A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data
Article first published online: 3 SEP 2010
© 2010, The International Biometric Society
Volume 67, Issue 2, pages 381–390, June 2011
How to Cite
Reich, B. J. and Bondell, H. D. (2011), A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data. Biometrics, 67: 381–390. doi: 10.1111/j.1541-0420.2010.01484.x
- Issue published online: 20 JUN 2011
- Article first published online: 3 SEP 2010
- Received July 2009. Revised June 2010. Accepted June 2010.
- Bayesian nonparametrics;
- Dirichlet process prior;
- Landscape genetics;
- Microsatellite data;
- Model-based clustering
Summary 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.