Journal article
A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data
BJ Reich, HD Bondell
Biometrics | WILEY | Published : 2011
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. © 2010, The International Biometric Society.
Grants
Awarded by National Institute of Mental Health
Funding Acknowledgements
The authors thank the editor, associate editor, and two referees for helpful comments and suggestions that greatly improved the manuscript. We also thank Drs Jung-Ying Tzeng and Jeffrey Thorne of NCSU for discussion of population genetics and the National Institutes of Health (1 R01 MH084022-01A1) and National Science Foundation (DMS-0705968 and CMG-0934595) for supporting this work.