@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 {16,
	title = {Testing for the indirect effect under the null for genome-wide mediation analyses.},
	journal = {Genet Epidemiol},
	volume = {41},
	year = {2017},
	month = {2017 Dec},
	pages = {824-833},
	abstract = {<p>Mediation analysis helps researchers assess whether part or all of an exposure{\textquoteright}s effect on an outcome is due to an intermediate variable. The indirect effect can help in designing interventions on the mediator as opposed to the exposure and better understanding the outcome{\textquoteright}s mechanisms. Mediation analysis has seen increased use in genome-wide epidemiological studies to test for an exposure of interest being mediated through a genomic measure such as gene expression or DNA methylation (DNAm). Testing for the indirect effect is challenged by the fact that the null hypothesis is composite. We examined the performance of commonly used mediation testing methods for the indirect effect in genome-wide mediation studies. When there is no association between the exposure and the mediator and no association between the mediator and the outcome, we show that these common tests are overly conservative. This is a case that will arise frequently in genome-wide mediation studies. Caution is hence needed when applying the commonly used mediation tests in genome-wide mediation studies. We evaluated the performance of these methods using simulation studies, and performed an epigenome-wide mediation association study in the Normative Aging Study, analyzing DNAm as a mediator of the effect of pack-years on FEV .</p>},
	keywords = {Basic Helix-Loop-Helix Transcription Factors, DNA Methylation, Epigenomics, Genome-Wide Association Study, Humans, Lung Neoplasms, Models, Genetic, Repressor Proteins},
	issn = {1098-2272},
	doi = {10.1002/gepi.22084},
	author = {Barfield, Richard and Shen, Jincheng and Just, Allan C and Vokonas, Pantel S and Schwartz, Joel and Baccarelli, Andrea A and VanderWeele, Tyler J and Lin, Xihong}
}
