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Data from: Expected genotype quality and diploidized marker data from genotyping-by-sequencing of Urochloa spp. tetraploids

Citation

Matias, Filipe Inácio et al. (2019), Data from: Expected genotype quality and diploidized marker data from genotyping-by-sequencing of Urochloa spp. tetraploids, Dryad, Dataset, https://doi.org/10.5061/dryad.4j2c7h6

Abstract

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 the phred-scaled probability of genotype misclassification, conditioned on the true genotype, which we call Expected Genotype Quality (EGQ). If the GBS method has 0.5% allelic error, then 17 reads are needed to classify simplex tetraploids as heterozygous with 95% accuracy (EGQ = 13) compared with 61 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. Cross-validation 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 based on EGQ is also needed, regardless of whether genotypes are called using a diploidized or allele dosage model.

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