Genotyping‐by‐sequencing through transcriptomics: implementation in a range of crop species with varying reproductive habits and ploidy levels

Abstract

SummaryThe application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker‐dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism (SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost‐effectiveness and sample throughput. Genotyping‐by‐sequencing (GBS) encompasses a range of protocols including resequencing of the transcriptome. This study describes a skim GBS‐transcriptomics (GBS‐t) approach developed to be broadly applicable, cost‐effective and high‐throughput while still assaying a significant number of SNP loci. A range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding were chosen, including perennial ryegrass, a diploid outbreeding forage grass; phalaris, a putative segmental allotetraploid outbreeding forage grass; lentil, a diploid inbreeding grain legume; and canola, an allotetraploid partially outbreeding oilseed. GBS‐t was validated as a simple and largely automated, cost‐effective method which generates sufficient SNPs (from 89 738 to 231 977) with acceptable levels of missing data and even genome coverage from c. 3 million sequence reads per sample. GBS‐t is therefore a broadly applicable system suitable for many crops, offering advantages over other systems. The correct choice of subsequent sequence analysis software is important, and the bioinformatics process should be iterative and tailored to the specific challenges posed by ploidy variation and extent of heterozygosity.

Diversity and Genome Analysis of Australian and Global Oilseed Brassica napus L. Germplasm Using Transcriptomics and Whole Genome Re-sequencing

“…GBS-t raw sequencing data of 540 samples from Malmberg et al (2017) were re-analyzed with the addition of 93 new samples which were sequenced following the same pipeline as previously described, resulting in a total of 633 samples, representing 627 canola varieties from 27 countries. These include 258 Australian samples, 271 European samples, 69 Asian samples, 8 North American samples, 2 New Zealand samples, 1 African sample, and 24 samples of unknown origin (full details provided in Supplementary Table S1 ).…”

“…Library preparation of the 93 additional samples was performed according to the methods described in Malmberg et al (2017) . Briefly, mRNA was extracted using a Dynabead method (Life Technologies, Carlsbad, CA, United States) and RNA-Seq libraries generated using the SureSelect stranded RNA library preparation kit (Agilent Technologies, Santa Clara, CA, United States).…”

“…Briefly, mRNA was extracted using a Dynabead method (Life Technologies, Carlsbad, CA, United States) and RNA-Seq libraries generated using the SureSelect stranded RNA library preparation kit (Agilent Technologies, Santa Clara, CA, United States). Around three million sequencing reads were generated per sample and after read quality trimming and alignment to the Darmor CDS reference genome ( Chalhoub et al, 2014 ), SNP genotyping of all 633 samples was performed according to the methods described in Malmberg et al (2017) , using the 226,855 high quality SNP loci previously generated by Malmberg et al (2017) , as well as filtering for a minimum mapping quality of 30.…”

“…This study aimed to generate the most comprehensive genome analysis of canola global germplasm to date that is lacking from the literature. As recent studies have focused on European and Chinese varieties, this study selected a significant number of Australian spring types as well as diverse global germplasm, which was initially interrogated for population structure using GBS-transcriptomics (GBS-t), which provides reduced representation of the genome for sequencing by extracting and converting mRNA to RNA-Seq libraries, allowing for cost effective genotyping of a large number of samples ( Malmberg et al, 2017 ). As a result of this assessment, a core collection of samples representative of global diversity was used for WGR, where DNA is extracted and the whole genome is re-sequenced, resulting in a higher cost per sample but providing more genomic information.…”

Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines

“…Phenotypic and pedigree selection can potentially exploit both types of resistance to breed varieties with durable field resistance to L. maculans (Brun et al, 2010; Delourme et al, 2014). Phenotyping for both qualitative and quantitative resistance can be done in field disease nurseries against a full fungal population (Kaur et al, 2009; Light et al, 2011) as well as in controlled environment conditions against specific single spore fungal isolates (Rouxel et al, 2003; Marcroft et al, 2012; Brun et al, 2010).…”