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Ma, J.Z.* ; Xiao, F.F.* ; Xiong, M.* ; Andrew, A.S.* ; Brenner, H.* ; Duell, E.J.* ; Haugen, A.* ; Hoggart, C.* ; Hung, R.J.* ; Lazarus, P.* ; Liu, C.L.* ; Matsuo, K.* ; Mayordomok, J.I.* ; Schwartz, A.G.* ; Staratschek-Jox, A.* ; Wichmann, H.-E. ; Yang, P.* ; Amos, C.I.*

Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer.

Hum. Hered. 73, 185-194 (2012)
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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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Statistical Power ; Genetic Association Studies ; Case-control Association Analysis ; Gene-environment Interaction Environmental Risk Factor ; Association Mapping ; Orthogonal Modeling; GENOME-WIDE ASSOCIATION; MISSING HERITABILITY; LOCI
Language english
Publication Year 2012
HGF-reported in Year 2012
ISSN (print) / ISBN 0001-5652
e-ISSN 1423-0062
Journal Human Heredity
Quellenangaben Volume: 73, Issue: 4, Pages: 185-194 Article Number: , Supplement: ,
Publisher Karger
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
POF-Topic(s) 30503 - Chronic Diseases of the Lung and Allergies
Research field(s) Genetics and Epidemiology
PSP Element(s) G-503900-001
PubMed ID 22889990
DOI 10.1159/000339906
WOS ID WOS:000309381500001
Scopus ID 84864798530
Erfassungsdatum 2012-10-26
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