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

Ma, Jianzhong, Xiao, Feifei, Xiong, Momiao et al. (2012). Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer. . 73(4), 185-194. 10.1159/000339906

cited authors

  • 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, Erich; Yang, Ping; Amos, Christopher I

fiu authors

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.

publication date

  • January 1, 2012

keywords

  • Case-Control Studies
  • Computer Simulation
  • Databases, Factual
  • Early Detection of Cancer
  • Gene Frequency
  • Gene-Environment Interaction
  • Genetic Loci
  • Genetic Predisposition to Disease
  • Genetics, Population
  • Genome-Wide Association Study
  • Humans
  • Logistic Models
  • Lung Neoplasms
  • Models, Genetic
  • Polymorphism, Single Nucleotide
  • Quantitative Trait, Heritable
  • Smoking

Digital Object Identifier (DOI)

Medium

  • Print-Electronic

start page

  • 185

end page

  • 194

volume

  • 73

issue

  • 4