- THIS ARTICLE
- Full Text
- Full Text (PDF)
- Data Supplement
-
All Versions of this Article:
genetics.107.077263v1
177/2/861 most recent - Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Kitada, S.
- Articles by Kishino, H.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Kitada, S.
- Articles by Kishino, H.
Originally published as Genetics Published Articles Ahead of Print on July 29, 2007.
Genetics, Vol. 177, 861-873, October 2007, Copyright © 2007
doi:10.1534/genetics.107.077263
Empirical Bayes Inference of Pairwise FST and Its Distribution in the Genome
Shuichi Kitada*,1,
Toshihide Kitakado* and
Hirohisa Kishino
* Faculty of Marine Science, Tokyo University of Marine Science and Technology, Minato, Tokyo 108-8477, Japan and
Graduate School of Agriculture and Life Sciences, University of Tokyo, Bunkyo, Tokyo 113-8657, Japan
1 Corresponding author: Tokyo University of Marine Science and Technology, 4-5-7 Konan, Minato, Tokyo, 108-8477, Japan.
E-mail: kitada{at}kaiyodai.ac.jp
Populations often have very complex hierarchical structure. Therefore, it is crucial in genetic monitoring and conservation biology to have a reliable estimate of the pattern of population subdivision. FST's for pairs of sampled localities or subpopulations are crucial statistics for the exploratory analysis of population structures, such as cluster analysis and multidimensional scaling. However, the estimation of FST is not precise enough to reliably estimate the population structure and the extent of heterogeneity. This article proposes an empirical Bayes procedure to estimate locus-specific pairwise FST's. The posterior mean of the pairwise FST can be interpreted as a shrinkage estimator, which reduces the variance of conventional estimators largely at the expense of a small bias. The global FST of a population generally varies among loci in the genome. Our maximum-likelihood estimates of global FST's can be used as sufficient statistics to estimate the distribution of FST in the genome. We demonstrate the efficacy and robustness of our model by simulation and by an analysis of the microsatellite allele frequencies of the Pacific herring. The heterogeneity of the global FST in the genome is discussed on the basis of the estimated distribution of the global FST for the herring and examples of human single nucleotide polymorphisms (SNPs).