Improved Bayesian inference of hybrids using genome sequences

Abstract

A Bayesian hybrid inference method is developed which infers hybrids and back-crosses across two generations using sampled genomes from two populations. The method improves on that of Chakraborty and Rannala (2023) by accounting for uncertainty of population haplotype frequencies and correctly marginalizing over haplotypes while still modeling linkage and recombination across the genome. In analyses of simulated data the new method produced posterior probabilities nearly identical to the method of Chakraborty and Rannala (2023) when sample sizes were large. For small sample sizes, posterior probabilities produced by the new method tended to be lower as expected since it accounts for additional uncertainties of population haplotype frequencies. Statistical performance of the new method as measured by the ROC (Receiver Operation Characteristic) curve, appears equivalent to that of Chakraborty and Rannala (2023). The new method is applied to three recently published datasets for populations of kiwifruit (genus Actinidia), plateau fence lizard (Sceloporus tristichus) and puma (Puma concolor).