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A novel method for identifying nonlinear gene–environment interactions in case–control association studies

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Abstract

The genetic influences on complex disease traits generally depend on the joint effects of multiple genetic variants, environmental factors, as well as their interplays. Gene × environment (G × E) interactions play vital roles in determining an individual’s disease risk, but the underlying genetic machinery is poorly understood. Traditional analysis assuming linear relationship between genetic and environmental factors, along with their interactions, is commonly pursued under the regression-based framework to examine G × E interactions. This assumption, however, could be violated due to nonlinear responses of genetic variants to environmental stimuli. As an extension to our previous work on continuous traits, we proposed a flexible varying-coefficient model for the detection of nonlinear G × E interaction with binary disease traits. Varying coefficients were approximated by a non-parametric regression function through which one can assess the nonlinear response of genetic factors to environmental changes. A group of statistical tests were proposed to elucidate various mechanisms of G × E interaction. The utility of the proposed method was illustrated via simulation and real data analysis with application to type 2 diabetes.

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Abbreviations

BIC:

Bayesian information criterion

BMI:

Body mass index

G × E:

Gene–environment interaction

GENVEA:

Gene, Environment Association Studies Consortium

GWAS:

Genome-wide association study

HPFS:

Health Professionals Follow-up Study

LM:

Linear predictor model

LM-I:

Linear predictor model with interaction

MAF:

Minor allele frequency

NHS:

Nurses’ Health Study

SNP:

Single nucleotide polymorphism

T2D:

Type 2 diabetes mellitus

VC:

Varying-coefficient

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Acknowledgments

The authors wish to thank three anonymous referees for their constructive comments that greatly improved the manuscript. This work was partially supported by NSF grant DMS-1209112 and by National Natural Science Foundation of China grant 31371336. Funding support for the GWAS of Gene and Environment Initiatives in Type 2 Diabetes was provided through the NIH Genes, Environment and Health Initiative [GEI] (U01HG004399). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000091.v2.p1, through dbGaP accession number phs000091.v2.p1.

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The authors declare no conflict of interest.

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Authors and Affiliations

  1. Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA

    Cen Wu & Yuehua Cui

  2. Division of Medical Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China

    Yuehua Cui

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  1. Cen Wu
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  2. Yuehua Cui
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Correspondence to Yuehua Cui.

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Wu, C., Cui, Y. A novel method for identifying nonlinear gene–environment interactions in case–control association studies. Hum Genet 132, 1413–1425 (2013). https://doi.org/10.1007/s00439-013-1350-z

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