A Bayesian Approach to Inferring Rates of Selfing and Locus-Specific Mutation.

Journal Article (Journal Article)

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 reproduction under pure hermaphroditism, gynodioecy, and a model developed to describe the self-fertilizing killifish Kryptolebias marmoratus. 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 model. Our sampler is designed to accommodate additional information, including observations pertaining to the sex ratio, the intensity of inbreeding depression, and other aspects of reproduction. It can provide joint posterior distributions for the population-wide proportion of uniparental individuals, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual. Further, estimation of all basic parameters of a given model permits estimation of functions of those parameters, including the proportion of the gene pool contributed by each sex and relative effective numbers.

Full Text

Duke Authors

Cited Authors

  • Redelings, BD; Kumagai, S; Tatarenkov, A; Wang, L; Sakai, AK; Weller, SG; Culley, TM; Avise, JC; Uyenoyama, MK

Published Date

  • November 2015

Published In

Volume / Issue

  • 201 / 3

Start / End Page

  • 1171 - 1188

PubMed ID

  • 26374460

Pubmed Central ID

  • PMC4649643

Electronic International Standard Serial Number (EISSN)

  • 1943-2631

Digital Object Identifier (DOI)

  • 10.1534/genetics.115.179093


  • eng

Conference Location

  • United States