A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

Arunabha Majumdar; Kathryn S Burch; Tanushree Haldar; Sriram Sankararaman; Bogdan Pasaniuc; W James Gauderman; John S Witte

doi:10.1093/bioinformatics/btaa1083

A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

Bioinformatics. 2021 Apr 5;36(24):5640-5648. doi: 10.1093/bioinformatics/btaa1083.

Authors

Arunabha Majumdar^{1

2}, Kathryn S Burch³, Tanushree Haldar⁴, Sriram Sankararaman⁵, Bogdan Pasaniuc^{1

3}, W James Gauderman⁶, John S Witte²

Affiliations

¹ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
² Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
³ Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁴ Institute for Human Genetics, University of California, San Francisco, CA 94158, USA.
⁵ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁶ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA.

PMID: 33453114
DOI: 10.1093/bioinformatics/btaa1083

Abstract

Motivation: While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes.

Results: Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e. GxE pleiotropy), our approach offers substantial gain in power (18-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches.

Availability and implementation: We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

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