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Abstract Hardy–Weinberg proportions (HWP) are often explored to evaluate the assumption of random mating. However, in autopolyploids, organisms with more than two sets of homologous chromosomes, HWP 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 for detecting genotyping errors than testing for HWP (in a natural population) and testing for Mendelian segregation (in an experimental S1 population). Our methods are implemented in Version 2.0.2 of thehwepR package on the Comprehensive R Archive Networkhttps://cran.r‐project.org/package=hwep.
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Tests for segregation distortion in tetraploid F1 populations
Abstract Key message:In tetraploid F1 populations, traditional segregation distortion tests often inaccurately flag SNPs due to ignoring polyploid meiosis processes and genotype uncertainty. We develop tests that account for these factors. Abstract:Genotype data from tetraploid F1 populations are often collected in breeding programs for mapping and genomic selection purposes. A common quality control procedure in these groups is to compare empirical genotype frequencies against those predicted by Mendelian segregation, where SNPs detected to havesegregation distortionare discarded. However, current tests for segregation distortion are insufficient in that they do not account for double reduction and preferential pairing, two meiotic processes in polyploids that naturally change gamete frequencies, leading these tests to detect segregation distortion too often. Current tests also do not account for genotype uncertainty, again leading these tests to detect segregation distortion too often. Here, we incorporate double reduction, preferential pairing, and genotype uncertainty in likelihood ratio and Bayesian tests for segregation distortion. Our methods are implemented in a user-friendly R package, . We demonstrate the superiority of our methods to those currently used in the literature on both simulations and real data.
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- Award ID(s):
- 2132247
- PAR ID:
- 10566556
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Theoretical and Applied Genetics
- Volume:
- 138
- Issue:
- 1
- ISSN:
- 0040-5752
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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