Vol. 73, No. 4, 2012
Issue release date: September 2012
Free Access
Hum Hered 2012;73:185–194
Original Paper
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Natural and Orthogonal Interaction Framework for Modeling Gene-Environment Interactions with Application to Lung Cancer

Ma 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
email Corresponding Author

 goto top of outline Key Words

  • Statistical power
  • Genetic association studies
  • Case-control association analysis
  • Gene-environment interaction
  • Environmental risk factor
  • Association mapping
  • Orthogonal modeling

 goto top of outline Abstract

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 © 2012 S. Karger AG, Basel

 goto top of outline References
  1. Donnelly P: Progress and challenges in genome-wide association studies in humans. Nature 2008;456:728–731.
  2. Amos C, Wu X, Broderick P, et al: Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet 2008;40:616–622.
  3. Maher B: Personal genomes: the case of the missing heritability. Nature 2008;456:18–21.
  4. Manolio T, Collins F, Cox N, et al: Finding the missing heritability of complex diseases. Nature 2009;461:747–753.
  5. Alvarez-Castro J, Carlborg O: A unified model for functional and statistical epistasis and its application in quantitative trait loci analysis. Genetics 2007;176:1151–1167.

    External Resources

  6. Alvarez-Castro JM, Le Rouzic A, Carlborg O: How to perform meaningful estimates of genetic effects. PLoS Genet 2008;4:e1000062.

    External Resources

  7. Truong T, Hung R, Amos C, et al: Replication of lung cancer susceptibility loci at chromosomes 15q25, 5p15, and 6p21: a pooled analysis from the International Lung Cancer Consortium. J Natl Cancer Inst 2010;102:959–971.

 goto top of outline Author Contacts

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 camos@mdanderson.org

 goto top of outline Article Information

Received: February 11, 2012
Accepted after revision: June 6, 2012
Published online: August 9, 2012
Number of Print Pages : 10
Number of Figures : 4, Number of Tables : 1, Number of References : 7
Additional supplementary material is available online - Number of Parts : 1

 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 73, No. 4, Year 2012 (Cover Date: September 2012)

Journal Editor: Devoto M. (Philadelphia, Pa./Rome)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE

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