  We propose a Bayesian nonparametric model to infer population admixture,
extending the Hierarchical Dirichlet Process to allow for correlation between
loci due to Linkage Disequilibrium. Given multilocus genotype data from a
sample of individuals, the model allows inferring classifying individuals as
unadmixed or admixed, inferring the number of subpopulations ancestral to an
admixed population and the population of origin of chromosomal regions. Our
model does not assume any specific mutation process and can be applied to most
of the commonly used genetic markers. We present a MCMC algorithm to perform
posterior inference from the model and discuss methods to summarise the MCMC
output for the analysis of population admixture. We demonstrate the performance
of the proposed model in simulations and in a real application, using genetic
data from the EDAR gene, which is considered to be ancestry-informative due to
well-known variations in allele frequency as well as phenotypic effects across
ancestry. The structure analysis of this dataset leads to the identification of
a rare haplotype in Europeans.
