Leaf rust of barley is a serious disease caused by the biotrophic fungus Puccinia hordei Otth., which, under favorable conditions, may cause yield losses of up to 62% (Park et al., 2015), while in general loses are about 15–25% (Whelan et al., 1997). Symptoms of leaf rust vary from small chlorotic flecks to large orange-brown pustules of up to 0.5 mm in size, often surrounded by green islands (Clifford, 1985). Although several resistance genes in barley have been identified, the major challenge in control of barley leaf rust is the breakdown of resistance caused by mutations in effector (avirulence) genes of the pathogen, leading to occurrence of new virulent races on previously resistant plant cultivars in a short period of time (Park, 2003). Therefore, to combat leaf rust epidemics caused by newly occurring/generated virulent races and to achieve a sustainable disease control, the employment of new resistance genes using functional molecular markers in breeding schemes as well as the isolation of known ones in order to get detailed information on the structure and function is of prime importance. Furthermore, isolation of known resistance genes is a prerequisite that allow an efficient allele mining of genetic resources (Li et al., 2016) as well as allele editing, e.g., by CRISPR/Cas9 (Wang et al., 2014).

Since the first genetic study on leaf rust resistance (Waterhouse, 1927), 25 Rph (Resistance to P. hordei) genes have been mapped in barley (Kavanagh et al., 2017). Among them, two genes, namely Rph20 and Rph23, mediate an adult plant resistance (APR) (Hickey et al., 2011; Singh et al., 2015), while the remaining 23 (Rph1 to Rph19, Rph21, Rph22, Rph24, and Rph25) establish seedling resistance (Kavanagh et al., 2017). Rph5 and Rph6 on chromosome 3H (Zhong et al., 2003), Rph9 and Rph12 on chromosome 5H (Borovkova et al., 1998) and Rph15 and Rph16 on chromosome 2H have been described as alleles of the same gene (Weerasena et al., 2004). Only Rph7, Rph15, and Rph16 are still effective in Europe (Niks et al., 2000; Perovic et al., 2004) and the number of effective Rph genes available to breeders is decreasing rapidly (Kavanagh et al., 2017). Among all known Rph genes, only Rph1 has been isolated recently, using the newly developed cloning approach called Mutant Chromosome Sequencing (MutChromSeq) (Steuernagel et al., 2016) in combination with genetic mapping (Dracatos et al., 2018).

Molecular markers have been widely used in barley breeding for mapping of genes, marker-assisted selection, as well as in positional isolation of genes (Stein and Graner, 2005; Perovic et al., 2018). The most abundant molecular markers are single nucleotide polymorphism (SNP). Employing next generation sequencing (Ganal et al., 2018), SNPs are easily detectable in a high throughput manner and are therefore currently the markers of choice. The number of available SNP markers rapidly increased from about 180 EST markers to about 6,800 SNPs on the 9K Illumina iSelect chip up to 44,040 SNPs on the 50K Illumina Infinium array (Kota et al., 2003; Rostoks et al., 2005; Stein et al., 2007; Close et al., 2009; Muñoz-Amatriaín et al., 2011; Comadran et al., 2012; Bayer et al., 2017). The barley Genome Zipper (GZ) assembled 86% of the barley genes in a putative linear order (Mayer et al., 2011). Population sequencing methodology (POPSEQ) was developed as an integrated method to create a linear order of contigs using whole-genome-shotgun sequencing (WGS) data that resulted in the first ultra-high density map of the barley genome (Mayer et al., 2011; Mascher et al., 2013a). Assessment of the GZ and POPSEQ by Silvar et al. (2015) at seven loci mapped with higher genetic resolution revealed an accuracy of 97.8% with respect to the GZ and 99.3% to POPSEQ in comparison to consensus genetic maps. In addition to the above mentioned resources, advances in target capture/enrichment and next-generation sequencing, like GBS (Poland et al., 2012), exome capture (Mascher et al., 2013b), and barley reference genome sequence (Mascher et al., 2017) are available for marker development.

Although high resolution mapping allows precise zooming into targeted loci, the un-even distribution of crossovers along chromosomes (International Barley Genome Sequencing Consortium [IBSC], 2012) and the large variation in the genetic/physical ratio across the genome (Künzel et al., 2000) often hampers high-resolution genetic dissection. In barley, peri-centromeric regions (pCENR) comprise at least 48% of the physical genome but harbor only 14–22% of the total barley gene content (Mascher et al., 2017). The other extreme are hotspots of high recombination rates in telomeric regions (Bhakta et al., 2015). In case of the locus of Ryd3, which is located in a centromeric region, the physical/genetic ratio has been estimated at 14–60 Mb/cM, while the genome-wide average is 4.4 Mb/cM (Lüpken et al., 2014). At the rym4/rym5 locus, the ratio of physical to genetic distances was in the range between 0.8 and 2.3 Mb cM and have increased to over 30 Mb cM, although the gene has been mapped on the telomeric region of chromosome 3H (Stein and Graner, 2005). This indicates that a large number of meiotic events is essential for a sufficient genetic resolution to detect recombination events in close vicinity to the targeted genes, and highlights the need for development of HRMPs.

Diverse collections of barley germplasm were evaluated for detecting new sources of leaf rust resistance (Perovic et al., 2003). In this respect, the Rph_MBR1012 gene was mapped on the short arm of chromosome 1H (König et al., 2012), where only Rph4 has previously been localized (McDaniel and Hathcock, 1969; König et al., 2012). Prior to the recently cloned gene Rph1 by Dracatos et al. (2018), all efforts to isolate leaf rust resistance genes in barley were unsuccessful. An example of unsuccessful isolation is the case of Rph7 (Brunner et al., 2000; Scherrer et al., 2005). Hence, positional cloning is still one of the most efficient and reliable approaches to isolate a resistance gene in crop species with large genomes, such as wheat and barley (Krattinger et al., 2009). In barley, up to now five genes conferring resistance to fungal and viral pathogens have been isolated through map-based cloning, comprising mlo (Büschges et al., 1997; Simons et al., 1997), Mla6 (Halterman et al., 2001), Rpg1 (Brueggeman et al., 2002), rym4/rym5 (Pellio et al., 2005) and rym11 (Yang et al., 2014).

The aims of this study were to: (i) develop at HRMP for the Rph_MBR1012 resistance gene, (ii) saturate the locus using all available state-of-the-art genomic resources i.e., GBS, 50K Infinium and the barley reference genome, (iii) anchor the genetic map to the barley reference sequence (iv) characterize the putative candidate rust resistance genes by allele specific re-sequencing and (v) test the developed markers for their diagnostic value.

Leaf rust is an important fungal disease affecting barley production (Park, 2003). Fungicide application is an option to reduce yield losses but is not always efficient and cannot be considered as a sustainable disease management (Park et al., 2015). Thus, growing of resistant cultivars is the most economical and environmental friendly way to reduce yield losses caused by leaf rust (Kolmer, 1996). However, disease resistance provided by major Rph genes is often overcome due to the emergence of new P. hordei pathotypes (Niks, 1982; Steffenson et al., 1993; Park, 2003) indicating the need for introducing new sources of resistance into barley breeding as well as the need for isolating known ones toward deciphering the structure and function offering the possibility of developing functional markers for breeding and create new alleles by e.g., CRISPR/Cas9 (Kumar et al., 2018).

In this study we have shown the efficient use of the barley reference sequence in physical mapping and especially in marker saturation. Previously, Perovic et al. (2003) demonstrated that the barley landrace MBR1012 is resistant to the barley leaf rust isolate I-80, which later was mapped using 14 SSRs and three SNPs markers on barley chromosome 1HS (König et al., 2012). A null allele of the SSR marker GBMS187 was identified as the closest linked marker at 0.8 cM proximal to the resistance gene. The allelic status of Rph_MBR1012 and Rph4 (McDaniel and Hathcock, 1969), two genes mapped on the short arm on barley chromosome 1HS, is part of an ongoing experiment (Perovic et al., in preparation). The phenotypic evaluation conducted here revealed a hypersensitive reaction of the Rph_MBR1012 resistance gene (Figure 1), while the genetic analysis demonstrates that by using genetically mapped markers in combination with the genome sequence information (Mascher et al., 2017) the physical position of this locus can be determined easily. An initial size of the locus of 6.25 Mb that was estimated based on the published map was further downsized by the use of new marker resources and by increasing the genetic resolution.

For many years, mapping of resistance genes relied on the use of various molecular markers i.e., restriction fragment length polymorphism (RFLP) (Graner et al., 1991; Kleinhofs et al., 1993), random amplified polymorphic DNAs (RAPDs) (Williams et al., 1990; Chalmers et al., 1993), amplified fragment length polymorphism (AFLPs) (Vos et al., 1995; Qi et al., 1998) and SSRs (Ramsay et al., 2000; Varshney et al., 2007). For instance, the powdery mildew resistance gene mlo was identified by a combined use of RFLP and AFLP markers which was the first gene, isolated by map-based cloning in barley (Büschges et al., 1997). AFLP, RAPD and RFLP-derived markers were also used to saturate the Mla region (Wei et al., 1999). However, using these marker systems, gene isolation was a laborious and time consuming effort. Advances in molecular marker technologies as well as the previous version of the barley genome sequence already facilitated an accelerated fine mapping of disease resistance genes (Lüpken et al., 2013, 2014; Yang et al., 2014). New Illumina SNP genotyping assays, namely 9K and 50K (Comadran et al., 2012; Bayer et al., 2017), together with GBS (Poland et al., 2012) opened a new way for a more efficient and faster marker saturation of target loci in barley. In our study, above mentioned marker resources were used for a first marker saturation of Rph_MBR1012. During the simultaneous construction of a fine map and an initial marker saturation a set of 37 GZ and 9K iSelect SNP markers were randomly selected and mapped to our target interval of 8.0 cM reducing the target interval to 0.1 cM flanked by QBS97 and QBS98. Subsequently, the newly developed high-density barley 50K Infinium SNP markers (Bayer et al., 2017) and GBS markers, which were selected using the reference sequence in the shortened candidate interval (0.1 cM), resulted in the identification of nineteen additional polymorphic SNPs. These markers were converted into KASP markers and the Rph_MBR1012 locus was genetically further narrowed into an interval of 0.07 cM between the markers QBS127 and QBS98. In the target interval, ten markers i.e., QBS128, QBS129, QBS130, GBS626, GBR534, GBS546, QBS116, QBS117, QBS131 and QBS132 spanning 0.07 cM genetic distance between QBS127 (at 0.02 cM) and QB98 (at 0.05 cM) were co-segregating. Seven out of the ten co-segregating markers, namely QBS116 (50K), QBS117 (50K), QBS128 (GBS), QBS130 (GBS), QBS131 (GBS), GBS546 and GBR534, were located in five genes in the target interval.

Fine mapping of resistance genes is a bottleneck in gene isolation due to the presence of many genes within target intervals, an uneven recombination frequency and a lack of molecular markers, (Stein and Graner, 2005). The fine map for the Rph_MBR1012 region constructed in this study was based on a set of 56 molecular markers including four InDel, three present/absent, six SSRs, and 43 SNPs markers. Even though Rph_MBR1012 is located in the telomeric region, it turned out that recombination events are not distributed continuously along this region. Although Rph_MBR1012 is surrounded with two highly recombining regions at the telomere of chromosome 1HS, 0.58 and 0.6 Mb/cM, the locus is in very unfavorable region of 7.28 Mb/cM with a high number of co-segregating markers, again elucidating that the potential of map-based cloning still depends on the genomic context around the gene of interest. Uneven distribution of recombination frequencies along the genome (Künzel et al., 2000; Akhunov et al., 2003) and differences in local recombination rates, may cause regions even without any recombination over large physical distances which are not suited to map based cloning (Qi and Gill, 2001; Neu et al., 2002). Consequently, the efficiency of an effort of increasing the population size has always to be considered.

Genome-wide studies and multiple gene surveys recorded variation of the SNP frequencies in barley from one SNP per 240 bp, per 200 bp, and per 189 bp (Shavrukov, 2016). In contrast, one SNP per 7 bp in the leaf rust resistance Rph7 gene region evidently showed the usefulness of high-density SNP markers for the purpose of gene isolation in barley (Scherrer et al., 2005).

In addition to the resources used in our study the following genomic resources for marker saturation nowadays may be used: exome sequencing (Mascher et al., 2013b), RNA sequencing (RNAseq) (Wang et al., 2009) which is based on transcriptome profiling, resistance gene enrichment sequencing (RenSeq) (Andolfo et al., 2014) and WGS. These methods may serve to enhance the detection of polymorphism in the genome and to develop markers toward gene isolation in a short period of time. More recently, MutRenSeq that combines the complexity reduction of R gene targeted enrichment sequencing and computational analysis based on comparative genomics provides a tool for the rapid cloning of disease resistance (R) genes in plants (Steuernagel et al., 2016; Dracatos et al., 2018).

Anchoring of the markers against the barley reference sequence elucidated the physical size of 0.44 Mb for the interval harboring Rph_MBR1012. The order of all mapped markers were inconsistent with the order in the barley physical map (Stein et al., 2007). However, a large rearrangement of 15 markers within 1.34 Mb in the distal part of chromosome 1H was observed. This inversion is based on non-fixed orientation of the BAC-based sequence contig within the small scaffold having only one the anchor point (personal communication Martin Mascher).

Twenty-nine annotated genes were identified within the narrowed down interval between markers QBS127 and QBS98 comprising five disease associated resistance genes (R genes) which support the prior observation that many barley resistance genes are located distally in regions with high recombination frequency (International Barley Genome Sequencing Consortium [IBSC], 2012). It has been indicated, that more than 80% of all known R genes are of the NBS-LRR type (nucleotide-binding leucine rich repeat) (Shao et al., 2016). LRR domains have particular function in plant-pathogen recognition (Hong and Zhang, 2016). The annotation using Blastx against the non-redundant protein database of NCBI also indicates the presence of the NBS-LRR domain in all five disease resistance genes in the target interval. Disease resistance genes located in the target interval tend to cluster which is typical for NBS-LRR based resistance gene analogs (DeYoung and Innes, 2006). Since P. hordei is a biotrophic fungi and the fact that NBS-LRR resistance genes are only effective in conferring resistance to biotrophic or hemibiotrophic pathogens, but not against necrotrophic pathogens (Belkhadir et al., 2004) provides evidence that resistance is due to a gene carrying the NBS-LRR motif. Hence, full length re-sequencing of five disease resistance genes in parental lines was conducted. However, more than 80% similarity in the sequences of R genes considerably hampered sequencing, therefore in order to obtain a complete sequence of the disease resistance genes, new primer design will be conducted.

Marker validation of seven co-segregating markers in 51 already tested barley lines (König et al., 2012), as well as 12 other barley cultivars/lines, gave hint that new markers identified in this study are not all diagnostic for Rph_MBR1012. Based on our study, the markers GBS546 and GBS626 with 80.32 and 83.60% accuracies in prediction of Rph_MBR1012 are the best diagnostic markers and facilitate faster and easier detection of Rph_MBR1012 (and putative alleles) in barley breeding lines. Out of the selected markers QBS128 (HORVU1Hr1G000830/Disease resistance protein), QBS130 (HORVU1Hr1G000910/Disease resistance protein), QBS116, QBS117 and GBR534 (HORVU1Hr1G000940/copper ion binding), and marker GBS546 (HORVU1Hr1G000930/Low molecular weight glutenin subunit) were directly derived from putative candidate genes in the target interval but revealed a less diagnostic character. However, the diagnostic Rph_MBR1012 markers identified in this study could be very useful not only for discriminating between resistant and susceptible cultivars but also for pyramiding of Rph_MBR1012 with other resistance genes to aim a durable resistance in barley cultivars (Sharma Poudel et al., 2018).

In summary, by using high-throughput genotyping and sequencing techniques together with the barley reference sequence we succeeded to downsize the Rph_MBR1012 target interval to 0.44 Mb between markers QBS127 and QBS98 in comparison to 6.24 Mb in a previous study. This is an indispensable step toward isolation of this gene. Four strategies might be then considered in next step in order to define the loci underlying the resistance gene Rph_MBR1012; enhancing the map resolution via screening a new set of F₂ plants and using the new SNPs and InDel defined from candidate genes at target interval to develop the new markers for further marker saturation, screening a non-gridded BAC library from donor line MBR1012, overexpression of five detected disease resistance genes in the target interval in a susceptible barley cultivar, e.g., Scarlett and knock out the genes in resistant lines using CRISPR/cas9. The co-segregating and closely linked markers detected in this study, may be useful as probes for BAC library screening and construction of the physical map in MBR1012.

DP, DK, and FO conceived and designed the experiments, provided the experimental material and contributed to study design, subject recruitment and sample preparation. LF, DK, and DP performed the experiments. LF, JK, and DP analyzed the data. LF, JK, HD, FO, and DP interpreted the data. All authors wrote the manuscript, and read and approved the final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.