A Beta-Mixture Model for Assessing Genetic Population Structure

Rongwei Fu, Dipak K. Dey, Kent E. Holsinger

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

An important fraction of recently generated molecular data is dominant markers. They contain substantial information about genetic variation but dominance makes it impossible to apply standard techniques to calculate measures of genetic differentiation, such as F-statistics. In this article, we propose a new Bayesian beta-mixture model that more accurately describes the genetic structure from dominant markers and estimates multipleF STs from the sample. The model also has important application for codominant markers and single-nucleotide polymorphism (SNP) data. The number ofF STis assumed unknown beforehand and follows a random distribution. The reversible jump algorithm is used to estimate the unknown number of multipleF STs. We evaluate the performance of three split proposals and the overall performance of the proposed model based on simulated dominant marker data. The model could reliably identify and estimate a spectrum of degrees of genetic differentiation present in multiple loci. The estimates ofF STs also incorporate uncertainty about the magnitude of within-population inbreeding coefficient. We illustrate the method with two examples, one using dominant marker data from a rare orchid and the other using codominant marker data from human populations.

Original languageEnglish (US)
Pages (from-to)1073-1082
Number of pages10
JournalBiometrics
Volume67
Issue number3
DOIs
StatePublished - Sep 1 2011

Keywords

  • Allele frequency
  • Bayesian modeling
  • Beta mixture
  • F
  • Inbreeding coefficient
  • Reversible jump algorithm

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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