title: Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies.
abstract: BACKGROUND
  Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this "missing heritability" problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets.
  
  
  RESULTS
  Simulation studies indicated that the iBVS method was advantageous in its performance with highest AUC in both variable selection and outcome prediction, when compared to Stepwise and LASSO based strategies. In an analysis of a leprosy case-control study, iBVS selected 94 SNPs as predictors, while LASSO selected 100 SNPs. The Stepwise regression yielded a more parsimonious model with only 3 SNPs. The prediction results demonstrated that the iBVS method had comparable performance with that of LASSO, but better than Stepwise strategies.
  
  
  CONCLUSIONS
  The proposed iBVS strategy is a novel and valid method for Genome-wide Association Studies, with the additional advantage in that it produces more interpretable posterior probabilities for each variable unlike LASSO and other penalized regression methods.
authors.affiliation: School of Public Health, Shandong University, Jinan, Shandong, 250012, China. zhxiaoshuai@gmail.com.
authors.forename: Xiaoshuai
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Public Health, Shandong University, Jinan, Shandong, 250012, China. xuefzh@sdu.edu.cn.
authors.forename: Fuzhong
authors.initials: F
authors.lastname: Xue
authors.affiliation: Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Science, Jinan, Shandong, 250022, China. 1450407248@qq.com.
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
authors.affiliation: Bayessoft, Inc., 2221 Caravaggio Drive, Davis, CA, 95618, USA. dzhu2010@gmail.com.
authors.forename: Dianwen
authors.initials: D
authors.lastname: Zhu
authors.affiliation: School of Public Health, Chongqing Medical University, Chongqing, 400016, China. cellepy@gmail.com.
authors.forename: Bin
authors.initials: B
authors.lastname: Peng
authors.affiliation: Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, 94158, USA. joe.wiemels@ucsf.edu.
authors.forename: Joseph L
authors.initials: JL
authors.lastname: Wiemels
authors.affiliation: Bayessoft, Inc., 2221 Caravaggio Drive, Davis, CA, 95618, USA. dyang@bayessoft.com.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D002681:China; D055106:Genome-Wide Association Study; D006801:Humans; D007918:Leprosy / Q000235:genetics; D015999:Multivariate Analysis; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D012044:Regression Analysis; D013269:Stochastic Processes