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 multipleFSTs from the sample. The model also has important application for codominant markers and single-nucleotide polymorphism (SNP) data. The number ofFSTis assumed unknown beforehand and follows a random distribution. The reversible jump algorithm is used to estimate the unknown number of multipleFSTs. 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 ofFSTs 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 language | English (US) |
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Pages (from-to) | 1073-1082 |
Number of pages | 10 |
Journal | Biometrics |
Volume | 67 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2011 |
Keywords
- Allele frequency
- Bayesian modeling
- Beta mixture
- Inbreeding coefficient
- Reversible jump algorithm
ASJC Scopus subject areas
- General Immunology and Microbiology
- Applied Mathematics
- General Biochemistry, Genetics and Molecular Biology
- General Agricultural and Biological Sciences
- Statistics and Probability