@article {146,
	title = {Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals.},
	journal = {Am J Hum Genet},
	volume = {108},
	year = {2021},
	month = {2021 06 03},
	pages = {965-982},
	abstract = {<p>Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.</p>},
	keywords = {Case-Control Studies, Cohort Studies, Epilepsy, Exome, Genetic Markers, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Humans, Phenotype, Whole Exome Sequencing},
	issn = {1537-6605},
	doi = {10.1016/j.ajhg.2021.04.009}
}
@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}
}
