The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 9, Number 2 (2015), 640-664.
Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies
Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS or fGWAS. The Bayesian group lasso and the associated MCMC algorithms are developed to identify significant SNPs and estimate how they affect longitudinal traits through time-varying genetic actions. The model is generalized to analyze the genetic control of complex traits using subject-specific sparse longitudinal data. The statistical properties of the new model are investigated through simulation studies. We use the new model to analyze a real GWAS data set from the Framingham Heart Study, leading to the identification of several significant SNPs associated with age-specific changes of body mass index. The fGWAS model, equipped with the Bayesian group lasso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases.
Ann. Appl. Stat., Volume 9, Number 2 (2015), 640-664.
Received: December 2012
Revised: January 2015
First available in Project Euclid: 20 July 2015
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Li, Jiahan; Wang, Zhong; Li, Runze; Wu, Rongling. Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies. Ann. Appl. Stat. 9 (2015), no. 2, 640--664. doi:10.1214/15-AOAS808. https://projecteuclid.org/euclid.aoas/1437397105
- Convergence diagnostics and summary of parameter estimates. We plot the potential scale reduction factor (PSRF) against iterations and summarize the average estimates, standard errors and mean squared errors (MSEs) of corresponding Legendre coefficients for the first five genetic predictors.