New Methods Employing Multilocus Genotypes to Select or Exclude Populations as Origins of Individuals

TLDR

A new method assigns individuals of unknown origin to populations using genetic distance, compared to two likelihood‑based multilocus genotype methods. The method enables exclusion of a population as an individual’s origin, extending current assignment approaches. The assignment probability is derived from the distribution of genetic distance or genotype likelihood for each population, and the methods were evaluated via coalescent simulations varying divergence time, mutation model, sample size, and locus number. Likelihood‑based methods, particularly Bayesian, consistently outperform distance methods; under the infinite allele model they are more efficient than the stepwise model even at equal Fst, and Bayesian can achieve 100 % correct assignment with about ten microsatellite loci (H≈0.6) on 30–50 individuals per population when Fst≈0.1.

Abstract

Abstract A new method for assigning individuals of unknown origin to populations, based on the genetic distance between individuals and populations, was compared to two existing methods based on the likelihood of multilocus genotypes. The distribution of the assignment criterion (genetic distance or genotype likelihood) for individuals of a given population was used to define the probability that an individual belongs to the population. Using this definition, it becomes possible to exclude a population as the origin of an individual, a useful extension of the currently available assignment methods. Using simulated data based on the coalescent process, the different methods were evaluated, varying the time of divergence of populations, the mutation model, the sample size, and the number of loci. Likelihood-based methods (especially the Bayesian method) always performed better than distance methods. Other things being equal, genetic markers were always more efficient when evolving under the infinite allele model than under the stepwise mutation model, even for equal values of the differentiation parameter Fst. Using the Bayesian method, a 100% correct assignment rate can be achieved by scoring ca. 10 microsatellite loci (H ≈ 0.6) on 30–50 individuals from each of 10 populations when the Fst is near 0.1.