High-quality, gemone-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

Authors: VANDERZANDE, STIJN - Washington State University; HOWARD, NICHOLAS - University Of Minnesota; CAI, LICHUN - Michigan State University; DA SILVA LINGE, CASSIA - Clemson University; ANTANAVICIUTE, LAIMA - Clemson University; BINK, MARCO - Wageningen University And Research Center; KRUISSELBRINK, JOHANNES - Wageningen University And Research Center; Bassil, Nahla; GASIC, KSENIJA - Clemson University; IEZZONI, AMY - Michigan State University; VAN DE WEG, ERIC - Wageningen University; PEACE, CAMERON - Washington State University

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/12/2019
Publication Date: 6/27/2019
Citation: Vanderzande, S., Howard, N., Cai, L., Da Silva Linge, C., Antanaviciute, L., Bink, M., Kruisselbrink, J., Bassil, N.V., Gasic, K., Iezzoni, A., Van De Weg, E., Peace, C. 2019. High-quality, gemone-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow. PLoS One. https://doi.org/10.1371/journal.pone.0210928.
DOI: https://doi.org/10.1371/journal.pone.0210928

Interpretive Summary: We have developed a comprehensive workflow to enable quick identification of errors in a large dataset of genotypes first scored computationally. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project and are available through the Genome Database for Rosaceae.

Technical Abstract: High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTLTM software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.