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Natural and Orthogonal Interaction Framework for Modeling Gene-Environment Interactions with Application to Lung CancerMa J.a · Xiao F.a · Xiong M.b · Andrew A.S.c · Brenner H.g · Duell E.J.j · Haugen A.l · Hoggart C.m · Hung R.J.n · Lazarus P.d · Liu C.a · Matsuo K.o · Mayordomo J.I.k · Schwartz A.G.e · Staratschek-Jox A.h · Wichmann E.i · Yang P.f · Amos C.I.a
aDepartment of Genetics, The University of Texas MD Anderson Cancer Center, and bHuman Genetics Center, University of Texas School of Public Health, Houston, Tex., cDepartment of Community and Family Medicine, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, N.H., dDepartments of Pharmacology and Public Health Sciences, Penn State College of Medicine, Hearshey, Pa., eKarmanos Cancer Institute and Department of Oncology, Wayne State University School of Medicine, Detroit, Mich., and fMayo Clinic Cancer Center, Rochester, Minn., USA; gDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, hLife and Medical Sciences Bonn, Genomics and Immunoregulation, University of Bonn, Bonn, and iHelmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany; jUnit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, and kServicio de Oncologia Medica, Hospital Clinico Universitario, Zaragoza, Spain; lThe National Institute of Occupational Health, Oslo, Norway; mEpidemiology Unit, London School of Hygiene and Tropical Medicine, London, UK; nSamuel Lunenfeld Research Institute, Toronto, Ont., Canada; oDivision of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan Corresponding Author
Christopher I. Amos, PhD
Department of Genetics, Unit 209
University of Texas MD Anderson Cancer Center
1515 Holcombe Blvd., Houston, TX 77030 (USA)
Tel. +1 713 792 3020, E-Mail firstname.lastname@example.org
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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