A Beta-Mixture Model for Assessing Genetic Population Structure
Article first published online: 29 NOV 2010
© 2010, The International Biometric Society
Volume 67, Issue 3, pages 1073–1082, September 2011
How to Cite
Fu, R., Dey, D. K. and Holsinger, K. E. (2011), A Beta-Mixture Model for Assessing Genetic Population Structure. Biometrics, 67: 1073–1082. doi: 10.1111/j.1541-0420.2010.01506.x
- Issue published online: 14 SEP 2011
- Article first published online: 29 NOV 2010
- Received February 2009. Revised July 2010. Accepted August 2010.
- Allele frequency;
- Bayesian modeling;
- Beta mixture;
- Inbreeding coefficient;
- Reversible jump algorithm
Summary 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 multiple FSTs from the sample. The model also has important application for codominant markers and single-nucleotide polymorphism (SNP) data. The number of FST is assumed unknown beforehand and follows a random distribution. The reversible jump algorithm is used to estimate the unknown number of multiple FSTs. 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 of FSTs 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.