An efficient exact algorithm for identifying hybrids using population genomic sequences.

Abstract

The identification of individuals that have a recent hybrid ancestry (between populations or species) has been a goal of naturalists for centuries. Since the 1960s, codominant genetic markers have been used with statistical and computational methods to identify F1 hybrids and backcrosses. Existing hybrid inference methods assume that alleles at different loci undergo independent assortment (are unlinked or in population linkage equilibrium). Genomic datasets include thousands of markers that are located on the same chromosome and are in population linkage disequilibrium which violate this assumption. Existing methods may therefore be viewed as composite likelihoods when applied to genomic datasets and their performance in identifying hybrid ancestry (which is a model-choice problem) is unknown. Here, we develop a new program Mongrail that implements a full-likelihood Bayesian hybrid inference method that explicitly models linkage and recombination, generating the posterior probability of different F1 or F2 hybrid, or backcross, genealogical classes. We use simulations to compare the statistical performance of Mongrail with that of an existing composite likelihood method (NewHybrids) and apply the method to analyze genome sequence data for hybridizing species of barred and spotted owls.