Bayesian clustering using hidden Markov random fields in spatial population genetics

Olivier François, Sophie Ancelet, Gilles Guillot

Research output: Contribution to journalJournal articleResearchpeer-review


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 call implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it call 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.
Original languageEnglish
Issue number2
Pages (from-to) 805-816
Publication statusPublished - 2006
Externally publishedYes


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