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Copyright 2007 by the Genetics Society of America DOI: 10.1534/genetics.106.061317
 

Summary: Copyright 2007 by the Genetics Society of America
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
Manuscript received May 25, 2006
Accepted for publication December 24, 2006
ABSTRACT
Inferring population structure from genetic data sampled from some number of individuals is a for-
midable 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

  

Source: Andolfatto, Peter - Department of Ecology and Evolutionary Biology, Princeton University

 

Collections: Biology and Medicine