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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Detalles Bibliográficos
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
Descripción
Sumario: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

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