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Article

Identification of Candidate Genes and Genomic Regions Associated with Adult Plant Resistance to Stripe Rust in Spring Wheat

by
Amira M. I. Mourad
1,2,*,
Mohamed A. Abou-Zeid
3,
Shamseldeen Eltaher
4,
P. Stephen Baenziger
5 and
Andreas Börner
2,*
1
Department of Agronomy, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
2
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, D-06466 Seeland, Germany
3
Wheat Disease Research Department, Plant Pathology Research Institute, ARC, Giza 12619, Egypt
4
Department of Plant Biotechnology, Genetic Engineering and Biotechnology Research Institute (GEBRI), University of Sadat City, Sadat 79, Egypt
5
Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68583, USA
*
Authors to whom correspondence should be addressed.
Agronomy 2021, 11(12), 2585; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11122585
Submission received: 23 October 2021 / Revised: 3 December 2021 / Accepted: 10 December 2021 / Published: 19 December 2021
(This article belongs to the Special Issue Cereal Disease Resistance Genes and Fungal Effectors)
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Abstract

:
Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major disease that damages wheat plants and affects wheat yield all over the world. In recent years, stripe rust became a major problem that affects wheat yield in Egypt. New races appeared and caused breakdowns in the resistant genotypes. To improve resistance in the Egyptian genotypes, new sources of resistance are urgently needed. In the recent research, a set of 95 wheat genotypes collected from 19 countries, including Egypt, were evaluated for their resistance against the Egyptian race(s) of stripe rust under field conditions in the two growing seasons 2018/2019 and 2019/2020. A high genetic variation was found among the tested genotypes. Single marker analysis was conducted using a subset of 71 genotypes and 424 diversity array technology (DArT) markers, well distributed across the genome. Out of the tested markers, 13 stable markers were identified that were significantly associated with resistance in both years (p-value ≤ 0.05). By using the sequence of the DArT markers, the chromosomal position of the significant DArT markers was detected, and nearby gene models were identified. Two markers on chromosomes 5A and 5B were found to be located within gene models functionally annotated with disease resistance in plants. These two markers could be used in marker-assisted selection for stripe rust resistance under Egyptian conditions. Two German genotypes were carrying the targeted allele of all the significant DArT markers associated with stripe rust resistance and could be used to improve resistance under Egyptian conditions.

1. Introduction

Wheat (Triticum aestivum L.) is one of the most important crops all over the world [1,2]. During the life cycle of wheat, plants are attacked by different pathogens, including stripe rust (caused by Puccinia striiformis f. sp. tritici), which is considered a major global threat to wheat production [3,4,5]. Infection by stripe rust pathogens can occur at any time during the wheat growing season, from the one-leaf stage to maturity, resulting in a decreasing number of kernels/head and kernel mass/plant [6]. Applying fungicides can control stripe rust. However, fungicides are expensive, could be ineffective if not applied on time and harm the environment. On the other hand, planting resistant genotypes is a more effective, economical and easy method in controlling stripe rust disease [7,8].
In recent years, new stripe rust races appeared and spread rapidly worldwide; these races are highly aggressive and can survive under high-temperature conditions [9]. These new races broke the resistance in some Egyptian important genotypes such as Misr_2, Giza_168 and Sakha_61 [10]. Due to this situation, a search for new sources of resistance against the Egyptian rust race(s) is urgently required. Due to the low diversity of the genetic background in Egyptian wheat genotypes, the identification of new sources of resistance in worldwide spring wheat genotypes is urgently needed. Identified new sources of resistance could be used in future breeding programs to accelerate stripe rust resistance in Egyptian wheat.
The first step in breeding resistant genotypes is understanding the genetic control of the resistance [11,12]. At present, there are 80 genes identified for resistance to stripe rust in wheat, and more than 300 temporarily designated genes and QTL have been reported [13,14,15]. Furthermore, more than 160 quantitative trait loci (QTLs) have been designated in 49 regions on the 21 wheat chromosomes controlling stripe rust resistance [16,17]. All these resistance genes/QTLs could be classified into two different types: seedling resistance genes/QTLs (also known as all-stage resistance (ASR)) and adult plant resistance genes/QTLs (APR). An ASR gene is effective against a specific race or a small number of races and provides resistance beginning from the seedling growth stage through to the entire growth cycle. On the other hand, APR genes are race non-specific and are expressed only at the adult growth stage [6,7,18]. Identification and mapping of both ASR and APR resistance genes help wheat breeders to combine both resistance types into a single genetic background and hence achieve durable and long-lasting stripe rust protection in cultivars [6,7,8].
Many genetic approaches could be used in mapping resistance genes in plants, such as QTL mapping and genome-wide association study (GWAS). Both methods identify marker alleles associated with the targeted trait. Different types of molecular markers could be used to conduct these analyses, such as simple sequence repeat (SSR), diversity arrays technology (DArT), sequence-tagged sites (STS) or single nucleotide polymorphisms (SNPs). DArT markers were reported as a marker technology that provides high genome coverage, reduces the complexity of the genome and is cost-competitive [19]. This technology has recently gained increasing attention, although it was developed many years ago [20,21,22,23]. It has been used widely in mapping QTLs associated with different traits in plants, including stripe rust resistance in wheat [16,24,25,26,27]. The objectives of this study were to (1) identify the genetic diversity of the Egyptian and other spring wheat genotypes in their resistance to the Egyptian race(s) of stripe rust, (2) understand the genetic control of the resistance against the Egyptian race(s) of stripe rust, (3) identify some DArT markers and genomic regions associated with stripe rust resistance to be used in marker-assisted selection for stripe rust resistance in wheat and (4) select the best resistant genotypes to be used in improving stripe rust resistance under Egyptian conditions in future breeding programs.

2. Materials and Methods

2.1. Plant Materials

In this study, a set of 95 spring bread wheat genotypes were used to understand the genetic control of wheat resistance to the Egyptian race(s) of stripe rust. These genotypes represented new and old cultivars from 19 different countries collected by the USDA-ARS, Aberdeen, ID, USA (Figure S1 and Table S1). Seeds of all the tested genotypes were obtained from the USDA-ARS, Aberdeen, ID, USA, except seeds of the Egyptian genotypes that were obtained from the Egyptian Governorate.

2.2. Evaluation of Stripe Rust Resistance

The tested genotypes were evaluated for their resistance to the Egyptian race(s) of P. striiformis f. sp. tritici at the adult growth stage. The evaluation was conducted under field conditions using natural infection. The experiments were conducted for two years (2018/2019 and 2019/2020) at Sids Agricultural Research Station, Egypt. Both experiments were planted using a randomized complete block design. In each growing season, three replications were planted. Each tested genotype was represented in a single one-meter row with 30 cm row spacing in each replication. Furthermore, the experiment was surrounded by a spreader area planted with a mixture of Morocco and Thatcher, two highly susceptible varieties, to make sure that infection would occur. The spreader area was inoculated artificially with a mixture of urediniospores collected from the Egyptian fields in the previous growing seasons. The infection of the tested genotypes occurred naturally from the surrounding spreaders.
Adult plant resistance was evaluated using two different parameters, disease severity (DS) and infection type (IT) [28]. IT was recorded according to the Stakman et al., (1962) [29] scale (I, R, MR, MS and S), as described by Roelfs et al., (1992) [30]. It was then converted to numbers as described by Abou-Zaid and Mourad 2021 [31]. To calculate the coefficient of infection (CI), DS was multiplied by the converted IT as described by Shewaye, et. al. (2018) and Abouzeid and Mourad (2021) [31,32]. Based on the original IT, the genotype was considered resistant if it had MR, R or immune ITs (0–0.4). However, based on the DS, resistant genotypes should have 10% or less of leaf coverage. Therefore, genotypes that had CI% ≤ 4% were considered as resistant genotypes.

2.3. Statistical Analysis of Stripe Rust Resistance

The analysis of variance (ANOVA) was calculated for the CI values using R software [33], using the following model:
Yijk = µ+ gi+ rj+ yk+ gyik + eijk
where Yijk is an observation of genotype i in replication j which was planted in year k, µ is the general mean; gi, rj, and yk are the main effects of genotypes (fixed effects), replications (random effects) and years (random effects), respectively; gyik is the interaction of the genotypes in the two years; eijk is the error. The broad-sense heritability (H2) was calculated as described in Abou-Zeid and Mourad 2021 [31]

2.4. Genotyping of the Tested Genotypes Using DArT Markers

In the recent study, 71 genotypes of the tested genotypes were genotyped using 424 DArT markers. The DArT markers’ information was a part of a stripe rust resistance study carried out by Maccaferri et. al. (2015) [34] in fields in the U.S.A.. Marker information is available on the website of the U.S. National Plant Genome system (https://npgsweb.ars-grin.gov/gringlobal/search.aspx), (accessed on 9 December 2021). Available DArT markers were used to calculate polymorphic information content (PIC) using PowerMarker software [35], using the following formula:
PIC = 1 − ∑ j = 1 n P ij 2 − ∑ j = 1 n = 1   ∑ k = j + 1 n 2 P ij 2   P ik 2
where Pij and Pik are the frequencies of jth and kth alleles for marker i, respectively.
Besides this, principal component analysis (PCA) was used for the tested genotypes based on the available DArT markers using TASSEL software [36].

2.5. Single Marker Analysis (SMA) of Stripe Rust Adult Plant Resistance

CI data, as well as the available data of the 424 DArT markers of the tested 71 genotypes, were used to perform SMA to identify DArT markers significantly associated with adult resistance in the tested genotypes. Single marker analysis was performed using PowerMarker software V 3.25 [35]. For each significant marker, the phenotypic variation explained by the marker, as well as the allele effect of each marker, was estimated using TASSEL 5.0 software [36].

2.6. Gene Models Underlying Each Significant DArT Marker and Their Annotations

The sequence of the significant DArT markers was obtained from Diversity Array Technology, available at https://www.diversityarrays.com/technology-and-resources/sequences. The sequences of these markers were blasted against the wheat genome using the EnsemblPlants database available at this link: https://plants.ensembl.org/Triticum_aestivum/Tools/Blast. The best blasts were detected based on the chromosomal location of the DArT marker, the highest length of the blast, the highest percentage of identity (ID%) and the lowest p-value. In order to compare the chromosomal position of the best blast and the different available gene models with the highest confidence, possible gene models controlling the resistance were identified. The distance between the significant markers and high-confidence gene models was visualized using MapChart software [37]. Using the international wheat genome sequencing consortium (IWGSC), the functional annotation of these gene models was investigated and their association with stripe rust resistance was examined. Furthermore, the expression of the identified gene models was compared under controlled conditions with disease conditions at adult plant stages using the Wheat Expression Browser “http://www.wheat-expression.com/”, (accessed on 9 December 2021).

3. Results

3.1. Phenotypic Variation for Stripe Rust Resistance in the Tested Genotypes

Highly significant differences were identified among the tested genotypes for their resistance against the Egyptian race(s) of stripe rust pathogen, based on the analysis of variance (ANOVA). Highly significant differences were found between the years. The genotype x years interaction was highly significant, while no significant differences were found among the replications. Broad-sense heritability was 0.63 across the two years (Table 1). A highly significant correlation was found between the two years (r = 0.61, p-value > 0.01 (Figure S2).
The percentage of the coefficient of infection (CI) ranged from 0% to 100% in both years (2018/2019 and 2019/2020) (Figure 1). The susceptible check “Morocco” showed a very susceptible response to stripe rust, with a CI% of 100% and 93.33% in 2018/2019 and 2019/2020, respectively. Also, the susceptible check “Thatcher” had a CI% of 100% in 2018/2019 but was absent in 2019/2020. The number of susceptible genotypes (with a percentage of CI 50% or more) was higher in 2018/2019 than in 2019/2020, with a total number of 43 and 19 in 2018/2019 and 2019/2020, respectively. Out of the 19 genotypes that were susceptible in 2019/2020, 18 were susceptible in both 2018/2019 and 2019/2020 (Figure 2a and Table S2). On the other hand, 22 and 20 genotypes were resistant to stripe rust (CI ≤ 4%) in 2018/2019 and 2019/2020, respectively. Only ten genotypes had the same resistant reaction to stripe rust in both years (Figure 2b). These resistant genotypes are from five different countries: Egypt (three genotypes), Germany (two genotypes), Saudi Arabia (one genotype), the United Kingdom (one genotype) and the U.S.A (three genotypes). (Table S3).

3.2. Marker Distribution and Principal Coordinate Analysis

A total number of 424 polymorphic DArT markers were available for 71 genotypes. Out of these markers, 382 (90.09%) had a known chromosomal position, and they covered the whole wheat genome. The B genome was found to carry the highest number of markers, with a percentage of 45% (171 markers). The lowest number, on the other hand, was located on genome D, with a percentage of 17% (66 markers). There were 42 markers located on unknown chromosomal positions with a percentage of 10%. The total number of markers/chromosomes ranged from one marker on chromosome 4D to 38 makers on chromosome 3B (Figure S3a). The PIC value ranged from 0.1 (four markers) to 0.5 (30 markers) across the chromosomes. The majority of markers showed 0.4 of PIC value with a total number of 142 markers (Figure S3b).
The result of principle-component analysis (PCA) classified the tested genotypes into two groups, with a size of nine and 86 genotypes for groups 1 and 2, respectively. The common ten resistant genotypes are located in the second group, while common susceptible genotypes were distributed between both groups with the number of four and 14 genotypes in group 1 and group 2, respectively (Figure 3).

3.3. Single Marker Analysis (SMA) of Adult Plant Resistance to Stripe Rust

Single marker analysis identified 39 and 48 DArT markers significantly associated with stripe rust resistance (p-value ≤ 0.05) in 2018/2019 and 2019/2020, respectively (Figure 4, Tables S4 and S5). Out of these identified markers, 13 markers were stable and significantly associated with the resistance in both years. These stable markers were each located on chromosomes 2A, 2B, 3B, 3D, 5A, 5B and 6D, and two markers were located on chromosomes 6B, 7A and 7D, with the p-value ranging from 0.000 to 0.037 in 2018/2019 and from 0.000 to 0.048 in 2019/2020 (Table 2 and Figure 4b). The phenotypic variation explained by these markers (R2) ranged from 4.38% (WPT-743380) to 17.21% (WPT-9088) in 2018/2019 and from 0.09% (WPT-5736) to 20.30% (WPT-9589) in 2019/2020. Marker “WPT-9088” had the highest allelic effect, increasing stripe rust resistance by a percentage of 36.09% and 27.63% in 2018/2019 and 2019/2020, respectively. Meanwhile, marker “WPT-9589” had the highest allelic effect associated with decreasing resistance, with a value of 30.85% and 32.14% in 2018/2019 and 2019/2020, respectively.

3.4. Validation of the Identified Genomic Regions Associated with Stripe Rust Resistance

To validate the association between the significant markers and stripe rust resistance, the sequence of the significant DArT markers was obtained from the Diversity Array Technology website for all the 13 significant stable markers expect WPT-3976 (2A) and RPT-7068 (6D), which did not have available sequence information. These sequences were blasted against the wheat genome using the EnsemblPlants database. Based on the chromosomal location of the DArT markers, the length of the marker and the identified sequence, the highest percentage of identity (ID%) and the lowest p-value, the possible positions of the eleven significant DArT markers were detected (Table 3). Furthermore, gene models with high confidence located near or within the detected position were investigated. Out of the eleven markers, four markers (WPT-9088 (3B), WPT-0485 (3D), WPT-9589 (6B), and WPT-4835 (7A)) were located neither near nor within high-confidence gene models (Table 3). Five markers were located very near to high-confidence gene models (WPT-5736 (2B), WPT-9256 (6B), WPT-6447 (7A), WPT-743380 (7D) and WPT-744976 (7D)). Two markers were located within high-confidence gene models WPT-9094 on chromosome 5A and WPT-9724 on chromosome 5B (Figure 5). The functional annotation of all identified gene models has a strong relation to disease resistance in plants (Table 3).
In the remaining part of our manuscript, we focused only on the significant markers harboring high-confidence gene models. As a result, two markers–WPT-9094 (susceptible effect, 5A), and WPT-9724 (resistant effect, 5B)–were taken into account. The presence of these two markers and their coefficient of infection were investigated in the resistant and susceptible genotypes. The percentage of agreement between the presence of each marker and the expected reaction based on the marker effect was calculated in each group of genotypes (Figure 6). A high percentage of agreement was found for both markers in the resistant genotypes, with a percentage of 85.7%. Meanwhile, a lower percentage of agreement for both markers was found in the susceptible genotypes, with a percentage of 53.33% and 60.00% for WPT-9094 and WPT-9724, respectively.
Furthermore, the expression of the two identified gene models harboring significant markers was investigated using the Wheat-Expression database. Higher expression of both genes was found at the reproductive growth stage for both genes under disease conditions compared with the controls. However, the same expression of the TraesCS5A02G327200 gene model was found under both controlled and disease conditions at the vegetative growth stage. Besides this, very little expression was found for the TraesC25B02G020900 gene model under disease conditions with a value of 0.011 tpm compared with 0.00 tpm under controlled conditions (Figure 7).

3.5. Selection of Superior Genotypes for Stripe Rust Resistance in the Studied Materials

To genetically emphasize resistance in the common resistant genotypes listed in Table S3, the number of targeted alleles of the 13 DArT markers associated with the resistance was investigated in each genotype (Figure 8a). Unfortunately, no available DArT marker data were available for the three highly resistant Egyptian genotypes. The two German resistant genotypes (PI_313101 and PI_343730) contained all the targeted alleles for the 13-significant DArT markers. The Saudi Arabian genotype PI_574346 had the lowest number of targeted alleles (seven alleles). The number of targeted alleles in the genotypes from the U.K. and the U.S. ranged from eight to 12 alleles.
The genetic distance between each pair of the resistant genotypes was calculated to confirm their suitability to be crossed for improving stripe rust resistance (Figure 8b and Table S6). The genetic distance ranged from 0.20 between the two German genotypes (PI_313101 and PI_343730) to 0.74 between the German genotype PI_313101 and the American genotype PI_599988. The Saudi Arabian genotype PI_574346 was genetically near to the two American genotypes PI_620714 and PI_599988, while the third American genotype PI_595213 was found to have a small genetic distance from the English genotype PI_339818.

4. Discussion

The appearance of new stripe rust races has led to a lack of resistance in many Egyptian wheat genotypes. This situation threatens wheat production in Egypt, North Africa and the Middle East. As a result, new sources are urgently needed to improve resistance in the Egyptian wheat germplasm. The evaluation of the recent panel that represents new and old cultivars of spring wheat from different 19 countries will enable the identification of the possible source of resistance genes and enhance the future of wheat breeding in the Middle East.

4.1. Genetic Variation of Stripe Rust Resistance in the Tested Genotypes

The high percentage of CI% (more than 93.3%) in both susceptible checks–Morocco and Theater–indicated that the natural infection occurred sufficiently in both years and that our evaluation is accurate. Highly significant differences were found amo ng the tested genotypes in their adult plant resistance to the Egyptian races of stripe rust which indicated the success of our evaluation and the high genetic variation that existed in our tested materials (Table 1). The percentage of CI ranged from 0–100% in both years (2018/2019–2019/2020), confirming the high genetic variation. The presence of high genetic variation among the tested genotypes is very useful in selecting the resistant genotypes against the Egyptian stripe rust race(s). Highly significant differences were found between the years, and the genotypes x years interaction indicated the different responses of the genotypes in 2018/2019 and 2019/2020. The high degree of broad-sense heritability (H2B = 0.63) indicated that most of the phenotypic variation in stripe rust resistance is due to genotypic variation. This concluded that the phenotypic variation is stable, and that the selection of highly resistant genotypes will be successful in future breeding programs. The presence of a highly significant correlation between the two years (r = 0.61, p-value <0.01) could serve to increase the resistance in the different years under the Egyptian environmental conditions, as well as suggesting that selection in one year could be a good predictor of the next year.
Out of the tested genotypes, ten genotypes were resistant to stripe rust in both years, with a percentage of CI of 4% or less. These genotypes are cultivars from five different countries: Saudi Arabia (one genotype), the United Kingdom (one genotype), Germany (two genotypes), Egypt (three genotypes) and the United States (three genotypes) (Table S2). The three Egyptian genotypes (Misr-2, Sids-14 and Shandweel-1) were previously reported to be resistant to the Egyptian race(s) of stripe rust, indicating the reliability of our dataset [38,39,40]. However, a CI% of 4% was found in Sids-14 in the 2019/2020 growing season, indicating the beginning of the overcoming of resistance in these genotypes. As a result, there is an urgent need to improve resistance in the Egyptian genotypes. Understanding the genetic control of resistance in these three genotypes will enable wheat breeders to improve resistance by increasing genes, controlling resistance against the Egyptian races. Genotypes from the remaining four countries (Germany, Saudi Arabia, the United Kingdom and the U.S.A) could be used to improve resistance in the Egyptian germplasm if they contain different genetic systems that control the resistance. Moreover, integrating exotic resistant genotypes will help to increase genetic diversity, which could be highly useful not only for stripe rust resistance but also for other agronomic features.

4.2. Genetic Analysis of Adult Plant Resistance to Stripe Rust in the Tested Genotypes

To effectively utilize the resistance presented in the recently tested genotypes, it is important to understand the genetic characterization of stripe rust resistance. In the recent study, 424-DArT markers of 71 genotypes were used in the genetic analysis to identify the genetic control of the stripe rust adult plant resistance. DArT markers have been reported as a cost-effective and high-throughput marker system that has been used to characterize different agronomic traits, as well as biotic and abiotic stress resistance in wheat using association mapping (AM) [24,41,42,43,44,45,46]. The AM relies mainly on testing the association between a single marker and a phenotypic trait. This could be done using different kinds of analysis, such as genome-wide association study (GWAS), which requires more than 100 genotypes to be efficient [47], and SMA, which could be done using any number of genotypes. In the recent study, the number of genotypes (71) is appropriate for SMA analysis [48]. Furthermore, studied DArT markers are well distributed across the three hexaploid wheat genomes (A, B, and D), with the highest number of markers on genome B and the least number on genome D (Figure S3a). Previous studies concluded that genome B is the most diverse wheat genome, while genome D is the least diverse genome [49,50,51,52,53,54,55,56]. The average PIC value of the studied marker is 0.35, which is quite similar to previous studies [50,56,57]. This PIC value was reported as a good indicator of informative markers [58]. Based on the good distribution of the DArT markers and the appropriate PIC value, we can conclude that this set of DArT markers is highly appropriate for AM studies.
The PCA classified the tested genotypes into two groups. The resistant genotypes were located in the same group, which concluded the genetic similarity of the ten resistance genotypes despite their different origin. This could be illustrated by the gene flow of the different genotypes among the different regions. The high genetic similarity of the ten resistant genotypes concluded that improving stripe rust resistance in the Egyptian wheat background could be done using this set of genotypes. As a result, we can conclude that the studied panel is a highly diverse one, with a minimum effect of population structure on it, which makes this panel highly appropriate for AM [59].

4.3. Identification of Genomic Regions Associated with Stripe Rust Adult Plant Resistance

SMA identified a set of 13 stable DArT markers associated with stripe rust resistance in both years (Figure 4 and Table 2). Based on the chromosomal location of the identified DArT markers, there are several genomic regions distributed on ten different chromosomes controlling the resistance. Previous studies concluded that QTLs that explain less than 10% of the phenotypic variation are minor QTLs, while major QTLs explain more than 10% of the phenotypic variation [60,61,62]. In our study, the identified QTLs/genomic regions could be classified into minor QTLs (seven in 2018/2019 and four in 2019/2020) and major QTLs (six in 2018/2019 and nine in 2019/2020). The seven genomic regions which differ from minor to major effects in both years confirm the significant interaction between the genotypes and the years (GxY), as the response of the same genotype differed from year to year. Based on the allelic effect of the identified markers, six markers were found to be significantly associated with increasing the susceptibility of the genotypes to stripe rust, while the remaining seven markers were associated with increasing the resistance (Table 2). The identified significant markers were distributed across the wheat genome, which indicates the presence of many QTLs controlling resistance across the wheat genome. Previous studies concluded that there are many QTLs controlling stripe rust resistance in wheat, and that these QTLs are distributed across the wheat genome, which confirmed our results [31,63,64,65,66,67,68,69].
To further investigate the genetic association between the significant markers and stripe rust resistance, genomic regions carrying the sequence of the significant DArT markers were investigated. The available sequences were blasted against the wheat genome using the EnsemblPlants database to determine the exact position of these significant markers. EnsemblPlants was recorded as an integrative approach that provides the useful genome-sequence information of several plant species, including wheat [70]. The result of the sequence blasts in the EnsemblPlants database should be taken carefully, and filtration should be done based on the percentage of identity, p-value and blast length [70]. In our recent study, the high percentage of identity (ID%) that ranged from 85.5 to 100%, the highest blast length and low p-values indicated that the results obtained in this study could be trusted. Out of the 13 significant DArT markers, five were located near high-confidence gene models annotated to be related with disease resistance in plants, and two markers (WPT-9094 (5A), and WPT-9724 (5B)) were located within high-confidence gene models based on the IWGSC v2.0 database. The ID% of these two markers were very high, with a percentage of 98.4 and 99.8% for each marker, respectively. In addition, the blast lengths of both markers were very high, indicating that the identified position is the exact position of the targeted markers.
The DArT marker “WPT-9094” on chromosome 5A was found to be overlapping TraesCS5A02G327200 gene model with a size of 230bp, indicating that this marker is a part of the identified gene model. The functional annotation of this gene model was associated with 5′-3′ exoribonuclease 3. The putative function of this protein is the exosome complex exonuclease RRP46 (Ribosomal RNA-processing protein 46), which is a protein involved in rRNA processing and a component of the exosome 3′-->5′ exonuclease complex. Recent studies concluded that some ribosomal genes are involved in plant defense and cell death [71,72,73]. Therefore, the abundance of ribosomal RNA and protein modifies the plant’s metabolism, destroys the plant’s defense system and facilitates cell-death development upon the pathogen attack [74]. The functional annotation of this gene model is in agreement with the allele effect of the associated DArT marker, which confirms our results. Furthermore, the WPT-9094 DArT marker was mapped on the position 58.3 cM on the long arm of chromosome 5A, and was surrounding two QTLs controlling stripe rust adult plant resistance [66]. Two stripe rust resistance genes were previously mapped on 5AL, Yr48 and Yr34 [69]. However, comparing the position of their linked markers (WPT-7061 and KASP markers) and the position of WPT-9094, they were found to be far from each other and harboring different gene models (TraesCS5A02G558500, for WPT-7061 marker). In addition, previous studies identified major stable QTLs on the long arm of chromosome 5A (220–226 cm), controlling stripe rust adult plant resistance [75]. Based on this study, the identified gene model is far away from previously reported gene models associated with stripe rust resistance. Comparing our results with those of previous studies, we can conclude that the gene model identified by the WPT-9094 marker could be a novel gene associated with stripe rust adult plant resistance. The higher percentage of agreement between the absence of the significant DArT marker in the resistance genotypes (85.7%) and lower agreement with the presence of this marker in the susceptible genotypes concluded that the WPT-9094 marker could be used for MAS for stipe rust resistance under Egyptian conditions. More studies should be conducted to provide more information about the gene model and the identified DArT marker.
The sequence of the DArT marker “WPT-9724” on chromosome 5B was found to be the same sequence as that of the gene model TraesCS5B02G020900. This gene model was found to be functionally annotated as Glutathione S-transferase. This enzyme plays an important role in plant-pathogen interaction, which increases plant resistance against pathogens by the detoxification of toxic lipid hydroperoxides that accumulate during infections and induce a systemic resistance response (ISR) to subsequent pathogen infections [76]. The allele effect of the DArT marker was found to increase resistance and reduce stripe rust symptoms, which confirms the function annotation of the linked gene model. The DArT marker “WPT-9724” has been mapped on the long arm of the 5B chromosome with the position of 199.7 cM [77]. It was previously reported to be associated with stem rust resistance in wheat [78]. This DArT marker was not associated with stripe rust resistance under U.S. conditions based on Maccaferri et al.’s (2015a) QTL map, which concluded that stripe rust races are different in the U.S.A. from those that exist in Egypt, and both have two different genetic systems controlling their resistance in wheat. Some stripe rust resistance genes such as Yr3, in addition to many QTLs controlling stripe rust resistance, were mapped on chromosome 5B, which supported our results. However, no information is available about the effectiveness of the Yr3 resistance gene against the Egyptian race(s). Based on our result, the gene model TraesCS5B02G020900 seems to be a novel stripe rust resistance gene that could be markedly selected using the DArT marker WPT-9724. The high agreement between the presence of this DArT marker in the resistant genotypes (85.7%) and the absence of the marker in the susceptible genotypes (60%) could be explained as the resistant alleles masking other resistance alleles, although when they are present, other resistant alleles could be involved. However, these results concluded that more caution should be taken when using this DArT marker in MAS for stripe rust resistance in wheat. More studies are needed to provide more information about the association between the DArT marker and the identified gene model.

4.4. Selection of Superior Genotypes to Stripe Rust Resistance in the Currently Studied Genotypes

To confirm the possibility of improving stripe rust resistance against the Egyptian race(s) using the currently studied materials, the number of the targeted alleles of the common significant DArT markers was investigated in each one of the high resistance genotypes. Unfortunately, no DArT markers were available for the three Egyptian resistant genotypes. As a result, we could not confirm the possibility of improving resistance in the current Egyptian genotypes. However, we would be able to select the best genotypes to be crossed together in order to pyramid many resistance genes against the Egyptian race(s). Genotypes with as many as pyramid resistance genes could be used directly as an Egyptian cultivar if they are highly adapted to the Egyptian environment, or could be crossed with the Egyptian genotypes after confirming that they carry different resistance alleles. The presence of different targeted alleles/genotypes confirms that resistance against the Egyptian race(s) of stripe rust could be improved using the current materials. The two German genotypes seem to be the best genotypes to improve resistance against the Egyptian race, as they carry all the targeted alleles of the thirteen DArT markers.
The genetic distance between the two German genotypes and the five remaining genotypes was high and ranged from 0.56 to 0.74. Previous studies concluded that crossing between genotypes with a high genetic distance produces lines with a high combining ability and distinct alleles controlling the trait [79]. Therefore, crossing between any of these two German genotypes and the remaining five genotypes will be useful. In addition, the genetic distance between each pair of the remaining five genotypes was high, confirming the successful crossing to pyramid many resistant genes in one genotype.

5. Conclusions

In conclusion, the high genetic variation in the 95 genotypes tested for their resistance against the Egyptian race(s) of stripe rust indicates that stripe rust-resistant genotypes could be selected from the studied materials. A set of thirteen DArT markers significantly associated with the resistance were identified. Out of these significant markers, two markers, WPT-9094 on chromosome 5A and WPT-9724 on chromosome 5B, were located within gene models functionally annotated to be associated with disease resistance in the plant. These two markers could be used in MAS for stripe rust resistance in wheat. The two gene models seem to be novel genes controlling resistance, because to our knowledge their locations do not match the location of any genes previously associated with stripe rust resistance. The two highly resistant German genotypes could be used to improve resistance against the Egyptian race(s) of stripe rust, as they contain all the targeted alleles of the significant DArT markers and are genetically highly distant from the remaining resistant genotypes; thus crossing will be successful.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/agronomy11122585/s1, Figure S1: Histogram represents the number of genotypes/country of the 95-spring wheat genotypes used in the current study, Figure S2: Correlation between the coefficient of infection (CI%) in 2018/2019 and 2019/2020, Figure S3: Histogram represents the distribution of the 424-DArT markers in the spring wheat tested genotypes (a) and the distribution of polymorphic information content (PIC) values of the tested markers, Histogram represents the distribution of polymorphic information content (PIC) values of the tested markers (b). Table S1: List of the 95-spring wheat genotypes used in the current study, their country of origin, and their response to the stripe rust infection (disease severity and infection type) in the two growing seasons. Table S2: Common susceptible genotypes (50% or more) in both years. Table S3: Common resistant genotypes (4% or less) in both years. Table S4: Significant DArT markers for stripe rust resistance in the season of 2018/2019, Table S5: Significant DArT markers for stripe rust resistance in the season of 2019/2020, Table S6: The genetic distance between each pair of the high resistance genotypes base on the 424 DArT markers.

Author Contributions

A.M.I.M. design the experiment, performed the genetic and phenotyping analysis, discussed the results, and drafted the manuscript. M.A.A.-Z. performed the phenotyping for stripe rust resistance. S.E. helped in data analysis. P.S.B. reviewed the manuscript. A.B. reviewed the manuscript. The authors agreed to be accountable for the content of the work. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially funded by the cultural affairs and mission sector, the Egyptian government. The publication of this article was funded by the Open Access Fund of the Leibniz Association.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available as this manuscript is part of an ongoing study.

Acknowledgments

The authors would like to thank Sid’s station team of wheat breeding for helping in conducting the experiment.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The response of the 95-tested genotypes to stripe rust at the adult plant stage under the Egyptian conditions at 2018/2019 and 2019/2020.
Figure 1. The response of the 95-tested genotypes to stripe rust at the adult plant stage under the Egyptian conditions at 2018/2019 and 2019/2020.
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Figure 2. The Venn diagram represents the number of genotypes with the same response in the two evaluation years (2018/2019 and 2019/2020). (a) number of susceptible genotypes with a percentage of coefficient of infection (CI%) of 50% or more, (b) number of resistant genotypes with a CI% of 4% or less.
Figure 2. The Venn diagram represents the number of genotypes with the same response in the two evaluation years (2018/2019 and 2019/2020). (a) number of susceptible genotypes with a percentage of coefficient of infection (CI%) of 50% or more, (b) number of resistant genotypes with a CI% of 4% or less.
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Figure 3. Principle coordinate analysis of the studied spring wheat genotypes based on the 424 DArT markers.
Figure 3. Principle coordinate analysis of the studied spring wheat genotypes based on the 424 DArT markers.
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Figure 4. Single marker analysis for stripe rust resistance in the tested genotypes: (a) the number of significant markers in each year, (b) Distribution of the chromosomal locations of the significant markers across the 21 wheat chromosomes.
Figure 4. Single marker analysis for stripe rust resistance in the tested genotypes: (a) the number of significant markers in each year, (b) Distribution of the chromosomal locations of the significant markers across the 21 wheat chromosomes.
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Figure 5. The chromosomal position of the significant DArT markers identified on chromosome 5A (a) and chromosome 5B (b), the identified gene models that harboring these markers in bp and their functional annotation based on the international wheat genome sequencing consortium (IWGSC V.2).
Figure 5. The chromosomal position of the significant DArT markers identified on chromosome 5A (a) and chromosome 5B (b), the identified gene models that harboring these markers in bp and their functional annotation based on the international wheat genome sequencing consortium (IWGSC V.2).
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Figure 6. Percentage of agreement between the phenotypic reaction (resistant and susceptible) of the tested genotypes and the presence of the significant DArT markers in the genome of the tested genotypes.
Figure 6. Percentage of agreement between the phenotypic reaction (resistant and susceptible) of the tested genotypes and the presence of the significant DArT markers in the genome of the tested genotypes.
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Figure 7. The expression of the two gene models harboring significant DArT markers in transcripts per million (TPM). Blue and orange columns represent the expression of the gene models at the reproductive growth stage under controlled and disease conditions, respectively, while grey and yellow columns represent the expression of the two models at the vegetative growth stage under the same conditions.
Figure 7. The expression of the two gene models harboring significant DArT markers in transcripts per million (TPM). Blue and orange columns represent the expression of the gene models at the reproductive growth stage under controlled and disease conditions, respectively, while grey and yellow columns represent the expression of the two models at the vegetative growth stage under the same conditions.
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Figure 8. (a) Number of targeted alleles of DArT markers significantly associated with stripe rust resistance in the highly resistant genotypes (with a coefficient of infection of 4% or less), (b) The genetic distance between each pair of the high resistance genotypes with a coefficient of infection of 4% or less.
Figure 8. (a) Number of targeted alleles of DArT markers significantly associated with stripe rust resistance in the highly resistant genotypes (with a coefficient of infection of 4% or less), (b) The genetic distance between each pair of the high resistance genotypes with a coefficient of infection of 4% or less.
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Table
Table 1. Analysis of variance for stripe rust resistance in the 95-spring wheat genotypes.
Table 1. Analysis of variance for stripe rust resistance in the 95-spring wheat genotypes.
Sourced.fM.S.
Years (Y)127122.56 **
Replications (R)21839.41
Genotypes (G)863799.96 **
GY851402.18 **
GYR289941.92
Heritability63.10
** p < 0.01.
Table
Table 2. Significant stable DArT markers for stripe rust resistance across the tested years (2018/2019 and 2019/2020) based on single marker analysis (p-value 0.05).
Table 2. Significant stable DArT markers for stripe rust resistance across the tested years (2018/2019 and 2019/2020) based on single marker analysis (p-value 0.05).
DArT MarkerChromosomep-ValueAllele Effect (%)R2 (%)
201920202019202020192020
WPT-39762A0.0150.044−24.36−19.2111.559.80
WPT-57362B0.0370.0481.6613.894.550.09
WPT-90883B0.0000.002−36.09−27.6317.2115.00
WPT-04853D0.0050.027−23.89−20.1410.5010.24
WPT-90945A0.0100.00419.0224.987.5816.81
WPT-97245B0.0300.046−16.66−13.816.385.90
WPT-92566B0.0330.001−17.84−26.546.9519.42
WPT-95890.0010.00030.8532.1413.3820.30
RPT-70686D0.0160.01320.3822.048.3713.42
WPT-48357A0.0090.023−24.21−16.9913.969.79
WPT-64470.0020.002−25.38−24.0514.9617.55
WPT-7433807D0.0090.01519.1013.374.3810.98
WPT-7449760.0080.01815.2619.115.5910.71
Table
Table 3. Possible chromosomal location of the 11 significant DArT markers, gene models that are located within or near the DArT markers and their functional annotation based on the PlantEnsemble database.
Table 3. Possible chromosomal location of the 11 significant DArT markers, gene models that are located within or near the DArT markers and their functional annotation based on the PlantEnsemble database.
MarkerChro.Marker Length (bp)Blast Length (bp)Blast PositionID%p-ValueGene ModelGene Position (bp)Gene Annotation
WPT-57362B49434798127191-79812726997.10%6.1 × 10−6TraesCS2B02G625200798128178-798132084UniProtKB-W5B9H0
TraesCS2B02G625300798134799-798139436E3 ubiquitin-protein ligase
WPT-90883B51851047651816-47652325100%0NANANA
WPT-04853D376376605639199-60563957498.90NANANA
WPT-90945A518516536984272-53698478798.40%0TraesCS5A02G327200536984557-5369894015′-3′ exoribonuclease 3
WPT-97245B57357320040488-2004106099.8%0TraesCS5B02G02090020040488-20041060Glutathione S-transferase
WPT-92566B748143711392100-71139224297.21.20 × 10−63TraesCS6B02G452700711372963-711375917Hepatocellular carcinoma-associated antigen 59 family protein, expressed
WPT-9589 572101715201767-715201867961.1 × 10−38NANANA
WPT-64477A568120691517984-69151811485.55.30 × 10−19TraesCS7A02G502500691521202-691522644Eukaryotic aspartyl protease family protein
WPT-4835574120691517990-69151810986.72.2 × 10−21NANANA
WPT-7433807D8697918220754-822154499.90TraesCS7D02G0184008223605-8224570NBS-LRR disease resistance protein-like protein
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Mourad, A.M.I.; Abou-Zeid, M.A.; Eltaher, S.; Baenziger, P.S.; Börner, A. Identification of Candidate Genes and Genomic Regions Associated with Adult Plant Resistance to Stripe Rust in Spring Wheat. Agronomy 2021, 11, 2585. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11122585

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Mourad AMI, Abou-Zeid MA, Eltaher S, Baenziger PS, Börner A. Identification of Candidate Genes and Genomic Regions Associated with Adult Plant Resistance to Stripe Rust in Spring Wheat. Agronomy. 2021; 11(12):2585. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11122585

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Mourad, Amira M. I., Mohamed A. Abou-Zeid, Shamseldeen Eltaher, P. Stephen Baenziger, and Andreas Börner. 2021. "Identification of Candidate Genes and Genomic Regions Associated with Adult Plant Resistance to Stripe Rust in Spring Wheat" Agronomy 11, no. 12: 2585. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11122585

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