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An exposure‐weighted score test for genetic associations integrating environmental risk factors
Author(s) -
Han Summer S.,
Rosenberg Philip S.,
Ghosh Arpita,
Landi Maria Teresa,
Caporaso Neil E.,
Chatterjee Nilanjan
Publication year - 2015
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12328
Subject(s) - logistic regression , statistics , genetic association , probit model , score test , genome wide association study , probit , range (aeronautics) , computer science , econometrics , biology , statistical hypothesis testing , mathematics , genetics , engineering , single nucleotide polymorphism , genotype , gene , aerospace engineering
Summary 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.