Inference of Population Structure Using Multilocus Genotype Data

TLDR

The study assumes a model with K populations, each defined by allele frequencies at each locus, where K may be unknown. The paper proposes a model‑based clustering method to infer population structure and assign individuals to populations using multilocus genotype data. The method probabilistically assigns individuals to one or more populations based on multilocus genotypes, without assuming a specific mutation model, and works with unlinked markers. The method achieves highly accurate assignments with as few as seven microsatellite loci, demonstrated on an endangered bird species. Software is available at http://www.stats.ox.ac.uk/~pritch/home.html.

Abstract

Abstract We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.