Bayesian Tests for Random Mating in Autopolyploids

Poster presented at the Plant and Animal Genome Conference on January 16, 2023.

Abstract: Hardy-Weinberg equilibrium is often explored to evaluate the assumption of random mating. However, in autopolyploids, organisms with more than two sets of homologous chromosomes, Hardy-Weinberg equilibrium and random mating are different hypotheses that require different statistical testing approaches. Currently, the only available methods to test for random mating in autopolyploids (i) heavily rely on asymptotic approximations and (ii) assume genotypes are known, ignoring genotype uncertainty. Furthermore, these approaches are all frequentist, and so do not carry the benefits of Bayesian analysis, including ease of interpretability, incorporation of prior information, and consistency under the null. Here, we present Bayesian approaches to test for random mating, bringing the benefits of Bayesian analysis to this problem. Our Bayesian methods also (i) do not rely on asymptotic approximations, being appropriate for small sample sizes, and (ii) optionally account for genotype uncertainty via genotype likelihoods. We validate our methods in simulations, and demonstrate on two real datasets how testing for random mating is more useful than testing for equilibrium (in a natural population) and testing for Mendelian segregation (in an experimental S1 population). Our methods are implemented in Version 2.0.0 of the hwep R package on the Comprehensive R Archive Network https://cran.r-project.org/package=hwep.