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An MCMC Approach for Joint Inference of Population Structure and Inbreeding Rates from Multi-Locus Genotype Data

Non-random mating induces correlations in allelic states within and among loci that can be exploited to understand the genetic structure of natural populations (WRIGHT, 1965). For many species, it is of considerable interest to quantify the contribution of two forms of non-random mating to patterns of standing genetic variation: inbreeding (mating among relatives) and population substructure (limited dispersal of gametes). Here, we extend the popular Bayesian clustering approach STRUCTURE (PRITCHARD et al., 2000) for simultaneous inference of inbreeding or selfing rates and population-of-origin classification using multilocus genetic markers. This is accomplished by eliminating the assumption of Hardy-Weinberg equilibrium within clusters, and, instead, calculating expected genotype frequencies based on inbreeding or selfing rates. We demonstrate the need for such an extension by showing that selfing leads to spurious signals of population substructure using the standard STRUCTURE algorithm with a bias towards spurious signals of admixture. We gauge the performance of our method using extensive coalescent simulations and demonstrate that our approach can correct for this bias. We also apply our approach to understanding the population structure of the wild relative of domesticated rice, Oryza rufipogon, an important partially selfing grass species. Using a sample of n = 16 individuals sequenced at 111 random loci, we find strong evidence for existence of two subpopulations which correlates well with geographic location of sampling and estimate selfing rates for both groups that are consistent with estimates from experimental data (s ≈ 0.48− 0.70).