Document Detail


A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk.
MedLine Citation:
PMID:  24490143     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.
Authors:
Lewei Duan; Duncan C Thomas
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Publication Detail:
Type:  Journal Article     Date:  2013-12-31
Journal Detail:
Title:  International journal of genomics     Volume:  2013     ISSN:  2314-436X     ISO Abbreviation:  Int J Genomics     Publication Date:  2013  
Date Detail:
Created Date:  2014-02-03     Completed Date:  2014-02-03     Revised Date:  2014-06-11    
Medline Journal Info:
Nlm Unique ID:  101605206     Medline TA:  Int J Genomics     Country:  Egypt    
Other Details:
Languages:  eng     Pagination:  406217     Citation Subset:  -    
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Grant Support
ID/Acronym/Agency:
P01 ES011627/ES/NIEHS NIH HHS; P30 CA014089/CA/NCI NIH HHS; P30 ES007048/ES/NIEHS NIH HHS; R01 ES016813/ES/NIEHS NIH HHS; R01 ES019876/ES/NIEHS NIH HHS

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