A spatial dirichlet process mixture model for clustering population genetics data

Biometrics
Brian J Reich, Howard D Bondell

Abstract

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.

References

Nov 3, 2004·Genetics·Gilles GuillotJean François Cosson
Aug 23, 2006·Nature Reviews. Genetics·Laurent Excoffier, Gerald Heckel
Sep 20, 2006·Nature Reviews. Genetics·David J Balding
Jan 24, 2007·Genetics·John P Huelsenbeck, Peter Andolfatto
Jul 4, 2009·Bioinformatics·Eric DurandOlivier François

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Citations

Jan 11, 2014·BMC Infectious Diseases·Elaine O NsoesieMadhav V Marathe
Dec 21, 2011·Environmetrics·Jungsoon ChoiMd Monir Hossain
Apr 27, 2012·Statistical Methods in Medical Research·Jungsoon ChoiJihong Liu
Nov 4, 2020·Ecological Applications : a Publication of the Ecological Society of America·Rahel SollmannDevin S Johnson
Mar 24, 2015·Deutsches Ärzteblatt International·Harald Binder, Maria Blettner

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