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 back crosses. 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.

Hierarchical heuristic species delimitation under the multispecies coalescent model with migration

“…When there exist contact zones between populations and one can estimate the proportion of hybridization and back-crossing ( h ), an appropriate criterion may be the rate ratio m / h , which measures reproductive isolation: a value of 1 means that introgressed alleles are neutral and have the same chance of being retained as a native allele in the recipient population, while m / h ≪ 1 means that introgressed alleles are strongly deleterious and purged from the population by natural selection, indicating the existence of (post-zygotic) reproductive isolation (Westram et al ., 2022). Note that genetic/genomic data can be used to identify hybrids and estimate h (Anderson and Thompson, 2002; Chakraborty and Rannala, 2023) and to estimate the effective migration rate m (Hey, 2010; Hey et al ., 2018; Gronau et al ., 2011; Flouri et al ., 2023). It may also be noted that a seemingly small migration rate ( m ), in the order of M = Nm = 0.5 may have a major impact on the genetic history of the populations (Long and Kubatko, 2018; Jiao and Yang, 2021).…”

Genomic Tools in Biological Invasions: Current State and Future Frontiers

“…When invasion events are analyzed using genomic data, it is possible to identify the presence of hybrids between divergent populations or species ( Chakraborty and Rannala 2023 ), as well as contemporary gene flow ( Wilson and Rannala 2003 ). If intraspecific genetic admixture is sufficiently recent (i.e.…”

Reidentification of hybridization events with transcriptomic data and phylogenomic study in seabuckthorn

“…By determining whether two different allozymes are produced at a speci c gene locus, it is possible to determine whether that locus is heterozygous (Harris, 1997). When identifying the hybrid F1 generation, If loci with xed differences exist, F1 hybrids will be heterozygous for all such loci (Chakraborty & Rannala, 2023).…”