linkage map of a barley RIL population. In the low resolution mapping step, by taking the advantages of GBS along with the accurate phenotyping assessment, the MlLa-H locus was initially assigned within an interval of 3 cM, corresponding to 3.5 Mbp at the physical scale. For the high resolution mapping step, the SNPs derived by GBS were being easily converted to individuals CAPS markers to saturate the target region, screen the recombinants and reduce the target interval. The low cost of GBS, makes it an attractive approach to create a dense genetic map and to allocate precisely any resistance QTL interval in mapping and breeding populations.
As the amount and quality of generated sequence data per run keep increasing, GBS has become a cost-competitive alternative to other whole-genome genotyping platforms (Elshire et al., 2011b). In addition, for crops with big genomes like barley, this technique is technically less challenging compared to exome sequencing owing to reduced sample handling and few PCR and purification steps, making it a highly rapid approach (He et al., 2014b). In fact, following the DNA extraction, the library preparation for 200 lines takes only less than one week. Although creating a dense genetic map is an important step in the genetic mapping of a locus, increasing the size of mapping population and marker saturation within the target interval are essential steps to obtain a higher resolution at the chromosomal region containing the target gene. In the current study, a cluster of related disease resistance genes, RLK and NBS-LRR genes, were located in close physical proximity at the targeted interval. The deviation of the observed recombination frequency from the average predicted value for the telomeric region of the barley chromosomes offers clear clues for suppressed recombination at this interval which was also detected in ‘Vada’
haplotype. In this situation, the population size enlargement cannot help to obtain a higher resolution. Resistance gene enrichment sequencing (RenSeq) approach is an innovative approach that could have been used instead of classical gene mapping to further facilitate the candidate
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gene identification process (Jupe et al., 2013). It can be used for mapping of resistance loci in segregating populations but also for rapid cloning of R genes via its combination with mutagenesis (Mutagenesis and Resistance gene enrichment sequencing: MutRenSeq). The latter approach is especially useful in the regions where dissection of the resistance locus through recombination is not realistic like the situation observed in current study (Steuernagel et al., 2016). Both RenSeq and MutRenSeq approaches are based on enrichment sequencing, eliminating the necessity to sequence the whole genome. In fact, all genomic regions complementary to the R-encoding genes of the reference genome are captured by baits of 120 nucleotides (Steuernagel et al., 2017). This provides an opportunity to explore the allelic diversity at R genes. Jupe et al. (2013) applied RenSeq to identify NBS-LRR alleles that co-segregate with the underlying resistance in a wild potato population that was segregating for late blight resistance. This approach highly facilitated the development of the closest markers for the Rpi-rzc1 gene conferring broad-spectrum resistance to potato late blight within a cluster of candidate R genes (Śliwka et al., 2012). Both approaches can be applied to quickly map all functional R genes to control important crop diseases, and to identify previously uncharacterized R encoding sequences (Jupe et al., 2012, 2013; Steuernagel et al., 2016). Jupe et al. (2013) demonstrated that a large fraction of the identified R loci mapped on genomic regions for which no gene models were provided by the Potato Genome Sequencing Consortium (PGSC). Thus, it is highly proposed to consider RenSeq as a helpful technique to improve the current barley reference genome annotations for R genes. In the current study, it was shown that the automated annotation for two R genes located in the MlLa-H interval, was incomplete and gene prediction software failed to annotate some parts/exons. This might also be true for the rest of the genome. Using RenSeq, it was realized that many R genes that were annotated by PGSC as partial, were in reality full length, with large gaps/missing some sequences (Jupe et al., 2012; Steuernagel et al., 2016). The main limitations in assemblies are direct consequences of extreme abundance of repetitive elements in the genome, and the severely reduced frequency of meiotic recombination in pericentromeric regions (Mascher et al., 2017). As in RenSeq, the complexity of the genome is significantly reduced in a non-random manner, this limitation might be overcome. Nevertheless, Steuernagel et al. (2016) reported that by using Illumina 250-bp paired-end sequencing reads in RenSeq and MutRenSeq, they also faced difficulties to bridge a gap by a 2,920-bp intron located between two exons of Sr22 gene. This could be due to the short reads obtained from Illumina sequencing as well as high sequence similarity of large R gene sub-families which might cause
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some misassemblies or some incompleteness of predicted annotations (Steuernagel et al., 2017).
This limitation could be overcome if the DNA and even, RNA samples of respective individuals are being sequenced using a long-read sequencing technology such as PacBio (Steuernagel et al., 2016; Witek et al., 2016; Bevan et al., 2017). One drawback of RenSeq is that the broad sequence diversity among parental R gene families prohibited the identification of the individual R genes responsible for resistance which can be easily be solved by using MutRenSeq. Steuernagel et al.
(2016) used RenSeq in combination with mutagenesis to identify R genes that mediate resistance. By using ethyl methanesulfonate (EMS)-derived loss of function mutants, and crossing with the wild-type resistant genotype, they created the independent M2 families and did screening for susceptible mutants in each family. In conjunction with a Triticeae NBS-LRR–specific bait library and sequencing of the wild-type and susceptible mutants, they successfully cloned two major dominant wheat stem rust genes, Sr22 and Sr45. This approach is a very novel and practical approach for the cloning of R genes regardless of where they reside within the genome (on telomeric or centromeric regions) and whether they have experienced suppression recombination due to SV or not (Jupe et al., 2012; Bevan et al., 2017).
By EMS mutagenesis of the resistant plant (HOR2573) in the current study and creation of independent M2 families and screening for susceptible mutants for various disease resistant traits like resistance to leaf rust, leaf blight, powdery mildew and etc., all the possible resistance genes present in this accession can be identified. For the target enrichment, it is proposed to add the annotated RLK family genes from Triticeae gene recourses to available Triticeae NBS-LRR–specific bait library and do sequencing of the wild-type and
susceptible-mutants, data analysis and finally candidate calling. For mapping, de novo assembly of the enriched sequences of the resistant wild-type can be used as a reference. This approach provides important clues on SV and PAV within R genes on the genome-wide scale (Steuernagel et al., 2017). The key advantages of this approach are that it is highly rapid compared to classical map-based cloning approach (<24 months), without a need for fine mapping and the construction of physical contig spanning the target interval. Furthermore, this approach allows to rescue R genes from crop wild relatives-introgressions (from H. bulbosum) or barley accessions found in Genebanks which are not currently being used in breeding program owing to linkage drag, and provide an opportunity to clone rapidly R genes that could be used in multi-R gene pyramiding efforts, a strategy that promises more durable disease resistance in crops (Bevan et al., 2017; Steuernagel et al., 2017).
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