@article {86,
	title = {A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits.},
	journal = {Am J Hum Genet},
	volume = {106},
	year = {2020},
	month = {2020 01 02},
	pages = {71-91},
	abstract = {<p>Gene-environment interactions (GxE) can be fundamental in applications ranging from functional genomics to precision medicine and is a conjectured source of substantial heritability. However, unbiased methods to profile GxE genome-wide are nascent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and binary traits. To address this gap, we propose a simple, unifying mixed model for gene-environment interaction (GxEMM). In simulations and theory, we show that GxEMM can dramatically improve estimates and eliminate false positives when the assumptions of existing methods fail. We apply GxEMM to a range of human and model organism datasets and find broad evidence of context-specific genetic effects, including GxSex, GxAdversity, and GxDisease interactions across thousands of clinical and molecular phenotypes. Overall, GxEMM is broadly applicable for testing and quantifying polygenic interactions, which can be useful for explaining heritability and invaluable for determining biologically relevant environments.</p>},
	keywords = {Adult, Animals, Computer Simulation, Female, Gene-Environment Interaction, Genetic Markers, Genome-Wide Association Study, Humans, Male, Mental Disorders, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenomics, Phenotype, Rats},
	issn = {1537-6605},
	doi = {10.1016/j.ajhg.2019.11.015},
	author = {Dahl, Andy and Nguyen, Khiem and Cai, Na and Gandal, Michael J and Flint, Jonathan and Zaitlen, Noah}
}
