Abstract
Given the economic and environmental importance of allopolyploids and other species with highly duplicated genomes, there is a need for accurate genotyping methodology that distinguishes paralogs in order to yield Mendelian markers. Methods such as comparing observed and expected heterozygosity are frequently used for identifying collapsed paralogs, but have limitations in genotyping-by-sequencing datasets, in which observed heterozygosity is difficult to estimate due to undersampling of alleles. These limitations are especially pronounced when the species is highly heterozygous or the expected inheritance is polysomic. We introduce a novel statistic, Hind/HE, that uses the probability of sampling reads of two different alleles at a sample*locus, instead of observed heterozygosity. The expected value of Hind/HE is the same across all loci in a dataset, regardless of read depth or allele frequency. In contrast to methods based on observed heterozygosity, it can be estimated and used for filtering loci prior to genotype calling. We also introduce an algorithm that can choose among multiple alignment locations for a given sequence tag in order to optimize the value of Hind/HE for each locus, correcting alignment errors that frequently occur in highly duplicated genomes. Our methodology is implemented in polyRAD v1.2, available at https://github.com/lvclark/polyRAD.
