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Article

QTL Mapping for Seedling and Adult Plant Resistance to Leaf and Stem Rusts in Pamyati Azieva × Paragon Mapping Population of Bread Wheat

by
Yuliya Genievskaya
1,
Saule Abugalieva
1,2,
Aralbek Rsaliyev
3,
Gulbahar Yskakova
3 and
Yerlan Turuspekov
1,4,*
1
Laboratory of Molecular Genetics, Institute of Plant Biology and Biotechnology, Almaty 050040, Kazakhstan
2
Department of Biodiversity and Bioresources, Faculty of Biology and Biotechnology, al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
3
Laboratory of Phytosanitary Safety, Research Institute of Biological Safety Problems, Gvardeisky 080409, Zhambyl Region, Kazakhstan
4
Agrobiology Faculty, Kazakh National Agrarian University, Almaty 050010, Kazakhstan
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(9), 1285; https://doi.org/10.3390/agronomy10091285
Submission received: 6 August 2020 / Revised: 21 August 2020 / Accepted: 22 August 2020 / Published: 30 August 2020
(This article belongs to the Special Issue Molecular Marker Technology for Crop Improvement)
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Abstract

:
Leaf rust (LR) and stem rust (SR) pose serious challenges to wheat production in Kazakhstan. In recent years, the susceptibility of local wheat cultivars has substantially decreased grain yield and quality. Therefore, local breeding projects must be adjusted toward the improvement of LR and SR disease resistances, including genetic approaches. In this study, a spring wheat segregating population of Pamyati Azieva (PA) × Paragon (Par), consisting of 98 recombinant inbred lines (RILs), was analyzed for the resistance to LR and SR at the seedling and adult plant-growth stages. In total, 24 quantitative trait loci (QTLs) for resistance to rust diseases at the seedling and adult plant stages were identified, including 11 QTLs for LR and 13 QTLs for SR resistances. Fourteen QTLs were in similar locations to QTLs and major genes detected in previous linkage mapping and genome-wide association studies. The remaining 10 QTLs are potentially new genetic factors for LR and SR resistance in wheat. Overall, the QTLs revealed in this study may play an important role in the improvement of wheat resistance to LR and SR per the marker-assisted selection approach.

1. Introduction

Bread wheat (Triticum aestivum L.) is one of the major cereal crops in the world. In 2018/2019, the global production of wheat was 734.7 million metric tons, ranking second place amongst the grains after maize [1]. It is used mostly as flour for the production of a large variety of leavened and flat breads and the manufacturing of a wide range of other baked products [2]. In 2018/2019, Kazakhstan was ranked the 12th largest wheat producer in the world [3]. In Kazakhstan, wheat is cultivated on about 13 million hectares annually. The country produces up to 20–25 million tons of bread wheat per year and exports up to 5–7 million tons of the grain [4]. The primary goals of modern wheat breeding programs worldwide include enhancing grain yield and quality and increasing resistance to biotic and abiotic stresses to ensure global food security [5]. Biotic stresses include dangerous fungal diseases and particularly the most common representatives of the Puccinia genus: Puccinia recondita Rob. ex Desm f. sp. tritici, causing leaf rust (LR), and Puccinia graminis Pers. f. sp. tritici Eriks. & Henn., which is responsible for stem rust (SR) of wheat.
LR generally causes light to moderate yield losses ranging from 1% to 20% over a large area, but when the disease is severe prior to heading time, it may destroy up to 90% of the wheat crop [6]. For example, in Kazakhstan, epiphytotic development of the pathogen on spring wheat during 2000–2001 resulted in 50–100% LR severity on commercial cultivars in the Akmola region (northern Kazakhstan), which is the main wheat-growing region in the country [7].
SR is another important rust disease that is often considered the most devastating of the wheat rust diseases because it can cause complete crop loss over a large area within a short period of time [8]. In 2016, northern Kazakhstan was subjected to an epiphytotic outbreak of SR, resulting in 50% disease development severity in the field, decreasing wheat yield and grain quality [7]. Nowadays, local farmers prefer the usage of fungicides to protect wheat fields from LR and SR; however, this method is harmful to the environment and more expensive than breeding and growing genetically resistant wheat cultivars [9].
LR and SR resistances are controlled by a diverse group of genes, designated as Lr and Sr, respectively [10]. In the last 100 years, approximately 80 Lr resistance genes have been identified and described in bread wheat, durum wheat, and diploid wheat species [10], and the list is still growing. For SR, nearly 60 Sr genes have been identified to date in wheat and its wild relatives [10]. Generally, resistance to rust diseases can be broadly categorized into two types. The first is resistance at all growth stages (called seedling resistance), detected at the seedling stage and expressed until the plant dies. This type of resistance is controlled by the R type of genes, and the majority of Lr and Sr genes belong to this group. The efficacy of the R gene is pathogen-strain-dependent [11]. The second type of resistance is adult plant resistance (APR), where genes are ineffective during the seedling stage but provide robust resistance at maturity [11]. For example, LR resistance genes Lr12, Lr13, Lr22a, Lr22b, Lr34, Lr35, Lr46, Lr48, Lr49, and Lr67, and SR resistance genes Sr2 and Sr57 are well-characterized APR genes [10]. Durable rust resistance is more likely to be the APR type rather than the seedling type [12]; both types are important for wheat breeding [11].
Two of the most effective methods of quantitative trait locus (QTL) mapping are based on association panels and biparental segregating populations [13]. Both of these methods provide the means to investigate the genome and describe the etiology of complex quantitative traits, including disease resistance [14,15,16,17]. Genetic maps are a key tool enabling genetic linkage studies and searches for novel loci responsible for traits. Modern high-throughput sequencing technologies allow for the high-accuracy genotyping of large collections with genetically diverse germplasms [18,19] and segregating mapping populations, such as doubled haploids (DHs), recombinant inbred lines (RILs), F2, and backcross (BC) populations [20]. Linkage maps were successfully used for the QTL analyses of wheat yield components [21], grain quality traits [22], abiotic [23], and biotic stress factors, including pests [24].
The primary goal of this study was to identify QTLs involved in seedling and adult plant resistance of bread wheat to LR and SR under environmental conditions in southern and southeastern Kazakhstan. To meet this goal, the Pamyati Azieva × Paragon (PA × Par) RILs mapping population (MP) was studied in field and greenhouse (GH) conditions. Previously, this population was successfully used for the analysis of yield-related traits [25] and adult plant resistance to LR and SR in south-east and northern Kazakhstan in 2018 [26,27]. Hence, the current study adds the investigation of seedling resistance in the MP to LR and SR races. In addition to one-year studies in south-east and north Kazakhstan, this work covers the analysis of LR and SR resistance in the MP in southern Kazakhstan in 2018 and 2019.

2. Materials and Methods

2.1. Plant Material and Genotyping

The biparental mapping population PA × Par composed of 98 RILs was developed in greenhouse conditions of the John Innes Centre (Norwich, UK) during 2011–2015 under the ADAPTAWHEAT project [28]. The RIL population was obtained via a single-seed descent method using two parental cultivars: Paragon (elite spring cultivar originated from the U.K.) and Pamyati Azieva (a commercial spring cultivar originating from Russia and registered in Kazakhstan) [29]. Both cultivars were chosen due to their diverse genetic backgrounds and different manifestations of yield traits as well as resistance to diseases. The RIL population was further developed for F8 generation in the fields of southeastern Kazakhstan.
The RILs and two parental cultivars were genotyped using the Illumina’s iSelect 20K single nucleotide polymorphism (SNP) array at the TraitGenetics Company (TraitGenetics GmbH, Gatersleben, Germany). The genotypic data were filtered from markers with >10% missing data and with <0.1 minor allele frequency and consisted of 4595 polymorphic SNP markers.

2.2. Phenotyping of Seedling Resistance in Greenhouse

For the comprehensive study of the PA × Par MP response to LR and SR pathogens, the resistance was evaluated at the seedling and adult plant-growth stages. Race-specific resistance at the seedling stage was assessed in a greenhouse of the Research Institute of Biological Safety Problems (RIBSP, Gvardeisky, Zhambyl region, southern Kazakhstan). For the inoculation of RILs seedlings (7–10 days after sowing) in greenhouse conditions, three races of P. graminis and three races of P. recondita with different levels of virulence to Sr and Lr genes, respectively, were used (Table 1). Inoculated plants were placed in the boxes of the greenhouse with appropriate temperature conditions (22 ± 2 °C for SR, 18 ± 2 °C for LR) and illumination (10,000–15,000 lux, 16 h’ light period) [30,31,32]. RIL reaction was assessed on the 14th day after inoculation, according to the scale reported by Stakman [33]. The experiment was performed in two independent replicates.
Races of P. graminis were differentiated in 2018 [34] using the North American nomenclature [35] with the assistance of five sets of SR-differentiating wheat cultivars. Races of P. recondita were also identified in 2018 using 20 Thatcher near-isogenic lines (NILs) sets of Lr genes [36,37,38]. For the nomenclature of P. recondita races, Virulence Analysis Tools [39] were used.

2.3. Adult Plant Resistance and Yield Components in Field Conditions

APR in the field was tested in two environments: the RIBSP and the Kazakh Research Institute of Agriculture and Plant Industry (KRIAPI, Almalybak, Almaty region, southeastern Kazakhstan) (Table 2).
In RIBSP fields, mixed races of LR and SR urediniospores common in Kazakhstan were applied as inoculum. The inoculum was activated at a temperature of 37–40 °C for 30 min, followed by watering in a humid chamber at a temperature of 18–22 °C for 2 h. At the booting stage, individual plants were treated with an aqueous suspension of leaf and stem rust urediniospores dissolved in Tween 80 detergent. After inoculation, the plots were covered with plastic wrap for 16–18 h. In KRIAPI fields, inoculation occurred with local LR and SR pathogen populations in uncontrolled natural conditions.
Thus, experiments were conducted in three independent environments, including the study of seedling resistance in greenhouse conditions, the study of APR at RIBSP (controlled inoculation), and APR at KRIAPI (uncontrolled inoculation). In both field conditions, phenotyping of APR to LR and SR was performed in two independent replicates at the stage of grain ripening with the maximum level of disease manifestation. Disease assessment was performed using the scale of Stakman for SR [33] and the scale of Mains and Jackson for LR [40]. The severity of rust infection on leaf and stem surfaces was evaluated using the modified Cobb scale [41,42]. To meet the data format required for linkage analysis, the results of LR and SR evaluations at both seedling and adult plant-growth stages were converted to the 0–9 linear disease scale as described by Zhang et al. [43].
To identify the influence of LR and SR severity on the productivity of the studied population, two important yield-related components, thousand kernel weight (TKW, g) and kernel yield per plot (YP, g/m2), were also evaluated.

2.4. Statistical Analysis of Phenotypic Data and QTL Mapping

Phenotypic data processing, descriptive statistics, and one-tailed correlation tests were performed with SPSS Statistics v. 22 (SPSS Inc., Chicago, IL, USA).
The composite interval mapping (CIM) method with the Kosambi mapping function was used for the detection of QTLs by Windows QTL Cartographer v2.5 [44]. The threshold value for the logarithm of odds (LOD) score was calculated based on 1000 permutations and was 3.0 for all experiments with a walking step of 1 cM. QTLs were detected for each environment and replication separately (seedling resistance in GH, APR at KRIAPI, and APR at RIBSP). QTLs identified in individual environments and/or replications overlapping in 20 cM intervals and associated with the same trait were considered as identical [45]. Genetic maps with QTLs were drawn using MapChart v. 2.32 software [46]. For the markers with the same positions, only one single nucleotide polymorphism (SNP) maker was selected for the map.
All genes present within the interval of 500 kb to the left and 500 kb to the right (1 Mb in total) from the peak marker were identified using the Ensembl Plant database [47]. As a reference, the genome of T. aestivum RefSeq v1 was used. The exact position of the peak SNP in the genome was determined using a BLAST tool [48]. Proteins and RNA gene products were identified using the UniProt database [49] via cross-reference from Ensembl Plant.

3. Results

3.1. Phenotyping Variations of Seedling and Adult Plant Resistance in Mapping Population

The values of the resistance to target diseases in parents and 98 RILs are summarized in Table 3.
The average seedling resistance of RILs to LR races was between 5.9 and 6.1 points, corresponding to the moderately susceptible (MS) level. The major part of the population belonged to the MS group, with only several lines observed in the susceptible (S) group. Several lines were also in the resistant (R) group to the races TQKHT and TRTHT. Parental cultivars demonstrated an MS level of resistance to studied LR races, except for PA, which was susceptible to the race TQKHT. As for seedling resistance to SR, the average level in the RILs population was MS to all three SR races. However, unlike in the case of LR, the distribution of lines among resistance groups was different. Races TKRTF and RKRTF were divided between MS and S groups with a dominance of the S reaction to TKRTF and MS reaction to RKRTF. R and moderately resistant (MR) levels were detected only in the race PKCTC. Levels of resistance in parental cultivars were similar to races TKRTF (S) and RKRTF (MS), but PA demonstrated higher resistance to the race PKCTC than Par.
At the adult plant stage, the reactions of parents and RILs to LR and SR were significantly different between the studied environments. At RIBSP, the average reaction of RILs to LR was MR, with almost even distribution among all possible reactions observed in the population. At KRIAPI, the average level of resistance was MS with a dominance of MS and S reactions in the population. The parental cultivars demonstrated the same reaction to LR at KRIAPI, but at RIBSP, Par was more resistant than PA. For SR at the adult plant stage at RIBSP, the majority of RILs were in the S group, and the average level was MS. At KRIAPI, the largest part of the population was in the R group and the average level was MR. Parental cultivars also were in the S group at RIBSP and the R group at KRIAPI.
The analysis of variance showed that the resistance of RILs to LR at the seedling growth stage was significantly affected by the RIL genotype (p < 0.01) and the race of LR pathogen (p < 0.05), but not by genotype × race interaction (Table 4). For SR resistance, all factors had a significant influence (p < 0.001) on the resistance at the seedling stage (Table 4).

3.2. Correlations among SR and LR Seedling and Adult Plant Resistance and Influence of APR on the Yield-Related Traits

Significant positive correlations were found among the reactions to all three LR races at the seedling stage, as well as between APR to LR at KRIAPI and seedling resistance to LR races TQTMQ and TRTHT (Table 5). APR to LR at KRIAPI was also positively correlated with APR to SR at KRIAPI and seedling resistance to SR race TKRTF. For the other SR races, race PKCTC had a positive correlation with APR to SR at KRIAPI, and race RKRTF was negatively correlated with LR race TQTMQ. The only significant correlation of APR to SR at RIBSP was associated with LR race TRTHT.
The negative influence of LR and SR severities at the adult plant stage on the wheat YP and TKW was confirmed by significant negative correlations (p < 0.01) between these traits at RIBSP (Table 6). At KRIAPI, the severity of LR at the adult plant stage was negatively correlated with YP only.

3.3. Identification of QTLs for Seedling and Adult Plant Resistance to LR in the RIL Population

A total of 11 QTLs for resistance to LR were identified at the seedling and adult plant-growth stages. Out of these 11 QTLs, eight QTLs were detected for different LR races at the seedling stage, two QTLs were for APR, and one QTL was observed for both seedling and adult plant resistance (Table 7, Figure 1). QTLs for LR resistance were located on 10 chromosomes of the A, B, and D genomes. The phenotypic variations explained by an individual QTL ranged from 11.6% to 25.7%. Because all QTLs for LR resistance identified in this study explained more than 10% of the phenotypic variation, they were considered major QTLs [50]. The LOD score of QTLs for LR resistance was in the range of 3.2–8.6.
For the LR race TQTMQ, three QTLs identified on chromosomes 4A, 5B, and 7B were revealed. They explained 12.1–15.7% of the phenotypic variations. The alleles of all three QTLs associated with the increase in resistance to LR originated from PA. For the second LR race TQKHT, two QTLs on chromosomes 6A and 7D explained 25.7% and 11.6% of the variations in phenotype, respectively. Both QTLs associated with higher resistance to race TQKHT originated from Paragon. For the third LR race TRTHT, three QTLs on chromosomes 3A, 4D, and 6A were observed. Identified QTLs explained 12.3–16.2% of the variation in resistance to race TQKHT. The alleles of QTLs QLr.ipbb-3A.2 and QLr.ipbb-6A.5 increasing resistance were from Paragon, and the allele of QLr.ipbb-4D.1 was from PA.
Two QTLs for APR to LR at RIBSP were identified on chromosomes 1B and 7A and explained 13.6% and 11.7% of phenotypic variation, respectively. For both APR QTLs, alleles associated with increased LR resistance were from Paragon. One QTL for APR to LR at KRIAPI was also detected at the seedling stage for the resistance to LR race TQKHT on chromosome 1A. It explained 16.0% of LR resistance variation. In both cases, alleles associated with higher resistance originated from Paragon.

3.4. QTLs for SR Resistance at Seedling and Adult Plant Stages Identified in PA × Par Mapping Population

A total of 13 QTLs were detected in this study for SR resistance at the seedling and adult plant-growth stages. Among them, seven race-specific QTLs were identified at the seedling stage (three QTLs for race TKRTF, three QTLs for race PKCTC, and one QTL for race RKRTF), three QTLs were observed for APR (two QTLs at KRIAPI and one QTL at RIBSP), and three QTLs were revealed in both the seedling and adult stages (Table 8, Figure 2). The identified QTLs for SR resistance were distributed among nine chromosomes of A, B, and D genomes and explained from 8.9% to 39.1% of the variation in the resistance to SR. In total, 11 out of 13 QTLs for SR resistance had R2 > 10% and could be considered major QTLs. The LOD score for the detected QTL varied from 3.0 to 6.8.
Three QTLs for resistance to SR race TKRTF were identified, including two QTLs mapped on chromosome 6B and one QTL on 2D. These QTLs explained 10.0–19.2% of the variation in SR resistance to race TKRTF. QTLs QSr.ipbb-6B.6 and QSr.ipbb-6B.7 had alleles increasing SR resistance to race TKRTF carried by PA, whereas resistance allele QSr.ipbb-2D.2 originated from Paragon. For the second SR race PKCTC, two QTLs for resistance to this race were located on chromosome 2B and one QTL was on 5B. The phenotypic variance conditioned by these QTLs varied from 8.9% to 14.7%. The third SR race RKRTF allowed the identification of one race-specific QTL on chromosome 5A explaining 24.7% of the phenotypic variation. Its allele, associated with an increase in SR resistance, originated from Paragon.
Three QTLs for APR to SR were identified on chromosomes 1D, 3D, and 5A, and explained from 10.8% to 18.0% of SR resistance variation. Two QTLs identified at KRIAPI had alleles increasing resistance to SR originating from Paragon, and the allele of QTL at RIBSP was from PA. The last three QTLs for LR resistance occurred multiple times in the experiment and are located on chromosomes 1B, 2A, and 6B. The QTL QSr.ipbb-1B.4 was detected as race-specific to TKRTF at the seedling stage and as APR QTL at KRIAPI. It explained 15.7% of SR resistance variation and had alleles increasing resistance originating from Paragon at the seedling stage and PA at the adult plant stage. The QTL QSr.ipbb-2A.2 was identified as effective against SR race RKRTF at the seedling stage and as APR QTL at RIBSP. This QTL explained 39.1% of the phenotypic variation, and alleles increasing SR resistance at both growth stages were inherited from PA. The QTL QSr.ipbb-6B.5 was discovered at the seedling stage to race TKRTF and at the adult plant stage at RIBSP. The QTL explained 17.3% of SR resistance variations. Its alleles increasing resistance originated from PA in the case of seedling resistance and from Paragon at the adult growth stage.

3.5. Comparison of Identified QTLs with Previous Works and Gene Identification

The QTLs identified in this study were analyzed in comparison with previously reported QTLs for LR and SR resistance in the PA × Par RILs population [27] and with QTLs for LR and SR resistance at RIBSP identified using genome-wide association study (GWAS) [51]. The location of each identified QTL was compared to the genetic positions of known Lr and Sr genes (Table 9). In total, four candidate Lr genes and four QTLs were found for five QTLs associated with LR resistance in this study. In the analysis of QTLs for SR resistance, we found similarities with the genetic locations of eight previously identified QTLs and/or candidate Sr genes.
The region of each QTL was analyzed for the presence of protein-coding genes in the interval 500 kb upstream and 500 kb downstream from the most significant SNP (Table S1). The analysis of LR-associated QTL regions suggested the presence of 158 genes ranging from 6 (QLr.ipbb-7B.3 and QLr.ipbb-7D.1) to 22 (QLr.ipbb-1B.4 and QLr.ipbb-6A.4) genes per interval. A similar search for SR-associated QTL regions indicated the presence of 226 genes ranging from 5 (QSr.ipbb-2B.4) to 29 (QSr.ipbb-1B.4) genes per interval. Among these 158 genes identified for QTLs associated with LR, 48.9% coded for proteins with functions known in T. aestivum, 48.6% for uncharacterized proteins, and 2.5% for RNAs. For QTLs associated with SR, 56.6% of genes coded for proteins uncharacterized in T. aestivum, 41.2% described protein-coding genes, and 2.2% coded for RNAs. Among genes coding for uncharacterized proteins, sequences similar to the 24 QTL regions for LR and 39 QTL regions for SR were identified in other grass species (Table S1). Orthologous genes with their sequence similarity level higher than 70% were selected and are listed.

4. Discussion

4.1. General Resistance of RILs in Studied Environments

At the seedling stage, the majority of RILs and parental cultivars showed MS and S levels of resistance to all races of LR and SR, except for the SR race PKCTC, where several lines were identified as R and MR (Table 3). The ANOVA test showed a more significant influence of pathogen genotype (race) on the resistance of RILs rather than the genotype of wheat lines (Table 4). This result indicated that these genetic factors associated with resistance are race-specific. In the world and in Kazakhstan, breeding programs are mostly focused on the combination of seedling resistance and APR in new cultivars. Pyramiding of seedling gene(s) with slow rusting APR gene(s) usually results in higher resistance of the crop. This agrees with wheat R genes conferring resistance to LR (Lr1, Lr10, Lr21) and SR (Sr22, Sr33, Sr35, Sr45, Sr50) being cloned and widely used in wheat breeding [55]. However, the significant positive correlations among LR races observed in this study (Table 5) suggested the involvement of genetic factors that are effective against all three races. The presence of strong positive correlations between APR to LR and SR at KRIAPI also indicated that genes conferring LR resistance are either closely linked or may have a pleiotropic effect on genes that control SR resistance [26,56]. Positive correlations were simultaneously observed between seedling resistance to LR races TQTHQ and TKTHT and APR to LR at KRIAPI, as well as between seedling resistance to SR race PKCTC and APR to SR at KRIAPI (Table 5). The relationship between race-specific seedling and broad adult plant resistances could be influenced by the presence of LR and SR races in the fields at KRIAPI. This also suggested that the wheat germplasm growing in this region could be effectively and rapidly screened for resistance to LR and SR at the seedling stage in a greenhouse [57].
LR and SR resistances are complex traits [58]; this was confirmed by the range of reactions to pathogens and the presence of transgressive segregations. Even when parents demonstrated the same level of resistance, such as APR to SR, RILs still showed transgressive phenotypes in the direction of either resistance (RIBSP) or susceptibility (KRIAPI) (Table 3). This phenomenon is not rare; it was previously described for many other quantitatively inherited wheat traits; for example, in studies of grain quality traits [22], grain Zn and Fe concentrations [59], grain yield and plant height [60], and rust diseases [61,62].

4.2. QTL Mapping for Leaf Rust Resistance

Alleles conferring increased resistance of QTLs for LR race TQKHT and APR at RIBSP originated from Par (Table 7). The higher LR resistance of Par in comparison with PA indicated that the U.K. cultivar is a promising source for wheat breeding programs in Kazakhstan. PA was simultaneously found to be a source for QTLs with increased LR resistance to race TQTMQ.
The 11 QTLs for the resistance to LR at the seedling and adult plant-growth stages can be divided into two categories: (1) similar to QTLs previously detected for LR resistance and (2) presumably novel QTLs. The first category consisted of 6 out of 11 QTLs for LR resistance (Table 9). Four of the QTLs for LR resistance with similar genetic positions (QLr.ipbb-1A.2 (APR at KRIAPI), QLr.ipbb-6A.4 (seedling resistance to TQKHT), QLr.ipbb-6A.5 (seedling resistance to TRTHT), and QLr.ipbb-7B.3 (seedling resistance to TQTMQ) were previously identified in a GWAS study performed at RIBSP in 2018/2019 at the adult plant stage [50]. Hence, multiple occurrences of QTLs associated with the resistance to LR in different conditions and environments indicated the broad stability of these loci. QLr.ipbb-7B.3 may be associated with the gene Lr14 located in a similar region of the genome (Table 9). The effectiveness of allele Lr14a was described for northern Kazakhstan and Lr14b for eastern and western Kazakhstan [7]. Lr14 was also described as an effective resistance factor to TQTMQ (Table 1). The APR QTL QLr.ipbb-1B.4 is associated with the gene Lr21, positioned in close proximity to the peak of the QTL (Table S1). This gene was described as effective in southeastern Kazakhstan [7]. The last QTL from the first group, QLr.ipbb-3A.2, is probably associated with genes Lr63 and Lr66 (Table 9). Unfortunately, information is lacking about the role of these genes in the wheat-growing areas of Kazakhstan. However, Lr63 and Lr66 are known to condition low to intermediate infection types to most of P. recondita isolates [63]. The remaining five QTLs identified for LR resistance are presumably novel genetic factors, since there were no reliable matches between their positions in the genome and previously identified QTLs or genes.

4.3. QTLs for Stem Rust

In 13 QTLs for the resistance to SR identified in this study, alleles presumably increasing resistance originated from both PA and Par (Table 8). Similar to LR resistance, SR-resistance-associated QTLs could be divided into two loci groups, where the first group has similar genetic positions with previously reported QTLs for SR resistance (Table 9), and the second group has none of those matches. The first group includes 8 out of 13 QTLs identified for SR resistance. For three of them—QSr.ipbb-2B.4 (seedling resistance to PKCTC), QSr.ipbb-6B.5 (seedling resistance to TKRTF and APR in RIBSP), and QSr.ipbb-6B.7 (seedling resistance to TKRTF)—QTLs for SR resistance with similar positions in the genome were identified in a previous work involving the PA × Par mapping population [27] and in a GWAS study using resistance data obtained from RIBSP [51]. Similar to the LR study, these findings may indicate the stability of identified QTLs. In addition to the information with QTL similarities, several specific Sr genes seem to be associated with QTLs from this study (Table 9). One of the most interesting findings was the identification of three QTLs on distal ends of chromosomes 2A, 2B, and 2D responsible for seedling resistance to SR races RKRTF, PKCTC, and TKRTF, respectively. These QTLs could be associated with the gene Sr32, which was mapped in these regions of chromosomes 2A [64], 2B [65], and 2D [66]. The gene was previously reported as effective against Ug99 and related SR races [66]. The other Sr genes involved in resistance to SR races in the Ug99 lineage and possibly associated with QTL from this study are Sr31 (resistant to TTKSF and TTKSP), Sr36 (all Ug99 lineage races, except TTTSK), and Sr39 (all Ug99 lineage races) [67]. Among the SR races used in this study, Sr36 was described as effective against PKCTC (Table 1). The resistance pattern is similar to QSr.ipbb-2B.4, which is located in a nearby region of the chromosome. The second group of the genetic factors consisted of the remaining five QTLs that could be novel QTLs associated with resistance to SR.

4.4. QTLs Cluster on Chromosome 1B

The QTLs associated with several traits are common in wheat. It may occur due to pleiotropic effect or their tight linkage. For the resistance to wheat fungal diseases, pleiotropic APR genes Lr34/Yr18/Pm38/Sr57 [68], Lr46/Yr29/Pm39/Sr58 [69], and Lr67/Yr46/Pm46/Sr55 [70] were previously described. Among the QTLs identified for LR and SR resistance in this study, two QTLs (QLr.ipbb-1B.4 and QSr.ipbb-1B.4) occupy the same interval on chromosome 1B (Table 7 and Table 8, Figure 1 and Figure 2). In addition, the QLr.ipbb-1B.4 interval contains the resistance gene Lr21 less than 500 kb from the significant peak, whereas the interval of QSr.ipbb-1B.4 has genes for disease resistance proteins and resistance-related kinases next to the peak marker (Table S1). Lr21 was described as effective for southeastern Kazakhstan [7]. Common markers in these intervals suggest the usefulness for marker-assisted breeding of these QTLs to develop wheat cultivars with durable rust resistance for gene pyramiding [11].

5. Conclusions

Overall, 24 QTLs for the resistance to rust diseases at the seedling and adult plant stages were identified in this study, including 11 QTLs for LR and 13 QTLs for SR. Among the QTLs associated with LR, eight QTLs were race-specific and detected at the seedling stage, two QTLs were at the stage of the adult plant, and one QTL was identified in both stages. The QTLs for LR-resistance explained from 11.6% (QLr.ipbb-7D.1) to 25.7% (QLr.ipbb-6A.4) of the phenotypic variation and were detected on 10 chromosomes. The increased resistance to LR in TQTMQ race-specific QTLs originated from PA; in QTLs specific for the race TQKHT and APR, alleles were from Par. For TRTHT, the origin of resistance alleles in identified QTLs was both parental cultivars. For SR resistance, seven QTLs were race-specific and detected at the seedling stage, three QTLs were identified at the adult plant stage, and three QTLs were identified at both growth stages. SR-associated QTLs explained from 8.9% (QSr.ipbb-2B.4) to 39.1% (QSr.ipbb-2A.2) of variation in SR resistance and were mapped on nine chromosomes. The alleles increasing resistance to SR originated from both parents: effective alleles in six QTLs were from Par, in five QTLs from PA, and two QTLs had a different origin of resistance at the seedling and adult plant stages. Among the QTLs from this study, 10 QTLs were putative and 14 matching QTLs were found in previous works involving the PA × Par population, a GWAS study at RIBSP, and possible candidate resistance genes. The cluster of QTLs associated with both LR and SR resistances was identified on chromosome 1B. Thus, the QTLs revealed in this study may play an essential role in the improvement of wheat resistance to LR and SR via marker-assisted selection.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4395/10/9/1285/s1, Table S1. The list of protein- and RNA-coding genes 500 kb upstream and 500 kb downstream from the most significant SNP of the QTL.

Author Contributions

Conceptualization, S.A. and Y.T.; methodology, A.R. and Y.T.; formal analysis, Y.G. and G.Y.; investigation, Y.G., A.R., and S.A.; resources, Y.T. and A.R.; data curation, Y.G., G.Y., and Y.T.; writing—original draft preparation, Y.G.; writing—review and editing, Y.G., S.A., A.R., G.A., and Y.T.; supervision, Y.T.; project administration, S.A.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Agriculture of the Republic of Kazakhstan, grant number BR06249329. The APC was also funded by grant number BR06249329.

Acknowledgments

This work was conducted within the framework of the project “Development of new DNA markers associated with the resistance of bread wheat to the most dangerous fungal diseases in Kazakhstan” in Program “Development of the innovative systems for increasing the resistance of wheat varieties to especially dangerous diseases in the Republic of Kazakhstan”: BR06249329 supported by the Ministry of Agriculture of the Republic of Kazakhstan.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Statista. Available online: https://www.statista.com/ (accessed on 12 March 2020).
  2. Giraldo-Carbajo, P.; Barzana, M.E.B.; Manzano-Agugliaro, F.; Giménez, E. Worldwide Research Trends on Wheat and Barley: A Bibliometric Comparative Analysis. Agronomy 2019, 9, 352. [Google Scholar] [CrossRef] [Green Version]
  3. USDA. Available online: https://www.usda.gov/ (accessed on 17 March 2020).
  4. Statistics Committee. Ministry of National Economy of the Republic of Kazakhstan. Available online: https://stat.gov.kz/ (accessed on 15 March 2020).
  5. Tadesse, W.; Sanchez-Garcia, M.; Assefa, S.G.; Amri, A.; Bishaw, Z.; Ogbonnaya, F.C.; Baum, M. Genetic Gains in Wheat Breeding and Its Role in Feeding the World. Crop Breed. Genet. Genom. 2019, 1, e190005. [Google Scholar]
  6. Samborski, D.J. Wheat leaf rust. In The Cereal Rusts Vol. II: Diseases, Distribution, Epidemiology and Control; Roelfs, A.P., Bushnell, W.R., Eds.; Academic Press: Orlando, FL, USA, 1985; pp. 39–59. [Google Scholar]
  7. Koyshybaev, M. Wheat Diseases, 1st ed.; FAO: Ankara, Turkey, 2018; p. 365. [Google Scholar]
  8. Eversmeyer, M.G.; Kramer, C.L. Epidemiology of Wheat Leaf and Stem Rust in the Central Great Plains of the USA. Annu. Rev. Phytopathol. 2000, 38, 491–513. [Google Scholar] [CrossRef] [PubMed]
  9. Summers, R.W.; Brown, J.K.M. Constraints on breeding for disease resistance in commercially competitive wheat cultivars. Plant Pathol. 2013, 62, 115–121. [Google Scholar] [CrossRef]
  10. McIntosh, R.A.; Dubcovsky, J.; Rogers, W.J.; Morris, C.F.; Xia, X.C. Catalogue of Gene Symbols for Wheat: 2017 Supplement. Available online: https://shigen.nig.ac.jp/wheat/komugi/genes/macgene/supplement2017.pdf (accessed on 27 July 2020).
  11. Ellis, J.G.; Lagudah, E.; Spielmeyer, W.; Dodds, P.N. The past, present and future of breeding rust resistant wheat. Front. Plant Sci. 2014, 5, 641. [Google Scholar] [CrossRef] [Green Version]
  12. Bariana, H.S.; Hayden, M.J.; Ahmed, N.U.; Bell, J.A.; Sharp, P.J.; McIntosh, R.A. Mapping of durable adult plant and seedling resistances to stripe rust and stem rust diseases in wheat. Aust. J. Agric. Res. 2001, 52, 1247. [Google Scholar] [CrossRef]
  13. Xu, Y.; Li, P.; Yang, Z.; Yang, Z. Genetic mapping of quantitative trait loci in crops. Crop J. 2017, 5, 175–184. [Google Scholar] [CrossRef] [Green Version]
  14. Arruda, M.P.; Brown, P.; Brown-Guedira, G.; Krill, A.M.; Thurber, C.; Merrill, K.R.; Foresman, B.J.; Kolb, F.L. Genome-Wide Association Mapping of Fusarium Head Blight Resistance in Wheat using Genotyping-by-Sequencing. Plant Genome 2016, 9, 1–14. [Google Scholar] [CrossRef]
  15. Li, G.; Xu, X.; Tan, C.; Carver, B.F.; Bai, G.; Wang, X.; Bonman, J.M.; Wu, Y.; Hunger, R.; Cowger, C. Identification of powdery mildew resistance loci in wheat by integrating genome-wide association study (GWAS) and linkage mapping. Crop J. 2019, 7, 294–306. [Google Scholar] [CrossRef]
  16. Cheng, B.; Gao, X.; Cao, N.; Ding, Y.; Gao, Y.; Chen, T.; Xin, Z.; Zhang, L.Y. Genome-wide association analysis of stripe rust resistance loci in wheat accessions from southwestern China. J. Appl. Genet. 2020, 61, 37–50. [Google Scholar] [CrossRef] [Green Version]
  17. Ollier, M.; Talle, V.; Brisset, A.-L.; Le Bihan, Z.; Duerr, S.; Lemmens, M.; Goudemand, E.; Robert, O.; Hilbert, J.-L.; Buerstmayr, H. QTL mapping and successful introgression of the spring wheat-derived QTL Fhb1 for Fusarium head blight resistance in three European triticale populations. Theor. Appl. Genet. 2020, 133, 457–477. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Unamba, C.I.N.; Nag, A.; Sharma, R.K. Next Generation Sequencing Technologies: The Doorway to the Unexplored Genomics of Non-Model Plants. Front. Plant Sci. 2015, 6, 6. [Google Scholar] [CrossRef] [Green Version]
  19. Turuspekov, Y.; Plieske, J.; Ganal, M.; Akhunov, E.; Abugalieva, S. Phylogenetic analysis of wheat cultivars in Kazakhstan based on the wheat 90 K single nucleotide polymorphism array. Plant Genet. Resour. 2015, 15, 29–35. [Google Scholar] [CrossRef]
  20. Singh, M.; Upadhyaya, H.D. Genetic and Genomic Resources for Grain Cereals Improvement, 1st ed.; Academic Press: Cambridge, MA, USA, 2015; pp. 58–59. [Google Scholar]
  21. Hu, J.; Wang, X.; Zhang, G.; Jiang, P.; Chen, W.; Hao, Y.; Ma, X.; Xu, S.; Jia, J.; Kong, L.; et al. QTL mapping for yield-related traits in wheat based on four RIL populations. Theor. Appl. Genet. 2020, 133, 917–933. [Google Scholar] [CrossRef] [PubMed]
  22. Goel, S.; Singh, K.; Singh, B.; Grewal, S.; Dwivedi, N.; Alqarawi, A.A.; Allah, E.A.; Ahmad, P.; Singh, N.K. Analysis of genetic control and QTL mapping of essential wheat grain quality traits in a recombinant inbred population. PLoS ONE 2019, 14, e0200669. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Li, L.; Mao, X.; Wang, J.; Chang, X.; Reynolds, M.; Jing, R. Genetic dissection of drought and heat-responsive agronomic traits in wheat. Plant Cell Environ. 2019, 42, 2540–2553. [Google Scholar] [CrossRef] [Green Version]
  24. Emebiri, L.C.; Tan, M.K.; El-Bouhssini, M.; Wildman, O.; Jighly, A.; Tadesse, W.; Ogbonnaya, F.C. QTL mapping identifies a major locus for resistance in wheat to Sunn pest (Eurygaster integriceps) feeding at the vegetative growth stage. Theor. Appl. Genet. 2017, 130, 309–318. [Google Scholar] [CrossRef] [PubMed]
  25. Amalova, A.Y.; Yermekbayev, K.A.; Griffiths, S.; Abugalieva, S.I.; Turuspekov, Y.K.; Centre, U.K.J.I. Phenotypic variation of common wheat mapping population Pamyati Azieva x Paragon in south-east of Kazakhstan. Int. J. Biol. Chem. 2019, 12, 11–17. [Google Scholar] [CrossRef] [Green Version]
  26. Genievskaya, Y.; Amalova, A.; Ydyrys, A.; Sarbayev, A.; Griffiths, S.; Abugalieva, S.; Turuspekov, Y. Resistance of common wheat (Triticum aestivum L.) mapping population Pamyati Azieva × Paragon to leaf and stem rusts in conditions of south-east Kazakhstan. Eurasian J. Ecol. 2019, 61, 14–23. [Google Scholar]
  27. Genievskaya, Y.; Fedorenko, Y.; Sarbayev, A.; Amalova, A.; Abugalieva, S.; Griffiths, S.; Turuspekov, Y. Identification of QTLs for resistance to leaf and stem rusts in bread wheat (Triticum aestivum L.) using a mapping population of ‘Pamyati Azieva × Paragon’. Vavilov J. Genet. Breed. 2019, 23, 887–895. [Google Scholar] [CrossRef]
  28. ADAPTAWHEAT. Available online: https://www.jic.ac.uk/adaptawheat/ (accessed on 12 April 2020).
  29. Yermekbayev, K.; Turuspekov, Y.; Ganal, M.; Plieske, J.; Griffiths, S. Construction and utilization of the hexaploid map Pamyati Azieva × Paragon. In Proceedings of PlantGen (Plant Genetics, Genomics, Bioinformatics and Biotechnology), Almaty, Kazakhstan, 29 May–2 June 2017; Turuspekov, Y., Abugalieva, S., Eds.; Institute of Plant Biology and Biotechnology: Almaty, Kazakhstan, 2017. [Google Scholar]
  30. Chu, C.-G.; Friesen, T.L.; Xu, S.S.; Faris, J.D.; Kolmer, J.A. Identification of novel QTLs for seedling and adult plant leaf rust resistance in a wheat doubled haploid population. Theor. Appl. Genet. 2009, 119, 263–269. [Google Scholar] [CrossRef] [PubMed]
  31. Pretorius, Z.A.; Jin, Y.; Bender, C.M.; Herselman, L.; Prins, R. Seedling resistance to stem rust race Ug99 and marker analysis for Sr2, Sr24 and Sr31 in South African wheat cultivars and lines. Euphytica 2011, 186, 15–23. [Google Scholar] [CrossRef]
  32. Spanic, V.; Rouse, M.N.; Kolmer, J.A.; Anderson, J.A. Leaf and stem seedling rust resistance in wheat cultivars grown in Croatia. Euphytica 2014, 203, 437–448. [Google Scholar] [CrossRef]
  33. Stakman, E.C.; Stewart, D.M.; Loegering, W.Q. Identification of physiologic races of Puccinia graminis var. tritici. US Agric. Res. Serv. 1962, 617, 1–53. [Google Scholar]
  34. Rsaliyev, A.; Rsaliyev, S.S. Principal approaches and achievements in studying race composition of wheat stem rust. Vavilov J. Genet. Breed. 2019, 22, 967–977. [Google Scholar] [CrossRef]
  35. Roelfs, A.P. An International System of Nomenclature for Puccinia graminis f. sp. tritici. Phytopathology 1988, 78, 526. [Google Scholar] [CrossRef] [Green Version]
  36. Long, D.L.; Kolmer, J.A. A North American system of nomenclature for Puccinia recondita f. sp. tritici. Phytopathology 1989, 79, 525–529. [Google Scholar] [CrossRef]
  37. Kolmer, J.A.; Ordoñez, M.E. Genetic Differentiation of Puccinia triticina Populations in Central Asia and the Caucasus. Phytopathology 2007, 97, 1141–1149. [Google Scholar] [CrossRef] [Green Version]
  38. Kolmer, J.A.; Kabdulova, M.G.; Mustafina, M.A.; Zhemchuzhina, N.S.; Dubovoy, V. Russian populations of Puccinia triticina in distant regions are not differentiated for virulence and molecular genotype. Plant Pathol. 2014, 64, 328–336. [Google Scholar] [CrossRef]
  39. Schachtel, G.A.; Dinoor, A.; Herrmann, A.; Kosman, E. Comprehensive Evaluation of Virulence and Resistance Data: A New Analysis Tool. Plant Dis. 2012, 96, 1060–1063. [Google Scholar] [CrossRef] [Green Version]
  40. Mains, E.B.; Jackson, H.S. Physiologic specialization in the leaf rust of wheat Puccinia triticiana Erikss. Phytopathology 1926, 16, 89–120. [Google Scholar]
  41. Peterson, R.F.; Campbell, A.B.; Hannah, A.E. A Diagrammatic Scale for Estimating Rust Intensity on Leaves and Stems of Cereals. Can. J. Res. 1948, 26, 496–500. [Google Scholar] [CrossRef]
  42. Roelfs, A.; Singh, R.; Saari, E.E. Rust Diseases of Wheat: Concepts and Methods of Disease Management; CIMMYT: Mexico City, Mexico, 1992; p. 45. [Google Scholar]
  43. Zhang, D.; Bowden, R.; Bai, G. A method to linearize Stakman infection type ratings for statistical analysis. In Proceedings of the Borlaug Global Rust Initiative 2011 Technical Workshop, Saint Paul, MN, USA, 13–16 June 2011. [Google Scholar]
  44. Windows QTL Cartographer V2.5. Available online: https://brcwebportal.cos.ncsu.edu/qtlcart/WQTLCart.htm (accessed on 9 February 2020).
  45. Rong, J.; Feltus, F.A.; Waghmare, V.N.; Pierce, G.J.; Chee, P.W.; Draye, X.; Saranga, Y.; Wright, R.J.; Wilkins, T.A.; May, O.L.; et al. Meta-analysis of Polyploid Cotton QTL Shows Unequal Contributions of Subgenomes to a Complex Network of Genes and Gene Clusters Implicated in Lint Fiber Development. Genetics 2007, 176, 2577–2588. [Google Scholar] [CrossRef]
  46. Voorrips, R.E. MapChart: Software for the graphical presentation of linkage maps and QTLs. J. Hered. 2002, 93, 77–78. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. EnsemblPlant. Triticum Aestivum. Available online: https://plants.ensembl.org/Triticum_aestivum/Info/Index (accessed on 27 February 2020).
  48. BLAST Tool. Triticum Aestivum. Available online: https://plants.ensembl.org/Triticum_aestivum/Tools/Blast (accessed on 2 March 2020).
  49. UniProt. Available online: https://www.uniprot.org/ (accessed on 2 March 2020).
  50. Collard, B.C.; Jahufer, M.Z.Z.; Brouwer, J.B.; Pang, E.C.K. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 2005, 142, 169–196. [Google Scholar] [CrossRef]
  51. Genievskaya, Y.; Abugalieva, S.; Rsaliyev, A.; Turuspekov, Y. Genome-wide association mapping for resistance to leaf, stem, and yellow rusts of bread wheat in conditions of South Kazakhstan. PeerJ 2020. submitted work under review. [Google Scholar]
  52. Aoun, M.; Breiland, M.; Turner, M.K.; Loladze, A.; Chao, S.; Xu, S.S.; Ammar, K.; Anderson, J.A.; Kolmer, J.A.; Acevedo, M. Genome-Wide Association Mapping of Leaf Rust Response in a Durum Wheat Worldwide Germplasm Collection. Plant Genome 2016, 9, 1–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Yu, L.-X.; Barbier, H.; Rouse, M.N.; Singh, S.; Singh, R.P.; Bhavani, S.; Huerta-Espino, J.; Sorrells, M.E. A consensus map for Ug99 stem rust resistance loci in wheat. Theor. Appl. Genet. 2014, 127, 1561–1581. [Google Scholar] [CrossRef] [Green Version]
  54. Hart, G.E.; Gale, M.D.; McIntosh, R.A. Linkage maps of Triticum aestivum (hexaploid wheat, 2n = 42, genomes A, B & D) and T. tauschii (2n = 14, genome D). In Progress in Genome Mapping of Wheat and Related Species, Proceedings of the 3rd Publ Wkshp Int Triticeae Mapping Initiative, Mexico City, Mexico, 22–26 September 1992; Hoisington, D., McNab, A., Eds.; CIMMYT: Mexico City, Mexico, 1993; pp. 32–46. [Google Scholar]
  55. Periyannan, S.; Milne, R.J.; Figueroa, M.; Lagudah, E.; Dodds, P.N. An overview of genetic rust resistance: From broad to specific mechanisms. PLoS Pathog. 2017, 13, e1006380. [Google Scholar] [CrossRef] [Green Version]
  56. Figlan, S.; Terefe, T.G.; Shimelis, H.; Tsilo, T.J. Adult plant resistance to leaf rust and stem rust of wheat in a newly developed recombinant inbred line population. S. Afr. J. Plant Soil 2017, 35, 111–119. [Google Scholar] [CrossRef]
  57. Tadesse, W.; Reents, H.J.; Hsam, S.L.K.; Zeller, F.J. Relationship of seedling and adult plant resistance and evaluation of wheat germplasm against tan spot (Pyrenophora tritici-repentis). Genet. Resour. Crop Evol. 2010, 58, 339–346. [Google Scholar] [CrossRef]
  58. Niks, R.E.; Qi, X.; Marcel, T.C. Quantitative Resistance to Biotrophic Filamentous Plant Pathogens: Concepts, Misconceptions, and Mechanisms. Annu. Rev. Phytopathol. 2015, 53, 445–470. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Crespo-Herrera, L.A.; Govindan, V.; Stangoulis, J.C.R.; Hao, Y.; Singh, R.P. QTL Mapping of Grain Zn and Fe Concentrations in Two Hexaploid Wheat RIL Populations with Ample Transgressive Segregation. Front. Plant Sci. 2017, 8, 1800. [Google Scholar] [CrossRef] [Green Version]
  60. Asif, M.; Yang, R.C.; Navabi, A.; Iqbal, M.; Kamran, A.; Lara, E.P.; Randhawa, H.; Pozniak, C.; Spaner, D. Mapping QTL, selection differentials, and the effect of Rht-B1 under organic and conventionally managed systems in the Attila × CDC Go spring wheat mapping population. Crop Sci. 2015, 55, 1129–1142. [Google Scholar] [CrossRef]
  61. Tian, Z.M.; Zhang, L.P.; Sun, Q.L.; Shan, F.H.; Tian, Z.H. Research review on wheat QTLs. Inn. Mong. Agric. Sci. Technol. 2007, 3, 68–71. [Google Scholar]
  62. Bokore, F.E.; Knox, R.E.; Cuthbert, R.D.; Pozniak, C.J.; McCallum, B.D.; N’Diaye, A.; Depauw, R.M.; Campbell, H.L.; Munro, C.; Singh, A.; et al. Mapping quantitative trait loci associated with leaf rust resistance in five spring wheat populations using single nucleotide polymorphism markers. PLoS ONE 2020, 15, e0230855. [Google Scholar] [CrossRef] [Green Version]
  63. Kolmer, J.A.; Anderson, J.A.; Flor, J.M. Chromosome Location, Linkage with Simple Sequence Repeat Markers, and Leaf Rust Resistance Conditioned by Gene Lr63 in Wheat. Crop Sci. 2010, 50, 2392–2395. [Google Scholar] [CrossRef] [Green Version]
  64. McIntosh, R.A.; Wellings, C.R.; Park, R.F. Wheat Rusts, An Atlas of Resistance Genes; CSIRO Publications: Melbourne, Australia, 1995; pp. 93–99. [Google Scholar]
  65. McIntosh, R.A.; Dubcovsky, J.; Rogers, W.J.; Morris, C.F.; Appels, R.; Xia, X.C. Catalogue of gene symbols for wheat: 2011 supplement. Annu. Wheat Newsl. 2011, 57, 303–321. [Google Scholar]
  66. Mago, R.; Verlin, D.; Zhang, P.; Bansal, U.; Bariana, H.; Jin, Y.; Ellis, J.; Hoxha, S.; Dundas, I. Development of wheat–Aegilops speltoides recombinants and simple PCR-based markers for Sr32 and a new stem rust resistance gene on the 2S#1 chromosome. Theor. Appl. Genet. 2013, 126, 2943–2955. [Google Scholar] [CrossRef]
  67. Singh, R. Current status, likely migration and strategies to mitigate the threat to wheat production from race Ug99 (TTKS) of stem rust pathogen. CAB Rev. Perspect. Agric. Veter-Sci. Nutr. Nat. Resour. 2006, 1, 1–13. [Google Scholar] [CrossRef] [Green Version]
  68. Singh, R.P.; Herrera-Foessel, S.A.; Huerta-Espino, J.; Bariana, H.; Bansal, U.; McCallum, B.; Hiebert, C.; Bhavani, S.; Singh, S.; Lan, C.X.; et al. Lr34/Yr18/Sr57/Pm38/Bdv1/Ltn1 confers slow rusting, adult plant resistance to Puccinia graminis tritici. In Proceedings of the 13th International Cereal Rusts and Powdery Mildews Conference, Beijing, China, 28 August–1 September 2012. [Google Scholar]
  69. Singh, R.P.; Herrera-Foessel, S.A.; Huerta-Espino, J.; Lan, C.X.; Basnet, B.R.; Bhavani, S.; Lagudah, E.S. Pleiotropic gene Lr46/Yr29/Pm39/Ltn2 confers slow rusting, adult plant resistance to wheat stem rust fungus. In Proceedings of the Borlaug Global Rust Initiative, New Delhi, India, 19–22 August 2013. [Google Scholar]
  70. Herrera-Foessel, S.A.; Singh, R.P.; Lillemo, M.; Huerta-Espino, J.; Bhavani, S.; Singh, S.; Lan, C.; Calvo-Salazar, V.; Lagudah, E.S. Lr67/Yr46 confers adult plant resistance to stem rust and powdery mildew in wheat. Theor. Appl. Genet. 2014, 127, 781–789. [Google Scholar] [CrossRef] [PubMed]
Agronomy 10 01285 g001 550
Figure 1. Pamyati Azieva × Paragon genetic map with quantitative trait loci (QTLs) for adult plant resistance (APR) to leaf rust (LR) in two regions and seedling resistance to three LR races. The region containing the QTL is indicated by a vertical bar on the right and followed by the name of the QTL. Single nucleotide polymorphism (SNP) markers are shown on the right and their genetic positions (cM) on the left. The peak marker for each QTL is highlighted in color and bolded. Colors of QTL indicate APR or race-specific seedling resistance.
Figure 1. Pamyati Azieva × Paragon genetic map with quantitative trait loci (QTLs) for adult plant resistance (APR) to leaf rust (LR) in two regions and seedling resistance to three LR races. The region containing the QTL is indicated by a vertical bar on the right and followed by the name of the QTL. Single nucleotide polymorphism (SNP) markers are shown on the right and their genetic positions (cM) on the left. The peak marker for each QTL is highlighted in color and bolded. Colors of QTL indicate APR or race-specific seedling resistance.
Agronomy 10 01285 g001
Agronomy 10 01285 g002 550
Figure 2. Pamyati Azieva × Paragon genetic map with quantitative trait loci (QTLs) for adult plant resistance (APR) to stem rust (SR) in two regions and seedling resistance to three SR races. The region containing the QTL is indicated by a vertical bar on the right and followed by the name of the QTL. Single nucleotide polymorphism (SNP) markers are positioned on the right and their genetic positions (cM) are shown on the left. The peak marker for each QTL is highlighted in color and bold. Colors of QTL indicate APR or race-specific seedling resistance.
Figure 2. Pamyati Azieva × Paragon genetic map with quantitative trait loci (QTLs) for adult plant resistance (APR) to stem rust (SR) in two regions and seedling resistance to three SR races. The region containing the QTL is indicated by a vertical bar on the right and followed by the name of the QTL. Single nucleotide polymorphism (SNP) markers are positioned on the right and their genetic positions (cM) are shown on the left. The peak marker for each QTL is highlighted in color and bold. Colors of QTL indicate APR or race-specific seedling resistance.
Agronomy 10 01285 g002
Table
Table 1. Virulence/avirulence pattern of pathogen races used in the study.
Table 1. Virulence/avirulence pattern of pathogen races used in the study.
Disease (Pathogen)RaceAvirulent (Effective) GenesVirulent (Ineffective) Genes
LR (Puccinia recondita Rob. ex Desm f. sp. tritici)TQKHTLr24, 26, 3ka, 19, 25Lr1, 2a, 2c, 3, 9, 16, 11, 17, 30, 20, 29, 2b, 3bg, 14a, 15
TRTHTLr24, 19, 25Lr1, 2a, 2c, 3, 9, 16, 26, 3ka, 11, 17, 30, 20, 29, 2b, 3bg, 14a, 15
TQTMQLr24, 26, 20, 25, 14a, 15Lr1, 2a, 2c, 3, 9, 16, 3ka, 11, 17, 30, 19, 29, 2b, 3bg
SR (Puccinia graminis Pers. f. sp. tritici Eriks. & E. Henn.)TKRTFSr11, 30, 24, 31Sr5, 21, 9e, 7b, 6, 8a, 9g, 36, 9b, 17, 9a, 9d, 10, 38, Tmp, McN
PKCTCSr21, 11, 36, 9b, 30, 24, 31, 38Sr5, 9e, 7b, 6, 8a, 9g, 17, 9a, 9d, 10, Tmp, McN
RKRTFSr9e, 11, 30, 24, 31Sr5, 21, 7b, 6, 8a, 9g, 36, 9b, 17, 9a, 9d, 10, 38, Tmp, McN
LR, leaf rust; SR, stem rust.
Table
Table 2. Meteorological data on average temperature and precipitations during the vegetation period in the fields of Research Institute of Biological Safety Problems (RIBSP, southern Kazakhstan) and Kazakh Research Institute of Agriculture and Plant Industry (KRIAPI, southeastern Kazakhstan).
Table 2. Meteorological data on average temperature and precipitations during the vegetation period in the fields of Research Institute of Biological Safety Problems (RIBSP, southern Kazakhstan) and Kazakh Research Institute of Agriculture and Plant Industry (KRIAPI, southeastern Kazakhstan).
KRIAPI
MarchAprilMayJuneJuly
Temperature (°C)8.212.817.022.327.0
Precipitation (mm)27.3168.439.372.722.6
RIBSP
MarchAprilMayJuneJuly
Temperature (°C)13.017.024.029.033.0
Precipitation (mm)1.02.31.80.90.1
Table
Table 3. Descriptive statistics for leaf rust (LR) and stem rust (SR) resistance at two plant-growth stages in the Pamyati Azieva × Paragon mapping population.
Table 3. Descriptive statistics for leaf rust (LR) and stem rust (SR) resistance at two plant-growth stages in the Pamyati Azieva × Paragon mapping population.
Env.Plant-Growth StageRace (Disease)Parents IT 1RILs IT
PAParMeanRangeR (%)MR (%)MS (%)S (%)
GHSeedlingTQTMQ (LR)6 (3)5 (3-)6.1 ± 0.95–80089.810.2
TQKHT (LR)8 (4-)6 (3)5.9 ± 1.04–903.191.85.1
TRTHT (LR)6 (3)6 (3)5.9 ± 0.84–801.096.92.1
SeedlingTKRTF (SR)8 (4-)8 (4-)7.0 ± 1.45–90045.954.1
PKCTC (SR)5 (3-)8 (4-)5.4 ± 1.81–91.022.458.218.4
RKRTF (SR)6 (3)5 (3-)6.5 ± 1.45–90064.335.7
RIBSPAdultLR8 (30S)6 (50MS)3.8 ± 1.40–938.811.229.620.4
SR9 (80S)8 (40S)6.6 ± 2.91–91.023.518.457.1
KRIAPILR6 (40MS)6 (40MS)6.6 ± 1.72–909.264.326.5
SR0 (0)1 (10R)1.8 ± 2.30–856.126.518.41.0
1—parents IT scores are given in 0–9 numeric scale, traditional IT scores are given in parentheses. Env., environment; GH, greenhouse conditions; PA, Pamyati Azieva; Par, Paragon; IT, infection type; R, percentage of resistant lines (0–1 on 9-point scale); MR, percentage of moderately resistant lines (2–4 on 9-point scale); MS, percentage of moderately susceptible lines (5–7 on 9-point scale); S, percentage of susceptible lines (8–9 on 9-point scale).
Table
Table 4. ANOVA of plant genotype (Geno), pathogen race (Race), and plant genotype × pathogen race (Geno: Race) effects on seedling resistance to leaf rust (LR) and stem rust (SR) in the Pamyati Azieva × Paragon mapping population.
Table 4. ANOVA of plant genotype (Geno), pathogen race (Race), and plant genotype × pathogen race (Geno: Race) effects on seedling resistance to leaf rust (LR) and stem rust (SR) in the Pamyati Azieva × Paragon mapping population.
FactordfLRSR
MeanSFpMeanSFp
Geno972.5061.6300.0015.3802.6798.39 × 10–11
Race25.4203.5250.031136.94068.180<2.00 × 10–16
Geno: Race1941.3960.9080.7664.5402.2621.20 × 10–10
Residuals2941.537 2.010
df, degree of freedom; MeanS, mean square.
Table
Table 5. Correlations among race-specific seedling resistance and adult plant resistance (APR) to leaf rust (LR) and stem rust (SR) in the Pamyati Azieva × Paragon mapping population.
Table 5. Correlations among race-specific seedling resistance and adult plant resistance (APR) to leaf rust (LR) and stem rust (SR) in the Pamyati Azieva × Paragon mapping population.
LR TQTMQLR TQKHTLR TRTHTAPR LR RIBSPAPR LR KRIAPISR TKRTFSR PKCTCSR RKRTFAPR SR RIBSP
LR TQKHT0.193 *
LR TRTHT0.207 *0.283 **
APR LR RIBSP0.008 ns0.122 ns0.154 ns
APR LR KRIAPI0.251 **−0.028 ns0.192 *−0.124 ns
SR TKRTF−0.152 ns0.000 ns0.141 ns0.088 ns0.228 *
SR PKCTC−0.150 ns0.021 ns0.058 ns0.081 ns−0.009 ns−0.019 ns
SR RKRTF−0.202 *−0.006 ns0.091 ns−0.125 ns0.069 ns0.050 ns0.110 ns
APR SR RIBSP−0.071 ns0.096 ns0.197 *0.130 ns0.038 ns−0.075 ns−0.040 ns−0.029 ns
APR SR KRIAPI0.127 ns0.040 ns0.117 ns0.018 ns0.245 **−0.006 ns0.182 *0.015 ns−0.043 ns
APR, adult plant resistance; ns not significant; * p < 0.05; ** p < 0.01.
Table
Table 6. Correlations between leaf rust (LR) and stem rust (SR) resistance at the adult plant stage and yield-related traits in the Pamyati Azieva × Paragon mapping population.
Table 6. Correlations between leaf rust (LR) and stem rust (SR) resistance at the adult plant stage and yield-related traits in the Pamyati Azieva × Paragon mapping population.
TKW RIBSPYP RIBSPTKW KRIAPIYP KRIAPI
LR RIBSP−0.175 **−0.252 **––
SR RIBSP−0.490 **−0.474 **––
LR KRIAPI––−0.055 ns−0.200 *
SR KRIAPI––0.134 ns−0.151 ns
TKW, thousand kernel weight (g); YP, yield per plot (g/m2); ns not significant; * p < 0.05; ** p < 0.01.
Table
Table 7. The list of quantitative trait loci (QTLs) for adult plant resistance (APR) and race-specific seedling resistance to leaf rust (LR) identified in the Pamyati Azieva × Paragon recombinant inbred lines (RILs) population.
Table 7. The list of quantitative trait loci (QTLs) for adult plant resistance (APR) and race-specific seedling resistance to leaf rust (LR) identified in the Pamyati Azieva × Paragon recombinant inbred lines (RILs) population.
RaceEnv.QTLFlanking Markers (Left–Right)Chr.CI (cM)Peak (cM)Max. LODR2 (%)Add. Effect 1Allele 1
TQTMQGHQLr.ipbb-4A.2IAAV7104–Excalibur_c25699_1134A65.2–73.968.03.312.1−0.47PA
GHQLr.ipbb-5B.2Kukri_rep_c98079_222–BS00075815_515B87.4–99.294.54.315.2−0.57PA
GHQLr.ipbb-7B.3BS00023023_51–wsnp_Ex_c5653_99370627B93.5–98.994.74.915.7−0.79PA
TQKHTGHQLr.ipbb-6A.4Excalibur_rep_c105463_330–Ku_c37893_4956A31.5–42.040.88.625.70.63Paragon
GHQLr.ipbb-7D.1Kukri_c16416_647–BS00062644_517D74.0–84.984.14.211.60.41Paragon
TRTHTGHQLr.ipbb-3A.2Excalibur_c74666_291–RFL_Contig1896_12363A6.2–24.723.35.116.20.46Paragon
GHQLr.ipbb-4D.1TA020319-0161–BS00022436_514D7.1–16.214.33.712.3−0.39PA
GHQLr.ipbb-6A.5BobWhite_c30930_192–BS00022992_516A56.9–58.557.44.815.10.44Paragon
APRRIBSPQLr.ipbb-1B.4Excalibur_c29707_318–Kukri_c36151_1701B0–13.00.73.213.61.29Paragon
APRRIBSPQLr.ipbb-7A.3Ra_c4601_2417–CAP7_c7296_887A86.4–94.088.93.411.71.18Paragon
TQKHT/APRGH/KRIAPIQLr.ipbb-1A.2RAC875_c12348_720–BS00022824_511A75.9–88.282.14.616.00.86Paragon
CI, confidence interval; LOD, logarithm of odds; R2, phenotypic variance explained by the QTL; Chr., chromosome; Add., additive effect. 1—Additive effect of QTL indicates increasing of the trait expression explained by the allele of one parent (positive for PA and negative for Paragon). In the case of disease resistance, increased expression of the trait is undesired, and the effective allele is taken from the other parent (negative for PA and positive for Paragon).
Table
Table 8. The list of quantitative trait loci (QTLs) for APR and race-specific seedling resistance to stem rust (SR) identified in Pamyati Azieva × Paragon recombinant inbred lines (RILs) population.
Table 8. The list of quantitative trait loci (QTLs) for APR and race-specific seedling resistance to stem rust (SR) identified in Pamyati Azieva × Paragon recombinant inbred lines (RILs) population.
RaceEnv.QTLFlanking Markers (Left–Right)Chr.CI (cM)Peak (cM)Max. LODR2 (%)Add. Effect *Allele *
TKRTFGHQSr.ipbb-2D.2wsnp_Ex_rep_c80588_75758453–TA010191-13382D0–3.70.13.010.00.43Paragon
GHQSr.ipbb-6B.6wsnp_Ex_c12618_20079758–wsnp_Ex_c1249_23998946B46.0–54.347.84.214.1−0.57PA
GHQSr.ipbb-6B.7wsnp_Ku_c43368_50890819–wsnp_Ku_c4910_87933276B75.5–87.379.94.719.2−0.66PA
PKCTCGHQSr.ipbb-2B.3RAC875_rep_c71112_400–Kukri_c1175_15772B6.3–10.49.23.912.2−0.63PA
GHQSr.ipbb-2B.4BS00041323_51–RAC875_c32503_1342B75.9–78.378.02.98.90.54Paragon
GHQSr.ipbb-5B.1RAC875_rep_c114200_428–wsnp_RFL_Contig1548_7625475B137.6–146.3141.14.914.70.96Paragon
RKRTFGHQSr.ipbb-5A.3BS00036851_51–Excalibur_c27357_1465A184.7–194.4190.36.824.70.72Paragon
APRKRIAPIQSr.ipbb-1D.1BS00051826_51–BobWhite_rep_c65565_3591D61.7–65.564.35.018.02.24Paragon
RIBSPQSr.ipbb-3D.1Ra_c10284_405–Kukri_c5252_1073D0–11.41.93.111.3−0.99PA
KRIAPIQSr.ipbb-5A.2BS00089076_51–CAP11_c2623_1965A168.2–178.5172.73.010.80.72Paragon
TKRTF/APRGH/KRIAPIQSr.ipbb-1B.4Excalibur_c29707_318–Kukri_c92979_1951B0–15.212.64.215.70.59/–0.77Paragon/PA
RKRTF/APRGH/RIBSPQSr.ipbb-2A.2Kukri_c33374_1048–Tdurum_contig42153_58542A0.5–37.230.85.239.1−1.45PA
TKRTF/APRGH/RIBSPQSr.ipbb-6B.5RAC875_rep_c105224_352–Kukri_rep_c107077_3606B2.7–15.16.74.617.3−0.56/1.26PA/Paragon
* Additive effect of QTL indicates increasing of the trait expression explained by the allele of one parent (positive for PA and negative for Paragon). In the case of disease resistance, increased expression of the trait is undesired, and the effective allele is taken from the other parent (negative for PA and positive for Paragon).
Table
Table 9. Comparison of quantitative trait loci (QTLs) for leaf rust (LR) and stem rust (SR) resistance identified in this study in a Pamyati Azieva × Paragon mapping population with previously described QTLs and candidate Lr and Sr genes.
Table 9. Comparison of quantitative trait loci (QTLs) for leaf rust (LR) and stem rust (SR) resistance identified in this study in a Pamyati Azieva × Paragon mapping population with previously described QTLs and candidate Lr and Sr genes.
#TraitType of Resistance (Race)QTLReference QTLCandidate Genes
1LRTQKHT/APRQLr.ipbb-1A.2QLr.ipbb-1A.1 [51]-
2APRQLr.ipbb-1B.4-Lr21 [47]
3TRTHTQLr.ipbb-3A.2-Lr63, Lr66 [52]
4TQKHTQLr.ipbb-6A.4QLr.ipbb-6A.1 [51]-
5TRTHTQLr.ipbb-6A.5QLr.ipbb-6A.2 [51]-
6TQTMQQLr.ipbb-7B.3QLr.ipbb-7B.1 [51]Lr14 [52]
1SRTKRTF/APRQSr.ipbb-1B.4-Sr31 [53]
2APRQSr.ipbb-1D.1-Sr18 [54]
3RKRTF/APRQSr.ipbb-2A.2-Sr32-2A [53]
4PKCTCQSr.ipbb-2B.3-Sr32-2B, Sr39 [53]
5PKCTCQSr.ipbb-2B.4QSR.IPBB-2B [26]Sr36 [53]
6TKRTFQSr.ipbb-2D.2-Sr32-2D [53]
7TKRTF/APRQSr.ipbb-6B.5QSR.IPBB-6B.1 [27], QSr.ipbb-6B.3 [51]-
8TKRTFQSr.ipbb-6B.7QSR.IPBB-6B.2 [27], QSr.ipbb-6B.4 [51]Sr11 [54]

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Genievskaya, Y.; Abugalieva, S.; Rsaliyev, A.; Yskakova, G.; Turuspekov, Y. QTL Mapping for Seedling and Adult Plant Resistance to Leaf and Stem Rusts in Pamyati Azieva × Paragon Mapping Population of Bread Wheat. Agronomy 2020, 10, 1285. https://doi.org/10.3390/agronomy10091285

AMA Style

Genievskaya Y, Abugalieva S, Rsaliyev A, Yskakova G, Turuspekov Y. QTL Mapping for Seedling and Adult Plant Resistance to Leaf and Stem Rusts in Pamyati Azieva × Paragon Mapping Population of Bread Wheat. Agronomy. 2020; 10(9):1285. https://doi.org/10.3390/agronomy10091285

Chicago/Turabian Style

Genievskaya, Yuliya, Saule Abugalieva, Aralbek Rsaliyev, Gulbahar Yskakova, and Yerlan Turuspekov. 2020. "QTL Mapping for Seedling and Adult Plant Resistance to Leaf and Stem Rusts in Pamyati Azieva × Paragon Mapping Population of Bread Wheat" Agronomy 10, no. 9: 1285. https://doi.org/10.3390/agronomy10091285

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