Expected Genotype Quality and Diploidized Marker Data from Genotyping-by-Sequencing of Urochloa spp. Tetraploids
Although genotyping-by-sequencing (GBS) is a well-established marker technology in diploids, the development of best practices for tetraploid species is a topic of current research. We determined the theoretical relationship between read depth and expected genotype quality (EGQ) for tetraploid vs. diploidized genotype calls. If the GBS method has 1% error, then 17 reads are needed to classify tetraploid samples as heterozygous vs. homozygous with 95% accuracy, compared with 63 reads to determine allele dosage. We developed an R script to convert tetraploid GBS data in Variant Call Format (VCF) into diploidized genotype calls and applied it to 267 interspecific hybrids of the tetraploid forage grass Urochloa (syn. Brachiaria). When reads were aligned to a mock reference genome created from GBS data of the U. brizantha cultivar Marandu, 25,678 bi-allelic SNPs were discovered, compared to approximately 3000 SNPs when aligning to the closest true reference genomes, Setaria viridis and S. italica. Crossvalidation revealed that missing genotypes were imputed by the Random Forest method with a median accuracy of 0.85, regardless of heterozygote frequency. Using the Urochloa spp. hybrids, we illustrated how filtering samples based only on GQ creates genotype bias; a depth threshold with corresponding EGQ equal to the GQ threshold is also needed, regardless of whether genotypes are called using a diploidized or allele dosage model.