Evaluation of a global spring wheat panel for stripe rust: Resistance loci validation and novel resources identification.

Stripe rust (incited by Puccinia striiformis f. sp. tritici) is airborne wheat (Triticum aestivum L.) disease with dynamic virulence evolution. Thus, anticipatory and continued screening in hotspot regions is crucial to identify new pathotypes and integrate new resistance resources to prevent potential disease epidemics. A global wheat panel consisting of 882 landraces and 912 improved accessions was evaluated in two locations in Egypt during 2016 and 2017. Five prevalent and aggressive pathotypes of stripe rust were used to inoculate the accessions during the two growing seasons and two locations under field conditions. The objectives were to evaluate the panel for stripe rust resistance at the adult plant stage, identify potentially novel QTLs associated with stripe rust resistance, and validate previously reported stripe rust QTLs under the Egyptian conditions. The results indicated that 42 landraces and 140 improved accessions were resistant to stripe rust. Moreover, 24 SNPs were associated with stripe rust resistance and were within 18 wheat functional genes. Four of these genes were involved in several plant defense mechanisms. The number of favorable alleles, based upon the associated SNPs, was significant and negatively correlated with stripe rust resistance score, i.e., as the number of resistances alleles increased the observed resistance increased. In conclusion, generating new stripe rust phenotypic information on this panel while using the publicly available molecular marker data, contributed to identifying potentially novel QTLs associated with stripe rust and validated 17 of the previously reported QTLs in one of the global hotspots for stripe rust.

Introduction

Wheat (Triticum aestivum L.) stripe (syn. yellow) rust caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating wheat diseases in the world. Stripe rust can cause yield losses from 10 to 100% [1]. Utilization of rust-resistant genotypes is the most economical and environmentally sound approach to reduce stripe rust damage, as it protects grain yield and reduces the need for fungicides [2]. Global efforts dedicated to identifying stripe rust-resistance genes, resulted in 74 officially designated and more than 40 temporarily named stripe rust-resistant genes [2-4]. Most of the identified stripe rust resistance genes (Yr)are race-specific resistance genes [5] and thus vulnerable to the evolving Yr pathotypes. Stripe rust evolves new pathotypes quickly through mutation and somatic hybridization [6], and because it is airborne, races can migrate to other regions and become regionally or globally predominant [7]. For this reason, pathotype non-specific resistance is considered more durable and effective against many stripe rust pathotypes. For example, Yr18 has remained durable and effective against stripe rust for 50 years [8]. Therefore, breeding durable stripe rust-resistant genotypes remains one of the key objectives for wheat pathologists and breeders.

Identifying adult plant stripe resistant genotypes is often done in the field due to the limitations of growing adult plants in controlled environments. However, screening under the field conditions requires the presence of the pathogen and conducive conditions for infection [9]. Also, field disease screening is affected by the seasonal variability of temperature and precipitation, which in return affect the susceptibility of the host [10]. Therefore, it is often desirable to artificially inoculate the host plant under field conditions to assure the presence of common races in the region [11]. To evaluate plant materials for new stripe rust pathotype(s) that is not naturally present in a breeder`s environment; artificial inoculation is not acceptable as it will release new pathotypes into the environment. In this case, evaluation should be undertaken at the rust hotspots, e.g., where the new pathotypes naturally occur [1]. For example, the worldwide wheat collections were screened for stripe rust resistance in Pakistan [12] and India [13], both of which are considered hotspot regions for this disease.

Field evaluation is expensive, time-consuming and, as mentioned earlier, highly affected by environmental conditions. The advent of relatively inexpensive, high throughput molecular marker platforms makes the marker-assisted selection (MAS) a viable approach to tracking resistance genes. In MAS, DNA molecular markers are used to select for desirable traits based on previous knowledge of the association between a specific marker and that trait. Therefore, establishing a marker-trait relationship is the first step in developing MAS protocols for any given trait. The two requirements to build the marker-trait association are to have accurate phenotypic information and reliable marker data. Furthermore, establishing accurate marker-trait associations require large populations to obtain a higher power by increasing the recombination frequency and the frequency of rare alleles [14]. Maccaferri et al. (2015) [15] evaluated 1000 spring wheat accessions using four stripe rust pathotypes. They were able to identify 97 SNP linked with stripe rust resistance. Muleta et al. (2017) [16] evaluated 1,163 spring wheat accessions for stripe rust resistance and were able to identify 11 and 7 genomic regions in significant associations with stripe rust resistance at the adult and seedling stages, respectively. Kertho et al. (2015) [17] evaluated 567 winter wheat accessions for the stripe and leaf rust (Puccinia triticina Eriks) resistance and identified 65 and eight significant markers associated with leaf rust and stripe rust, respectively. These studies identified markers or QTLs associated with stripe rust resistance. Such markers, after validation, will be useful in breeding resistant genotypes, which will save time and resources [18].

Currently, accurate phenotyping has become the major bottleneck and funding constraint of MAS applications [14]. Thus, we focused our efforts and resources on conducting extensive phenotyping of global wheat collection under the field conditions in Egypt. The five prevalent and aggressive pathotypes in Egypt during the last five years of stripe rust are 0E0, 6E4, 70E20, 128E28, and 134E244 [19]. Several of the most important wheat cultivars such as “Misr2”, “Giza168” and “Sakha 61” known to be resistant to the previous five most important pathotypes, recently have become susceptible under the field conditions in Egypt. During the last 10 years, stripe rust races have evolved in Egypt and became more prevalent and aggressive. For example, infections of stripe rust were observed on several lines with stripe rust resistance genes, i.e., Yr1, Yr17 and Yr32 in northern Egypt during 2012 [20]. Aggressive virulent pathotypes to Yr27 were detected on Yr27 resistant lines such as Yr27/6*Avocet S, and Ciano 97. Additionally, during 2015, the warrior pathotype [21,22] (virulent on Yr1, Yr2, Yr3, Yr4, Yr6, Yr7, Yr9, Yr17, Yr25, Yr32, and YrSp) was detected.

Wheat accessions used in this study were obtained from several geographic regions and included landraces, experimental lines, and cultivars. Most of these lines have not been tested for stripe rust resistance in Egypt, one of the world hotspots for stripe rust [23]. Thus, testing this large number of accessions in Egypt should add an important regional perspective to the previous studies that were conducted using the same plant materials in other regions. Furthermore, in the current study, we used the same SNP markers platform (9K SNP) that was used in recent stripe rust studies [15,16,17]. Thus, phenotypic information generated from the current study coupled with the genotypic data can be used to identify new or environmental specific (local) QTLs and to validate recently reported stripe rust QTLs under the Egyptian environmental conditions. The objectives of this study were to 1- Evaluate a comprehensive spring wheat collection for stripe rust resistance during the adult plant growth stage to identify new resistant genotypes, 2- Identify potential QTLs associated with stripe resistance, and 3- Validate previously identified stripe rust associated QTLs or identify new QTLs under the Egyptian field conditions.

Materials and methods

Ethics statement

The fields used in the current study located within commercial wheat production regions in Egypt. Additionally, yellow rust races used in the study were among the most common races in these regions. Thus, we did not introduce any race that is not naturally present in the growing environment. Furthermore, any field activities were conducted properly within the Egyptian laws and regulations by an Agriculture research center (ARC) specialist (Second author on this paper). Therefore, no specific permissions were required for locations or field activities. Furthermore, we confirm that the field studies conducted in the current study did not involve endangering indigenous or protected species.

Plant materials and experimental conditions

The present study was conducted in two consecutive growing seasons (2015/2016 and 2016/2017; hereafter referred to by their harvest season, 2016, and 2017) and two locations in Behira governorate, Egypt, i.e., Elbostan, and Elkhazan. Hence the study was done in four environments (2 locations × 2 growing seasons). Elbostan location is an experimental farm for Damanhour University (30°46′46′′ N, 30°82′32′′ E), representing the newly reclaimed land, while Elkhazan location is a grower farm (31°05'35.2"N, 30°30'10.4"E) located in the Nile valley representing long-term farmed soil.

The seeds of all accessions were provided by the USDA-ARS, National Small Grains Collection (NSGC) located in Aberdeen, ID, USA. The accessions originated from 107 countries, including 35 accessions from Egypt and represented old and new accessions for the period from 1920 to 2012. The accessions included 882 landraces; 912 improved accessions (493 experimental lines and 419 cultivars) and 317 with unknown improvement category from a global spring wheat collection. Accessions details, i.e., pedigree, selection history, and origin can be found on the T3/wheat website (https://triticeaetoolbox.org/wheat/). Each accession was planted in two replicates using a randomized incomplete block design [24] in plots of four rows wide with 25 cm between rows and two meters long. The incomplete blocks consisted of 50 accessions in addition to three check cultivars, i.e., “Sids13”, Gimmiza9, and Giza168. A spreader cultivar i.e., “Morocco” was planted around each replicate as a border of one meter wide. For field inoculation with stripe rusts, within each environment Morocco was sprayed with a mist of water and dusted with mixture of urediniospores at sunset, before dew formation, using 200 mg of five prevalent and aggressive pathotypes of stripe rust, i.e., 0E0, 6E4, 70E20, 128E28, and 134E244 [19] mixed with a talcum powder at a ratio of 1 : 20 (v/v) (spores: talcum powder). The inoculation of the spreader plants was conducted at the booting stage [25]. The urediniospores of the stripe rust were obtained from the Wheat Diseases Research Department, Plant Pathology Research Institute, Agricultural Research Center, Giza, Egypt. Standard agronomic practices including recommended fertilizer application and irrigation schedule were followed at each location.

Disease assessment

Scoring stripe rust was conducted based on the Field Response (FR) fand the percentage of infected tissue (severity). The field responses used were Immune = 0, no uredinia or other macroscopic sign of infection, R = resistant, small uredinia surrounded by necrosis; MR = Moderately resistant, medium to large uredinia surrounded by necrosis; MS = moderately susceptible, medium to large uredinia surrounded by chlorosis; S = susceptible, large uredinia without necrosis or chlorosis [26]. To facilitate the statistical analysis, the field responses were converted into 0, 2, 4, 6 and 8 for immune, resistant, moderately resistant, moderately susceptible and susceptible, respectively for each replicate. Additionally, the genotypes that had across environments`average field response in the range from 1 to 3 were considered resistant; while those in the range from 3 to 5 were considered moderately resistant. Furthermore, the genotypes that had across environments`average field response in the range from 5 to 6 were considered moderately susceptible, and those fall in the range from 6 to 8 were considered susceptible.

SNP genotyping

Wheat accessions included in this study were genotyped through the Triticeae Coordinated Agriculture Project (TCAP) using Illumina GoldenGate platform (Illumina Inc., San Diego, CA) at the USDA-ARS genotyping laboratory in Fargo, ND, USA [27]. Marker data were coded as x = {-1, 0, 1}, where -1 represents homozygous for the minor allele, 0 represents heterogeneous, and 1 represents homozygous for the major allele. The single nucleotide polymorphism (SNP) markers were filtered by removing SNPs with missing values > 10% and minor allele frequency [MAF] < 5%. The filtration step resulted in 3216 high-quality SNPs, missing values were imputed using random forest regression [28], which was applied using the MissForest R/package [29]. Filtered SNP markers were plotted in Manhattan plots using “wnsp 2013 consensus map”; available on: https://triticeaetoolbox.org/wheat/ [30].

Statistical analysis

Analysis of variance was carried out by fitting the following model [31]: where Y is a vector of FR scores or ACI values measured on the plot, μ is the overall mean, E is the effect of ith environment, EB is jth incomplete block nested within th complete block and ith environment (random), G is the effect of mth genotype, EG is the interaction effect between ith environment and mth genotype, and ԑ is the experimental error. Homogeneity of variance across locations and growing seasons was tested using Bartlett’s Test [32]. Means within and across locations were compared using Tukey’s honest significant difference (HSD) [33].

The broad sense heritability (H2) was estimated as follows: Where is the genetic variance; is the genotype by environment variance and is the residual variance.

Ensembling machine learning approach applied in the random forest algorithm was used to build a classification model using the accessions with known improvement class, i.e., improved or landraces, across the 3215 SNP markers (variables). SNP marker data on the accessions within the known improvement class was used to estimate the model parameters, which were then used to obtain a classification rule to group the accessions of the unknown improvement class into either improved or landraces [34]. Classification accuracy was estimated by randomly masking the improvement class for a set of accessions, and then using the random forest classification model to estimate the masked improvement class. The observed (previously known) and estimated improvement class was used to calculate the percentage of the misclassified accessions, as an average after replicating the previous process 100 times [35].

After classifying the unknown accessions, genetic variability among accessions within landraces and improved accessions was investigated using SNP markers by estimating pairwise allele sharing matrix among all accessions [36]. Eigenvector decomposition of the standardized allele sharing matrix was used to investigate the relationships among accessions within the improved lines and the landraces in which the first two principal components were plotted against each other while color coding accessions according to their improvement class using prcomp function in R software [37]. Then, a heatmap with a dendrogram was generated using heatmap.2 and hclust functions in R software [37] to visually identify the overall patterns in the studied materials. Polymorphic information content (PIC) was calculated according to Smith et al. (1997) [38].

The best linear unbiased estimates (BLUE) for stripe rust scores and SNP markers were subjected to association analysis using a mixed linear model (MLM) in R package GAPIT [39]. The association analysis was carried out by performing a linear mixed model with restricted maximum likelihood estimates as follows: Where Y is a vector of the stripe rust scores from the field responses, μ is a vector of intercepts, u is a vector of n×1 of random polygene background effects, e is a vector of random experimental errors with mean 0 and covariance matrix Var (e), Z is a matrix relating Y to u. Var(u) = 2KVg, where K is a known n×n matrix of realized relationship matrix, Vg is a scalar of the unknown genetic variance. m is a vector of fixed effect due to SNP markers, W is a matrix that relates Y to m. Var (e) = RVR, where R is an n×n matrix, and VR is scalar with unknown residual variance. P-values estimated from the association model were subjected to false discovery rate (FDR) corrections using Q-value estimates applied in the R package q-value [40]. The sequence of each significant SNP markers were used in the Triticeae Toolbox database to identify genes associated with YR and their functional annotation using IWGSC RefSeqv1.0 wheat reference genome [41].

Results

Genetic characterization of the studied accessions

Plotting the accessions using the first two principal components (Fig 1A) indicated that the accessions were grouped into two major groups. The first group contained the improved accessions while the second contained landraces. The unknown accessions were scattered across both groups implying that the unknown accessions included both improved accessions and landraces. The random forest model classified the unknown accessions (317) into improved (147) and landraces (170), with an estimated 91% classification accuracy (Fig 1B). The distribution of minor allele frequency (MAF), and the polymorphism information content (PIC) for the landraces and the improved accessions used in the current study are presented in Fig 2A and 2B, respectively. For landraces and improved accessions, PIC-values ranged from 0.09 to 0.37. The overall mean and median for PIC-values estimated from landraces were 0.28 and 0.30, respectively (Fig 2A). The PIC-value mean and median for the improved accessions were 0.30 and 0.33, respectively. The overall mean and median of MAF for the landraces were 0.26 and 0.29, respectively. The MAF mean and median for the improved accessions was 0.30 for both parameters (Fig 2B).

Fig 1

Accessions distribution based on the improvement degree, i.e, improved, landraces and unknown, using the first two principal components, before [A] and after [B] classifying the unknown accessions.

Fig 2

The distribution of polymorphism information content [PIC] [A] and allele frequency [B] for the improved accessions and landraces.

The distribution of the pair-wise shared alleles for the improved accessions and the landraces (Fig 3) indicated that the landraces tend to have less shared alleles compared to the improved accessions. The mean and median of the shared alleles among the landraces were 0.60 and 0.61, respectively. While the mean and median for the improved accessions were 0.69 and 0.70, respectively. Furthermore, the overall heatmap for the pairwise shared alleles in the landraces compared with the improved accessions indicated that the landraces tend to have fewer shared alleles (S1 Fig)

Fig 3

The distribution of the pair-wise shared alleles for the improved accessions and the landraces.

Phenotypic evaluation

Field response scores across environments were normally distributed with 14.3% CV and heritability of 85%. Consequently, hereafter we will report the FR results only. The analysis of variance for the FR observations indicated a significant effect for the four environments and genotypes. However, no significant effect was detected for the genotypes × environments interaction (S1 Table). Based on the average of the FR across environments, the percentages of the resistant (R), moderately resistant (MR), moderately susceptible (MS), and susceptible (S) accessions in the landraces were 8, 27, 26, and 29%, respectively. The percentages of the field responses for the improved accessions were 24, 50, 16 and 10% for the R, MR, MS and S types, respectively. Across environments, the values of the field responses for the local check cultivars were 4.6, 3.75 and 3.37 for Gimmiza9, Giza168, and Sids13, respectively. Thus, the three check cultivars were considered moderately resistant. The overall mean of the resistance scores for the improved accessions and landraces was 4, and 5.25, respectively, indicating that improved accessions were more resistant to the stripe rust pathotypes present. Furthermore, from the improved accessions, 140 (13% of this class) genotypes found to be more resistant to stripe rust compared to the best check cultivar (Fig 4 and S1 Table). Also, 51 (5% of this class) landraces outperformed the best check cultivar in terms of resistance to stripe rust (Fig 4 and S2 Table).

Fig 4

The distribution of the yellow rust resistance score across environments for the improved accessions and landraces.

Marker-Trait association for stripe rust

Linkage disequilibrium (LD) was estimated for the improved accessions and landraces using 3215 SNP markers. LD declined to 50% of its initial values at 10 cM for the improved accessions and 11.75 cM for the landraces (Fig 5). Bayesian information criteria (BIC) was used to determine the optimum number of principal components (PCA) to use in order to account for the population stratification. BIC results indicated that the mixed model with no PCA was the optimum model to use. Furthermore, to validate the previous results, the percentage of variance that the first PCA accounted for was calculated, which was less than 1% of the total variance. Therefore, we reported the results of the association mapping using only the kinship (K) matrix which accounted for most of the stratification among genotypes in the landraces and improved accessions.

Fig 5

Genome-wide linkage disequilibrium (LD) decays for the landraces and the improved accessions using 3215 SNP markers.

Genotype × Environment interaction was not significant, thus we applied genome-wide association mapping analysis (GWAS) using the means across environments for the landraces and the improved accessions. Overall, GWAS identified 24 SNPs that were associated with stripe rust resistance (Table 1). Eight SNPs were common between landraces and improved accessions, while the rest were either significant only for the landraces (eight SNPs) or the improved accessions (another eight SNPs) (Fig 6). The common SNPs were located on chromosomes 1B (IWA4349 and IWA6787), 2B (IWA4349 and IWA6787), 5A (IWA6988), 6A (IWA5142), and 7B (IWA3415 and IWA3416). Of the eight SNPs that were found to be significantly linked with stripe rust in the landraces only, five of these eight SNPs were located in chromosome 1A (IWA6644, IWA3182, IWA5150, IWA4351 and IWA6649), and three in 1B (IWA5370, IWA7331, and IWA3892). The eight SNPs that were found to be significantly associated with stripe rust resistance in the improved accessions were located on chromosomes 1B (IWA7048 and IWA4155), 2B (IWA4096, IWA4095, IWA4097 and IWA7371), 5A (IWA7880) and 5B (IWA3514) (Fig 6).

Table 1

Significant markers associated with stripe rust resistance in the landraces and improved accessions.

ImprovementDegree	SNP	chrom	Position(cM)	P.value	MAF	Additiveeffect	R2
Landraces	IWA6644	1A	8.3	1.25E-07	0.48	0.29	4.50
	IWA3182	1A	9.5	3.58E-11	0.28	0.24	0.79
	IWA5150	1A	9.9	2.13E-18	0.23	0.30	10.01
	IWA4351	1A	11.6	7.4E-27	0.36	0.32	5.94
	IWA6649	1A	11.6	7.81E-26	0.36	0.26	6.19
	IWA5370	1B	11	3.66E-07	0.11	0.25	0.96
	IWA7331	1B	11	4.21E-08	0.07	0.23	0.70
	IWA4349	1B	13.2	8.73E-16	0.33	0.39	8.04
	IWA6787	1B	13.2	6.41E-13	0.35	0.21	7.05
	IWA3892	1B	123.4	1.61E-06	0.37	0.36	0.90
	IWA7799	2B	46.9	5.59E-12	0.23	0.23	1.37
	IWA6121	2B	206.2	4.65E-06	0.20	0.38	2.70
	IWA6988	5A	190.4	1.93E-07	0.40	0.39	6.66
	IWA5142	6A	131.8	2.43E-06	0.35	0.30	2.83
	IWA3415	7B	164.9	5.73E-12	0.19	0.50	7.99
	IWA3416	7B	164.9	4.22E-12	0.19	0.17	7.83
Improved Accessions	IWA4349	1B	13.2	2.7E-22	0.17	0.56	10.61
	IWA6787	1B	13.2	1.93E-18	0.23	0.18	9.04
	IWA7048	1B	22.9	5.7E-08	0.09	0.35	0.83
	IWA4155	1B	96.4	5E-15	0.49	0.37	2.41
	IWA7799	2B	46.9	2.74E-19	0.43	0.39	2.61
	IWA4096	2B	199.3	2.78E-08	0.28	0.39	2.43
	IWA4095	2B	200.5	3.46E-07	0.26	-1.18	1.95
	IWA4097	2B	200.5	1.88E-06	0.26	-2.10	1.89
	IWA7371	2B	200.5	1.36E-06	0.26	-0.89	1.90
	IWA6121	2B	206.2	3.21E-09	0.33	0.31	1.89
	IWA6988	5A	190.4	1.17E-11	0.15	0.35	2.66
	IWA7880	5A	190.4	3.92E-14	0.23	0.38	2.53
	IWA3514	5B	22.9	4.24E-17	0.36	0.39	2.44
	IWA5142	6A	131.8	6.62E-09	0.45	0.39	3.86
	IWA3415	7B	164.9	9.56E-27	0.39	0.48	14.55
	IWA3416	7B	164.9	4.04E-27	0.39	0.27	14.53

R: Percentage of the variance explained. Bold SNP names refer to significant markers in the landraces and the improved accessions. MAF: minor allele frequency

Fig 6

Manhattan plot for stripe rust results obtained from genome-wide association mapping for all accessions (A), improved accessions (B) and landraces (C).

Among the significant SNPs, IWA7331 and IWA7048 had the lowest minor allele frequency of 0.07 and 0.09, respectively. The percentage of variance explained by the significant markers, in the landraces, ranged from 0.70 to 10.01%, while in the improved accessions, it ranged from 0.83 to 14.53% (Table 1). All significant markers in the landraces had a positive additive effect on the stripe rust resistance. While, in the improved accessions three of the significant markers in chromosome 2B (IWA4095, IWA4097 and IWA7371) had a negative additive effect.

The effect of the number of favorable alleles on the stripe rust resistance (Fig 7) indicated that as the number of favorable alleles increases, the stripe rust score decreased (evel of resistance increased). Furthermore, there was a highly significant negative correlation between the number of favorable alleles in the landraces (r = - 0.65, P-value < 0.01)and improved accessions (r = -0.73, P-value < 0.01) with respect to stripe rust FR scores (Fig 7).

Fig 7

Boxplot for the number of favorable alleles effect on stripe rust resistance for the improved accessions and the landraces.

The results of the in silico analysis indicated that the 24 SNPs identified in our study are located in 18 wheat functional genes. Several of the significant SNPs identified were located in the same gene. For example, IWA4351 and IWA6649 are located in TraesCS1A01G015900 gene, While, IWA4349 and IWA6787 are located in TraesCS1B01G046300 gene. Furthermore, IWA4095, IWA4096, IWA4097 and IWA7371 SNPs are located in TraesCS2B01G501500 gene (Table 2).

Table 2

Represents genes that have significant stripe rust-resistant markers.

SNP	Allele	Gene ID	Function
IWA3182	A/G	TraesCS1A01G012500	Transforming growth factor-beta receptor-associated protein 1
IWA3415	A/G	TraesCS7B01G476800	Calcium-binding EF hand protein-like
IWA3416	T/C	TraesCS7B01G476800	Calcium-binding EF hand protein-like
IWA3514	T/C	TraesCS5B01G019600	Amino acid permease family protein, putative, expressed
IWA3892	A/G	TraesCS1B01G449600	Auxilin-like protein 1
IWA4095	A/G	TraesCS2B01G501500	UHRF1-binding protein 1
IWA4096	T/C	TraesCS2B01G501500	UHRF1-binding protein 1
IWA4097	A/G	TraesCS2B01G501500	UHRF1-binding protein 1
IWA4155	A/G	TraesCS1B01G396200	1,2-dihydroxy-3-keto-5-methylthiopentene dioxygenase
IWA4349	T/C	TraesCS1B01G046300	ACT domain-containing protein
IWA4351	A/G	TraesCS1A01G015900	Serine/threonine-protein kinase
IWA5142	T/C	TraesCS6A01G348400	Amino acid transporter family protein
IWA5150	T/G	TraesCS1A01G015200	Tubulin-specific chaperone cofactor E-like protein
IWA5370	T/G	TraesCS1B01G030800	ABC transporter ATP-binding protein
IWA6121	A/G	TraesCS2B01G495700	RNA-binding family protein
IWA6644	T/C	TraesCS1A01G005800	Mei2-like protein
IWA6649	T/C	TraesCS1A01G015900	Serine/threonine-protein kinase
IWA7048	T/C	TraesCS1B01G069300	DUF810 family protein
IWA7331	T/G	TraesCS1B01G041300	Transducin/WD40 repeat protein
IWA7371	T/C	TraesCS2B01G501500	UHRF1-binding protein 1
IWA7799	T/G	TraesCS2B01G074100	basic helix-loop-helix [bHLH] DNA-binding superfamily protein
IWA7880	T/G	TraesCS5A01G537100	Nitrate transporter 1.1

Discussion

Stripe rust is an airborne wheat disease with dynamic virulence evolution, thus anticipatory and continuous screening in the hotspot regions is crucial to identify and integrate new resistance resources and predict disease epidemics [42]. Plant breeders must remain vigilant as the stripe rust spores have the capacity for long-distance migration via airborne pathways [43,44]. The previous history and circumstances combined with the anticipated global warming might stimulate the presence of new stripe rust pathotypes, which creates an urgent need to develop new stripe rust resistant lines [45]. Therefore, identifying such lines in the stripe rust hotspot regions, i.e., Egypt, which currently experiences the presence of new pathotypes, can be beneficial to other breeders in other geographic regions that likely to face the same challenge in the future [46].

Despite the fact that the environments used in our study were different statistically, the magnitude of differences was rather small. Moreover, a highly significant statistical difference was detected among genotypes, but genotype × environment interaction was not significant indicating the environments were similar in their pathogenicity. These results may be due to successfully inoculating the accessions with the same five pathotypes across environments. Tsilo et al. (2014) [47] reported a similar effect of the artificial inoculation with a known mixture of leaf rust pathotypes in five environments, in which accessions in the artificially inoculated environments tended to have similar effects across environments. Furthermore, the surrounding farms around the experimental locations during the two growing seasons were commercially cultivated with the same wheat cultivars, i.e, Gimmiza9 and Sids12, which were officially the recommended cultivars for both locations. Inoculating with the same stripe rust pathotypes, in addition to having the same surrounding cultivars, might have created a similar environmental effect on the studied accessions [48]. These results help to explain the observed high broad sense heritability (85%) which also indicates that most of the variance observed in the current study can be attributed to differences among the studied accessions [49].

Investigating the difference among accessions indicated that landraces tend to have more minor alleles and fewer pairwise shared alleles compared to the improved accessions. One explanation for this result is the active germplasm exchange among the spring wheat breeding programs. Also, selection for traits such as plant height and grain yield might also have resulted in a change in the frequency of other linked alleles [50]. Additionally, the overall mean of stripe rust score across environments in the landraces was 31% higher than that in the improved accessions, indicating that breeding efforts during the last century have resulted in improved stripe rust resistance. Nevertheless, 42 landraces were resistant to stripe rust and outperformed the resistant check cultivars indicating that excellent levels of resistance in the landraces have evolved. The resistant landraces through the natural selection might contain novel resistance genes or combinations of resistance gene that would be valuable for stripe rust breeding efforts [51]. Overall, our results indicated that the landraces and improved accessions are structurally different. That structural difference, caused by differences in minor allele frequency and pairwise alleles sharing, can be beneficial or detrimental. It can be useful in identifying complementary genomic regions or genes to improve resistance, but it can also increase the false discovery rate in the genome-wide association mapping (GWAS) studies if the landraces and improved accessions were fitted simultaneously to the same model [4].

Consequently, separate GWAS models were fitted for the landraces and the improved accessions. In the same context, linkage disequilibrium (LD) measured in the improved accessions tended to decay more rapidly than that measured in the landraces. The causes for the fast decay in the improved accessions is due to recombination (most likely resulting from breeders`designed crosses using shared germplasm) followed by selection for particular alleles [52]. The LD decay with genetic distance observed our study indicates that GWAS is a useful approach to identifying SNPs linked to stripe rust resistance genes in the landraces and the improved accessions. GWAS results indicated that eight SNPs were significantly linked with stripe rust resistance exclusively in the landraces. Five of these eight SNPs were in chromosome 1A in the region from 8.3 to 11.6 cM. This region in the 1A chromosome contains two QTLs, i.e., QYrid.ui-1A_RioBlanco [15] and QYr.tam-1A_Avocet-YrA [53]. Another two SNPs [IWA5370 and IWA7331] which were identified in previous studies and found to be linked with stripe rust resistance genes might represent Yr3a, Yr3b, Yr3c, or Yr21 [19]. IWA3892 is another SNP marker found to be associated with the stripe rust resistance gene in the landraces [15].

Eight SNP markers (IWA7048, IWA4155, IWA4096, IWA4095, IWA4097, IWA7371, IWA7880 and IWA3514) were significantly associated with stripe rust resistance genes in the improved accessions, but not in the landraces. IWA7048 was previously associated with grain total protein content [54]. However, no published reports were found to associate IWA4155 SNP marker with stripe rust resistance genes. Recently, IWA4096, IWA4095 and IWA4097 were found to be linked with stripe rust resistance genes in Ethiopian durum wheat (Triticum turgidum ssp. durum) [55]. IWA7371 is located in the same genomic region that contains Yr5 gene; however, no previous reports have identified an association between that marker and Yr5gene. Therefore, IWA7371 might be a novel marker for that gene or an unknown gene. Additionally, IWA7880 and IWA3514 SNP markers located in chromosomes 5A and 5B, respectively, had not previously been associated with stripe rust resistance genes.

An additional eight SNPs (IWA4349, IWA6787, IWA7799, IWA6121, IWA6988, IWA5142, IWA3415 and IWA3416) were significantly linked with stripe rust resistance genes in both the landraces and the improved accessions. Out of these eight markers, IWA4349 [47] and IWA6988 [15] were previously associated with stripe rust resistance genes. IWA6121 was found to be tightly linked to Yr5 [56]. IWA3415 and IWA3416 were found to be associated with stripe rust resistance gene in spring wheat [15]. Furthermore, three of the markers were found to be linked with stripe rust in the landraces and the improved accessions (IWA6787, IWA7799, and IWA5142) were not reported before. Therefore, most likely these SNPs are novel markers for stripe rust resistance.

Functional gene annotation for the significant 24 SNPs indicated that these SNPs are located in 18 wheat functional genes. Four of these 18 genes have known products that contribute directly or indirectly in several plant defense mechanisms. For example, receptor-like kinases (gene: TraesCS1A01G015900, SNP: IWA4351 and IWA6649) play a crucial role in the plant ability to recognize both general elicitors and specific pathogens through resistance (R) gene [57]. ACT domain-containing protein (gene: TraesCS1B01G046300, SNP: IWA4349 and IWA6787) serve as amino acid-binding sites in several feedback-regulated amino acid metabolic enzymes [58]. Calcium-binding EF-hand protein-like (gene: TraesCS7B01G476800, SNP: IWA3415 and IWA3416) involved in transmembrane signal transductions and is important for plant disease resistance [59]. UHRF1-binding protein1 (gene: TraesCS2B01G501500, SNP: IWA4095, IWA4096, IWA4097 and IWA7371) plays a critical role in DNA methylation and is a regulator of cell proliferation [60]. The other 14 genes have no known or published information about their contribution to the plant defense mechanisms.

Identification of favorable alleles for stripe rust resistance is a prerequisite to enhancing the resistance of the modern cultivars by introgression and accumulating several favorable alleles from the wheat gene pool through molecular markers. In this study, the correlation between several favorable alleles combinations and stripe rust resistance was highly significant and biologically meaningful. Therefore, these favorable alleles of stripe rust resistance would be useful for understanding and improving wheat stripe rust resistance.

Conclusion

This study is one of the first large-scale studies to be conducted in the Mediterranean basin and in one of the global stripe rust hotspot regions. Generating new stripe rust phenotypic information on the studied panel while using the publicly available molecular marker data, contributed to identifying potentially novel QTLs associated with stripe rust for this region and validated 17 of the previously reported QTLs under the field conditions.

Overall, the improved accessions tended to be more resistant to stripe rust compared to the landraces. Out of the 24 QTLs that were found to be significantly associated with stripe rust, 17 were previously reported, while seven are potentially novel. Stripe rust resistant accessions identified in the current study will be included in various crossing blocks to enhance stripe rust resistance in Egyptian elite lines. These and previous findings will contribute to plant breeders’ and pathologists’ efforts in Egypt, North Africa, and the Mediterranean basin to improve the overall resistance to stripe rust by providing new resources and highlighting the importance of using markers to improve selection accuracy.

Analysis of variance for field responses of stripe rust across environments.

(XLSX)

Click here for additional data file.

List of stripe rust resistant accessions, along with their improvement degree and country of origin.

(XLSX)

Click here for additional data file.

Heatmap estimated from the pairwise shared alleles for the improved accessions [A] and the landraces [B].

(TIF)

Click here for additional data file.

28 Jun 2019

PONE-D-19-14766

Evaluation of a Global Spring Wheat Panel for Stripe Rust: Resistance Loci Validation and Novel Resources Identification

PLOS ONE

Dear Dr Elbasyoni,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Dear Dr. Elbasyoni,

I received comments from the advisers on your manuscript "Evaluation of a Global Spring Wheat Panel for Stripe Rust: Resistance Loci Validation and Novel Resources Identification" which you submitted to PlosONE. Based on reviewer comments and my assessment I have decided that your manuscript could be reconsidered for publication should you be prepared to incorporate major revisions. When preparing revised manuscript, you are asked to carefully consider the reviewer comments, which can be found below, and submit a list of detailed and itemized responses to the comments. Please take attention to comment of reviewer one regarding use of an average data.

With kind regards,

Dragan

==============================

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Additional Editor Comments:

Dear Dr. Elbasyoni,

I received comments from the advisers on your manuscript "Evaluation of a Global Spring Wheat Panel for Stripe Rust: Resistance Loci Validation and Novel Resources Identification" which you submitted to PlosONE. Based on reviewer comments and my assessment I have decided that your manuscript could be reconsidered for publication should you be prepared to incorporate major revisions. When preparing revised manuscript, you are asked to carefully consider the reviewer comments, which can be found below, and submit a list of detailed and itemized responses to the comments. Please take attention to comment of reviewer one regarding use of an average data.

With kind regards,

Dragan

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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Reviewer #1: Comments

Major comments

1) Combined data analysis: The authors presented the disease score result as an average of disease scores across environments and over time. Since these are field experiments, there may be natural infections (the manuscript presented strong arguments showing natural infections in the experimental locations: lines 118-128, 470-472 and 569-570). The response of accessions may vary depending on the prevalent race in each location and year. Hence, calculating averages may not provide an accurate picture of the reaction of accessions in each location and year. For example, an accession with resistant reaction in one location (R = 2) but susceptible reaction in the second location (S = 8) will have an average disease score value of 5, which may be categorized as MR = 4 or MS = 6. Both MR and MS are not actually the disease reactions for that specific accession. The result has to be presented as “the accession had resistant reaction in location 1 but susceptible reaction in location 2”. The QTL analysis should also be done for each location and year separately (the authors may refer an article in Plant Genome doi:10.3835/plantgenome2016.10.0107).

2) Selection of resistant accessions: The authors presented in Table S2 (supporting information) accessions that were selected as resistant. They picked only accessions that were having resistant reaction (disease score of 3 and less). This will exclude accessions that were moderately resistant. An accession with MR disease reaction to yellow rust is considered as a good source of stripe rust resistance, hence have to be included in the selection. Sometimes depending on the disease severity accessions with MS reaction could also be a good source of adult plant resistance.

Minor comments

- Shorten the introduction

Line 140: …. Stripe rust resistance

Line 170: delete ‘was’

Line 181: Field disease reaction of germplasms to rusts (R, MR, MS, S) at adult plant stage is not referred as infection type (IT). IT is a term used to refer reaction to seedling reaction. The words for adult plant response are ‘Field Response’. Hence, change IT to field response throughout the manuscript.

Line 344: What does ‘infection value’ indicate? Use terms consistently across the manuscript.

Lines 344-346: What was the cut point to distinguish R from MR, MR from MS and MS from S? This has to be indicated in the materials and methods part. As it stands, Gimiza9 with value of 4.6 is closer to MR = 4 than MS = 6.

Line 347: what does resistance score represent? Be consistent

Line 466: delete ‘that’

Line 477: environments

Table 1. R2 values are between 0 – 1. Why are there values greater than 1?

TableS2. Field response of the selected genotypes in both years and location should be presented separately, i. e., Elbostan-2016, Elbostan-2017, Elkhazan-2016 and Elkhazan-2017.

Reviewer #2: Dear Dr. Ibrahim Elbasyoni,

the manuscript deals with stripe rust as one of the most important fungal wheat diseases. Occurence of new races has been observed many times in the past. Warrior races led in Europe to massive infections. Hence it is of high interest to evaluate genetic ressources which can increase the resistance of cultivars in the future. Therefore,as one result, shown in the manuscript, resistant genotypes can be selected for breeding, supported by SNP markers, which are easy to handle. However some aspects can improve the manuscript:

Please check the English spelling in some parts. At the beginning you use "Identifying" several times. Perhaps, some alternatives should be integrated into the text.

Other points:

Line:

64 to 66: Check sentence (tense).

69: change letters "Yr" to Italic style

entire Introduction:

PLease change both mentioned Yr.. to Italic style

Line 127:

Please include a citation into the sentence about Warrior races

Line 155: I would like to suggest to change from "an experimental farm for" to

"field station of"

Line 170: Divide into two words

Line 170 to 173: Please show approximately the spore amounts (e.g. 20 mg per

slot)... How was the germination rate of spores (if data are available). At which

time of the day, inoculation was performed.

Line 203: Please cite original source of wnsp 2013 consensus map

Line 390 (Table 1: Please indicate in the description of the Table,that R²

describes the percentage of explained phenotypic variance.

Line 418: change frequenc to frequency

Line 453: No information about the method to get the data for Table 2 were

provided in the Material and Method (MM) section. Is this a blast of markers to

genes in a database? Did you use the reference genome to check this informations?

Please insert method into MM section. Are these genes the best hits or did you

select genes, specific for defence reactions?

Table S1, Please show the high heritability of the trait in the results already.

This heritability is very high in comparison to other studies (as you discussed).

Please check Table S2. Why only breeding lines are provided, all are Type R

independend from the Yr score. Please show in the head of the table, what Yr

score means. All should be self explaining.

**********

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Reviewer #1: Yes: Belayneh Admassu Yimer

Reviewer #2: No

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12 Jul 2019

Dear Reviewer#1:

Thank you so much for making time to read our manuscript. All your valuable comments, edits and suggestions are highly appreciated and we will do our best to make the best use of it.

Below is our response detailed response to your comments:

#==================================================================================#

A- Major comments:

1) Combined data analysis: The authors presented the disease score result as an average of disease scores across environments and over time. Since these are field experiments, there may be natural infections (the manuscript presented strong arguments showing natural infections in the experimental locations: lines 118-128, 470-472 and 569-570). The response of accessions may vary depending on the prevalent race in each location and year. Hence, calculating averages may not provide an accurate picture of the reaction of accessions in each location and year. For example, an accession with resistant reaction in one location (R = 2) but susceptible reaction in the second location (S = 8) will have an average disease score value of 5, which may be categorized as MR = 4 or MS = 6. Both MR and MS are not actually the disease reactions for that specific accession. The result has to be presented as “the accession had resistant reaction in location 1 but susceptible reaction in location 2”. The QTL analysis should also be done for each location and year separately (the authors may refer an article in Plant Genome doi:10.3835/plantgenome2016.10.0107).

Thank you so much for your valuable comment and suggestion. We totally understand the merits of analyzing each location and year separately. However, as mentioned in the manuscript lines 335 and 336 all environments (locations and growing seasons were homogeneous) that means the lines grown in each location had the same pattern of response. Additionally, as shown in the text (lines 339 - 340) and table S1 no significant G*E was detected which also indicated that the lines had the same pattern of response across environments. In the current study, we were interested in identifying lines resistant to yellow rust across the studied environments that is why we are using that large number of accessions. As we also mentioned in the discussion, the studied accessions were surrounded by the same commercial cultivars across years. Therefore, we had no statistical or biological reason to conduct the analysis for each environment separately.

2) Selection of resistant accessions: The authors presented in Table S2 (supporting information) accessions that were selected as resistant. They picked only accessions that were having resistant reaction (disease score of 3 and less). This will exclude accessions that were moderately resistant. An accession with MR disease reaction to yellow rust is considered as a good source of stripe rust resistance, hence have to be included in the selection. Sometimes depending on the disease severity accessions with MS reaction could also be a good source of adult plant resistance.

We totally understand your point that MR and MS could contain resistant lines. We did not ignore MR and MS lines. As a mater of fact all data is already in the depository for others to use. We simply presented, in the supporting information, the most resistant lines, that does not mean we ignored MS and MR lines, we already described that in the text. However, the uncertainty attached to using MR and MS genotypes is high and mostly reevaluating these lines will result in resistance to specific race or races or susceptibility. Meanwhile, in the current study, we were able to identify 330 resistant wheat genotypes that found to be resistant across environments, that genotypes are being re-evaluated in other locations for stripe rust.

B-Minor comments

- Shorten the introduction:

Introduction was shortened

Line 140: …. Stripe rust resistance

The word resistance was added

Line 170: delete ‘was’

Deleted

Line 181: Field disease reaction of germplasms to rusts (R, MR, MS, S) at adult plant stage is not referred as infection type (IT). IT is a term used to refer reaction to seedling reaction. The words for adult plant response are ‘Field Response’. Hence, change IT to field response throughout the manuscript.

Thank you so much for the suggestion, IT was replaced by Field response throughout the manuscript.

Line 344: What does ‘infection value’ indicate? Use terms consistently across the manuscript.

Corrected accordingly

Lines 344-346: What was the cut point to distinguish R from MR, MR from MS and MS from S? This has to be indicated in the materials and methods part. As it stands, Gimiza9 with value of 4.6 is closer to MR = 4 than MS = 6.

The cut points are presented in the materials and methods lines: 182:184 …. That is precisely why we use the average across locations….

Line 347: what does resistance score represent? Be consistent

Corrected accordingly

Line 466: delete ‘that’

Deleted

Line 477: environments

Corrected

Table 1. R2 values are between 0 – 1. Why are there values greater than 1?

That is because we are present it as the percentage of the variance explained, Footnote was added under the table.

TableS2. Field response of the selected genotypes in both years and location should be presented separately, i. e., Elbostan-2016, Elbostan-2017, Elkhazan-2016 and Elkhazan-2017.

Please see our response to your first comment.

Dear Reviewer#2:

Thank you so much for making time to read our manuscript. All your valuable comments, edits and suggestions are highly appreciated and we will do our best to make the best use of it.

Below is our response detailed response to your comments:

Response to reviwer#2:

Reviewer #2: Dear Dr. Ibrahim Elbasyoni,

the manuscript deals with stripe rust as one of the most important fungal wheat diseases. Occurence of new races has been observed many times in the past. Warrior races led in Europe to massive infections. Hence it is of high interest to evaluate genetic ressources which can increase the resistance of cultivars in the future. Therefore,as one result, shown in the manuscript, resistant genotypes can be selected for breeding, supported by SNP markers, which are easy to handle. However some aspects can improve the manuscript:

Please check the English spelling in some parts. At the beginning you use "Identifying" several times. Perhaps, some alternatives should be integrated into the text.

Synonyms to the word identifying ware used

Spelling check was conducted

Other points:

Line:

64 to 66: Check sentence (tense).

Sentence checked

69: change letters "Yr" to Italic style

entire Introduction:

PLease change both mentioned Yr.. to Italic style

When we refer to gene names as italic, while when referring to yellow rust as a disease we used the regular style.

Line 127:

Please include a citation into the sentence about Warrior races

References were inserted

Line 155: I would like to suggest to change from "an experimental farm for" to

"field station of"

Thank you for the suggestion, but we are required to use “experimental farm” in my institution.

Line 170: Divide into two words

The suggested correction was conducted

Line 170 to 173: Please show approximately the spore amounts (e.g. 20 mg per

slot)... How was the germination rate of spores (if data are available). At which

time of the day, inoculation was performed.

The total weight of stripe rust spores that were used in inoculation was 200 mg per environment. Dusting was carried out in the early evening (at sunset) before dew formation. There is no data available about the rate of spore germination.

Line 203: Please cite original source of wnsp 2013 consensus map

Reference for the “wnsp2013” was inserted

Line 390 (Table 1: Please indicate in the description of the Table,that R²

describes the percentage of explained phenotypic variance.

R2 description was included

Line 418: change frequenc to frequency

Suggested correction was conducted

Line 453: No information about the method to get the data for Table 2 were

provided in the Material and Method (MM) section. Did you use the reference genome to check this informations?

Please insert method into MM section.

Description for the gene identification was added

Is this a blast of markers to genes in a database?

Yes, we used the the Triticeae Toolbox database to search for gene identification and functional annotation using IWGSC RefSeqv1.0

Are these genes the best hits or did you select genes, specific for defence reactions?

Yes, genes selected were the best hits

Table S1, Please show the high heritability of the trait in the results already.

We did not understand the meaning of this point? If you want us to include the heritability values in the table, we already talked about it in the text.

This heritability is very high in comparison to other studies (as you discussed).

As you mentioned we provided an explanation for the high broad sense heritability observed in our study, please see lines from 521 to 535 in the discussion

Please check Table S2. Why only breeding lines are provided, all are Type R independend from the Yr score. Please show in the head of the table, what Yr score means. All should be self explaining.

In the header of the table we added: “yellow rust score and field response”

IT was replaced by field response as suggested by reviwer#1, also “type” was replaced by “field response”.

TablS2: contains 122 breeding lines , 111 cultivar and 97 landraces.

Furthermore, in table S2 we are presenting only the resistant accessions across all environments, please see materials and methods [ lines 186:198].

27 Aug 2019

PONE-D-19-14766R1

Evaluation of a Global Spring Wheat Panel for Stripe Rust: Resistance Loci Validation and Novel Resources Identification

PLOS ONE

Dear Dr Elbasyoni,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by Oct 11 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

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Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Dragan Perovic, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Dear Dr Elbasyoni,

according to reports of our reviewers you manuscript could be considered for publication if you are willing to incorporate minor changes.

Hope that your respond will be quick in order to avoid further delays.

Regards

Dragan

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1) The cut points described in lines 196 - 198 are for single observations (R = 2, MR = 4, MS = 6 and S = 8). They don't take in to account average values. What happens if the average value is 5? Will it be MR or MS? That is why you need to describe your cut off point. On the same lines (355 - 358) you described Gimiza9 with average value of 4.6 as MS. I don't believe it's MS, rather it is closer to MR than MS. Describing the cut off value will avoid such confusions.

2) Lines 201 - 202: The authors described how they calculated the ACI; but at no point did they use the ACI values in the results or discussions part. Hence, you may delete the description in the materials and methods part.

3) The introduction part is still too long. It can be significantly shortened.

Reviewer #2: Dear Dr Ibrahim Elbasyoni,

all comments have been addressed. Critical comments have been considered. Please change only line 107: "accession" to accessions. The manuscript provides now valuable information's about genotypes, molecular markers, usable for resistance breeding against yellow rust. The northern regions of Africa are effected by many abiotic and biotic stresses. Yellow rust epidemics have been observed in European countries as well, so that genotypes carrying effective resistances in this study are usable in the future as the basis for an increased resistance level of elite material.

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Reviewer #1: Yes: Belayneh Admassu Yimer

Reviewer #2: Yes: Dr. Albrecht Serfling

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4 Sep 2019

6. Review Comments to the Author

Reviewer #1: 1) The cut points described in lines 196 - 198 are for single observations (R = 2, MR = 4, MS = 6 and S = 8). They don't take in to account average values. What happens if the average value is 5? Will it be MR or MS? That is why you need to describe your cut off point. On the same lines (355 - 358) you described Gimiza9 with average value of 4.6 as MS. I don't believe it's MS, rather it is closer to MR than MS. Describing the cut off value will avoid such confusions.

Thank you for your comment, we added detailed description for the cutoff in the materials and methods as you suggested.

2) Lines 201 - 202: The authors described how they calculated the ACI; but at no point did they use the ACI values in the results or discussions part. Hence, you may delete the description in the materials and methods part.

ACI description was removed from the materials and methods

3) The introduction part is still too long. It can be significantly shortened.

Unnecessary parts were removed from the introduction

Reviewer #2: Dear Dr Ibrahim Elbasyoni,

all comments have been addressed. Critical comments have been considered. Please change only line 107: "accession" to accessions.

Thank you so much for your comment the required correction was conducted

The manuscript provides now valuable information's about genotypes, molecular markers, usable for resistance breeding against yellow rust. The northern regions of Africa are effected by many abiotic and biotic stresses. Yellow rust epidemics have been observed in European countries as well, so that genotypes carrying effective resistances in this study are usable in the future as the basis for an increased resistance level of elite material.

Thank you so much for the positive comment.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

9 Sep 2019

Evaluation of a Global Spring Wheat Panel for Stripe Rust: Resistance Loci Validation and Novel Resources Identification

PONE-D-19-14766R2

Dear Dr. Elbasyoni,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Dragan Perovic, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Dr Elbasyoni,

it is my pleasure to accept your manuscript for publishing at PlosONE.

Regards

Dragan

Reviewers' comments:

23 Oct 2019

PONE-D-19-14766R2

Evaluation of a Global Spring Wheat Panel for Stripe Rust: Resistance Loci Validation and Novel Resources Identification

Dear Dr. Elbasyoni:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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With kind regards,

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on behalf of

Dr. Dragan Perovic

Academic Editor

PLOS ONE