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On Identifying the Optimal Number of Population Clusters via the Deviance Information Criterion

Author

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  • Hong Gao
  • Katarzyna Bryc
  • Carlos D Bustamante

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.

Suggested Citation

  • Hong Gao & Katarzyna Bryc & Carlos D Bustamante, 2011. "On Identifying the Optimal Number of Population Clusters via the Deviance Information Criterion," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-8, June.
    • Handle: RePEc:plo:pone00:0021014
    DOI: 10.1371/journal.pone.0021014
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    Cited by:

    1. Yordan P Raykov & Alexis Boukouvalas & Fahd Baig & Max A Little, 2016. "What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
    2. Bryc, Katarzyna & Bryc, Wlodek & Silverstein, Jack W., 2013. "Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations," Theoretical Population Biology, Elsevier, vol. 89(C), pages 34-43.

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