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.
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() 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.
Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2019) Semi-parametric Bayesian variable selection for gene-environment interactions. https://arxiv.org/abs/1906.01057
Wu, C., Li, S., and Cui, Y. (2012). Genetic Association Studies: An Information Content Perspective. Current Genomics, 13(7), 566–573
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
Wu, C. and Cui, Y. (2013). Boosting signals in gene–based association studies via efficient SNP selection. Briefings in Bioinformatics, 15(2):279–291
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
Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics. Briefings in Bioinformatics, 16(5), 873–883
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
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)
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
Wu, C., Zhou, F., Ren, J., Li, X., Jiang, Y., Ma, S. (2019). A Selective Review of Multi-Level Omics Data Integration Using Variable Selection. High-Throughput, 8(1)
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