Kai Yu Sholom Wacholder William Wheeler Zhaoming Wang Neil Caporaso Maria Teresa Landi Faming Liang |
| author_sort | Kai Yu |
| collection | DOAJ |
| description | An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene-environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene-environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene-environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene-environment interaction based on the single-marker approach is far from significant. |
| first_indexed | 2024-04-12T10:31:26Z |
| format | Article |
| id | doaj.art-43b8bae31b7f4ab69bb35f35811baf17 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-04-12T10:31:26Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-43b8bae31b7f4ab69bb35f35811baf172022-12-22T03:36:51ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042012-01-0181e100248210.1371/journal.pgen.1002482A flexible Bayesian model for studying gene-environment interaction.Kai YuSholom WacholderWilliam WheelerZhaoming WangNeil CaporasoMaria Teresa LandiFaming LiangAn important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene-environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene-environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene-environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene-environment interaction based on the single-marker approach is far from significant.http://europepmc.org/articles/PMC3266891?pdf=render |
| spellingShingle | Kai Yu Sholom Wacholder William Wheeler Zhaoming Wang Neil Caporaso Maria Teresa Landi Faming Liang A flexible Bayesian model for studying gene-environment interaction. PLoS Genetics |
| title | A flexible Bayesian model for studying gene-environment interaction. |
| title_full | A flexible Bayesian model for studying gene-environment interaction. |
| title_fullStr | A flexible Bayesian model for studying gene-environment interaction. |
| title_full_unstemmed | A flexible Bayesian model for studying gene-environment interaction. |
| title_short | A flexible Bayesian model for studying gene-environment interaction. |
| title_sort | flexible bayesian model for studying gene environment interaction |
| url | http://europepmc.org/articles/PMC3266891?pdf=render |
| work_keys_str_mv | AT kaiyu aflexiblebayesianmodelforstudyinggeneenvironmentinteraction AT sholomwacholder aflexiblebayesianmodelforstudyinggeneenvironmentinteraction AT williamwheeler aflexiblebayesianmodelforstudyinggeneenvironmentinteraction AT zhaomingwang aflexiblebayesianmodelforstudyinggeneenvironmentinteraction AT neilcaporaso aflexiblebayesianmodelforstudyinggeneenvironmentinteraction AT mariateresalandi aflexiblebayesianmodelforstudyinggeneenvironmentinteraction AT famingliang aflexiblebayesianmodelforstudyinggeneenvironmentinteraction AT kaiyu flexiblebayesianmodelforstudyinggeneenvironmentinteraction AT sholomwacholder flexiblebayesianmodelforstudyinggeneenvironmentinteraction AT williamwheeler flexiblebayesianmodelforstudyinggeneenvironmentinteraction AT zhaomingwang flexiblebayesianmodelforstudyinggeneenvironmentinteraction AT neilcaporaso flexiblebayesianmodelforstudyinggeneenvironmentinteraction AT mariateresalandi flexiblebayesianmodelforstudyinggeneenvironmentinteraction AT famingliang flexiblebayesianmodelforstudyinggeneenvironmentinteraction |