A spatial dirichlet process mixture model for clustering population genetics data

Biometrics. 2011 Jun;67(2):381-90. doi: 10.1111/j.1541-0420.2010.01484.x. Epub 2010 Sep 3.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Animals
  • Bayes Theorem
  • Cluster Analysis*
  • Computer Simulation
  • Genetics, Population / methods*
  • Microsatellite Repeats
  • Montana
  • Mustelidae / genetics