An integrated approach to identify environmental modulators of genetic risk factors for complex traits

Brunilda Balliu; Ivan Carcamo-Orive; Michael J Gloudemans; Daniel C Nachun; Matthew G Durrant; Steven Gazal; Chong Y Park; David A Knowles; Martin Wabitsch; Thomas Quertermous; Joshua W Knowles; Stephen B Montgomery

doi:10.1016/j.ajhg.2021.08.014

An integrated approach to identify environmental modulators of genetic risk factors for complex traits

Am J Hum Genet. 2021 Oct 7;108(10):1866-1879. doi: 10.1016/j.ajhg.2021.08.014. Epub 2021 Sep 27.

Authors

Affiliations

¹ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: bballiu@ucla.edu.
² Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute and Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA.
³ Biomedical Informatics Training Program and Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁴ Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁵ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁶ Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
⁷ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁸ New York Genome Center, New York, NY 10013, USA; Department of Computer Science, Columbia University, New York, NY 10027, USA.
⁹ Department of Pediatrics and Adolescent Medicine, Division of Pediatric Endocrinology, Ulm University, Ulm 89075, Germany.
¹⁰ Department of Medicine, Division of Cardiology and Cardiovascular Institute, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹¹ Department of Medicine, Division of Cardiology and Cardiovascular Institute, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: knowlej@stanford.edu.
¹² Department of Pathology and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.

Abstract

Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal-muscle-, fat-, and liver-relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWASs from 31 distinct polygenic traits, we show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS Catalog. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.

Keywords: GWAS; colocalization; eQTL; gene expression; gene-by-environment interactions.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Autoimmune Diseases / etiology
Autoimmune Diseases / pathology
Gene-Environment Interaction*
Genetic Predisposition to Disease*
Genome-Wide Association Study*
Humans
Mental Disorders / etiology
Mental Disorders / pathology
Metabolic Diseases / etiology
Metabolic Diseases / pathology
Multifactorial Inheritance*
Phenotype
Polymorphism, Single Nucleotide*
Quantitative Trait Loci*

Abstract

Publication types

MeSH terms

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