| spinBayes-package {spinBayes} | R Documentation |
spinBayes: Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection
Description
Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear G×E interactions simultaneously (Ren et al. (2020) doi:10.1002/sim.8434). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.
Within the Bayesian framework, we propose a partially linear varying coefficient model (PLVC) for G×E interactions. The varying coefficient functions capture the possible non-linear G×E interaction, and the linear component models the G×E interactions with linear assumptions. The changing of basis with B splines is adopted to separate the coefficient functions with varying, non-zero constant and zero forms, corresponding to cases of nonlinear interaction, main effect only (no interaction) and no genetic interaction at all.
Details
The user friendly, integrated interface BVCfit() allows users to flexibly choose the fitting methods they prefer. There are three arguments in BVCfit() that control the fitting method
| sparse: | whether to use the spike-and-slab priors to achieve sparsity. |
| VC: | whether to separate the coefficient functions with varying effects |
| and non-zero constant (main) effects. | |
| structural: | whether to use varying coefficient functions for modeling |
| non-linear GxE interactions. |
BVCfit() returns a BVCfit object that contains the posterior estimates of each coefficients. S3 generic functions BVSelection(), predict(), plot() and print() are implemented for BVCfit objects. BVSelection() takes a BVCfit object and returns the variable selection results. predict() takes a BVCfit object and returns the predicted values for new observations.
References
Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2020). Semiparametric Bayesian variable selection for gene-environment interactions. Statistics in Medicine, 39(5): 617–638. doi:10.1002/sim.8434.
Zhou, F., Ren, J., Lu, X., Ma, S., and Wu, C. (2021). Gene-Environment Interaction: A Variable Selection Perspective. Methods in Molecular Biology, 2212:191-223. doi:10.1007/978-1-0716-0947-7_13. PMID: 33733358.
Wu, C., Jiang, Y., Ren, J., Cui, Y., Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures. Statistics in Medicine, 37:437–456. doi:10.1002/sim.7518.
Wu, C., Zhong, P.-S., and Cui, Y. (2018). Additive varying–coefficient model for nonlinear gene–environment interactions. Statistical Applications in Genetics and Molecular Biology, 17(2). doi:10.1515/sagmb-2017-0008.
Jiang, Y., Huang, Y., Du, Y., Zhao, Y., Ren, J., Ma, S., Wu, C. (2017). Identification of prognostic genes and pathways in lung adenocarcinoma using a Bayesian Approach. Cancer Informatics, 1(7).
Wu, C., Shi, X., Cui, Y., and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions. Statistics in Medicine, 34(30): 4016–4030. doi:10.1002/sim.6609.
Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics. Briefings in Bioinformatics, 16(5), 873–883. doi:10.1093/bib/bbu046.
Wu, C., Cui, Y., and Ma, S. (2014). Integrative analysis of gene–environment interactions under a multi–response partially linear varying coefficient model. Statistics in Medicine, 33(28), 4988–4998. doi:10.1002/sim.6287.
Wu, C., and Cui, Y. (2013). Boosting signals in gene–based association studies via efficient SNP selection. Briefings in Bioinformatics, 15(2):279–291. doi:10.1093/bib/bbs087.
Wu, C., and Cui, Y. (2013). A novel method for identifying nonlinear gene–environment interactions in case–control association studies. Human Genetics, 132(12):1413–1425. doi:10.1007/s00439-013-1350-z.
Wu, C., Zhong, P.S., and Cui, Y. (2013). High dimensional variable selection for gene-environment interactions. Technical Report. Michigan State University.
Wu, C., Li, S., and Cui, Y. (2012). Genetic Association Studies: An Information Content Perspective. Current Genomics, 13(7), 566–573. doi:10.2174/138920212803251382.
See Also
Useful links: