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

Jianzhong Ma, Feifei Xiao, Momiao Xiong, Angeline S. Andrew, Hermann Brenner, Eric J. Duell, Aage Haugen, Clive Hoggart, Rayjean J. Hung, Philip Lazarus, Changlu Liu, Keitaro Matsuo, Jose Ignacio Mayordomo, Ann G. Schwartz, Andrea Staratschek-Jox, Erich Wichmann, Ping Yang, Christopher I. Amos

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)185-194
Number of pages10
JournalHuman Heredity
Issue number4
StatePublished - Sep 2012


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

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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