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June 2015 Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies
Jiahan Li, Zhong Wang, Runze Li, Rongling Wu
Ann. Appl. Stat. 9(2): 640-664 (June 2015). DOI: 10.1214/15-AOAS808

Abstract

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.

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Jiahan Li. Zhong Wang. Runze Li. Rongling Wu. "Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies." Ann. Appl. Stat. 9 (2) 640 - 664, June 2015. https://doi.org/10.1214/15-AOAS808

Information

Received: 1 December 2012; Revised: 1 January 2015; Published: June 2015
First available in Project Euclid: 20 July 2015

zbMATH: 06499924
MathSciNet: MR3371329
Digital Object Identifier: 10.1214/15-AOAS808

Rights: Copyright © 2015 Institute of Mathematical Statistics

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Vol.9 • No. 2 • June 2015
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