title: On identifying the optimal number of population clusters via the deviance information criterion.
abstract: Inferring population structure using bayesian clustering programs often requires a priori specification of the number of subpopulations, K, from which the sample has been drawn. Here, we explore the utility of a common bayesian model selection criterion, the Deviance Information Criterion (DIC), for estimating K. We evaluate the accuracy of DIC, as well as other popular approaches, on datasets generated by coalescent simulations under various demographic scenarios. We find that DIC outperforms competing methods in many genetic contexts, validating its application in assessing population structure.
authors: Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Stanford, California, United States of America. hgao98@stanford.edu
authors: Hong
authors: H
authors: Gao
authors: Katarzyna
authors: K
authors: Bryc
authors: Carlos D
authors: CD
authors: Bustamante
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D005828:Genetics, Population / Q000379:methods*; D006801:Humans; D015233:Models, Statistical