Estimation of Admixture Proportions: A Likelihood-Based Approach Using Markov Chain Monte Carlo

Corresponding author: Lounès Chikhi, Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London NW1 2HE, United Kingdom., l.chikhi{at}ucl.ac.uk (E-mail)

Communicating editor: D. CHARLESWORTH

When populations are separated for long periods and then brought into contact for a brief episode in part of their range, this can result in genetic admixture. To analyze this type of event we considered a simple model under which two parental populations (P₁ and P₂) mix and create a hybrid population (H). After that event, the three populations evolve under pure drift without exchange during T generations. We developed a new method, which allows the simultaneous estimation of the time since the admixture event (scaled by the population size t_i = T/N_i, where N_i is the effective population size of population i) and the contribution of one of two parental populations (which we call p₁). This method takes into account drift since the admixture event, variation caused by sampling, and uncertainty in the estimation of the ancestral allele frequencies. The method is tested on simulated data sets and then applied to a human data set. We find that (i) for single-locus data, point estimates are poor indicators of the real admixture proportions even when there are many alleles; (ii) biallelic loci provide little information about the admixture proportion and the time since admixture, even for very small amounts of drift, but can be powerful when many loci are used; (iii) the precision of the parameters' estimates increases with sample size (n = 50 vs. n = 200) but this effect is larger for the t_i's than for p₁; and (iv) the increase in precision provided by multiple loci is quite large, even when there is substantial drift (we found, for instance, that it is preferable to use five loci than one locus, even when drift is 100 times larger for the five loci). Our analysis of a previously studied human data set illustrates that the joint estimation of drift and p₁ can provide additional insights into the data.

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