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Cauchy combination methods for the detection of gene–environment interactions for rare variants related to quantitative phenotypes

Heredity volume 131, pages 241–252 (2023)Cite this article

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Abstract

The characterization of gene–environment interactions (GEIs) can provide detailed insights into the biological mechanisms underlying complex diseases. Despite recent interest in GEIs for rare variants, published GEI tests are underpowered for an extremely small proportion of causal rare variants in a gene or a region. By extending the aggregated Cauchy association test (ACAT), we propose three GEI tests to address this issue: a Cauchy combination GEI test with fixed main effects (CCGEI-F), a Cauchy combination GEI test with random main effects (CCGEI-R), and an omnibus Cauchy combination GEI test (CCGEI-O). ACAT was applied to combine p values of single-variant GEI analyses to obtain CCGEI-F and CCGEI-R and p values of multiple GEI tests were combined in CCGEI-O. Through numerical simulations, for small numbers of causal variants, CCGEI-F, CCGEI-R and CCGEI-O provided approximately 5% higher power than the existing GEI tests INT-FIX and INT-RAN; however, they had slightly higher power than the existing GEI test TOW-GE. For large numbers of causal variants, although CCGEI-F and CCGEI-R exhibited comparable or slightly lower power values than the competing tests, the results were still satisfactory. Among all simulation conditions evaluated, CCGEI-O provided significantly higher power than that of competing GEI tests. We further applied our GEI tests in genome-wide analyses of systolic blood pressure or diastolic blood pressure to detect gene–body mass index (BMI) interactions, using whole-exome sequencing data from UK Biobank. At a suggestive significance level of 1.0 × 10−4, KCNC4, GAR1, FAM120AOS and NT5C3B showed interactions with BMI by our GEI tests.

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Fig. 1: Q-Q plots of the null distributions of the test statistics with two choices of beta weights and a sample size of 4000.
Fig. 2: Q-Q plots of the null distributions of the test statistic for CCGEI-O with a sample size of 4000.
Fig. 3: False positive rate of different GEI tests at nominal 0.05 level.
Fig. 4: Statistical power of test statistics with different sample sizes, proportions of rare causal variants, and scenarios for genetic and interaction effects.
Fig. 5: Manhattan plots of genome-wide GEI analyses of gene–BMI interactions in SBP.
Fig. 6: Manhattan plots of genome-wide GEI analyses of gene–BMI interactions in DBP.
Fig. 7: Q-Q plots of genome-wide GEI analyses of gene–BMI interactions in SBP.
Fig. 8: Q-Q plots of genome-wide GEI analyses of gene–BMI interactions in DBP.

Data availability

This research was conducted using the UK Biobank Resource under Application Number 44080. Corresponding R codes for testing GEI effects in this article are available at GitHub: https://github.com/jlyx53/CCGEI.

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Acknowledgements

We are very grateful to the editor and two reviewers for their insightful comments and suggestions, which helped improve the quality of this manuscript.

Funding

This work was supported by the National Thousand Youth Talents Plan.

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  1. State Key Laboratory of Integrated Services Networks, Xidian University, 2 South Taibai Road, Xi’an, Shaanxi, 710071, China

    Xiaoqin Jin & Gang Shi

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  1. Xiaoqin Jin
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XJ and GS designed the study and conceptualized the idea for the analyses. XJ performed the analyses, analyzed data, interpreted the results and wrote the manuscript. XJ and GS supervised the study, and contributed to and reviewed the final manuscript.

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Correspondence to Xiaoqin Jin.

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Jin, X., Shi, G. Cauchy combination methods for the detection of gene–environment interactions for rare variants related to quantitative phenotypes. Heredity 131, 241–252 (2023). https://doi.org/10.1038/s41437-023-00640-7

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