A nested association mapping population identifies multiple small effect QTL conferring resistance against net blotch (Pyrenophora teres f. teres) in wild barley

The net form of net blotch caused by the necrotrophic fungus Pyrenophora teres f. teres is a major disease of barley, causing high yield losses and reduced malting and feed quality. Exploiting the allelic richness of wild barley proved to be a valuable tool to broaden the genetic base of resistance of modern elite cultivars. In this study, a SNP-based nested association mapping (NAM) study was conducted to map QTL for P. teres resistance in the barley population HEB-25 comprising 1,420 lines derived from BC1S3 generation. By scoring the percentage of infected leaf area followed by calculation of the average ordinate (AO) and scoring of the reaction type (RT) in two-year field trials a large variability of net blotch resistance across and within families of HEB-25 was observed. Genotype response to net blotch infection showed a range of 48.2% for AO (0.9–49.1%) and 6.4 for RT (2.2–8.6). NAM based on 5,715 informative SNPs resulted in the identification of 24 QTL for resistance against net blotch. Out of these, six QTL are considered novel showing no correspondence to previously reported QTL for net blotch resistance. Overall, variation of net blotch resistance in HEB-25 turned out to be controlled by small effect QTL. Results indicate the presence of alleles in HEB-25 differing in their effect on net blotch resistance. Results provide valuable information regarding the genetic architecture of the complex barley-P. teres f. teres interaction as well as for the improvement of net blotch resistance of elite barley cultivars.


Introduction
The net form of net blotch caused by the necrotrophic fungus Pyrenophora teres f. teres is a major disease of barley worldwide. Infections can cause high yield losses typically ranging from 10 to 40% with the potential to result in total yield loss if susceptible cultivars are grown [1,2]. Furthermore, infection results in a reduction of kernel size, plumpness, and bulk density, negatively affecting malting and feed quality [3]. PLOS  Barke (Hordeum vulgare ssp. vulgare). For more detailed information on population development see Maurer et al. [49]. Due to a loss of genotypes during field trials the analysis is based on 1,403 genotypes of the HEB-25 population.

Field trials
Field trials were conducted at the Julius Kuehn-Institute, Federal Research Centre for Cultivated Plants, in Quedlinburg, Germany, in 2014 and 2015 using a special experimental design called summer hill trial design developed by König et al. [17]. Genotypes were sown in rows of so called hill-plots comprising 25 seeds each, with a spacing of 0.5 m between hills. Spreader strips of susceptible varieties (Candesse and Stamm 4046) were sown between hill-plot rows with a row to row spacing of 1.0 m (S1 File). Trials were laid out in a randomized incomplete block design with two replicates of 18 incomplete blocks each. A resistant standard (gene bank accession HHOR 10860) was integrated three times in each of the incomplete blocks. Net blotch (P. teres f. teres) infected barley straw was incorporated in the topsoil before sawing to serve as a source of infection and to ensure homogenous disease pressure. Infected barley plants were harvested at the end of the first year and the straw was used as infection material for the second year. Field trials were sown in the first half of August as König et al. [17] had shown that at that time growing conditions in Germany are more favorable for P. teres f. teres in comparison to Rhynchosporium secalis, which is often opposite in spring, thereby preventing reliable scoring of P. teres f. teres resistance.

Phenotypic data
The percentage of infected leaf area (PILA), according to Moll et al. [55], and the reaction type (RT), applying the disease scale of Tekauz [56], were recorded at three consecutive dates, starting when disease symptoms were clearly visible in the susceptible spreader strips. A time period of two weeks between phenotyping dates was chosen to allow for a sufficient disease development. PILA data was used to calculate the area under the disease progress curve (AUDPC). AUDPC data was then used to calculate the average ordinate (AO) as a measure of infection severity: where (N) is the total number of observations, disease level at the ith observation is coded by (y i ), time at the ith observation is coded by (t i ) and the trial period in days is coded by (tp).

Statistical analysis
Phenotypic data analysis was performed using the software package SAS 9.4 (SAS Institute Inc., Cary, NC, USA) using proc mixed. Genotype, year, and genotype x year interaction were set as fixed. Design effects were set as random statement. Separate covariances were set for years to account for the difference in disease pressure between years. AO least-squares means (lsmeans) as well as RT lsmeans were used for subsequent NAM.
To estimate variance components to be used for the calculation of broad sense heritability, all model parameters were set as random. Broad sense heritability across years was calculated as: where genotypic variance is coded by (V G ), genotype x year variance is coded by (V GY ), and residual variance is coded by (V R ). The terms y and r indicate the number of years and replicates, respectively. Pearson's correlation coefficients were calculated with proc corr, using lsmeans per genotype as input.
Nested association mapping SNP genotyping was carried out using the barley Infinium iSelect 9K chip consisting of 7,864 SNPs [57]. SNPs showing >10% failure rate, >12.5% heterozygous calls, or being monomorphic over all 1,403 HEB lines were removed from the dataset. SNP filtering resulted in 5,715 informative SNPs used for NAM with an average genetic distance of 0.17cM and a maximum gap of 11.1cM between adjacent markers. Linkage disequilibrium (LD) across HEB-25 was calculated as r 2 between all mapped SNPs, excluding heterozygous genotypes, with the software package TASSEL 5.0 [58]. LD decay across intra-chromosomal SNPs was displayed by plotting r 2 between SNP pairs against their genetic distance. A second-degree smoothed loess curve was fitted in SAS with proc loess. The population-specific baseline r 2 was defined as the 95 th percentile of the distribution of r 2 for unlinked markers [59]. LD decay was defined as the distance at which the loess curve crosses the baseline. An identity-by-state approach was used to differentiate HEB genotypes. Parental genotype information enabled the identification of the exotic donor allele in each segregating HEB family. HEB lines showing a homozygous Barke genotype were assigned a value of 0, HEB lines showing a homozygous exotic genotype were assigned a value of 2, and heterozygous HEB lines were assigned a value of 1. Failed SNP calls were assigned a value using the mean imputation (MNI) approach [60]. For detailed information see Maurer et al. [49]. Assignment of SNPs to chromosomal positions was based on Maurer et al. [49].
NAM was performed using Model B of Liu et al. [61] verified to be best suited for genomewide association studies (GWAS) based on family-structured populations [62] and successfully applied in previous HEB-25 studies [49][50][51]. Model B is a multiple regression model including, next to a quantitative SNP effect and a qualitative family effect, quantitative cofactors that correct for population stratification and genetic background noise [62]. Marker trait associations were estimated by stepwise forward-backward regression based on minimizing the Bayesian information criterion (BIC [63]) taking all informative SNPs into consideration. Analysis was carried out with SAS 9.4 applying the proc glmselect procedure. SNPs were allowed to enter or leave the model at each step until the BIC estimate was not reduced any further. SNPs included in the final model were defined to be significant.
To increase the robustness of identified marker trait associations, a five-fold cross-validation (CV) was performed. In total, 200 CV runs (40 times five-fold CV) were performed. For this, 200 subsets were extracted out of the full genotype set. Subsets included 80% of genotypes of the full population each, randomly selected per HEB family. The subsets were taken as training sets for the identification of significant marker trait associations and for estimation of additive effects. The remaining 20% of genotypes were used as the validation set. Subsequently, the count of each significant marker over all training sets was recorded and referred to as detection rate (DR). This value was taken as a measure of robustness of the marker trait association. Markers with a DR of >50% were defined as particularly robust and used to assign resistance QTL.
Additive effects for each SNP were extracted as regression coefficient of the respective SNP directly from the NAM model described above. To obtain final estimates, additive effects of significant markers were averaged across all runs. Likewise final R 2 values for significant SNPs were obtained by averaging R 2 values of significant markers across all cross-validation runs. This way, the R 2 value can be interpreted as the percentage of variance explained by the investigated SNP marker. Furthermore, hotspots of marker trait associations were assigned to chromosome regions by determining the count and the mean additive effect of significant markers within 5cM.
A standard QTL interval of ±4cM around the markers with a DR >50% was defined, resembling the LD decay in HEB-25 (S2 File). In case the QTL was composed of more than one marker with a DR >50%, the marker showing the highest DR across all 200 cross-validation runs was defined as peak marker. QTL showing overlapping QTL intervals were combined to a single QTL interval.
To estimate the proportion of phenotypic variance explained by the full model, the unbiased estimator R 2 adj [64] was calculated for each subset by simultaneously modeling all of the significant markers in the linear model described above.
To determine the predictive ability R 2 pred of the full model for infection severity, the additive effects of markers estimated using the training sets were used to predict the phenotypic value of the remaining 20% of genotypes forming the validation sets [65]. Following Maurer et al. [50] R 2 pred was defined to be the squared Pearson product-moment correlation between predicted and observed phenotypic values. Subsequently, R 2 adj and R 2 pred values were averaged over all 200 CV runs to obtain final estimates.
Additional to the detection of marker trait associations across families, parent-specific QTL effects were calculated following the approach of Maurer et al. [52]. In a first step, the peak marker (SNP with highest DR >50% across all 200 cross-validation runs) of each QTL was selected and placed central in a 26cM interval (resembling the mean introgression size in HEB-25) to look for significant SNPs in this region. Due to model limitations reported in Maurer et al. [52] population-wide QTL located within this interval were pooled into one single parent-specific QTL. Subsequently 'Model-B' SNP effect estimates of all markers within this interval were cumulated for each of the 25 donors, following P n i SNP ðdonorÞ i Ãa i , where (i) iterates through all significant SNPs (n) in the respective QTL interval. SNP (donor) i represents the quantitative IBS donor genotype (i. e. 0 vs. 2) of the ith significant SNP and α i denotes the SNP effect estimate of this SNP obtained from 'Model-B'. Since SNPs show different IBS segregation patterns across the donors of HEB families a different cumulated effect was obtained for each donor. This procedure was conducted within each of the 200 cross-validation runs. Subsequently, the mean effect across all cross-validation runs was calculated and taken as the final parent-specific QTL effect estimate.

Comparison with previously identified QTL and analysis of identified QTL intervals
GrainGenes (https://wheat.pw.usda.gov/GG3/) and BARLEX (http://apex.ipk-gatersleben.de/ apex/f?p=284:10) databases were searched to obtain marker sequence information on previously reported QTL for net blotch resistance. If available, the marker sequence information was used to check for overlap of net blotch resistance QTL identified in this study with those 23 studies reported before and cited in the introduction. Only those QTL from previous studies were taken into consideration, which were placed in similar chromosomal regions as our QTL. The BARLEYMAP pipeline [66] was used as a common reference. Using this pipeline, the peak marker as well as flanking markers for known net blotch resistance QTL and markers identified in this study showing a DR >50% were blasted against the POPSEQ map [67] and the barley physical map [68]. Markers with a DR >50% identified in this study and located in a genetic distance of less than 4cM (resembling the LD decay in HEB-25, see S2 File) to markers of known resistance QTL were defined as potentially corresponding to previously reported resistance QTL. In addition, previously reported QTL for which no marker information could be obtained were compared to QTL detected in this study based on information given in the respective publication.
In addition, the BARLEYMAP pipeline [66] was used to identify potential candidate genes underlying the particularly robust QTL of this study by aligning the associated markers showing a DR >50% against the barley physical map [68] and the POPSEQ map [67]. The gene search was extended to an interval of ±4cM around markers with a DR >50% to account for the LD decay in HEB-25. Gene ontology (GO) terms defining defence response (0006952, 0050832), apoptotic process (0006915), peroxidase activity (0004601), response to (oxidative) stress (0006979, 0006950), ATP binding (0005524), nucleotide binding (0000166), protein binding (0005515), transporter activity (0005215), protein kinase activity (004672) were used to validate genes involved in resistance reactions [69]. Furthermore, GO terms defining reactions potentially involved, e.g. catalase activity, chitinase activity, cell wall, peroxisome, cell wall modification, and defence response to fungi, were considered (S8 File).

Phenotypic data
In both years the use of the summer hill trial design resulted in an elevated disease pressure across the whole field with spreader strips showing an AO close to 60%. The experimental conditions allowed for an optimal differentiation of the degree of P. teres f. teres resistance between genotypes. A large diversity in P. teres f. teres resistance of genotypes was observed for both traits studied with a highly significant variation (p <0.0001; Tukey-test) between as well as within families of the HEB-25 population (Fig 1A and 1B; S3 File).
A wide range of genotype responses to P. teres f. teres infection was observed in HEB-25 with a range of 48.2% for AO (0.95-49.1%) and 6.4 for RT (2.21-8.64), respectively (Table 1). Notably, several genotypes of the HEB-25 population showed a higher degree of resistance than the resistant check included in field trials (Fig 1A and 1B). The top 1% of all genotypes regarding P. teres f. teres resistance showed a mean AO value of 2.9% and a mean RT value of 3.8 (S3 File). The frequency distributions for both traits were slightly right skewed (S4 File). Because of the HEB-25 population design, the population means are close to the recurrent parent Barke (Table 1). Barke showed a high degree of susceptibility compared to the majority of wild donor parents. Only the wild donor of family 24 (Hordeum vulgare ssp. agriocrithon), originating from Tibet, China, showed a higher P. teres f. teres susceptibility than Barke (S5 File).
Two-year broad sense heritability was calculated to be h 2 = 0.62 for AO and h 2 = 0.65 for RT, respectively (Table 1). High correlations (Pearson's correlation coefficients; p <0.0001) were observed between the two resistance measures AO and RT with r = 0.86 and r = 0.76 for HEB-25 parents and for the HEB-25 population, respectively (S5 File).

Nested association mapping
NAM was performed for the two traits AO and RT, resulting in the identification of a high number of significant marker trait associations (Fig 2; S6 File). Most marker trait associations showed a DR below 50% across the 200 cross-validation runs. However, 11 and 13 particularly robust QTL being composed of one or more markers with a DR above 50% were identified for RT and AO, respectively (Table 2). Particularly robust marker trait associations were identified on all chromosomes except chromosome 1H in case of AO and on all chromosomes for RT. The QTL showing the peak marker with the highest DR (i_SCRI_RS_186193) is located in the centromeric region of chromosome 6H for both traits evaluated (Fig 2; Table 2). This QTL is composed of three SNPs with DR >50% in case of RT and two SNPs in case of AO. In both   Net blotch (Pyrenophora teres f. teres) resistance in a wild barley NAM population cases the peak marker showed a negative cross-validated mean effect, i.e. an increase of resistance in the presence of the wild allele compared to the Barke control allele. In general, this chromosome region showed the highest abundance of significant marker trait associations (Fig 2). A considerable variation in the wild allele effect estimates of adjacent markers was observed with an increase or decrease in trait values compared to the Barke control allele (Fig 2). Notably, the majority of detected QTL are composed of marker trait associations exhibiting opposed wild allele effect estimates (S6 File). For both traits QTL peak markers exhibited the same effect direction as the mean QTL estimate in all but one case, but differed in effect size (S7 File). Thus, NAM showed that QTL and peak marker effect are not necessarily identical. Across the whole population particularly robust QTL peak markers showed predominantly small to intermediate wild allele effect estimates and low R 2 values. Wild allele effects ranged from -5.99 to 9.64 in case of AO and from -0.98 to 0.51 in case of RT. The peak markers of QPt.7H-1 and QPt.3H-3 showed the highest effect estimate for AO and RT, respectively ( Table 2). R 2 values ranged from 0.04 to 14.88% explained variance in case of AO and from 0.08 to 9.23% in case of RT ( Table 2). The peak markers of QPt.2H-2 and QPt.2H-1 showed the highest R 2 value for AO and RT, respectively (Table 2).
Parent-specific QTL effects were calculated to obtain an effect estimate resembling the combined effect of all family specific markers the QTL is composed of. Due to previously mentioned model limitations (see Material and methods) QTL QPt.6H-1 and QPt.6H-3 were combined to one single parent-specific QTL (QPt.6H-1/3). Estimation of parent-specific QTL effects revealed a high variation in effect sizes of the wild allele among HEB families (S6 File).   [17], 3 [18], 4 [25], 5 [19], 6 [26], 7 [21], 8 [70] *for QTL defining the same position see [4,20,21] Between the two NAM studies a considerable overlap was observed. Of the particularly robust 11 and 13 QTL peak markers identified for AO and RT, four peak markers mapped to the same or to a nearby position (Fig 2). In three cases, QTL even shared the same peak marker (Table 2). However, trait-specific QTL existed likewise. Hotspots of marker trait associations, defined by a high number of significant markers in the respective 5cM interval, corresponded well with QTL peak marker positions in most cases. Similar to the observations in case of QTL peak markers, considerable overlap between trait hotspot regions of the two traits analysed existed (Fig 2).
The mean percentage of phenotypic variance explained by the full model (R 2 adj ) was calculated to be 68.9% for AO and 72.0% for RT (Table 3). Notably, for both traits a considerable fraction of the phenotypic variance was explained by the identified particularly robust QTL peak markers ( Table 2). The predictive ability (R 2 pred ) of the full model for infection severity was calculated to be 42.1% for AO and 43.3% for RT (Table 3).

Comparison with previously identified QTL
Comparison of net blotch resistance QTL identified in this study with those already reported in literature revealed that the majority of identified QTL mapped to chromosome regions known to be linked to net blotch resistance. In case of RT nine out of 11 QTL showed overlap with QTL intervals of previously reported resistance QTL or genes, whereas this was true for nine out of 13 for AO (Table 2). In detail, based on available data no overlap was found for QPt.1H-1, QPt.4H-2, QPt.5H-3, QPt.6H-4, QPt.7H-2, and QPt.7H-3. Out of these QTL, peak markers of QPt.1H-1, QPt.6H-4, QPt.7H-2, and QPt.7H-3 revealed negative CV mean effects (Table 2) indicating the existence of wild barley alleles conferring net blotch resistance. The alignment of SNPs with DR >50% against the physical barley map by means of the BARLEY-MAP pipeline resulted in the identification of a number of genes related to plant defence in the respective QTL intervals. In particular, leucine-rich repeat, NB-ARC, and Serine/threonine-protein kinase-like domain genes were found at high frequency. Details are given in S8 File.

Discussion
The high variation in P. teres f. teres infection severity observed in field trials clearly reflects the high genetic diversity present within the HEB-25 population, which is in line with findings of previous HEB-25 NAM studies focusing on developmental traits [49,50] and salinity tolerance [51]. The presence of significant differences not only between families but also within families demonstrates the high suitability of HEB-25 to identify population-wide as well as parent-specific QTL for P. teres f. teres resistance (Fig 1A and 1B; S3 File).
Phenotypic results of this study show that the high variation to net blotch resistance can be attributed to combined effects of a diverse set of predominantly highly resistant wild donor parents of HEB-25 and a relatively susceptible recurrent parent Barke. HEB-25 lines identified to possess a higher degree of resistance than the highly resistant check line included in field trials represent suitable candidates for pre-breeding programs (S3 File). Results of earlier studies by Maurer et al. [49,50] may be considered to select those net blotch resistance conferring HEB lines that combine high P. teres f. teres resistance with favorable yield related parameters. Advantageous is that integration of HEB-25 lines into pre-breeding programs will be faster to achieve than in case of the integration of wild accessions since a backcrossing step with cultivar Barke was already performed during population development.
The summer-hill trial design developed by König et al. [17] proved to be highly effective, allowing for a clear differentiation of genotype responses, thereby laying the basis for successful QTL identification with NAM. The high correlation between the two infection severity measures applied in this study and the relatively high heritabilities found prove that both measures allow a reliable scoring of genotypic resistance (Table 1; S5 File).
The occurrence of opposed wild allele effect estimates of closely linked markers in this study was also observed in previous HEB-25 studies by Maurer et al. [49,50,52] and likely arises from a combination of factors. Firstly, not all SNPs segregate in all genotypes and therefore, markers are likely to reflect only the mean wild allele effect of a fraction of the full population. As a result, closely linked markers segregating in different sets of genotypes of the full population can show opposed effect estimates because of different mean resistance levels of the two sets. Phenotypic results revealed that families differ in their mean resistance level (Fig 1A  and 1B). Therefore, it can be assumed that strongly differing sets are likely to be linked to different families and, thus, opposed effect estimates of closely linked SNPs can be caused by parent-specific alleles. Secondly, the presence of closely linked SNPs showing opposed wild allele effects can be caused by closely linked alleles with contrasting effects on P. teres f. teres resistance. A good example is the centromeric region of chromosome 6H that is known to harbor a number of closely linked P. teres f. teres resistance genes of which some are assumed to be in repulsion [4,19,25,26,70]. Therefore, we assume that the high number of closely linked markers with opposed wild allele effect estimates in the centromeric region of chromosome 6H identified in this study is likely to be partially caused by this complex cluster of resistance related genes.
In this study a rather stringent threshold for the acceptance of marker trait associations was defined. Therefore, minor QTL not passing this threshold but still influencing genotype response to P. teres f. teres are not considered. Defining a less stringent DR threshold of 10%, as applied in the study of Maurer et al. [50], would have resulted in a considerable higher number of QTL (Fig 2; S6 File). Nevertheless, hotspots of marker trait associations identified in this study may be used to narrow down regions potentially harboring minor QTL involved in the resistance response of genotypes to P. teres f. teres. However, when analyzing the hotspot information it has to be taken into account that in centromeric regions the number of markers is generally high and, therefore, centromeric regions should be interpreted with caution (Fig 2).
The detection of QTL despite low estimates across the whole population is a strong proof of the power of the NAM strategy in general and in particular the suitability and precision of the NAM model applied in this study (Fig 2; S6 File). The mean phenotypic variance explained by the full model and the calculated mean ability to predict the degree of infection of independent genotypes further supports the suitability of the applied model ( Table 3).
The high number of QTL linked to net blotch resistance detected in this study, the small CV mean effect estimates as well as the low percentage of phenotypic variance explained by the majority of QTL peak markers indicate a complex inheritance of adult plant P. teres f. teres resistance ( Table 2). This supports the conclusion drawn by Liu et al. [4] of a highly complex P. teres f. teres-barley interaction. Results of this study are comparable to previous NAM studies focusing on leaf blight in maize [38,41] that identified variation in resistance to be a result of the accumulation of numerous small effect loci with additive effects. Likewise, NAM studies focusing on rust fungi of wheat [47,48] resulted in the identification of a high number of QTL with predominantly small additive effect estimates. In addition, results of this study are comparable to the association study of Tamang et al. [71] focusing on resistance to the spot form of net blotch (P. teres f. maculata) and the association study of Richards et al. [21] focusing on seedling resistance to P. teres f. teres. The authors identified a high number of markers associated with resistance to P. teres f. maculata and P. teres f. teres, respectively, nearly all explaining only a low percentage of phenotypic variance.
Next to being the result of complex inheritance of P. teres f. teres resistance, small population-wide effects of QTL peak markers may also be attributed to the presence of alleles with differing effects on P. teres f. teres resistance. Namely, in case only a limited number of HEB-25 lines of the full population show a strong allele effect on resistance or contrasting allele effects among the 25 HEB donor parents exist at a marker position.
The importance of considering the influence of differing allele effects in HEB-25 on estimating a population-wide QTL peak marker effect is supported by results of the parent-specific QTL effect calculation (S6 File). An extreme example is the QTL QPt.7H-1. In this case, the high population-wide effect of the wild allele observed for the peak marker (wild barley allele effect on AO = +9.64) seems to be mainly caused by the strong effect of an allele or allele combination derived from the donor parent of HEB family F16 (wild barley parent-specific allele effect on AO in family F16 = +9.16). Comparable to this study, strongly varying parentspecific allele effects of QTL were observed likewise in the NAM studies of Bajgain et al. [47] and Li et al. [48] focusing on the identification of QTL conferring resistance to rust pathogens of wheat. Therefore, especially studies focusing on detailed analysis of specific QTL or the integration of net blotch resistance alleles in modern barley cultivars should use the parent-specific QTL effect information given in this study to select a resistance-carrying HEB line derived from the HEB family in which the estimated favorable QTL effect is maximized. Not including parent-specific QTL effect estimates in the selection decision may result in missing alleles whose strong favorable effect is masked by a high number of parent-specific alleles with an opposed effect on P. teres f. teres resistance (S6 File). However, in this regard it needs to be mentioned that parent-specific QTL effect estimates are likely to be slightly overestimated as each family comprises only a relatively small number of HEB-25 lines [52]. Thus, selection decisions should be based on a combined evaluation of population-wide and parent-specific estimates of the wild allele effect.
Several QTL identified in this study are located at chromosome positions not yet reported to be linked to P. teres f. teres resistance (Table 2). At the same time, QTL were identified that overlap with previously described P. teres f. teres resistance QTL. This fact is a strong proof of the reliability of the identified marker trait associations. NAM results are further supported by the fact that several QTL regions were independently identified by AO and RT (Fig 2; Table 2).
Out of the QTL that show no overlap some are located in the vicinity of previously reported P. teres f. teres resistance QTL. This is the case for QPt.1H-1 located in the vicinity of a QTL identified by Liu et al. [19], QPt.7H-2 located close to QTL QTL UHs -7H identified by König et al. [18], and QPt.7H-3 located in the vicinity of QTL QNFNBAPR.Al/S-7Hb identified by Lehmensiek et al. [5]. Furthermore, QPt.5H-3 is located in the region of a meta-QTL identified by Schweizer and Stein [72] effective against several fungal barley pathogens.
It has to be considered that previously reported P. teres f. teres resistance QTL were identified by the use of different isolates under different environmental conditions and mostly in seedling tests. Only QPt.5H-1, QPt.5H-2, and QPt.7H-1 showing overlap with QTL identified by König et al. [17] were identified under similar experimental conditions. Therefore, QTL identified in this study showing overlap with previously reported P. teres f. teres QTL should still be considered as distinct QTL until a test for allelism has been conducted.
Most of the identified particularly robust net blotch resistance QTL showed to be restricted to either AO or RT. These trait-specific QTL showed to be caused partly by the fact that for one trait the markers did not cross the defined DR threshold and thus, were not considered in this study, whereas for the other trait the markers crossed the threshold and were considered (Fig 2). In this case, for both traits DR peaks of markers were observed at the same or very close by positions and a less stringent threshold for the acceptance of marker trait associations (e. g. >10%, used by Maurer et al. [50]) would have resulted in the detection of the QTL based on both traits (Fig 2; S6 File). Furthermore, trait-specific QTL may be caused by the fact that the infection severity measure RT is less influenced by the degree of infection pressure, as a RT score indicative for susceptibility can be observed at a time point at which the fungus covers only a small percentage of the leaf (low AO value). Delaying the last phenotyping date, thus giving the fungus more time to spread across the leaf could have resulted in the detection of QTL regions based on both traits. Next to this study no other studies have been performed comparing AO and RT on the QTL level. Further research is needed to identify the underlying cause of these trait specific QTL.
The information given in this paper regarding genes located in a QTL region may assist in identifying the underlying genetic causes of a QTL effect (S8 File). The presence of leucinerich repeat, NB-ARC, and Serine/threonine-protein kinase-like genes in the QTL intervals at high frequency is in agreement with findings of previous studies indicating an important role of those gene families in the necrotrophic effector triggered reaction to P. teres f. teres infection [19,26]. Members of these gene families were also identified in other QTL studies focusing on necrotrophic and hemibiotrophic fungi [38,41,73]. The identification of various putative candidate genes by GO-term analysis may be viewed as a valuable source for subsequent studies focusing on the genetic basis of the P. teres f. teres-barley interaction (S8 File).
The overlap of QTL identified in this study with QTL identified to be linked to flowering time related genes [49,50] points towards the involvement of the flowering time pathway in the resistance reaction to P. teres f. teres. Studies on Arabidopsis thaliana [74,75] showed that QTL associated with resistance to the hemibiotrophic fungal pathogen Verticillium spp. mapped close to known flowering time genes and that the fungus influenced plant development. Association of flowering time with resistance to a necrothropic fungus has also been described in a study by Lyons et al. [76]. In this study a positive correlation between late flowering and resistance to Fusarium oxysporum in A. thaliana accessions was identified and the involvement of the photoperiodic pathway regulator GIGANTEA was shown. Furthermore, a negative correlation between days to anthesis and resistance to the hemibiotrophic maize pathogen Exserohilum turcicum has been identified [38,77].
Detailed analysis of the identified overlap of QTL of this study with flowering time related QTL identified by Maurer et al. [49,50] strongly points towards a negative correlation between flowering time and infection severity. Maurer et al. [49,50] identified the wild alleles of Ppd-H1 and HvCEN to cause early flowering, and, in contrast, the wild alleles of Vrn-H1 and Vrn-H3 to induce late flowering. In this study a resistance-decreasing effect of the wild allele was identified for peak markers of QTL overlapping with the Ppd-H1 and HvCEN QTL, and a resistance-increasing effect for peak markers of QTL overlapping with the Vrn-H1 and Vrn-H3 QTL. Nevertheless, based on our study only a comparison of QTL localisation and QTL effects was possible. Further studies including phenotypic data and trials conducted during the standard growing period are required for final assessment.
The results of this study provide valuable information not only for fundamental studies focusing on elucidating the complex P. teres f. teres-barley interaction, but also for improving net blotch resistance and biodiversity of modern elite barley cultivars. In future, a better understanding of the allelic diversity present at net blotch resistance QTL in HEB-25 will be achieved, after an ongoing exome capture effort will result in detailed information on sequence diversity between 26 parental alleles at each known gene of a QTL region. This way, it is expected to achieve a clearer estimate of haplotype-based allele effects in HEB-25 and to foster the identification and selection of wild barley alleles, which increase net blotch resistance in barley.