INFERRING ADMIXTURE PROPORTIONS FROM MOLECULAR-DATA

We derive here two new estimators of admixture proportions based on a coalescent approach that explicitly takes into account molecular infor mation as well as gene frequencies. These estimators can be applied to any type of molecular data (such as DNA sequences, restriction fragme nt length polymorphisms [RFLPs], dr microsatellite data) for which the extent of molecular diversity is related to coalescent times. Monte C arlo simulation studies are used to analyze the behavior of our estima tors. We show that one of them (m(Y)) appears suitable for estimating admixture from molecular data because of its absence of bias and relat ively low variance. We then compare it to two conventional estimators that are based on gene frequencies. m(Y) proves to be less biased than conventional estimators over a wide range of situations and especiall y for microsatellite data. However, its variance is larger than that o f conventional estimators when parental populations are not very diffe rentiated. The variance of m(Y) becomes smaller than that of conventio nal estimators only if parental populations have been kept separated f or about N generations and if the mutation rate is high. Simulations a lso show that several loci should always be studied to achieve a drast ic reduction of variance and that, for microsatellite data, the mean s quare error of m(Y) rapidly becomes smaller than that of conventional estimators if enough loci are surveyed. We apply our new estimator to the case of admired wolflike Canid populations tested for microsatelli te data.