Bayesian co-estimation of selfing rate and locus-specific mutation rates for a partially selfing population

TL;DR

This paper introduces a Bayesian method to jointly estimate selfing rates and locus-specific mutation rates in partially selfing populations, accounting for complex mating systems and genetic variation patterns.

Contribution

It extends coalescence models to include various mating systems and incorporates a Dirichlet Process Prior for mutation rate estimation, advancing inference accuracy.

Findings

Accurately estimates population-wide selfing rates.

Infers locus-specific mutation rates using a novel Bayesian approach.

Provides insights into recent outcrossing events in populations.

Abstract

We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about pure hermaphroditism, androdioecy, and gynodioecy. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens Sampling Formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet Process Prior (DPP) model. Among the parameters jointly inferred are the population-wide rate of self-fertilization, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual.