A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits

Andy Dahl; Khiem Nguyen; Na Cai; Michael J Gandal; Jonathan Flint; Noah Zaitlen

doi:10.1016/j.ajhg.2019.11.015

A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits

Am J Hum Genet. 2020 Jan 2;106(1):71-91. doi: 10.1016/j.ajhg.2019.11.015.

Authors

Andy Dahl¹, Khiem Nguyen², Na Cai³, Michael J Gandal⁴, Jonathan Flint⁵, Noah Zaitlen⁶

Affiliations

¹ Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA. Electronic address: [email protected].
² Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA.
³ Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
⁴ Department of Psychiatry, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁵ Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA.
⁶ Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA. Electronic address: [email protected].

Abstract

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.

Keywords: G-E correlation; GxE; disease subtypes; genetic heterogeneity; heritability; heteroskedasticity; linear mixed model; psychiatric disease.

MeSH terms

Adult
Animals
Computer Simulation
Female
Gene-Environment Interaction*
Genetic Markers*
Genome-Wide Association Study
Humans
Male
Mental Disorders / genetics*
Mental Disorders / pathology*
Middle Aged
Models, Genetic*
Multifactorial Inheritance / genetics*
Phenomics
Phenotype
Rats

Substances

Genetic Markers

Abstract

MeSH terms

Substances

Grants and funding