Abstract
Inferring population structure using Bayesian clustering programs often requires a priori specification of the number of subpopulations, , 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 . 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.
Highlights
A common problem in modern population genetics is identifying population substructure among a sample of individuals genotyped across a set of neutral genetic markers
By comparing the performance of Deviance Information Criterion (DIC) to other commonly used statistics on simulated data under a variety of population genetic scenarios, we find that it often outperforms other approaches and recommend it be considered by investigators interested in estimating K from genotype data
This is the underlying idea behind many model selection statistics such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)
Summary
A common problem in modern population genetics is identifying population substructure among a sample of individuals genotyped across a set of neutral genetic markers Bayesian clustering algorithms such as STRUCTURE [1,2] and BAPS [3] and their derivates [4,5,6,7,8] are commonly used for addressing this problem. A common way of dealing with this class of statistical problems (known as ‘‘model selection’’) is to use a penalizing function which weighs the fit of a model versus its complexity This is the underlying idea behind many model selection statistics such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The Deviance Information Criterion (DIC) is a recently proposed statistic for model selection when the posterior distribution of parameters in competing models are estimated using Markov chain Monte Carlo, as is the case with STRUCTURE and its derivatives [10]
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