SUMMARY: GBS-SNP-CROP is a bioinformatics pipeline originally developed to support the cost-effective genome-wide characterization of plant genetic resources through paired-end genotyping-by-sequencing (GBS), particularly in the absence of a reference genome. Since its 2016 release, the pipeline's functionality has greatly expanded, its computational efficiency has improved, and its applicability to a broad set of genomic studies for both plants and animals has been demonstrated. This note details the suite of improvements to date, as realized in GBS-SNP-CROP v.4.0, with specific attention paid to a new integrated metric that facilitates reliable variant identification despite the complications of homologs. Using the new de novo GBS read simulator GBS-Pacecar, also introduced in this note, results show an improvement in overall pipeline accuracy from 66% (v.1.0) to 84% (v.4.0), with a time saving of ∼70%. Both GBS-SNP-CROP versions significantly outperform TASSEL-UNEAK; and v.4.0 resolves the issue of non-overlapping variant calls observed between UNEAK and v.1.0. AVAILABILITY AND IMPLEMENTATION: GBS-SNP-CROP source code and user manual are available at https://github.com/halelab/GBS-SNP-CROP. The GBS read simulator GBS-Pacecar is available at https://github.com/halelab/GBS-Pacecar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2019) doi: 10.1093/bioinformatics/bty873, 35, 1783–1785. In the original article, there was an error in the formatting of Table 1. Comparative summary of GBS-SNP-CROP v.4.0 performance, based on a set of simulated data from GBS-Pacecar Note: In total, 25 000 SNPs and 10 000 indels were simulated across a genomic space of 100 000 GBS fragments. A total of 60 002 165 single-end reads were simulated for a population of 25 individuals (average of 2.4 million reads per genotype), with a sequencing error rate of 1.1%. See Supplementary Table S1 for more details UNEAK = TASSEL-UNEAK; GSC = GBS-SNP-CROP. The number of genotypes used for mock reference (MR) assembly. Computation time (minutes) required to run the full analysis on a Unix workstation with 16 GB RAM and a 2.6 GHz Dual Intel processor. Number of variants called by a pipeline (Note: a total of 35 000 variants were simulated, consisting of 25 000 SNPs and 10 000 indels). Percentage of called variants that could not be validated (false positives). Percentage of true, simulated variants that were not detected by the pipeline. Overall accuracy: 100 * [number of validated variants/(total number of simulated variants + number of non-validated variants)]. Comparative summary of GBS-SNP-CROP v.4.0 performance, based on a set of simulated data from GBS-Pacecar Note: In total, 25 000 SNPs and 10 000 indels were simulated across a genomic space of 100 000 GBS fragments. A total of 60 002 165 single-end reads were simulated for a population of 25 individuals (average of 2.4 million reads per genotype), with a sequencing error rate of 1.1%. See Supplementary Table S1 for more details UNEAK = TASSEL-UNEAK; GSC = GBS-SNP-CROP. The number of genotypes used for mock reference (MR) assembly. Computation time (minutes) required to run the full analysis on a Unix workstation with 16 GB RAM and a 2.6 GHz Dual Intel processor. Number of variants called by a pipeline (Note: a total of 35 000 variants were simulated, consisting of 25 000 SNPs and 10 000 indels). Percentage of called variants that could not be validated (false positives). Percentage of true, simulated variants that were not detected by the pipeline. Overall accuracy: 100 * [number of validated variants/(total number of simulated variants + number of non-validated variants)]. This has been corrected and the corrected table appears below.

The accurate determination of parent-progeny relationships within both in situ natural populations and ex situ genetic resource collections can greatly enhance plant breeding/domestication efforts and support plant genetic resource conservation strategies. Although a range of parentage analysis tools are available, none are designed to infer such relationships using genome-wide single nucleotide polymorphism (SNP) data in the complete absence of guiding information, such as generational groups, partial pedigrees, or genders. The R package (‘apparent’) developed and presented here addresses this gap. ‘apparent’ adopts a novel strategy of parentage analysis based on a test of genetic identity between a theoretically expected progeny (EP ij ), whose genotypic state can be inferred at all homozygous loci for a pair of putative parents ( i and j ), and all potential offspring (PO k ), represented by the k individuals of a given germplasm collection. Using the Gower Dissimilarity metric (GD), genetic identity between EP ij and PO k is taken as evidence that individuals i and j are the true parents of offspring k . Significance of a given triad (parental pair ij + offspring k ) is evaluated relative to the distribution of all GD ij|k values for the population. With no guiding information provided, ‘apparent’ correctly identified the parental pairs of 15 lines of known pedigree within a test population of 77 accessions of Actinidia arguta , a performance unmatched by five other commonly used parentage analysis tools. In the case of an inconclusive triad analysis due to the absence of one parent from the test population, ‘apparent’ can perform a subsequent dyad analysis to identify a likely single parent for a given offspring. Average dyad analysis accuracy was 73.3% in the complete absence of pedigree information but increased to 100% when minimal generational information (adults vs. progeny) was provided. The ‘apparent’ R package is a fast and accurate parentage analysis tool that uses genome-wide SNP data to identify parent-progeny relationships within populations for which no a priori knowledge of family structure exists.

BACKGROUND: With its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference. RESULTS: The GBS SNP-Calling Reference Optional Pipeline (GBS-SNP-CROP) developed and presented here adopts a clustering strategy to build a population-tailored "Mock Reference" from the same GBS data used for downstream SNP calling and genotyping. Designed for libraries of paired-end (PE) reads, GBS-SNP-CROP maximizes data usage by eliminating unnecessary data culling due to imposed read-length uniformity requirements. Using 150 bp PE reads from a GBS library of 48 accessions of tetraploid kiwiberry (Actinidia arguta), GBS-SNP-CROP yielded on average three times as many SNPs as TASSEL-GBS analyses (32 and 64 bp tag lengths) and over 18 times as many as TASSEL-UNEAK, with fewer genotyping errors in all cases, as evidenced by comparing the genotypic characterizations of biological replicates. Using the published reference genome of a related diploid species (A. chinensis), the reference-based version of GBS-SNP-CROP behaved similarly to TASSEL-GBS in terms of the number of SNPs called but had an improved read depth distribution and fewer genotyping errors. Our results also indicate that the sets of SNPs detected by the different pipelines above are largely orthogonal to one another; thus GBS-SNP-CROP may be used to augment the results of alternative analyses, whether or not a reference is available. CONCLUSIONS: By achieving high-density SNP genotyping in populations for which no reference genome is available, GBS-SNP-CROP is worth consideration by curators, researchers, and breeders of under-researched plant genetic resources. In cases where a reference is available, especially if from a related species or when the target population is particularly diverse, GBS-SNP-CROP may complement other reference-based pipelines by extracting more information per sequencing dollar spent. The current version of GBS-SNP-CROP is available at https://github.com/halelab/GBS-SNP-CROP.git.