@article {20,
	title = {Testing for gene-environment interaction under exposure misspecification.},
	journal = {Biometrics},
	volume = {74},
	year = {2018},
	month = {2018 Jun},
	pages = {653-662},
	abstract = {<p>Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.</p>},
	keywords = {Bias, Confounding Factors, Epidemiologic, Environmental Exposure, Gene-Environment Interaction, Humans, Linear Models, Models, Genetic, Scientific Experimental Error},
	issn = {1541-0420},
	doi = {10.1111/biom.12813},
	author = {Sun, Ryan and Carroll, Raymond J and Christiani, David C and Lin, Xihong}
}
@article {24,
	title = {Lessons Learned From Past Gene-Environment Interaction Successes.},
	journal = {Am J Epidemiol},
	volume = {186},
	year = {2017},
	month = {2017 Oct 01},
	pages = {778-786},
	abstract = {<p>Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G{\texttimes}E) has been a focus of research for several years. In this article, select examples of G{\texttimes}E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G{\texttimes}E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G{\texttimes}Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G{\texttimes}E in several examples. Future studies may benefit from these lessons learned.</p>},
	keywords = {Biomedical Research, Disease, Environmental Exposure, Gene-Environment Interaction, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Models, Biological},
	issn = {1476-6256},
	doi = {10.1093/aje/kwx230},
	author = {Ritz, Beate R and Chatterjee, Nilanjan and Garcia-Closas, Montserrat and Gauderman, W James and Pierce, Brandon L and Kraft, Peter and Tanner, Caroline M and Mechanic, Leah E and McAllister, Kimberly}
}
