Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics
- 1 October 2006
- journal article
- research article
- Published by Oxford University Press (OUP) in Genetics
- Vol. 174 (2), 805-816
- https://doi.org/10.1534/genetics.106.059923
Abstract
We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it can check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set.Keywords
This publication has 40 references indexed in Scilit:
- genalex 6: genetic analysis in Excel. Population genetic software for teaching and researchMolecular Ecology Notes, 2005
- gerud 2.0: a computer program for the reconstruction of parental genotypes from half‐sib progeny arrays with known or unknown parentsMolecular Ecology Notes, 2005
- Assumed and inferred spatial structure of populations: the Scandinavian brown bears revisitedMolecular Ecology, 2004
- Genetic Structure of Human PopulationsScience, 2002
- Hidden Markov Models and Disease MappingJournal of the American Statistical Association, 2002
- Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithmIEEE Transactions on Medical Imaging, 2001
- The Potts modelReviews of Modern Physics, 1982
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970
- Some generalized order-disorder transformationsMathematical Proceedings of the Cambridge Philosophical Society, 1952