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An exposure‐weighted score test for genetic associations integrating environmental risk factors

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  • Summer S. Han
  • Philip S. Rosenberg
  • Arpita Ghosh
  • Maria Teresa Landi
  • Neil E. Caporaso
  • Nilanjan Chatterjee

Abstract

Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene–environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome‐wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.

Suggested Citation

  • Summer S. Han & Philip S. Rosenberg & Arpita Ghosh & Maria Teresa Landi & Neil E. Caporaso & Nilanjan Chatterjee, 2015. "An exposure‐weighted score test for genetic associations integrating environmental risk factors," Biometrics, The International Biometric Society, vol. 71(3), pages 596-605, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:596-605
    DOI: 10.1111/biom.12328
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    1. Nilanjan Chatterjee & Raymond J. Carroll, 2005. "Semiparametric maximum likelihood estimation exploiting gene-environment independence in case-control studies," Biometrika, Biometrika Trust, vol. 92(2), pages 399-418, June.
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    1. Liang, Liang & Ma, Yanyuan & Carroll, Raymond J., 2019. "A semiparametric efficient estimator in case-control studies for gene–environment independent models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 38-50.

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