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Partition Description

Identify population sub-division and assign individuals to populations

Partition is a model-based statistical software package for identifying population sub-division and assigning individuals to populations, on the basis of their genotypes at co-dominant marker loci. The underlying population genetic model is appropriate for out-crossing diploid organisms.

Partition is a model-based statistical software package for identifying population sub-division (barriers to gene flow) and assigning individuals to populations, on the basis of their genotypes at co-dominant marker loci. Suitable genetic markers include microsatellites and allozymes. Unlinked, or loosely linked, marker loci should be used, so that the assumption of linkage equilibrium is a reasonable one.

The underlying population genetic model is appropriate for out-crossing diploid organisms. It assumes that the individuals in the sample belong to a number of separate source populations. These source populations are assumed to be at Hardy-Weinberg and linkage equilibrium, but the allelic composition of these source populations, and even the number of source populations represented in the sample, are treated as parameters whose values are unknown.

The parameter of interest is the partition of the set of sampled individuals, induced by the assignment of individuals to source populations. This parameter will be referred to as the sample partition.

The software package Partition consists of two separate programs. The first program (Partition.exe) is a Markov chain sampler which runs for a large number of iterations. This program generates a large sample of observations from the posterior distribution (of the sample partition). This output contains a great deal of information, which can be summarised in a number of different ways. The second program (PartitionView.exe) can be used to process the output from the Markov chain sampler, in order to provide such visual summaries.

One such visual representation is a tree of all the individuals in the sample (or more generally, a forest of separate trees). The heights of each node in the tree is equal to the posterior probability that all the individuals in the cluster defined by that node come form the same population. Another useful summary is the posterior distribution of the number of source populations represented in the sample. It also calculates the Bayes factor in support of a single source population against the alternative of more than one source population. A full explanation of the Bayesian inference method can be found in the accompanying article (LINK).

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