Document Detail

A spatial dirichlet process mixture model for clustering population genetics data.
MedLine Citation:
PMID:  20825394     Owner:  NLM     Status:  MEDLINE    
Identifying homogeneous groups of individuals is an important problem in population genetics. Recently, several methods have been proposed that exploit spatial information to improve clustering algorithms. In this article, we develop a Bayesian clustering algorithm based on the Dirichlet process prior that uses both genetic and spatial information to classify individuals into homogeneous clusters for further study. We study the performance of our method using a simulation study and use our model to cluster wolverines in Western Montana using microsatellite data.
Brian J Reich; Howard D Bondell
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2010-09-03
Journal Detail:
Title:  Biometrics     Volume:  67     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2011 Jun 
Date Detail:
Created Date:  2011-06-21     Completed Date:  2011-11-09     Revised Date:  2014-09-20    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  381-90     Citation Subset:  IM    
Copyright Information:
© 2010, The International Biometric Society.
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MeSH Terms
Bayes Theorem
Cluster Analysis*
Computer Simulation
Genetics, Population / methods*
Microsatellite Repeats
Mustelidae / genetics
Grant Support
1 R01 MH084022-01A1/MH/NIMH NIH HHS; P01 CA142538/CA/NCI NIH HHS; R01 ES014843/ES/NIEHS NIH HHS; R01 ES014843-01A2/ES/NIEHS NIH HHS; R01 MH084022/MH/NIMH NIH HHS; R01 MH084022-01A1/MH/NIMH NIH HHS; R01 MH084022-02/MH/NIMH NIH HHS; R01 MH084022-03/MH/NIMH NIH HHS

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