@article {23,
	title = {Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.},
	journal = {Am J Epidemiol},
	volume = {186},
	year = {2017},
	month = {2017 Oct 01},
	pages = {771-777},
	abstract = {<p>A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.</p>},
	keywords = {Animals, Disease, Gene-Environment Interaction, Genome-Wide Association Study, Genomics, Humans, Models, Animal, Sequence Analysis, RNA},
	issn = {1476-6256},
	doi = {10.1093/aje/kwx229},
	author = {Ritchie, Marylyn D and Davis, Joe R and Aschard, Hugues and Battle, Alexis and Conti, David and Du, Mengmeng and Eskin, Eleazar and Fallin, M Daniele and Hsu, Li and Kraft, Peter and Moore, Jason H and Pierce, Brandon L and Bien, Stephanie A and Thomas, Duncan C and Wei, Peng and Montgomery, Stephen B}
}
@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}
}
