Genome‐wide association mapping for field spot blotch resistance in South Asian spring wheat genotypes

Spot blotch caused by Bipolaris sorokiniana ((Sacc.) Shoemaker) (teleomorph: Cochliobolus sativus [Ito and Kuribayashi] Drechsler ex Dastur) is an economically important disease of warm and humid regions. The present study focused on identifying resistant genotypes and single‐nucleotide polymorphism (SNP) markers associated with spot blotch resistance in a panel of 174 bread spring wheat lines using field screening and genome‐wide association mapping strategies. Field experiments were conducted in Agua Fria, Mexico, during the 2019–2020 and 2020–2021 cropping seasons. A wide range of phenotypic variation was observed among genotypes tested during both years. Twenty SNP markers showed significant association with spot blotch resistance on 15 chromosomes, namely, 1A, 1B, 2A, 2B, 2D, 3A, 3B, 4B, 4D, 5A, 5B, 6A, 6B, 7A, and 7B. Of these, two consistently significant SNPs on 5A, TA003225‐0566 and TA003225‐1427, may represent a new resistance quantitative trait loci. Further, in the proximity of Tsn1 on 5B, AX‐94435238 was the most stable and consistent in both years. The identified genomic regions could be deployed to develop spot blotch‐resistant genotypes, particularly in the spot blotch‐vulnerable wheat growing areas.

access, and cost of living.As per FAO et al.'s (2022) report, approximately 2.3 billion people, that is, 30% of the total global population, are moderately or severely affected by food insecurity.Climate-resilient and higher yielding genotypes play a key role in the assurance of yield and income to farmers (Joshi et al., 2007).Therefore, more efforts are required to develop new, high-yielding cultivars having the potential to withstand biotic and abiotic stresses.
Among biotic stresses, several disease-causing pathogens are responsible for yield reduction in wheat.However, spot blotch (SB) caused by Bipolaris sorokiniana ((Sacc.)Shoemaker) (teleomorph: Cochliobolus sativus [Ito and Kuribayashi] Drechsler ex Dastur) is of major concern in warm wheat growing areas of the world, such as eastern India, Bangladesh, Nepal, China, Southeast Asia (Thailand, Philippines, Indonesia), Latin America (Bolivia, warmer regions of Brazil, Paraguay, northeast Argentina), and Africa (Tanzania and Zambia) (Chattopadhyay et al., 2022;Duveiller & Sharma, 2009;Juliana et al., 2022).Under hot and humid conditions, SB can cause 15%-20% production losses and may result in seed discoloration, shriveled seeds, and loss of viability (Chattopadhyay et al., 2022;Gupta et al., 2018;Singh et al., 2018).SB is estimated to affect 25 million ha of wheat globally, of which 10 million ha are under the rice-wheat cropping system in the Indian subcontinent alone (Duveiller et al., 2005;Joshi et al., 2007).In the rice-wheat cropping system, specific agronomic approaches, such as late sowing and unbalanced fertilizer application may increase disease frequency (Gupta et al., 2018;Joshi et al., 2007;Juliana et al., 2022).The SB pathogen thrives under high temperature (>26˚C) and high humidity, which is why late-sown wheat in South Asia normally has high SB infection (Gupta et al., 2018;Pandey et al., 2021).If predictions of a rise in mean temperature due to climate change prove accurate, the disease is expected to pose a threat to global wheat production (Gupta et al., 2018).
Bipolaris sorokiniana causes blotches on leaves, node canker, and possibly seedling blight (Chand et al., 2021;Singh et al., 2018).Dark brown lesions with a 1-to 2-mm diameter and non-chlorotic edges indicate early infection.The susceptible variety, however, sees these small leaf lesions grow into elongated light to dark brown spots that coalesce and induce necrosis of the leaf tissue (Chand et al., 2010;Duveiller et al., 2005).Just like other wheat diseases caused by hemibiotrophic fungi, host immunity to SB is yet to be discovered, and even resistant lines suffer yield losses under high disease pressure (Chand et al., 2021;Gupta et al., 2022;Singh et al., 2015).
Genetic studies suggested a quantitative genetic control for SB resistance in wheat, which is influenced by genotype-byenvironment interaction (Chand et al., 2021;Gupta et al., 2018;Joshi et al., 2004;Juliana et al., 2022).The development of SB resistance wheat cultivars may be accelerated by using molecular markers identified in bi-parental quantita-

Core Ideas
• Resistant germplasm identification to spot blotch of wheat.• Single-nucleotide polymorphism markers associated with spot blotch resistance.• Genome-wide association studies analysis for spot blotch resistance in Indian spring wheat panel.
tive trait loci (QTL) mapping and genome-wide association studies (GWASs) (Singh et al., 2018).Several QTLs associated with resistance to SB have been discovered on different chromosomes (Adhikari et al., 2012;Ahirwar et al., 2018;Bainsla et al., 2020;Gonzalez-Hernandez et al., 2009;Gurung et al., 2014;Jamil et al., 2018;Juliana et al., 2022;Kumar et al., 2015;Lillemo et al., 2013;Lu et al., 2016;Neupane et al., 2007;Sharma et al., 2007;Singh et al., 2018;Tomar et al., 2021;Zhang et al., 2020).So far, four QTLs with major effects have been assigned the designations Sb1 to Sb4.Sb1 was assigned to chromosome 7DS based on the findings of Lillemo et al. (2013).This gene was in close proximity to the cloned leaf rust resistance gene Lr34, which has been shown to exhibit pleiotropic effects on yellow rust (Yr18), stem rust (Sr57), powdery mildew (Pm38), and leaf tip necrosis (Ltn+) (He et al., 2022).The other three Sb genes have been well mapped, that is, Sb2 on 5B (Kumar et al., 2015), Sb3 on 3B (Lu et al., 2016), and Sb4 on 4BL (Zhang et al., 2020), but so far they have not been cloned.The objectives of this research were to identify novel genomic areas linked with SB resistance and resistance donors that could be used in breeding in a panel of 174 genotypes from India, Bangladesh, Nepal, and the International Maize and Wheat Improvement Center (CIMMYT), Mexico.The panel was previously used by Phuke et al. (2020) and He et al. (2021) for studies on tan spot and wheat blast, respectively.

Plant material and genotyping
A panel of 174 spring wheat genotypes from CIMMYT-Mexico (97), India (30), Bangladesh (19), and Nepal (28) were used (Table S1).These genotypes represent the contemporary elite breeding lines from the respective organizations or countries.
The panel was genotyped with Illumina Infinium 20k wheat array (15k + 5k add-on array) at Trait Genetics GmbH, Germany, where samples were analyzed on Infinium ultra-HD chips carrying 24 samples each.The single-nucleotide polymorphism (SNP) markers were filtered according to the following criteria: uncertain position (1695 markers), minor allele frequency less than 5% (2707 markers), and missing data points of more than 10% (222 markers), which resulted in 11,499 markers available for GWAS analysis.Marker locations on the reference whole genome sequence (IWGSC: Chinese Spring RefSeq v1.0,International Wheat Genome Sequencing 2018) were obtained from the following database: https://wheat-urgi.versailles.inra.fr/.

Disease screening
Field experiments for SB were conducted in CIMMYT's Agua Fria station, Mexico, during the 2019-2020 (hereafter referred to as 2020) and 2020-2021 (referred to as 2021) cropping seasons.The experiment was conducted in a randomized complete block design with two replications.The experimental unit consisted of 1-m double-row plots, and four checks: Chirya-3 (resistant), Francolin (moderately resistant), Ciano T79, and Sonalika (susceptible) were included.
The virulent local isolates of B. sorokiniana previously isolated and kept in freezers at −20˚C were reactivated and cultured on V8 medium for 5-7 days at 22-25˚C for mycelia growth.Subsequently, the isolates were multiplied on sorghum seeds previously soaked and autoclaved.Bipolaris sorokiniana inoculated sorghum grains were placed in flasks and then incubated for 6 weeks at room temperature with frequent shaking to mix the grains and promote better fungus coverage.The sorghum grains were then scattered at the base of the plants in the middle of the double row for field inoculation after 21 days of planting at the 3-to 5-leaf stage.Four to five weeks after inoculation, disease severity was visually scored for each plot using the double-digit scale (00-99) (Saari & Prescott, 1975).The first digit (D1) indicates disease progress in canopy height from the ground level, and the second digit (D2) refers to severity measured based on the diseased leaf area.Both D1 and D2 were scored on a scale of 1-9.The disease evaluation was repeated four times at 7-to 10day intervals.For each evaluation, the percentage of disease severity was estimated based on the following formula: The area under the disease progress curve (AUDPC) was calculated from the four disease evaluations using the following formula: where Y i is the SB severity at time t i , t i + 1 − t i is the time interval (days) between two disease scores, and n is the number of times when SB was recorded.

Linkage disequilibrium and population structure
Using all 11,499 SNP markers, a kinship matrix and clusters of individual genotypes were calculated, whereas a heat map was generated using a classical equation (VanRanden, 2008) in R program.TASSEL 5 (http://www.maizegenetics.net)was used to determine the linkage disequilibrium (LD) parameters among the SNP markers, which were then displayed against the physical distances.
Numeric transformation of genotypic data was performed using the R package GAPIT3 (Wang & Zhang, 2021) in accordance with the required format of the Structure 2.3.4 software (Pritchard et al., 2000), in which the admixture model was used for structure analysis.The burn-in period was set to 100,000, followed by 500,000 markers chain Monte Carlo (MCMC) replications.The subpopulation test range was retained from K1 to K10, each having five iterations (runs).The real subpopulations were assessed using the K method (Earl & VonHoldt, 2012), which was then validated by the Evanno et al. (2005) method using the STRUCTURE HARVESTER tool (Earl & VonHoldt, 2012).The standard deviation and the average logarithm of the probability of the observed likelihood (LnP[D]) were derived from the output summary.LnP(D) was calculated for each stage of the MCMC for each class (K = 1-10) by computing the log-likelihood for the data.

Principal component analysis
Principal component analysis (PCA) was performed using 11,499 SNPs and 174 genotypes in fixed and random model circulating probability unification (FarmCPU) (Liu et al., 2016).The first two principal components were drawn to show the clustering among genotypes.The intrachromosomal LD was calculated as the pairwise marker correlations (r 2 ) between the SNP markers plotted against the physical distance for significant marker-trait associations (MTAs).The long-distance LD and spline were fitted to the LD-decay graph using r 2 values of less than 0.99 using ggplot2 v3.30 in R v3.5.2.

Statistics and genome-wide association analysis
The combined analysis of variance (ANOVA) was carried out for the 2-year experiment, and three variance components, that is, genotypic variance  2 , experimental variance  2  , and interaction of genotype and experiment variance  2  , were estimated for SB using restricted maximum likelihood (Patterson & Thompson, 1971) estimation procedure using software META-R v. R-3.3.1.
Broad-sense heritability was estimated using the following formula: where  2  and  2  are the genotype and error variance, respectively,  2  is the genotype-by-environment interaction variance, nEnvs is the number of environments, and nrps is the number of replications.
The Bartlet test assessed the homogeneity of error variance before pooling the two-experiment data for GWAS analysis.MTA was performed using a mixed-linear model (MLM) and FarmCPU.The GWAS analysis using the FarmCPU model was performed using the R software package GAPITv.3.5 (Wang et al., 2021).The GWAS study was conducted for the two experiments separately, as well as for pooled experimental data.The markers were declared significant using Bonferroni correction with significant cutoff (p-values, 3.0 E-06) calculated at the alpha level of 0.2 using 11,499 markers to reduce false discovery rate in both MLM and FarmCPU models.
In TASSEL v.5 (Bradbury et al., 2007), the MLM (Yu et al., 2006) was fitted, where population structure was used as a fixed effect and kinship was used as a random effect.Two principal components were used to account for population structure (Price et al., 2006), and kinship was obtained by the centered identity-by-state method (Endelman & Jannink, 2012).We ran the MLM using the optimum compression level and the "population parameters previously determined" (Zhang et al., 2010) options in TASSEL.The p-values for the significance tests of the marker-trait associations, the marker effects, and the percentage of the SB variation explained by each marker were obtained.The LD between the consistent markers was analyzed using TASSEL version 5, and the standardized disequilibrium coefficients (D0) (Lewontin, 1964), the correlations between alleles at the two marker loci (r 2 ), and the p-values for the existence of LD using the two-sided Fisher's exact test were obtained.Markers with high r 2 values, D' values, and p-values for the test of disequilibrium equal to zero were grouped into LD blocks.

Gene functional annotations
GWAS results were further analyzed to test if the identified MTAs fall within the known genomic regions using functional annotation from the reference genome assembly (IWGSC Ref Seq v1.0).From the genome annotations provided by IWGSC, functional annotation of the genes, either containing significant SNPs or close by them, was collected and checked for their possible association with SB resistance.Protein functions were then retrieved from annotated data using literature mining.Only the genes in the exact genomic location were considered, and the number of base pairs added changed for each marker based on how close it was to the genes.The interval was then explored for predicted genes, and annotations from the IWGSC (https://www.wheatgenome.org/) were obtained.For several genes, the IWGSC annotations were not available.Thus, they were evaluated based on orthologous genes in related species with known predicted functions using the comparative genomics tool in Plant Ensembl.The Triticum aestivum gene transcripts and their available domains in Ensembl were also used (using the show transcript table link).Moreover, the JBrowse tool from T3/Wheat (https://wheat.triticeaetoolbox.org/;Blake et al., 2016) and GBrowse from URGI (https://urgi.versailles.inra.fr/gb2/gbrowse/wheat_survey_sequence _annotation) were also used to identify annotation to SNP markers.

Stacking resistance alleles
Resistance alleles were determined by comparing the mean of corrected AUDPC between alleles using the Wilcoxon test implemented in the R package "ggpubr" (Kassambara, 2020).Wheat lines were grouped by the number of resistance alleles they contained, and Tukey's honest significant difference test (p < 0.05) implemented in the R/multcomView package was used to compare whether there were significant differences in mean disease severities between groups.

Haplotype analysis
One stable SNP marker, 5B_AX-94435238, was selected for haplotype analysis.A pairwise comparison of corrected disease severities between haplotypes was conducted using the Wilcoxon test implemented in the R package "ggpubr" (Kassambara, 2020).Corrected AUDPC values were obtained using best linear unbiased prediction analysis for 2020 and 2021 and mean (across the environments) using Meta R (Alvarado et al., 2020).

Phenotypic evaluation and heritability
The AUDPC for SB ranged from 433.38 to 1813.43 in 2020 and from 351.77 to 1435.67 in 2021, displaying considerable phenotypic variation with a continuous distri- bution of lines in both years (Figure 1).Among the tested entries, CIM 33 (TEPOCA T 89) was the most susceptible (AUDPC = 1644.76),while CIM 34 (MILAN) was the most resistant (AUDPC = 558.91).The AUDPC for the resistant check Chirya 3 was recorded at 756.06, while it was 1744.69 for the susceptible check Ciano T79 (Table 1).The ANOVA revealed the highest heritability in 2021 (0.94), followed by 2020 (0.93), as well as a high heritability across years (0.86).ANOVA revealed significant effects from genotype, year, and genotype-by-year interaction (p < 0.0001; Table 2).

SNP density and principal component analysis
Among polymorphic SNP markers, 40.9% (5754), 50.8% (7142), and 8.3% (1167) were from the A, B, and D genomes, respectively.With a genomic coverage of 13.9 GB and 14,063 markers across the genome, the average marker density was 1.9 Mb between markers.The lowest marker density, 7.03 Mb between markers, was at chromosome 4D, while the highest, 0.54 Mb between markers, was observed at chromosome 2B.The average distance between markers for A, B, and D genomes was 0.89, 0.84, and 3.92 Mb, respectively.
Population structure analysis revealed four groups, of which Group 1 (G-I) consisted of 73 lines, majorly belonging to South Asia.Group 2 (G-II) consisted of 41 lines, Group 3 (G-III) consisted of 34 lines, while Group 4 (G-IV) consisted of 26 lines, mostly belonging to the CIMMYT origin (Figure 2).Based on pedigree information, most lines within the group shared descendents from common parents.Principle component analysis of a similar structure was observed, as shown in Figure 3.The heat map showed a high kinship relationship among lines (Figure 3).

Candidate genes for marker-trait associations
The significant SNPs identified from the GWAS analysis were further studied for the known candidate genes relevant to disease resistance using the recently annotated wheat reference sequence (RefSeq V1.0).Numerous plant protein families encoding proteins with diseaseresistance-associated domains, such as LRR, PKS, and the MADS transcription factor, were identified in the genomic regions harboring the significant SNPs.The SNP 3A_BS00023028_51 is located within the genomic region 3A:721341896-721361525 that contains two genes, TraesCS3A02G495300 and TraesCS3A02G495400, which The Plant Genome F I G U R E 2 Population structure of 174 diverse spring wheat germplasms revealed by structure and neighbor joining tree.encode for NAD kinase/diacylglycerol kinase-like domain and leucine-rich repeat protein kinase-like domain, respectively.

Stacking of R alleles
Nineteen significant markers obtained from the model "MLM" were selected to investigate the effects of pyramid-T A B L E 3 Single-nucleotide polymorphisms (SNPs) associated with spot blotch (SB) resistance and the candidate genes.

Haplotype analysis
Haplotype analysis was conducted for three consistently significant SNPs, that is, 5B_AX-94435238 linked to Tsn1, and 5A_TA003225-0566 and 5A_TA003225-1427 linked to a possibly new QTL.For 5B_AX-94435238, the susceptible allele "T" was consistently associated with higher disease severity compared to the resistant allele "C" in both years and the mean (Figure 6).Similarly, the "T" allele of 5A_TA003225-0566 and the "G" allele of 5A_TA003225-1427 were consistently associated with SB resistance in the panel (Figure 7).

DISCUSSION
This study screened a panel of bread wheat genotypes from CIMMYT and South Asia for field SB resistance.Based on the results, several resistant and moderately resistant genotypes were identified, primarily from CIMMYT germplasm.This confirms earlier findings that advanced lines from the CIMMYT bread wheat breeding program possess moderate resistance to SB (Singh et al., 2015).The continuous distribution of disease scores across all experiments indicates the quantitative character of resistance driven by the additive action of numerous QTLs and genes (Ayana et al., 2018;Joshi et al., 2004;Kumar et al., 2007Kumar et al., , 2009;;Neupane et al., 2007;Singh et al., 2018;Singh et al., 2023).ANOVA revealed significant effects of year and genotype-by-year, highlighting the need for multiple evaluations for SB across years/locations to identify lines with stable resistance and reliable markers for breeding (Chattopadhyay et al., 2022;Roy et al., 2021;Singh et al., 2015).
The genetic architecture of resistance to SB in the GWAS panel was proven to be polygenic, and 20 significant SNP markers related to SB resistance were found on 15 chromosomes.The significant marker on 5BL, AX-94435238, was close to Tsn1 and likely represented the QTL at this gene locus.The role of ToxA-Tsn1 interaction in susceptibility to SB disease has been reported by several researchers (Friesen et al., 2018;McDonald et al., 2018;Navathe et al., 2020), and because the Mexican population of B. sorokiniana also carries ToxA (Wu et al., 2021), no wonder why this locus turned out to be significant.This underlines the importance of deploying genotypes devoid of Tsn1 in geographic areas vulnerable to SB.Additionally, because ToxA is also widely present in pathogen populations of tan spot and Septoria nodorum blotch, eliminating Tsn1 from prevalent wheat varieties is also helpful in controlling these two diseases (Navathe et al., 2020).
Apart from AX-94435238, SNPs 5A_TA003225-0566 and 5A_TA003225-1427 were the only consistently detected markers in this study.They were mapped in a chromosomal region 5A:414410915-414411822, where no QTL for SB resistance has been identified and thus likely represents a new QTL.On the same chromosomal arm, the significant marker 5A_Kukri_c6266_260 was in the genomic region 5A:607678848-607685776, not very far (20.23Mb)from Vrn-A1 that has often been associated with field SB resistance (He et al., 2021;Singh et al., 2018;Zhu et al., 2014); thus, it might be involved in disease escape instead of genetic resistance.Earliness is often associated with SB escape, and a strategy for selecting genotypes based on the early-flowing allele of Vrn-A1 and employing Sb2 or other SB resistance QTLs to increase SB resistance has been well documented (He et al., 2020;Roy et al., 2021).
The marker Kukri_c59960_211 was located on 2DL at 455333875 bp, with the closest marker for SB resistance being Kukri_c31121_1460 at 607423420 (Ayana et al., 2018), implying that they may represent different QTL.Additionally, the SNP marker 3A_BS00023028_51 in the genomic region 3A:721341896-721361525 is linked to genes encoding the protein kinase-like domain, leucine-rich repeat, and NAD kinase/diacylglycerol kinase superfamily, which is crucial for disease resistance (Tomar et al., 2021).
Two significant markers on 3BS, wsnp_Ex_ c2723_5047696 and BS00102646_51, were located in the genomic region 3B: 6532701-6682983, which is different from that reported by Bainsla et al. (2020) at 17.1 Mb but fell into the QTL region for Sb3 at 6.1-7.1 Mb (Lu et al., 2016).Therefore, these two SNPs likely represent the Sb3 gene, which was initially reported in a Chinese genotype and recently identified in CIMMYT germplasm (Juliana et al., 2022), highlighting the important role of this gene in conferring SB resistance.The remaining significant SNPs were located within or close to the reported QTLs for SB resistance, for example, 6D_AX-94499500 was found in the genomic region 6D:10905028-10947752, which is close to the 6DS QTL reported in Tomar et al. (2021).This study identified multiple significant SNPs on different chromosomal regions, confirming the quantitative inheritance of SB resistance in the studied panel of wheat genotypes.Nevertheless, a few QTLs on 3BS (at Sb3), 5AL, and 5BL (at Tsn1) showed relatively big effects and are thus very suitable for marker-assisted selection (MAS).The effects of stacking resistance alleles confirmed the contribution of minor QTLs in reducing the severity of SB.For example, SW8488*2/KURUKU that possessed the most resistance alleles (18) had a SB severity that was 43% less than genotypes with fewer than five resistance alleles, indicating a clear association between increased number of resistance alleles with decreased SB severity.Using the marker-assisted backcross method, these QTLs can be transferred into well-known susceptible varieties or deployed to develop new varieties (He et al., 2022).Genotypes identified in the current study that showed good SB resistance could be utilized in future breeding programmes as resistance donors, and the significant SNP markers could be used to trace their corresponding QTL to speed up the breeding progress.

A C K N O W L E D G M E N T S
The financial support received by the first and last authors from the Indian Council of Agriculture Research (ICAR), India, is acknowledged.

C O N F L I C T O F I N T E R E S T S T A T E M E N T
The authors declare no conflicts of interest.

D A T A AVA I L A B I L I T Y S T A T E M E N T
All data supporting the findings of this study are available within the paper and within its Supporting Information.

F
I G U R E 1 Phenotypic distribution of 174 spring wheat genotypes for area under the disease progress curve (AUDPC) in spot blotch (SB) during (a) 2020 and (b) 2021.The lines in the boxplot represent the median of the distribution, while the black dots are outliers.

F
Manhattan plots representing 21 chromosomes (a-b) show the significant markers detected by fixed and random model circulating probability unification (FarmCPU) model for spot blotch (SB) during 2020 and 2021, while (c-e) show the significant markers detected by mixed-linear model (MLM) model for SB during 2020, 2021 and mean area under the disease progress curve (AUDPC).

F
I G U R E 5 Boxplot for effects of stacking numbers of alleles indicating increased resistance to spot blotch (SB) in the 174 spring wheat lines.AUDPC, area under the disease progress curve.NS, not significant.**p < 0.01.***p < 0.001.

F
Allelic distribution of significant single-nucleotide polymorphism (SNP) AX-94435238 tightly linked to the Tsn1 on chromosome 5B during 2020, 2021 and mean area under the disease progress curve (AUDPC).

F
I G U R E 7 Allelic distribution of significant single-nucleotide polymorphism (SNP) TA003225.0566and TA003225.1427associated with spot blotch (SB) resistance on chromosome 5A.AU T H O R C O N T R I B U T I O N S Umesh Kamble: Conceptualization; data curation; formal analysis; writing-review and editing.Xinyao He: conceptualization; data curation; formal analysis; writing-review and editing.Sudhir Navathe: Formal analysis; writingreview and editing.Manjeet Kumar: Formal analysis; writing-review and editing.Madhu Patial: Formal analysis; writing-review and editing.Muhammad Rezaul Kabir: Formal analysis; resources; writing-review and editing.Gyanendra Singh: Formal analysis; resources; writingreview and editing.Gyanendra Pratap Singh: Formal analysis; resources; writing-review and editing.Arun Kumar Joshi: Conceptualization; formal analysis; funding acquisition; project administration; resources; supervision; writingreview and editing.Pawan Kumar Singh: Conceptualization; data curation; formal analysis; funding acquisition; project administration; resources; supervision; writing-review and editing.
List of top wheat genotypes showing resistance to B. sorokiniana under field conditions.
T A B L E 1Note: CIM indicates the germplasm originated from International Maize and Wheat Improvement Center (CIMMYT), and IND indicates the germplasm originated from India.Abbreviation: AUDPC, area under the disease progress curve.T A B L E 2 Statistics and analysis of variance of the 174 advanced lines using area under the disease progress curve (AUDPC).