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

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)596-605
Number of pages10
Issue number3
StatePublished - Sep 1 2015
Externally publishedYes


  • Environmental exposures
  • GWAS
  • Gene-environment interaction
  • SNPs
  • Score test

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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