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Identification of QTLs conferring resistance to scald (Rhynchosporium commune) in the barley nested association mapping population HEB-25

BMC Genomics volume 21, Article number: 837 (2020) Cite this article

Abstract

Background

Barley scald, caused by the fungus Rhynchosporium commune, is distributed worldwide to all barley growing areas especially in cool and humid climates. Scald is an economically important leaf disease resulting in yield losses of up to 40%. To breed resistant cultivars the identification of quantitative trait loci (QTLs) conferring resistance to scald is necessary. Introgressing promising resistance alleles of wild barley is a way to broaden the genetic basis of scald resistance in cultivated barley. Here, we apply nested association mapping (NAM) to map resistance QTLs in the barley NAM population HEB-25, comprising 1420 lines in BC1S3 generation, derived from crosses of 25 wild barley accessions with cv. Barke.

Results

In scald infection trials in the greenhouse variability of resistance across and within HEB-25 families was found. NAM based on 33,005 informative SNPs resulted in the identification of eight reliable QTLs for resistance against scald with most wild alleles increasing resistance as compared to cv. Barke. Three of them are located in the region of known resistance genes and two in the regions of QTLs, respectively. The most promising wild allele was found at Rrs17 in one specific wild donor. Also, novel QTLs with beneficial wild allele effects on scald resistance were detected.

Conclusions

To sum up, wild barley represents a rich resource for scald resistance. As the QTLs were linked to the physical map the identified candidate genes will facilitate cloning of the scald resistance genes. The closely linked flanking molecular markers can be used for marker-assisted selection of the respective resistance genes to integrate them in elite cultivars.

Background

Rhynchosporium commune, a haploid fungus, is the causal agent of scald or barley leaf blotch, an important foliar disease of barley (Hordeum vulgare ssp. vulgare L.). R. commune has been classified as a hemibiotroph fungus which occurs in all barley growing areas around the world, especially in cool, humid climates [1]. The typical disease symptoms are tan necrotic lesions with dark brown margins, which occur after a latent period [2]. Besides barley the genus Rhynchosporium is able to infect further species, e. g. rye [1] and Lolium species [3]. Scald can cause yield losses of up to 40%, decreases grain quality [2, 4] and is considered a major economic barley disease, especially in the UK, Australia and Tunisia [5,6,7].

In practice the pathogen is mainly controlled by growing resistant cultivars or chemical protection, while phytosanitary techniques, e. g. ploughing or crop rotation can also help to protect the crop [1, 2]. In general, winter barley cultivars show a higher partial resistance to scald than spring barley cultivars [2]. Because the pathogen itself is highly diverse [1, 2, 8] scald is able to overcome crop protection methods like fungicides or specific cultivation methods as well as resistance genes within a few growing seasons, especially when extensively used [2]. In addition, the high genetic variation may enable the pathogen to cope with climate warming [9].

Since decades breeders and scientists deal with the complex interaction of barley and R. commune, which is only partly understood. Up to now, nine major resistance genes (R genes) and many quantitative trait loci (QTLs) have been identified and have been mapped on a consensus bin-map [2]. Björnstad et al. [10] suggested a nomenclature to classify R genes against Rhynchosporium using the Rrs/rrs terminology considering that some described resistance genes are alleles of the same gene. Part of the R gene suite identified thus far are derived from wild Hordeum species, e. g. Rrs13, Rrs14 and Rrs15 from Hordeum vulgare ssp. spontaneum [11,12,13,14,15,16] or Rrs16 from Hordeum bulbosum [17]. Most of them are mapped and markers for marker-assisted selection (MAS) are developed, but except Rrs2 [18] none of them are diagnostic. So far, none of the scald resistance genes has been cloned [1, 19].

The Rrs1 locus was the first resistance locus described in barley, which is a powerful and still effective resistance locus against scald in barley [20], although the fungus is able to overcome Rrs1 by losing the avirulence gene NIP1 (necrosis-inducing peptide 1, [21]). Rrs1 represents a complex locus with either many tightly linked genes or multiple alleles at a single gene allocated to the centromeric region of chromosome 3HL [2, 22]. Although Rrs1 is a major resistance gene sometimes even in resistant plants the fungus is able to complete its life cycle and sporulate [23,24,25]. Additionally, Patil et al. [26] mapped a second resistance locus, named Rrs4CI11549 22 cM distal to Rrs1 on chromosome 3HL. For Rrs2, located on 7HS [27], eight diagnostic markers are described [18]. The genomic region near Rrs2 was re-sequenced in a diverse set of wild and cultivated barley. The nucleotide diversity was higher in wild than cultivated barley and the domestication signal in this region was weak [28]. Pectin esterase inhibitor (PEI) genes were analysed as possible candidate genes for Rrs2, but this could not be confirmed [29]. Rrs13 is derived from a wild barley accession and located on the short arm of chromosome 6H [12, 30]. In this region are several QTL for scald resistance [31,32,33,34,35]. Rrs14 originated from a wild population of Hordeum vulgare ssp. spontaneum from Iran and is located between the seed storage protein loci Hor1 and Hor2 (hordein) on 1H [14]. Two different loci conferring resistance were both named Rrs15 whereby one is located on 2H and the other on 7H [2]. The single dominant gene on the long arm of chromosome 7H is derived from an Israeli accession of wild barley [16]. The locus on 2H originated from Hordeum vulgare and is named Rrs15CI8288 according to the resistance donor CIho8288 [36]. To avoid confusion Zhan et al. [2] suggested to rename the locus on 2H as Rrs17. The first resistance gene against scald from the secondary gene-pool of barley is Rrs16Hb on chromosome 4HS. It is derived from a recombinant hybrid between H. vulgare and H. bulbosum [17]. A recently identified resistance locus on chromosome 6H is called Rrs18 and was mapped distal from Rrs13 on chromosome 6H [37, 38].

Possibly some QTLs are alleles of known resistance genes [39]. Furthermore, some QTLs independently identified in different studies may be allelic or even identical [2]. Schweizer and Stein [40] integrated 166 QTLs from 28 studies to identify meta-QTLs mediating resistance to several fungal pathogens. Twenty meta-QTLs were detected over all chromosomes including ten regions associated with scald resistance on all chromosomes except chromosome 5H. Looseley et al. [19] identified altogether 17 QTLs by means of genome-wide association study (GWAS) in two different data sets (European spring barley and old list trials). The QTLs are in the region of Rrs1, Rrs3, Rrs13, Rrs15b and Rrs16, but are probably not the resistance genes. Wang et al. [41] combined in total 43 QTLs and seven genes conferring quantitative and qualitative resistance, which had been located on individual maps, in a consensus map. Again they found QTLs/genes on all chromosomes except 5H.

The majority of genetic studies on scald resistance has been conducted at the seedling stage focusing on major gene resistance presumably based on problems with the field tests [2, 42]. Normally there is a good correlation between seedling and adult plant resistance with some exceptions [2, 33]. Disease escape may be one reason why plants seem to be more resistant in the field than in the greenhouse [2]. Disease escape is mainly based on unfavourable growing conditions for the fungi like drought and temperature as well as plant height, maturity and canopy structure limiting the spreading of the pathogen [2]. Therefore, the resistance QTLs on 3H [32, 33, 43, 44] in the region of sdw1 possibly are pleiotropic effects of the semi-dwarfing gene [2]. Adult plant resistance (APR) is based on many minor genes with small effects [41, 45]. Accordingly, several QTLs for APR were detected on chromosome 2H, 3H, 4H, 6H and 7H [31,32,33, 41,42,43, 46, 47].

During the last decade the concept of nested association mapping (NAM) was established as a method to identify QTLs with high precision and high statistical power by combining advantages of classical linkage mapping and association mapping. In NAM a multitude of highly divergent (exotic) parents are crossed with one recurrent elite cultivar. This way potentially useful exotic alleles can be investigated in an adapted background, which is of special importance for allele mining of exotic resistance genes. In this regard NAM has been successfully applied to identify exotic sources of pathogen resistance in maize [48,49,50], barley [51,52,53] and wheat [54, 55]. Vatter et al. [52, 53] could show that the wild barley NAM population HEB-25 contains numerous novel QTLs for net blotch, leaf rust and stripe rust. However, resistances were mostly conferred by the combination of multiple small-effect QTLs rather than single major QTLs.

The aims of the study were I) to screen the HEB-25 population for scald resistance; II) to detect QTLs linked to resistance against R. commune; III) to compare the identified QTLs with known resistance genes and QTLs; IV) and to find highly resistant HEB-25 lines for introgression of the resistance improving alleles in pre-breeding programs.

Results

Scald resistance

Of the 26 HEB parents only HID_138 (donor of HEB family 13 (F13)), HID_380 (F24) and Barke (recurrent parent) were clearly susceptible in the greenhouse tests. The accession HID_144 (F15) segregated for resistance and the remaining accessions were resistant. Fifteen accessions showed no symptoms at all and the other seven resistant accessions showed only small lesions (Additional file 1: Figure S1). All 25 HEB families were segregating for scald resistance. HEB family 01 showed the highest average susceptibility (2.90) to scald and F12 the highest resistance (1.36) (Additional file 2: Table S1).

Marker data HEB-25 parents

According to marker data regarding Rrs1, Rrs2, Rrs17 and Rrs18, 14 wild barley parents carry at least one positive marker allele associated with known scald resistance genes.

Twelve parents of the HEB families carry resistant marker alleles at Rrs1, four carry resistant marker alleles at Rrs17 and three carry resistant marker alleles at Rrs18 (Additional file 3: Table S2). Three of them (HID_055 (F03), HID_065 (F05) and HID_144 (F15)) carry resistant marker alleles both at Rrs1 and Rrs17, while HID_138 (F13) carries resistant marker alleles both at Rrs1 and Rrs18. HID_101 (F09) carries resistant marker alleles both at Rrs17 and Rrs18.

The remaining eleven donors including Barke carry no positive alleles of Rrs1, Rrs17 or Rrs18. According to the marker results of the diagnostic marker e11_2 no positive Rrs2 allele is present in the HEB-25 parents.

GWAS/association

NAM revealed eight major QTLs, distributed across all seven barley chromosomes that were reliably detected in more than 30 cross-validation runs (Table 1, Fig. 1). In addition, a few minor QTLs were also detected (Fig. 1, Additional file 4: Table S3). All QTLs together reached a prediction ability (R2val) of 0.31. QTL effects were highly specific between families. The impact of the wild alleles according to GWAS ranged from a decline in resistance of + 0.54 (QRs.3H (Rrs1), F23) to resistance improvement of − 2.22 (QRs.2H (Rrs17), F05) scoring units. QTL effects differed strongly at QRs.2H and QRs.3H, indicated by negative and positive wild allele effects revealed in different HEB families. At the most robust QTL (QRs.2H, detected 76 times) the donor of HEB family 5 (F05) had a resistance-improving effect of 2.22 scoring units, whereas almost all remaining donors showed slight resistance-decreasing effects (Fig. 2, Additional file 5: Table S4). The donor alleles at QRs.3H on average showed resistance-improving effects of ≈ 1.7 scoring units in 18 families, whereas F03 and F23 showed comparatively minor resistance-decreasing effects at this QTL. By joint consideration of the eight major QTLs the donor of family 5 showed the most promising resistance effect by improving it by 5.2 scoring units (Fig. 3, Additional file 5: Table S4).

Table 1 QTL summary for scald resistance
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Fig. 1
figure 1

Circos plot indicating QTLs involved in scald resistance. Barley chromosomes are indicated as coloured bars on the inner circle. Grey connector lines represent the link between the genetic position (in cM) of SNPs in the inner circle and the physical position on the outer circle (in Mbp). QTLs and resistance genes from literature are indicated inside the circle and their position is given as outlined boxes on the cM scale. The dots represent the detection rate of each SNP in 100 cross-validation runs with horizontal reference lines at 0, 50 and 100 detections. Red dots represent an average trait-increasing effect, while blue dots represent an average trait-decreasing effect across all HEB families. Black lines on the outer track indicate the range of SNPs on the physical map that have been cumulated for estimating the family-specific effect, which is presented above. Here, the lower box indicates the family with the minimal GWAS effect, while the upper box represents the family with the maximal GWAS effect. The colour code indicates the strength of the effects as a heat map, i.e. darker colour represents a stronger effect. Figure created by use of Circos [58]

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Fig. 2
figure 2

Heat map of family-specific effects at major scald QTLs. For each QTL (columns) GWAS effects of different HEB families (rows) are shown. The colours range from − 2.5 (dark blue) to 2.5 (dark red) scoring units difference as compared to the reference Barke allele. The minimum effect was obtained for F05 at QRs.2H (− 2.22), while the maximum effect was obtained for F23 at QRs.3H (+ 0.54)

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Fig. 3
figure 3

Cumulated donor effect of 8 major scald QTLs. Grey-shaded bars represent the estimated donor effect for each HEB family. Effect was obtained by summarizing family-specific QTL effects of the eight major QTLs, which are represented as coloured dots

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Comparison with previously identified genes and QTL

Comparing the identified scald resistance QTLs (Table 1) with QTLs and genes reported in literature (Additional file 6: Table S5) showed that three of the eight identified QTLs are in regions where resistance genes are mapped: QRs.2H co-localizes with Rrs17, QRs.3H with Rrs1 and QRs.6H with Rrs13. The two QTLs QRs4.H and QRs.5H are in regions where QTLs have been reported. QRs.1H, QRs.7H-1 and QRs.7H-2 might represent novel QTLs associated with scald resistance as they are distantly located from Rrs14, Rrs2 and Rrs15. In the regions of the resistance genes Rrs4, Rrs16 and Rrs3 no QTL was detected in the HEB-25 population.

Discussion

To find and describe new alleles of scald resistance the wild barley NAM population HEB-25 was tested for scald resistance against a specific R. commune isolate (LfL07A) in controlled greenhouse trials. Eight reliable QTLs were identified, distributed over all chromosomes with two QTLs on chromosome 7H including novel QTLs. The detected variation in resistance in the lines of the HEB-25 infected with Rhynchosporium commune reflected a great genetic diversity in the NAM population as shown before for resistance to net blotch (Pyrenophora teres F. teres) [52], stripe rust (Puccinia striiformis f. sp. hordei) and leaf rust (Puccinia hordei) [53].

Most parents of HEB-25 were resistant, only the four parents HID_138 (F13), HID_144 (F15), HID_380 (F24) and Barke (recurrent parent) were susceptible. There were discrepancies between marker genotyping at four known resistance genes described in literature and the phenotyping scores, as for instance HID_138 should carry positive marker alleles at Rrs1 and Rrs18 (Additional file 3: Table S2). These discrepancies might be explained, because the markers are not diagnostic (except e11_2 for Rrs2 [18]), hinting on their limited applicability in wild barley.

Based on the QTL results, for each family the respective donor effect was indirectly derived from accumulating the effects obtained at the eight major QTLs (Fig. 3). According to this the most susceptible donors were observed in F01 (− 0.28 scoring units as compared to Barke), F13 (− 0.47), F24 (− 0.61) and F23 (− 0.93), followed by F17 (− 1.03) and F15 (− 1.15). The direct assessment of scald resistance in the greenhouse revealed the donors of F13, F15 and F24 as the most susceptible ones. The Pearson correlation coefficient of 0.63 between both approaches generally confirms their accordance (Additional file 7: Figure S2). The slight discrepancies for some wild barley accessions (e.g. HID_003 (F01), HID_249 (F17) and HID_359 (F23)) might have been observed due to the heterogenic nature of the wild barley accessions, in the sense that phenotyping was conducted with other plants of the same accession than those being used for initial crossing during HEB-25 development. The segregating susceptibility of HID_144 (F15) between replicates in the greenhouse tests confirms this assumption. The occurrence of heterogeneous seed stocks in genebanks is a common observation and strategies to avoid this are discussed [59].

The most resistant donors according to the cumulated QTL effects are from F05 and F09, followed by F04, F06, F11, F12 and F14. In F05 and F09 all QTLs except QRs.7H-1 have a positive effect on the resistance. Therefore, especially lines from family F05 (e.g. HEB_05_037, HEB_05_041) should be the first choice for breeding to improve scald resistance and to accumulate the respective scald resistance QTLs.

In the following, for each chromosome the detected QTLs are compared to the literature and potential candidate genes are discussed.

Chromosome 1H

The QTL QRs.1H was detected at a position of 43.15 cM on 1H. All 25 HEB donors except from family F01 have an allele at the QTL that improves resistance, but only in F25 the positive effect is greater than 0.5 rating scores. A known resistance gene on chromosome 1H is Rrs14, located in the telomere region of the short arm [19]. In the same region two QTLs linked to the marker Bmac0213 [42, 47] and a meta-QTL [40] were identified. Another QTL (Qsc-1H), distinct from Rrs14, was detected by Daba et al. [60]. However, none of the mentioned resistance loci corresponds to QRs.1H from this study. There are 16 high confidence genes with predicted function in the region of the QTL’s peak marker (BOPA2_12_11266) on chromosome 1H (Additional file 8: Table S6). The obvious candidate gene for resistance is the disease resistance protein RGA2 (resistance gene analog 2) [61, 62].

Chromosome 2H

The QTL QRs.2H in the NAM population is in the region of Rrs17 (formerly Rrs15), which is the only known resistance gene on 2H and is located on the short arm close to the telomere [19, 36]. For QRs.2H (Rrs17) only four families showed an improvement in resistance with F05 (− 2.22 scoring units) having the highest impact. This is the highest resistance improvement found in this study. In the region of the QTL on chromosome 2H there are 16 genes (Additional file 8: Table S6), which can play a role in defence, among them three disease resistance proteins [63] and seven kinases [64, 65]. Especially the lectin-domain containing receptor kinase A4.1 [66] and the two LRR receptor-like serine/threonine-protein kinases [67] are clear candidate genes.

Chromosome 3H

A single QTL (QRs.3H) was found on 3H in the region of Rrs1 [22]. It represents the main scald resistance QTL in HEB-25 as it explains 35% of genotypic variance. According to GWAS all families except F01, F03 and F23 carry a favourable allele that improves the resistance score up to 2.15 (F21) units compared to Barke. In 18 families the resistance score is improved by more than 0.5 scoring units. Around 50 QTLs are described on 3H, most of them in the region of Rrs1 (reviewed in [68]). Rrs1 was linked repeatedly to marker MWG680 [20, 26, 39, 69, 70] and the marker 11_0315 is derived from the same SNP [22]. The 3H QTL identified by Zantinge et al. [71] is linked to seedling and adult plant resistance and located in the region of Rrs1BC240 [70]. The marker identified by Zantinge [71] is located in a gene with a SWAP/Surp (Suppressor-of-White-Apricot) protein domain for which the role in resistance is unknown, other genes identified in the region may play a role in defence, e. g. leucine rich repeats (LLRs) [71]. Recently, a further major QTL for scald resistance at seedling and adult plant growth stage was identified in Yerong [72], confirming a resistance QTL in Yerong identified by Li and Zhou (2011) [42]. It was shown by differential variety screening and physical mapping that the locus is different from Rrs1 [72]. In our study, from the thirteen candidate genes in the QTL interval on 3H (Additional file 8: Table S6) the two kinases are the obvious candidate genes like on chromosome 2H. Also the beclin-1-like protein [73, 74], the homeodomain-like superfamily protein [75] and the exocyst complex component 7 [76] have been shown to play a role in defence, too.

Sdw1, located on the long arm of 3H and causing a semi-dwarf phenotype [77], can influence resistance via escape mechanism, hence resistance QTLs in this region are probably pleiotropic effects of plant height [2]. As expected, no QTL could be detected in this region, because on the one hand only young plants were monitored and on the other hand the advantages of higher plants regarding suppressed spreading of the pathogen is reduced under controlled greenhouse conditions. Also in the region of Rrs4 [26] on 3H no QTL was detected in HEB-25.

Chromosome 4H

QRs.4H is not in the region of a known resistance gene like Rrs3 and Rrs16 [19], but a QTL has been described in this region before [31, 32, 78]. The powdery mildew resistance gene mlo is located in a similar region [32, 34]. The QTL on 4H includes six candidate genes (Additional file 8: Table S6) with a possible role in defence reactions. The disease resistance-responsive (dirigent-like protein) family protein [79] is the obvious candidate. All families have the positive allele slightly improving resistance between 0.02 and 0.62 rating scores compared to Barke, in F03, F12, F23 and F25 greater than 0.5.

Chromosome 5H

There is no known resistance gene on 5H, but QTLs have been described before corresponding to the Qrs.5H position identified in HEB-25. All HEB-25 families except F18 carry a favourable allele, six of them increase resistance by more than 0.5 scoring units compared to Barke. In the eleven genes in the region of the QTL on chromosome 5H (Additional file 8: Table S6) with a link to resistance again a kinase is found and a cluster of cytochrome P450 superfamily proteins [80].

Chromosome 6H

The peak marker of QRs.6H falls in the region of Rrs13 [19] rather than in the closely located Rrs18 region [38]. All families have the positive allele, the greatest improvement in resistance show F04 and F07 (scores < − 1.0). There are other QTLs in this region [31,32,33,34,35, 69]. As on chromosome 1H and 2H disease resistance proteins [61,62,63, 72] are found in the region of the QTL (Additional file 8: Table S6) and as on chromosome 2H, 3H and 5H protein kinases [64, 81,82,83] are plausible candidate genes.

Chromosome 7H

On 7H the two QTLs QRs.7H-1 and QRs.7H-2 were detected. However, their peak markers do not correspond to Rrs2 [18] or Rrs15 [19]. In case of Rrs2 this is supported by the marker scores of the diagnostic marker e11_2 [18], which indicate the absence of the resistant allele in all HEB families. At QRs.7H-1 most families carry a resistance decreasing allele of up to 0.5 scoring units. In contrast, sixteen families carry a favourable allele at QRs7H.2 with F09 showing the greatest improvement in resistance of 1.65 scoring units compared to Barke, representing a potentially new effective source of scald resistance for future breeding. Of the genes at QRs.7H-2 (Additional file 8: Table S6) the most evident candidate genes are two protein kinases [64, 82]. There are further genes in the region that can play a role in defence like several BTB/POZ domain-containing proteins [84] and two peroxidase superfamily proteins [85, 86]. The QRs.7H-1 region contains ten potential candidate genes with a link to resistance, among them GDSL esterase/lipase [87], ring E3 ubiquitin ligase [88] and acidic endochitinase [89].

Conclusions

In the NAM population HEB-25 numerous genetic sources of scald resistance could be observed. Both known resistance genes (Rrs1, Rrs13 and Rrs17) and novel QTLs with ample variation among the different wild donors of HEB-25 could be detected. The most impactful resistance allele was obtained at Rrs17 in HEB family 05. Novel genetic variation that may be utilized in future breeding programs could furthermore be revealed for F09 at a novel QTL on chromosome 7H (QRs.7H-2). However, one has to consider that our results are based on a single R. commune isolate. As an outcome of the study the link to the physical map and the identified candidate genes will facilitate cloning the scald resistance genes. Furthermore, the segregating subpopulations of selected HEB-25 BC1S3 lines can be utilized for fine mapping through the heterogeneous inbred family (HIF) concept [90]. At least the closely linked markers can be used for marker-assisted selection of the underlying resistance genes and marker based introgression of the new identified resistance genes in pre-breeding programs.

Methods

NAM population HEB-25

The NAM population HEB-25 [56], consisting of 1420 individual BC1S3 lines in 25 wild barley derived families, was used in this study. HEB-25 is the result of initial crosses between the elite spring barley cultivar Barke (released in 1996) and 25 highly divergent exotic wild barley accessions (Hordeum v. ssp. spontaneum and H. v. ssp. agriocrithon). F1 plants of the initial crosses were backcrossed once with Barke. For detailed information about the population design, see Maurer et al. [56]. All seeds were obtained from the Chair of Plant Breeding at Martin Luther University Halle. The exotic wild barley accessions were obtained from the Max Planck Institute for Plant Breeding Research (Cologne, Germany) and their origin is described in Badr et al. [91].

Resistance assessment in greenhouse

In total, 1379 BC1S3:5 lines of the NAM population as well as the 26 parents were tested for scald resistance in seven independent greenhouse experiments with four replicates per line. Isolate ‘LfL07A’ of Rhynchosporium commune from the collection of the Bavarian State Research Center for Agriculture (Germany) was used to evaluate the resistance level. The fungal isolates were prepared for inoculation as described in Hofmann et al. [22].

The greenhouse test was conducted according to Hofmann et al. [22]. In brief, four plants of each tested line were grown at 18 °C in the greenhouse to the three-leaf stage and then spray-inoculated (in average 280,000 spores/ml). Afterwards plants were kept for 48 h in dark with 100% humidity. Rating on the second leaf started around 14 days after infection and continued three times every other day applying a scale of 0 (resistant) to 4 (susceptible) [19, 92], extended by half steps. The rating score of the third date (18 days post infection) was used as the final phenotypic value, as it represented the time point with the best differentiation between resistant and susceptible lines. Least squares means (LSMeans) were calculated for each HEB line to adjust for the random factor of different experiments with PROC MIXED (SAS Institute Inc., Cary, NC, USA) (Additional file 2: Table S1). The reference genotype for phenotyping was the highly susceptible cultivar Beatrix [22], which was highly susceptible in all tests (score 4.0).

Genotyping

Locus-specific markers (HEB-25 parents)

Genomic DNA of the HEB-25 parents was isolated from freeze-dried barley leaves (single plant) according to Behn et al. [93]. To test the linkage between resistance and markers known to be linked to resistance genes the parents of the NAM population were genotyped with the molecular markers 11_1476, 11_0205, 11_0315 [22] and SCRI_RS_221644 [19] for Rrs1, e11 for Rrs2 [18], scsnp07305, STS_2048 and GBS0346 for Rrs13/Rrs18 [37, 38], and Gems13 for Rrs15CI8288/Rrs17 [35, 36] (Additional file 3: Table S2). The marker alleles of the highly susceptible cultivar Beatrix were used as negative references at all marker loci.

Genome-wide markers (HEB-25 population)

DNA of pooled BC1S3:8 plants of each HEB line was extracted according to the manufacturer’s protocol, using the BioSprint 96 DNA Plant Kit and a BioSprint work station (Qiagen, Hilden, Germany), and finally dissolved in distilled water at approximately 50 ng/μl for genotyping with the recently developed barley Infinium iSelect 50 K chip [57] at TraitGenetics, Gatersleben, Germany. SNP markers that did not meet the quality criteria (polymorphic in at least one HEB family, < 10% failure rate, < 12.5% heterozygous calls) were removed from the data set. Altogether, 33,005 SNPs met the quality criteria and were analysed in this study. Fifty-five of the investigated 1379 lines were eliminated due to inconsistent genotypes between BC1S3 and BC1S3:8. Based on the Barke reference genotype, the wild barley allele can be specified in each segregating family. To setup the quantitative identity-by-state (IBS) matrix the state of the homozygous Barke allele was coded as 0, while HEB lines that showed a homozygous wild barley genotype were assigned a value of 2. Consequently, heterozygous HEB lines were assigned a value of 1. If a SNP was monomorphic in one HEB family but polymorphic in a second family, lines of the first HEB family were assigned a genotype value of 0, since their state is not different from the Barke allele. Gaps in the genotype matrix resulting from missing data points (0.84%) were filled applying the mean imputation (MNI) approach [94].

The genotype matrix is deposited at e!DAL [95, 96].

Nested association mapping

We used model ‘IBS-M’ [97], initially introduced as Model-A of Liu et al. [98], a multiple linear regression model with SNP markers being included as main effects using the quantitative IBS genotype matrix scores, to conduct genome-wide association mapping on LSMeans of each HEB line trait performance. The analysis was carried out by means of model selection with SAS PROC HPREG. This procedure can select the best model based on a set of predefined possible factors. In our case, all 33,005 SNPs were initially defined as possible factors. Significant SNPs were then determined by stepwise forward-backward regression. SNPs were allowed to enter or leave the model at each step based on the p-value (< 0.001) calculated for the marginal F-test of that term. SNPs included in the final model are hereafter referred to as significant SNPs. A SNP’s effect estimate can be interpreted as the allele substitution effect and represents the regression coefficient of the respective SNP in the final model. Note that all significant SNPs’ effect estimates are modelled at the same time in the final model.

Cross-validation

A five-fold cross-validation was run 20 times to increase the robustness of the results. For this, 100 subsets were extracted out of the total phenotypic data. Each subset consisted of 80% randomly-chosen HEB lines per family. This set was used as the training set to define significant markers and to estimate their effects, while the remaining 20% of lines were used as the validation set. The phenotypes of the validation set lines were predicted based on marker effects estimated in the training set. Prediction ability (R2val) was then calculated as the squared Pearson product–moment correlation between the observed and predicted phenotypes of the validation set, while R2train represents the model fit of the training set [97].

To define QTL regions, we calculated a SNP marker’s detection rate (DR) as the number of times, out of 100 cross-validation runs, it was included in the final model. Robust QTLs were defined if they were detected more than 30 times.

Cumulating SNPs to estimate parent-specific QTL effects

To estimate a parent-specific QTL effect from model ‘IBS-M’ we applied the cumulation method as presented in Maurer et al. [97]. This procedure was conducted within each of the 100 cross-validation runs and the mean of them was taken as the final parent-specific QTL effect estimate. Moreover, all SNPs from the respective QTL interval were fitted in a linear model to estimate the QTL’s explained genotypic variance (R2) in the whole dataset.

Comparison with previously identified genes and QTLs, candidate genes, link to physical map

Physical map positions of the barley Infinium iSelect 50 K chip were taken from Bayer et al. [57]. If no position was given the position estimate was derived from markers that revealed the highest linkage disequilibrium to the marker under consideration. In case of 4722 SNPs that were shared between the 9 k [99] and the 50 k chip the genetic positions of the 50 k markers were taken from Maurer et al. [56]. The genetic positions of the remaining markers were estimated based on the genetic positions of physically adjacent markers from the 9 k chip. The position of the major resistance genes against scald were integrated in the map according to Looseley et al. [19]. Sdw1 was integrated in the map by blasting the HvGA20ox2 sequence (GenBank: KX611234.1) underlying this locus [100] against the barley genome sequence [101]. Candidate genes for found QTLs were identified via BARLEYMAP [102] (accessed on 25 October 2019), by screening a 2 Mb region surrounding the QTL peak markers for high confidence genes on the physical map [101].

An overview of QTLs conferring resistance including candidate genes described in literature is given in Additional file 8: Table S6.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files (Additional file 2: Table S1, Additional file 3: Table S2). The genotype matrix is deposited at e!DAL [95, 96].

Abbreviations

APR:

Adult plant resistance

CG:

Candidate gene

DR:

Detection rate

GWAS:

Genome-wide association study

HEB:

Halle exotic barley

HID:

Hordeum identity (donor of HEB family)

LSMeans:

Least squares means

NAM:

Nested association mapping

QTL:

Quantitative trait locus

References

  1. Avrova A, Knogge W. Rhynchosporium commune: a persistent threat to barley cultivation. Mol Plant Pathol. 2012;13:986–97. https://doi.org/10.1111/j.1364-3703.2012.00811.x..

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhan J, Fitt BDL, Pinnschmidt HO, Oxley SJP, Newton AC. Resistance, epidemiology and sustainable management of Rhynchosporium secalis populations on barley. Plant Pathol. 2008;57:1–14. https://doi.org/10.1111/j.1365-3059.2007.01691.x.

    Article  Google Scholar 

  3. King KM, West JS, Brunner PC, Dyer PS, Fitt BDL. Evolutionary relationships between Rhynchosporium lolii sp nov and other Rhynchosporium species on grasses. PLoS ONE. 2013;8:e72536. https://doi.org/10.1371/journal.pone.0072536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Paulitz TC, Steffenson BJ. Biotic stress in barley: disease problems and solutions. In: Ullrich SE, editor. Barley: production, improvement and uses. London: Blackwell Publishing Ltd; 2011. p. 307–54.

    Google Scholar 

  5. Fitt BDL, Atkins SD, Fraaije BA, Lucas JA, Newton AC, Looseley ME, et al. Role of inoculum sources in Rhynchosporium population dynamics and epidemiology on barley. HGCA Final report. 2010; Project Number RD-2004-3099. https://projectblue.blob.core.windows.net/media/Default/Research%20Papers/Cereals%20and%20Oilseed/pr486.pdf.

  6. McLean MS, Hollaway GJ. Suppression of scald and improvements in grain yield and quality of barley in response to fungicides and host-plant resistance. Australasian Plant Pathol. 2018;47:13–21. https://doi.org/10.1007/s13313-017-0529-5.

    Article  CAS  Google Scholar 

  7. Bouajila A, Abang MM, Haouas S, Udupa S, Rezgui S, Baum M, Yahyaoui A. Genetic diversity of Rhynchosporium secalis in Tunisia as revealed by pathotype, AFLP, and microsatellite analyses. Mycopathologia. 2007;163:281–94. https://doi.org/10.1007/s11046-007-9012-0.

    Article  PubMed  Google Scholar 

  8. Xi K, Burnett PA, Tewari JP, Chen MH, Turkington TK, Helm JH. Histopathological study of barley cultivars resistant and susceptible to Rhynchosporium secalis. Phytopathology. 2000;90:94–102. https://doi.org/10.1094/PHYTO.2000.90.1.94.

    Article  Google Scholar 

  9. Stefansson TS, McDonald BA, Willi Y. Local adaptation and evolutionary potential along a temperature gradient in the fungal pathogen Rhynchosporium commune. Evol Appl. 2012;6:524–34. https://doi.org/10.1111/eva.12039.

    Article  Google Scholar 

  10. Björnstadt A, Patil V, Tekauz A, Maröy AG, Skinnes H, Jensen A, et al. Resistance to scald (Rhynchosporium secalis) in barley (Hordeum vulgare) studied by near-isogenic lines: I. markers and differential isolates. Phytopathology. 2002;92:710–20.

    Article  Google Scholar 

  11. Abbott DC, Brown AHD, Burdon JJ. Genes for scald resistance from wild barley (Hordeum vulgare ssp spontaneum) and their linkage to isozyme markers. Euphytica. 1992;61:225–31. https://doi.org/10.1007/BF00039662.

    Article  CAS  Google Scholar 

  12. Abbott DC, Lagudah ES, Brown AHD. Identification of RFLPs flanking a scald resistance gene on barley chromosome 6. J Heredity. 1995;86:152–4.

  13. Garvin DF, Brown AHD, Burdon JJ. Inheritance and chromosome locations of scald-resistance genes derived from Iranian and Turkish wild barleys. Theor Appl Genet. 1997;94:1086–91. https://doi.org/10.1007/s001220050519.

    Article  CAS  Google Scholar 

  14. Garvin DF, Brown AHD, Raman H, Read BJ. Genetic mapping of the barley Rrs14 scald resistance gene with RFLP, isozyme and seed storage protein markers. Plant Breed. 2000;119:193–6. https://doi.org/10.1046/j.1439-0523.2000.00456.x.

    Article  CAS  Google Scholar 

  15. Genger RK, Williams KJ, Raman H, Read BJ, Wallwork H, Burdon JJ, Brown AHD. Leaf scald resistance genes in Hordeum vulgare and Hordeum vulgare ssp spontaneum: parallels between cultivated and wild barley. Aust J Agric Res. 2003;54:1335. https://doi.org/10.1071/AR02230.

    Article  CAS  Google Scholar 

  16. Genger RK, Nesbitt K, Brown AHD, Abbott DC, Burdon JJ. A novel barley scald resistance gene: genetic mapping of the Rrs15 scald resistance gene derived from wild barley, Hordeum vulgare ssp. spontaneum. Plant Breed. 2005;124:137–41. https://doi.org/10.1111/j.1439-0523.2005.01085.x.

    Article  CAS  Google Scholar 

  17. Pickering RA, Ruge-Wehling B, Johnston P, Schweizer GF, Ackermann P, Wehling P. The transfer of a gene conferring resistance to scald (Rhynchosporium secalis) from Hordeum bulbosum into H. vulgare chromosome 4HS. Plant Breed. 2006;125:576–9. https://doi.org/10.1111/j.1439-0523.2006.01253.x.

    Article  CAS  Google Scholar 

  18. Hanemann A, Schweizer GF, Cossu R, Wicker T, Röder MS. Fine mapping, physical mapping and development of diagnostic markers for the Rrs2 scald resistance gene in barley. Theor Appl Genet. 2009;119:1507–22. https://doi.org/10.1007/s00122-009-1152-9.

    Article  CAS  PubMed  Google Scholar 

  19. Looseley ME, Griffe LL, Büttner B, Wright KM, Middlefell-Williams J, Bull H, et al. Resistance to Rhynchosporium commune in a collection of European spring barley germplasm. Theor Appl Genet. 2018;131:2513–28. https://doi.org/10.1007/s00122-018-3168-5.

    Article  CAS  PubMed  Google Scholar 

  20. Graner A, Tekauz A. RFLP mapping in barley of a dominant gene conferring resistance to scald (Rhynchosporium secalis). Theor Appl Genet. 1996;93:421–5. https://doi.org/10.1007/BF00223185.

    Article  CAS  PubMed  Google Scholar 

  21. Schürch S, Linde CC, Knogge W, Jackson LF, McDonald BA. Molecular population genetic analysis differentiates two virulence mechanisms of the fungal avirulence gene NIP1. Mol Plant-Microbe Interact. 2004;17:1114–25.

    Article  Google Scholar 

  22. Hofmann K, Silvar C, Casas AM, Herz M, Büttner B, Gracia MP, et al. Fine mapping of the Rrs1 resistance locus against scald in two large populations derived from Spanish barley landraces. Theor Appl Genet. 2013;126:3091–102. https://doi.org/10.1007/s00122-013-2196-4.

    Article  CAS  PubMed  Google Scholar 

  23. Ayres PG, Owen H. Resistance of barley varieties to establishment of subcuticular mycelia by Rhynchosporium secalis. Trans Br Mycol Soc. 1971;57:233–40.

    Article  Google Scholar 

  24. Thirugnanasambandam A, Wright KM, Atkins SD, Whisson SC, Newton AC. Infection of Rrs1 barley by an incompatible race of the fungus Rhynchosporium secalis expressing the green fluorescent protein. Plant Pathol. 2011;60:513–21. https://doi.org/10.1111/j.1365-3059.2010.02393.x.

    Article  Google Scholar 

  25. Zhan J, Yang L, Zhu W, Shang L, Newton AC. Pathogen populations evolve to greater race complexity in agricultural systems--evidence from analysis of Rhynchosporium secalis virulence data. PLoS One. 2012;7:e38611. https://doi.org/10.1371/journal.pone.0038611.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Patil V, Björnstadt A, MacKey J. Molecular mapping of a new gene Rrs4CI11549 for resistance to barley scald (Rhynchosporium secalis). Mol Breeding. 2003;12:169–83. https://doi.org/10.1023/A:1026076511073.

    Article  CAS  Google Scholar 

  27. Schweizer GF, Baumer M, Daniel G, Rugel H, Röder MS. RFLP markers linked to scald (Rhynchosporium secalis) resistance gene Rh2 in barley. Theor Appl Genet. 1995;90:920–4.

    Article  CAS  Google Scholar 

  28. Fu Y-B. Population-based resequencing analysis of wild and cultivated barley revealed weak domestication signal of selection and bottleneck in the Rrs2 scald resistance gene region. Genome. 2012;55:93–104. https://doi.org/10.1139/g11-082.

    Article  CAS  PubMed  Google Scholar 

  29. Marzin S, Hanemann A, Sharma S, Hensel G, Kumlehn J, Schweizer GF, Röder MS. Are PECTIN ESTERASE INHIBITOR genes involved in mediating resistance to Rhynchosporium commune in barley? PLoS One. 2016;11:e0150485. https://doi.org/10.1371/journal.pone.0150485.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Abbott DC, Burdon JJ, Jarosz AM, Brown AHD, Müller WJ, Read BJ. The relationship between seedling infection types and field reactions to leaf scald in clipper barley backcross lines. Aust J Agric Res. 1991;42:801–9.

  31. Spanner D, Shugar LP, Choo TM, Falak I, Briggs KG, Legge WG, et al. Mapping of disease resistance loci in barley on the basis of visual assessment of naturally occurring symptoms. Crop Sci. 1998;38:843–50. https://doi.org/10.2135/cropsci1998.0011183X003800030037x.

    Article  Google Scholar 

  32. Jensen J, Backes G, Skinnes H, Giese H. Quantitative trait loci for scald resistance in barley localized by a non-interval mapping procedure. Plant Breed. 2002;121:124–8. https://doi.org/10.1046/j.1439-0523.2002.00685.x.

    Article  CAS  Google Scholar 

  33. Cheong J, Williams KJ, Wallwork H. The identification of QTLs for adult plant resistance to leaf scald in barley. Aust J Agric Res. 2006;57:961. https://doi.org/10.1071/AR05389.

    Article  CAS  Google Scholar 

  34. Shtaya MJY, Marcel TC, Sillero JC, Niks RE, Rubiales D. Identification of QTLs for powdery mildew and scald resistance in barley. Euphytica. 2006;151:421–9. https://doi.org/10.1007/s10681-006-9172-x.

    Article  Google Scholar 

  35. Wagner C, Schweizer GF, Krämer M, Dehmer-Badani AG, Ordon F, Friedt W. The complex quantitative barley-Rhynchosporium secalis interaction: newly identified QTL may represent already known resistance genes. Theor Appl Genet. 2008;118:113–22. https://doi.org/10.1007/s00122-008-0881-5.

    Article  CAS  PubMed  Google Scholar 

  36. Schweizer GF, Herz M, Mikolajewski S, Brenner M, Hartl L, Baumer M. Genetic mapping of a novel scald resistance gene Rrs15CI8288 in barley. Proceedings of the 9th international barley genetics symposium; 2004. p. 258–65.

    Google Scholar 

  37. Hofmann K. Phenotypic assessment and genetic mapping of genes conferring resistance to leaf scald (Rhynchosporium commune) in barley (Hordeum vulgare) [promotion]. Gießen: Justus-Liebig-Universität Gießen; 2014.

    Google Scholar 

  38. Coulter M, Büttner B, Hofmann K, Bayer MM, Ramsay L, Schweizer GF, et al. Characterisation of barley resistance to rhynchosporium on chromosome 6HS. Theor Appl Genet. 2018;132:1089–107. https://doi.org/10.1007/s00122-018-3262-8.

    Article  CAS  PubMed  Google Scholar 

  39. Björnstadt A, Gronnerod S, MacKey J, Tekauz A, Crossa J, Martens H. Resistance to barley scald (Rhynchosporium secalis) in the Ethiopian donor lines ‘Steudelli’ and ‘jet’, analyzed by partial least squares regression and interval mapping. Hereditas. 2004;141:166–79. https://doi.org/10.1111/j.1601-5223.2004.01817.x.

    Article  Google Scholar 

  40. Schweizer P, Stein N. Large-scale data integration reveals colocalization of gene functional groups with meta-QTL for multiple disease resistance in barley. Mol Plant-Microbe Interact. 2011;24:1492–501. https://doi.org/10.1094/MPMI-05-11-0107.

    Article  CAS  PubMed  Google Scholar 

  41. Wang Y, Gupta S, Wallwork H, Zhang X-Q, Zhou G, Broughton S, et al. Combination of seedling and adult plant resistance to leaf scald for stable resistance in barley. Mol Breeding. 2014;34:2081–9. https://doi.org/10.1007/s11032-014-0164-6.

    Article  CAS  Google Scholar 

  42. Li HB, Zhou MX. Quantitative trait loci controlling barley powdery mildew and scald resistances in two different barley doubled haploid populations. Mol Breeding. 2011;27:479–90. https://doi.org/10.1007/s11032-010-9445-x.

    Article  Google Scholar 

  43. Sayed H, Backes G, Kayyal H, Yahyaoui A, Ceccarelli S, Grando S, et al. New molecular markers linked to qualitative and quantitative powdery mildew and scald resistance genes in barley for dry areas. Euphytica. 2004;135:225–8. https://doi.org/10.1023/B:EUPH.0000014939.83612.a0.

    Article  CAS  Google Scholar 

  44. von Korff M, Wang H, Léon J, Pillen K. AB-QTL analysis in spring barley. I. Detection of resistance genes against powdery mildew, leaf rust and scald introgressed from wild barley. Theor Appl Genet. 2005;111:583–90. https://doi.org/10.1007/s00122-005-2049-x.

    Article  CAS  Google Scholar 

  45. Wallwork H, Grcic M, Li CD, Hayden MJ, Chalmers KJ, Mather DE. Use of specific differential isolates of Rhynchosporium commune to detect minor gene resistance to leaf scald in barley seedlings. Australasian Plant Pathol. 2014;43:197–203. https://doi.org/10.1007/s13313-013-0264-5.

    Article  CAS  Google Scholar 

  46. Backes G, Graner A, Foroughi-Wehr B, Fischbeck G, Wenzel G, Jahoor A. Localization of quantitative trait loci (QTL) for agronomic important characters by the use of a RFLP map in barley (Hordeum vulgare L.). Theor Appl Genet. 1995;90:294–302. https://doi.org/10.1007/BF00222217.

    Article  CAS  PubMed  Google Scholar 

  47. Yun SJ, Gyenis L, Hayes PM, Matus I, Smith KP, Steffenson BJ, Muehlbauer GJ. Quantitative trait loci for multiple disease resistance in wild barley. Crop Sci. 2005;45:2563–72. https://doi.org/10.2135/cropsci2005.0236.

    Article  CAS  Google Scholar 

  48. Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, et al. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet. 2011;43:163–8. https://doi.org/10.1038/ng.747.

    Article  CAS  PubMed  Google Scholar 

  49. Poland JA, Bradbury PJ, Buckler ES, Nelson RJ. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci U S A. 2011;108:6893–8. https://doi.org/10.1073/pnas.1010894108.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Benson JM, Poland JA, Benson BM, Stromberg EL, Nelson RJ. Resistance to gray leaf spot of maize: genetic architecture and mechanisms elucidated through nested association mapping and near-isogenic line analysis. PLoS Genet. 2015. https://doi.org/10.1371/journal.pgen.1005045.

  51. Schnaithmann F, Kopahnke D, Pillen K. A first step toward the development of a barley NAM population and its utilization to detect QTLs conferring leaf rust seedling resistance. Theor Appl Genet. 2014;127:1513–25. https://doi.org/10.1007/s00122-014-2315-x.

    Article  PubMed  Google Scholar 

  52. Vatter T, Maurer A, Kopahnke D, Perovic D, Ordon F, Pillen K. A nested association mapping population identifies multiple small effect QTL conferring resistance against net blotch (Pyrenophora teres F. teres) in wild barley. PLoS ONE. 2017;12:e0186803. https://doi.org/10.1371/journal.pone.0186803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Vatter T, Maurer A, Perovic D, Kopahnke D, Pillen K, Ordon F. Identification of QTL conferring resistance to stripe rust (Puccinia striiformis f. sp. hordei) and leaf rust (Puccinia hordei) in barley using nested association mapping (NAM). PLoS ONE. 2018;13:e0191666. https://doi.org/10.1371/journal.pone.0191666.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Bajgain P, Rouse MN, Tsilo TJ, Macharia GK, Bhavani S, Jin Y, Anderson JA. Nested association mapping of stem rust resistance in wheat using genotyping by sequencing. PLoS One. 2016;11:e0155760. https://doi.org/10.1371/journal.pone.0155760.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Li H, Singh S, Bhavani S, Singh RP, Sehgal D, Basnet BR, et al. Identification of genomic associations for adult plant resistance in the background of popular south Asian wheat cultivar, PBW343. Front Plant Sci. 2016;7:1674. https://doi.org/10.3389/fpls.2016.01674.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Maurer A, Draba V, Jiang Y, Schnaithmann F, Sharma R, Schumann E, et al. Modelling the genetic architecture of flowering time control in barley through nested association mapping. BMC Genomics. 2015;16:290. https://doi.org/10.1186/s12864-015-1459-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bayer MM, Rapazote-Flores P, Ganal M, Hedley PE, Macaulay M, Plieske J, et al. Development and evaluation of a barley 50k iSelect SNP array. Front Plant Sci. 2017;8:1792. https://doi.org/10.3389/fpls.2017.01792.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639–45. https://doi.org/10.1101/gr.092759.109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Mascher M, Schreiber M, Scholz U, Graner A, Reif JC, Stein N. Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding. Nat Genet. 2019;51:1076–81. https://doi.org/10.1038/s41588-019-0443-6.

    Article  CAS  PubMed  Google Scholar 

  60. Daba SD, Horsley R, Brueggeman R, Chao S, Mohammadi M. Genome-wide association studies and candidate gene identification for leaf scald and net blotch in barley (Hordeum vulgare L.). Plant Dis. 2019;103:880–9. https://doi.org/10.1094/PDIS-07-18-1190-RE.

    Article  CAS  PubMed  Google Scholar 

  61. Sekhwal MK, Li P, Lam I, Wang X, Cloutier S, You FM. Disease resistance gene analogs (RGAs) in plants. Int J Mol Sci. 2015;16:19248–90. https://doi.org/10.3390/ijms160819248.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Loutre C, Wicker T, Travella S, Galli P, Scofield S, Fahima T, et al. Two different CC-NBS-LRR genes are required for Lr10-mediated leaf rust resistance in tetraploid and hexaploid wheat. Plant J. 2009;60:1043–54. https://doi.org/10.1111/j.1365-313X.2009.04024.x.

    Article  CAS  PubMed  Google Scholar 

  63. Ellis J, Dodds PN, Pryor T. Structure, function and evolution of plant disease resistance genes. Curr Opin Plant Biol. 2000;3:278–84. https://doi.org/10.1023/A:1026571130477.

    Article  CAS  PubMed  Google Scholar 

  64. Lehti-Shiu MD, Shiu S-H. Diversity, classification and function of the plant protein kinase superfamily. Philos Trans R Soc Lond Ser B Biol Sci. 2012;367:2619–39. https://doi.org/10.1098/rstb.2012.0003.

    Article  CAS  Google Scholar 

  65. Andersen EJ, Ali S, Byamukama E, Yen Y, Nepal MP. Disease resistance mechanisms in plants. Genes (Basel). 2018. https://doi.org/10.3390/genes9070339.

  66. Wang Z, Cheng J, Fan A, Zhao J, Yu Z, Li Y, et al. LecRK-V, an L-type lectin receptor kinase in Haynaldia villosa, plays positive role in resistance to wheat powdery mildew. Plant Biotechnol J. 2018;16:50–62. https://doi.org/10.1111/pbi.12748.

    Article  CAS  PubMed  Google Scholar 

  67. Goff KE, Ramonell KM. The role and regulation of receptor-like kinases in plant defense. Gene Regulation and Systems Biology. 2007;1:167–75. https://doi.org/10.1177/117762500700100015.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Zhang X, Ovenden B, Milgate A. Recent insights into barley and Rhynchosporium commune interactions. Mol Plant Pathol. 2020;21:1111–28. https://doi.org/10.1111/mpp.12945.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Gronnerod S, Maröy AG, MacKey J, Tekauz A, Penner GA, Björnstadt A. Genetic analysis of resistance to barley scald (Rhynchosporium secalis) in the Ethiopian line ‘Abyssinian’ (CI668). Euphytica. 2002;126:235–50. https://doi.org/10.1023/A:1016368503273.

    Article  CAS  Google Scholar 

  70. Genger RK, Brown AHD, Knogge W, Nesbitt K, Burdon JJ. Development of SCAR markers linked to a scald resistance gene derived from wild barley. Euphytica. 2003;134:149–59. https://doi.org/10.1023/B:EUPH.0000003833.63547.78.

    Article  CAS  Google Scholar 

  71. Zantinge J, Xue S, Holtz M, Xi K, Juskiw P. The identification of multiple SNP markers for scald resistance in spring barley through restriction-site associated sequencing. Euphytica. 2019;215:152. https://doi.org/10.1007/s10681-018-2317-x.

    Article  CAS  Google Scholar 

  72. Zhang R, Zheng F, Wei S, Zhang S, Li G, Cao P, Zhao S. Evolution of disease defense genes and their regulators in plants. Int J Mol Sci. 2019. https://doi.org/10.3390/ijms20020335.

  73. Lv X, Pu X, Qin G, Zhu T, Lin H. The roles of autophagy in development and stress responses in Arabidopsis thaliana. Apoptosis. 2014;19:905–21. https://doi.org/10.1007/s10495-014-0981-4.

    Article  CAS  PubMed  Google Scholar 

  74. Fernandez D, Santos P, Agostini C, Bon M-C, Petitot A-S, Silva MC, et al. Coffee (Coffea arabica L.) genes early expressed during infection by the rust fungus (Hemileia vastatrix). Mol Plant Pathol. 2004;5:527–36. https://doi.org/10.1111/j.1364-3703.2004.00250.x.

    Article  CAS  PubMed  Google Scholar 

  75. Swain S, Singh N, Nandi AK. Identification of plant defence regulators through transcriptional profiling of Arabidopsis thaliana cdd1 mutant. J Biosci. 2015;40:137–46. https://doi.org/10.1007/s12038-014-9498-9.

    Article  CAS  PubMed  Google Scholar 

  76. Martin-Urdiroz M, Deeks MJ, Horton CG, Dawe HR, Jourdain I. The exocyst complex in health and disease. Front Cell Dev Biol. 2016;4:24. https://doi.org/10.3389/fcell.2016.00024.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Xu Y, Jia Q, Zhou G, Zhang X-Q, Angessa T, Broughton S, et al. Characterization of the sdw1 semi-dwarf gene in barley. BMC Plant Biol. 2017;17:11. https://doi.org/10.1186/s12870-016-0964-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Daba SD. Genetic diversity and genome-wide association mapping of agronomic, disease resistance, and quality traits in barley accessions from Ethiopia, ICARDA, and the US [promotion]. Fargo, North Dakota: North Dakota State University of Agriculture and Applied Sciences; 2015.

    Google Scholar 

  79. Paniagua C, Bilkova A, Jackson P, Dabravolski S, Riber W, Didi V, et al. Dirigent proteins in plants: modulating cell wall metabolism during abiotic and biotic stress exposure. J Exp Bot. 2017;68:3287–301. https://doi.org/10.1093/jxb/erx141.

    Article  CAS  PubMed  Google Scholar 

  80. Xu J, Wang X-y, Guo W-z. The cytochrome P450 superfamily: key players in plant development and defense. J Integr Agric. 2015;14:1673–86. https://doi.org/10.1016/S2095-3119(14)60980-1.

    Article  CAS  Google Scholar 

  81. Rayapuram C, Jensen MK, Maiser F, Shanir JV, Hornshøj H, Rung JH, et al. Regulation of basal resistance by a powdery mildew-induced cysteine-rich receptor-like protein kinase in barley. Mol Plant Pathol. 2012;13:135–47. https://doi.org/10.1111/j.1364-3703.2011.00736.x.

    Article  CAS  PubMed  Google Scholar 

  82. Couto D, Zipfel C. Regulation of pattern recognition receptor signalling in plants. Nat Rev Immunol. 2016;16:537–52. https://doi.org/10.1038/nri.2016.77.

    Article  CAS  PubMed  Google Scholar 

  83. Liu P-L, Du L, Huang Y, Gao S-M, Yu M. Origin and diversification of leucine-rich repeat receptor-like protein kinase (LRR-RLK) genes in plants. BMC Evol Biol. 2017;17:47. https://doi.org/10.1186/s12862-017-0891-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Pajerowska-Mukhtar KM, Emerine DK, Mukhtar MS. Tell me more: roles of NPRs in plant immunity. Trends Plant Sci. 2013;18:402–11. https://doi.org/10.1016/j.tplants.2013.04.004.

    Article  CAS  PubMed  Google Scholar 

  85. Steiner-Lange S, Fischer A, Boettcher A, Rouhara I, Liedgens H, Schmelzer E, Knogge W. Differential defense reactions in leaf tissues of barley in response to infection by Rhynchosporium secalis and to treatment with a fungal avirulence gene product. Mol Plant-Microbe Interact. 2003;16:893–902. https://doi.org/10.1094/MPMI.2003.16.10.893.

    Article  CAS  PubMed  Google Scholar 

  86. Gamble L. Molecular characterisation of the Rhynchosporium commune interaction with barley [promotion]. Dundee: University of Dundee; 2016.

    Google Scholar 

  87. Lee H-J, Park OK. Lipases associated with plant defense against pathogens. Plant Sci. 2019;279:51–8. https://doi.org/10.1016/j.plantsci.2018.07.003.

    Article  CAS  PubMed  Google Scholar 

  88. Duplan V, Rivas S. E3 ubiquitin-ligases and their target proteins during the regulation of plant innate immunity. Front Plant Sci. 2014;5:42. https://doi.org/10.3389/fpls.2014.00042.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Kumar M, Brar A, Yadav M, Chawade A, Vivekanand V, Pareek N. Chitinases—potential candidates for enhanced plant resistance towards fungal pathogens. Agriculture. 2018;8:88. https://doi.org/10.3390/agriculture8070088.

    Article  CAS  Google Scholar 

  90. Tuinstra MR, Ejeta G, Goldsbrough PB. Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci. Theor Appl Genet. 1997;95:1005–11. https://doi.org/10.1007/s001220050654.

    Article  CAS  Google Scholar 

  91. Badr A, Müller K, Schäfer-Pregl R, El Rabey H, Effgen S, Ibrahim HH, et al. On the origin and domestication history of barley (Hordeum vulgare). Mol Biol Evol. 2000;17:499–510. https://doi.org/10.1093/oxfordjournals.molbev.a026330.

    Article  CAS  PubMed  Google Scholar 

  92. Jackson LF, Webster RK. The dynamics of a controlled population of Rhynchosporium secalis, changes in race composition and frequencies. Phytopathology. 1976;66:726–8.

    Article  Google Scholar 

  93. Behn AP, Hartl L, Schweizer GF, Wenzel G, Baumer M. QTL mapping for resistance against non-parasitic leaf spots in a spring barley doubled haploid population. Theor Appl Genet. 2004;108:1229–35. https://doi.org/10.1007/s00122-003-1559-7.

    Article  CAS  PubMed  Google Scholar 

  94. Rutkoski JE, Poland JA, Jannink J-L, Sorrells ME. Imputation of unordered markers and the impact on genomic selection accuracy. G3 (Bethesda). 2013;3:427–39. https://doi.org/10.1534/g3.112.005363.

    Article  Google Scholar 

  95. Maurer A, Pillen K. 50k Illumina Infinium iSelect SNP Array data for the wild barley NAM population HEB-25. e!DAL - Plant Genomics and Phenomics Research Data Repository (PGP). 2019. https://doi.org/10.5447/ipk/2019/20.

  96. Arend D, Lange M, Chen J, Colmsee C, Flemming S, Hecht D, Scholz U. e!DAL - a framework to store, share and publish research data. BMC Bioinformatics. 2014. https://doi.org/10.1186/1471-2105-15-214.

  97. Maurer A, Sannemann W, Léon J, Pillen K. Estimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations. Heredity. 2017;118:477–85. https://doi.org/10.1038/hdy.2016.121.

    Article  CAS  PubMed  Google Scholar 

  98. Liu W, Gowda M, Steinhoff J, Maurer HP, Würschum T, Longin CFH, et al. Association mapping in an elite maize breeding population. Theor Appl Genet. 2011;123:847–58. https://doi.org/10.1007/s00122-011-1631-7.

    Article  PubMed  Google Scholar 

  99. Comadran J, Kilian B, Russell J, Ramsay L, Stein N, Ganal M, et al. Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley. Nat Genet. 2012;44:1388–92. https://doi.org/10.1038/ng.2447.

    Article  CAS  PubMed  Google Scholar 

  100. Jia Q, Li C, Shang Y, Zhu J, Hua W, Wang J, et al. Molecular characterization and functional analysis of barley semi-dwarf mutant Riso no. 9265. BMC Genomics. 2015. https://doi.org/10.1186/s12864-015-2116-x.

  101. Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544:427–33. https://doi.org/10.1038/nature22043.

    Article  CAS  PubMed  Google Scholar 

  102. Cantalapiedra CP, Boudiar R, Casas AM, Igartua E, Contreras-Moreira B. BARLEYMAP: physical and genetic mapping of nucleotide sequences and annotation of surrounding loci in barley. Mol Breeding. 2015;35:3389. https://doi.org/10.1007/s11032-015-0253-1.

    Article  CAS  Google Scholar 

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Acknowledgments

We are grateful to Alfred Barth for excellent technical assistance.

Funding

This work was financially supported by the Interdisciplinary Centre for Crop Plant Research (IZN), Halle. The Bavarian State Ministry of Food, Agriculture and Forestry supported the work with research funding. The funding bodies were neither involved in the design of the study nor in the collection, analysis, and interpretation of data nor in writing the manuscript. Open Access funding enabled and organized by Projekt DEAL.

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Authors and Affiliations

  1. Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany

    Bianca Büttner & Günther Schweizer

  2. Martin Luther University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Halle, Germany

    Vera Draba, Klaus Pillen & Andreas Maurer

Authors
  1. Bianca Büttner
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  2. Vera Draba
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  3. Klaus Pillen
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  4. Günther Schweizer
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  5. Andreas Maurer
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Contributions

BB acquired, analysed and interpreted data. VD and AM analysed and interpreted data. KP and GS designed and conceptualized the work and acquired funding. BB, GS, KP and AM drafted and revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Andreas Maurer.

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Supplementary Information

Additional file 1: Figure S1

: Overview of scald symptoms.

Additional file 2: Table S1

: Raw data and LSMeans for scald susceptibility.

Additional file 3: Table S2

: Genotyping results of HEB-25 parents with locus-specific markers.

Additional file 4: Table S3

: GWAS results including all markers that were detected in at least one cross-validation run.

Additional file 5: Table S4

: Family-specific effects of eight major scald QTL defined in GWAS.

Additional file 6: Table S5

: Summary of reported scald resistance genes and QTLs.

Additional file 7: Figure S2

: Correlation of real HID phenotype scores and their derived donor effect obtained from GWAS results.

Additional file 8: Table S6

: Barleymap output of high confidence candidate genes for each of the eight major QTLs.

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Büttner, B., Draba, V., Pillen, K. et al. Identification of QTLs conferring resistance to scald (Rhynchosporium commune) in the barley nested association mapping population HEB-25. BMC Genomics 21, 837 (2020). https://doi.org/10.1186/s12864-020-07258-7

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Keywords

  • HEB-25
  • Hordeum vulgare
  • Hordeum vulgare ssp. spontaneum
  • Rrs
  • Wild barley
  • Scald resistance
  • Rhynchosporium commune
  • Nested association mapping (NAM)
  • Greenhouse trials

BMC Genomics

ISSN: 1471-2164

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