Abstract
Testing for gene–environment (GE) interactions in a gene-set defined by a biological pathway can help us understand the interplay between genes and environments and provide insight into disease etiology. A self-contained gene-set analysis can be performed by combining gene-level p-values using approaches such as the Gamma Method. In a gene-set analysis of genetic main effects, permutation approaches are commonly used to avoid inflated probability of a type 1 error caused by correlation of genes within the same pathway. However, when testing interaction effects, it is typically not possible to construct an exact permutation test. We therefore propose using a parametric bootstrap. For testing an interaction term, this approach requires fitting the null model, which only contains main effects; however, for a gene-set GE interaction model, the number of main effects can be large and therefore they may not be estimable. To estimate the main effects of SNPs in a gene-set, we propose modeling them as random effects. We then repetitively simulate null data from this model and analyze it to generate the null distribution of gene-set GE p-values, allowing for an empirical assessment of significance of the global GE effect in the gene-set of interest. Through simulation, we demonstrate that this approach maintains correct type I error, and is well powered to detect GE interactions. We apply our method to test whether the association of obesity with bipolar disorder (BD) is modified by genetic variation in the Wnt signaling pathway.
Similar content being viewed by others
Log in or create a free account to read this content
Gain free access to this article, as well as selected content from this journal and more on nature.com
or
References
Fan Q, Verhoeven VJM, Wojciechowski R, et al. Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error. Nat Commun. 2016; 29(7):11008.
Thomas D. Gene-environment-wide association studies: emerging approaches. Nat Rev Genet. 2010;11:259–72.
Lin X, Lee S, Christiani DC, Lin X. Test for interactions between a genetic marker set and environment in generalized linear models. Biostatistics. 2013;14:667–81.
Lin X, Lee S, Wu MC, et al. Test for rare variants by environment interactions in sequencing association studies. Biometrics. 2016; 72(1):156–164.
Coombes B, Basu S, Mcgue M. A combination test fors detection of gene-environment interaction in cohort studies. Genet Epidemiol. 2017. https://doi.org/10.1002/gepi.22043.
Fridley BL, Biernacka JM. Gene set analysis of SNP data: benefits, challenges, and future directions. Eur J Hum Genet. 2011;19:837–43.
Biernacka JM, Jenkins GD, Wang L, Moyer AM, Fridley BL. Use of the gamma method for self-contained gene-set analysis of SNP data. Eur J Hum Genet. 2012. https://doi.org/10.1038/ejhg.2011.236.
de Leeuw CA, Neale BM, Heskes T, Posthuma D. The statistical properties of gene-set analysis. Nat Rev Genet. 2016;17:353–64.
de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:1–19.
Bužková P, Lumley T, Rice K. Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions. Ann Hum Genet. 2011;75:36–45.
Efron B. Bootstrap methods: another look at the jackknife. In: Breakthroughs in statistics. Annals Stat., 7 (1979), pp. 1-26
Winham SJ, Cuellar-Barboza AB, Oliveros A, et al. Genome-wide association study of bipolar disorder accounting for effect of body mass index identifies a new risk allele in TCF7L2. Mol Psychiatry. 2014;19:1010–6.
Lee JM, Dedhar S, Kalluri R, Thompson EW. The epithelial--mesenchymal transition: new insights in signaling, development, and disease. J Cell Biol. 2006;172:973–81.
Abu-Baker A, Laganiere J, Gaudet R, et al. Lithium chloride attenuates cell death in oculopharyngeal muscular dystrophy by perturbing Wnt/$β$-catenin pathway. Cell Death Dis. 2013;4:e821.
Valvezan AJ, Klein PS. GSK-3 and Wnt signaling in neurogenesis and bipolar disorder. Front Mol Neurosci. 2012;5. p. 1
Fisher R. Statistical methods for research workers. 1925. https://doi.org/10.1056/NEJMc061160.
Yang J, Lee SH, Goddard ME, Visscher PM. {GCTA}: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.
Hodges JS. Richly parameterized linear models: additive, time series, and spatial models using random effects. CRC Press; 2013. Boca Raton, FL.
Mazo Lopera MA, Coombes BJ, de Andrade M. An efficient test for gene-environment interaction in generalized linear mixed models with family data. Int J Environ Res Public Health. 2017;14:1134.
Chen H, Wang C, Conomos M, et al. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am J Hum Genet. 2016;98:653–66.
Smith EN, Bloss CS, Badner JA, et al. Genome-wide association study of bipolar disorder in European American and African American individuals. Mol Psychiatry. 2009;14:755–63.
Acknowledgements
This work was supported by a generous gift from the Marriot Family and the Mayo Clinic Center for Individualized Medicine.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Coombes, B.J., Biernacka, J.M. Application of the parametric bootstrap for gene-set analysis of gene–environment interactions. Eur J Hum Genet 26, 1679–1686 (2018). https://doi.org/10.1038/s41431-018-0236-x
Received:
Revised:
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1038/s41431-018-0236-x
