Originally published as Genetics Published Articles Ahead of Print on January 21, 2007.

Genetics, Vol. 175, 1787-1802, April 2007, Copyright © 2007
doi:10.1534/genetics.106.061317

Inference of Population Structure Under a Dirichlet Process Model

John P. Huelsenbeck^*,1 and Peter Andolfatto

^* Department of Integrative Biology, University of California, Berkeley, California 94720 and Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California, San Diego, California 92093-0116

¹ Corresponding author: Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California, La Jolla, CA 92093-0116.
E-mail: johnh{at}berkeley.edu

Inferring population structure from genetic data sampled from some number of individuals is a formidable statistical problem. One widely used approach considers the number of populations to be fixed and calculates the posterior probability of assigning individuals to each population. More recently, the assignment of individuals to populations and the number of populations have both been considered random variables that follow a Dirichlet process prior. We examined the statistical behavior of assignment of individuals to populations under a Dirichlet process prior. First, we examined a best-case scenario, in which all of the assumptions of the Dirichlet process prior were satisfied, by generating data under a Dirichlet process prior. Second, we examined the performance of the method when the genetic data were generated under a population genetics model with symmetric migration between populations. We examined the accuracy of population assignment using a distance on partitions. The method can be quite accurate with a moderate number of loci. As expected, inferences on the number of populations are more accurate when = 4N_eu is large and when the migration rate (4N_em) is low. We also examined the sensitivity of inferences of population structure to choice of the parameter of the Dirichlet process model. Although inferences could be sensitive to the choice of the prior on the number of populations, this sensitivity occurred when the number of loci sampled was small; inferences are more robust to the prior on the number of populations when the number of sampled loci is large. Finally, we discuss several methods for summarizing the results of a Bayesian Markov chain Monte Carlo (MCMC) analysis of population structure. We develop the notion of the mean population partition, which is the partition of individuals to populations that minimizes the squared partition distance to the partitions sampled by the MCMC algorithm.

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