English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54389/89220 (61%)
Visitors : 10568603      Online Users : 23
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109748

    Title: Bayesian Nonparametric Clustering and Association Studies for Candidate SNP Observations
    Authors: Wang, Charlotte;Ruggeri, Fabrizio;Hsiao, Chuhsing K.;Argiento Raffaele
    Keywords: Bayesian Clustering;Bayesian Nonparametric;Random partitions;Dirichlet process mixture model;GWAS;Logistic regression
    Date: 2017-01-01
    Issue Date: 2017-03-04 02:10:52 (UTC+8)
    Abstract: Clustering is often considered as the first step in the analysis when dealing with an enormous amount of Single Nucleotide Polymorphism (SNP) genotype data. The lack of biological information could affect the outcome of such procedure. Even if a clustering procedure has been selected and performed, the impact of its uncertainty on the subsequent association analysis is rarely assessed. In this research we propose first a model to cluster SNPs data, then we assess the association between the cluster and a disease. In particular, we adopt a Dirichlet process mixture model with the advantages, with respect to the usual clustering methods, that the number of clusters needs not to be known and fixed in advance and the variation in the assignment of SNPs to clusters can be accounted. In addition, once a clustering of SNPs is obtained, we design an individualized genetic score quantifying the SNP composition in each cluster for every subject, so that we can set up a generalized linear model for association analysis able to incorporate the information from a large-scale SNP dataset, and yet with a much smaller number of explanatory variables. The inference on cluster allocation, the strength of association of each cluster (the collective effect on SNPs in the same cluster), and the susceptibility of each SNP are based on posterior samples from Markov chain Monte Carlo methods and the Binder loss information. We exemplify this Bayesian nonparametric strategy in a genome-wide association study of Crohn's disease in a case-control setting.
    Relation: International Journal of Approximate Reasoning 80, p.19-35
    DOI: 10.1016/j.ijar.2016.07.014
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

    Files in This Item:

    File Description SizeFormat
    Bayesian Nonparametric Clustering and Association Studies for Candidate SNP Observations.pdf1466KbAdobe PDF1View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback