A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy

Abstract

BackgroundGenotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools.ResultsWe compared the performance of five GBS pipelines using low-coverage Illumina sequence data from three soybean populations. To address issues identified with existing methods, we developed GB-eaSy, a GBS bioinformatics workflow that incorporates widely used genomics tools, parallelization and automation to increase the accuracy and accessibility of GBS data analysis. Compared to other GBS pipelines, GB-eaSy rapidly and accurately identified the greatest number of SNPs, with SNP calls closely concordant with whole-genome sequencing of selected lines. Across all five GBS analysis platforms, SNP calls showed unexpectedly low convergence but generally high accuracy, indicating that the workflows arrived at largely complementary sets of valid SNP calls on the low-coverage data analyzed.ConclusionsWe show that GB-eaSy is approximately as good as, or better than, other leading software solutions in the accuracy, yield and missing data fraction of variant calling, as tested on low-coverage genomic data from soybean. It also performs well relative to other solutions in terms of the run time and disk space required. In addition, GB-eaSy is built from existing open-source, modular software packages that are regularly updated and commonly used, making it straightforward to install and maintain. While GB-eaSy outperformed other individual methods on the datasets analyzed, our findings suggest that a comprehensive approach integrating the results from multiple GBS bioinformatics pipelines may be the optimal strategy to obtain the largest, most highly accurate SNP yield possible from low-coverage polyploid sequence data.

Adapting Genotyping-by-Sequencing and Variant Calling for Heterogeneous Stock Rats

“…There are multiple reasons we chose to develop our own computational pipeline for GBS rather than using existing workflows. Foremost, the prominent GBS analysis pipelines were developed and optimized for use in crop species (Sonah et al 2013;Catchen et al 2013;Glaubitz et al 2014;Torkamaneh et al 2017;Wickland et al 2017), some of which are polyploid and have differing levels of variation and LD than outbred rodent populations. Additionally, there were elements of each pipeline that did not meet our needs or lacked customizability.…”

“…However, those publications used protocols that had not been optimized, leaving significant room for improvement in genotype quality and marker density. Additionally, although several tools and workflows for the analysis of GBS data have been described, including Stacks (Catchen et al 2013), IGST-GBS (Sonah et al 2013), TASSEL-GBS (Glaubitz et al 2014), Fast-GBS (Torkamaneh et al 2017), and GB-eaSy (Wickland et al 2017), the majority were developed and optimized for use in plant species. Given the lack of well-developed genomic resources in these species, they do not leverage the wealth of genomic data available for model organisms such as rats.…”

Optimizing ddRADseq in Non-Model Species: A Case Study in Eucalyptus dunnii Maiden

“…This software was developed mainly for organisms without reference genomes and high-depth RAD sequencing. Additionally, Stacks is between the pipelines, with high accuracy for SNPs calling on this kind of data [34,35].…”

Rapid polygenic selection generates fine spatial structure among ecological niches in a well-mixed population

“…Pooled libraries were sequenced on one lane of the Illumina HiSeq 4000 in 2×150 bp paired-end mode yielding approximately 467 million paired-end reads (>140 Gb). Single nucleotide polymorphism (SNP) calling was performed using the GBeaSy analysis pipeline [115] with the following filter settings: minimum read length of 30bp after barcode and adapter trim, minimum phred-scaled variant quality of 30 and minimum read depth of 5 at the sample level. This yielded a total of 3,775,496 SNPs that were further filtered using VCFtools 0.1.13 [116].…”