Abstract
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. 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 have segregation distortion are 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, menbayes. We demonstrate the superiority of our methods to those currently used in the literature on both simulations and real data.