Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer

Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of...

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Autores: Ma, Jianzhong, Xiao, Feifei, Xiong, Momiao, Andrew, Angeline S., Brenner, Hermann, Duell, Eric J., Haugen, Aage, Hoggart, Clive, Hung, Rayjean J., Lazarus, Philip, Liu, Changlu, Matsuo, Keitaro, Mayordomo, Jose Ignacio, Schwartz, Ann G., Staratschek-Jox, Andrea, Wichmann, H.-Erich, Yang, Ping, Amos, Christopher I.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2012
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/126563
Acceso en línea:https://hdl.handle.net/2445/126563
Access Level:acceso abierto
Palabra clave:Càncer de pulmó
Interacció cel·lular
Lung cancer
Cell interaction
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spelling Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancerMa, JianzhongXiao, FeifeiXiong, MomiaoAndrew, Angeline S.Brenner, HermannDuell, Eric J.Haugen, AageHoggart, CliveHung, Rayjean J.Lazarus, PhilipLiu, ChangluMatsuo, KeitaroMayordomo, Jose IgnacioSchwartz, Ann G.Staratschek-Jox, AndreaWichmann, H.-ErichYang, PingAmos, Christopher I.Càncer de pulmóInteracció cel·lularLung cancerCell interactionObjectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits. Copyright (C) 2012 S. Karger AG, BaselKarger2018201820122018info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion17 p.application/pdfhttps://hdl.handle.net/2445/126563Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1159/000339906Human Heredity, 2012, vol. 73, num. 4, p. 185-194https://doi.org/10.1159/000339906(c) Karger, 2012info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1265632026-05-29T05:05:01Z
dc.title.none.fl_str_mv Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
title Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
spellingShingle Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
Ma, Jianzhong
Càncer de pulmó
Interacció cel·lular
Lung cancer
Cell interaction
title_short Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
title_full Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
title_fullStr Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
title_full_unstemmed Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
title_sort Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer
dc.creator.none.fl_str_mv Ma, Jianzhong
Xiao, Feifei
Xiong, Momiao
Andrew, Angeline S.
Brenner, Hermann
Duell, Eric J.
Haugen, Aage
Hoggart, Clive
Hung, Rayjean J.
Lazarus, Philip
Liu, Changlu
Matsuo, Keitaro
Mayordomo, Jose Ignacio
Schwartz, Ann G.
Staratschek-Jox, Andrea
Wichmann, H.-Erich
Yang, Ping
Amos, Christopher I.
author Ma, Jianzhong
author_facet Ma, Jianzhong
Xiao, Feifei
Xiong, Momiao
Andrew, Angeline S.
Brenner, Hermann
Duell, Eric J.
Haugen, Aage
Hoggart, Clive
Hung, Rayjean J.
Lazarus, Philip
Liu, Changlu
Matsuo, Keitaro
Mayordomo, Jose Ignacio
Schwartz, Ann G.
Staratschek-Jox, Andrea
Wichmann, H.-Erich
Yang, Ping
Amos, Christopher I.
author_role author
author2 Xiao, Feifei
Xiong, Momiao
Andrew, Angeline S.
Brenner, Hermann
Duell, Eric J.
Haugen, Aage
Hoggart, Clive
Hung, Rayjean J.
Lazarus, Philip
Liu, Changlu
Matsuo, Keitaro
Mayordomo, Jose Ignacio
Schwartz, Ann G.
Staratschek-Jox, Andrea
Wichmann, H.-Erich
Yang, Ping
Amos, Christopher I.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Càncer de pulmó
Interacció cel·lular
Lung cancer
Cell interaction
topic Càncer de pulmó
Interacció cel·lular
Lung cancer
Cell interaction
description Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits. Copyright (C) 2012 S. Karger AG, Basel
publishDate 2012
dc.date.none.fl_str_mv 2012
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/126563
url https://hdl.handle.net/2445/126563
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1159/000339906
Human Heredity, 2012, vol. 73, num. 4, p. 185-194
https://doi.org/10.1159/000339906
dc.rights.none.fl_str_mv (c) Karger, 2012
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Karger, 2012
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17 p.
application/pdf
dc.publisher.none.fl_str_mv Karger
publisher.none.fl_str_mv Karger
dc.source.none.fl_str_mv Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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