Novel resistance loci for quantitative resistance to Septoria tritici blotch in Asian wheat (Triticum aestivum) via genome-wide association study

doi:10.21203/rs.3.rs-4144941/v1

Septoria tritici blotch (STB), caused by the ascomycete fungus Zymoseptoria tritici, poses severe challenges to wheat cultivation worldwide. Deployment of resistant cultivars renders a practical way to control this disease. Therefore, the identification of resistant sources and genes/QTLs is imperative. We attempted to elucidate the genomic architecture for adult-plant STB resistance in a Septoria Association Mapping Panel (SAMP), which has 181 cultivars and advanced breeding genotypes from bread wheat breeding programs in India and Bangladesh. Field experiments identified several accessions such as BGD52 (CHIR7/ANB//CHIR1), BGD54 (CHIR7/ANB//CHIR1), IND92 (WH 1218), IND8 (DBW 168) and IND75 (PBW 800) possessing high levels of resistance. Genetic analysis indicated 21 stable quantitative trait nucleotides (QTNs) for STB resistance on all wheat chromosomes except 2D, 3A, 3D, 4A, 4D, 5D, 6B, 6D and 7A, most of which were found on previously identified chromosome regions for STB resistance. Three QTNs exhibited bigger phenotypic effects and were identified in all three experiments, including Q.STB.5A.1, Q.STB.5B.1and Q.STB.5B.3. Additionally, QTNs on chromosomes 1A (Q.STB.1A.1), 2A (Q.STB_DH.2A.1, Q.STB.2A.3), 2B (Q.STB.2B.4), 5A (Q.STB.5A.1, Q.STB.5A.2) and 7B (Q.STB.7B.2) might represent novel resistance QTL. The resistant genotypes and molecular markers identified in the present study could be used for STB resistance breeding programmes around the globe.

Background

Septoria tritici blotch (STB) disease causes yield losses of up to 50 per cent in susceptible wheat cultivars and can pose serious threat to wheat production. In this study, genomic architecture for adult-plant STB resistance in a Septoria Association Mapping Panel (SAMP) having 181 cultivars and genomic regions governing STB resistance in a South Asian wheat panel were looked for.

Results

The study found STB resistance sources, genomic regions, QTLs, haplotypes, pleiotropic SNPs, and candidate genes in Asian bread wheat genotypes. Five of the discovered QTNs i.e. on chromosomes 1A (Q.STB.1A.1), 2A (Q.STB_DH.2A.1, Q.STB.2A.3), 2B (Q.STB.2B.4), 5A (Q.STB.5A.1, Q.STB.5A.2) and 7B (Q.STB.7B.2) were potentially unique.

Conclusion

Our findings demonstrate the importance of Asian bread wheat as a source of STB resistance alleles and novel stable QTNs for wheat breeding initiatives to generate durable and broad-spectrum Z. tritici-resistant wheat cultivars.

Wheat (Triticum aestivum L.), an important foodgrain with economic impact, reaped significant production gains during the green revolution. However, the spectacle in distended wheat production is drastically restrained and challenged by discrete biotic stresses, amongst which prominent fungal pathogens severely decrease the yield and quality of wheat crops. Among these biotic forties, wheat production is antagonists by a foliar disease caused by the haploid ascomycete pathogenic fungus Zymoseptoria tritici (syn. Mycosphaerella graminicola; anamorph: Septoria tritici), the causal agent of Septoria tritici blotch (STB) disease. The reduction in photosynthetic capacity due to chlorotic and necrotic blotches symptomized by STB causes yield losses of up to 50 per cent in susceptible wheat cultivars [1,2,3]. The disease causes persistent challenges to world wheat growers of temperate climate regimes like Europe, the Mediterranean, Africa, the Americas, and Australia [4,5]. The changing climate, favouring moderate rainfall and temperature conditions (20-25°C with germination at temperatures ranging from 2°C to 37°C) increases incidences of STB and yield losses [6,7,8]. Amongst multifaceted disease control strategies, resistance genes/loci discovery and their utilization in resistance breeding are an appealing perspective to achieve an economical, durable, and environmentally friendly control of STB in wheat.

Breeding for qualitative resistance is considerably easier but is subjected to breakdowns periled by the emergence of new virulence in pathogen populations. Such "boom-to-bust" resistances to STB have been observed and reported by different researchers [9,10,11,12,13,14], and a classic example was exhibited by the US cultivar "Gene" with Stb4 gene, which displayed exceptional resistance to STB in the early 1990s [15] but became susceptible by the mid-1990s [16] due to the evolution of virulence amongst local Z. tritici populations. Similarly, the wheat variety "Tadinia", having a single dominant resistance gene, Stb4, was used in the California wheat breeding programme and remained effective for 15 years [17] but later became ineffective. Therefore, breeding for STB resistance encompassing quantitative resistance is the most effective, durable, and preferred method [3,18].

Up to now, 23 resistance genes: Stb1 to Stb20, StbSm3, StbWW, and TmStb1 [19,20,21] have been designated, with two of these, Stb6 and Stb18q have been cloned, encoding a wall-associated receptor kinase-like protein and a plasma membrane cysteine-rich receptor-like kinase, respectively [11,14]. Additionally, several quantitative trait loci (QTLs) and many marker-trait associations (MTAs) for resistance to Z. tritici have been identified and mapped on the different wheat chromosomes [20]. However, 3BL, 6BS and 7DL chromosome arms seem especially involved in quantitative resistance, highlighting the remarkable complexity and diversity of the genetic underpinnings of STB resistance [19].

So far, STB has not yet been reported in South Asia, and no reports on resistant sources in South Asian wheat germplasm and the underlying resistance mechanism are currently available. Therefore, characterization of the genomic regions governing STB resistance in a South Asian wheat panel is important to supplement the germplasm resources of wheat against STB. Here, we present a genome-wide association study for quantitative resistance to STB under field conditions in a diverse panel of 181 bread-wheat lines. We anticipate that this study provides breeders with a rich basis for improving durable STB resistances in future wheat cultivars.

Resistance spectra of wheat accessions to Zymoseptoria tritici

The 181 wheat genotypes tested for STB severity showed different AUDPC scores, with the mean AUDPC scores ranging from 356.58 (BGD 52) to 1384.84 (IND 51) (Table S1). The frequency distribution curve indicated a near-normal distribution for the STB severity (Fig. 1a). Among the 181 SAMP accessions, BGD52 (CHIR7/ANB//CHIR1), BGD54 (CHIR7/ANB//CHIR1), IND92 (WH 1218), IND8 (DBW 168) and IND75 (PBW 800) showed high levels of resistance to STB, with mean AUDPC score of 356.58, 369.86, 370.27, 372.43 and 484.16, respectively (Table S1, Fig. 1b), as compared to susceptible check "Huirivis #1". (1185.49). wheat lines from the cross CHIR7/ANB//CHIR1 showed increased resistance.

Descriptive statistical analysis revealed that the mean STB severity showed seasonal fluctuation and was higher during the 2019 growing season (918.74) (Table S2). The genotypic effect was significant (p<0.01) for STB resistance in individual years, and moderate to high heritability estimates were obtained (Table S3). The combined analysis of variance over years highlighted that the effect of genotype, year, and their two-way interaction (genotype × years) were all highly significant for STB severity (Table 1). The analysis of pooled data revealed that the genotypic variance (35613.20) was higher than the environmental effect (1353.32) for STB. The broad sense heritability of STB severity was high (h²= 88%).

Pearson's correlation analysis of the means over all environments revealed that STB severity was significantly negatively associated with days to heading (r=-0.61) and plant height (r=-0.46) traits (Table 2).

Population structure, diversity, and linkage disequilibrium (LD) analysis

Initially, a total of 13191 SNPs were scored, of which 9924 (75.23%) were mapped to known chromosomal positions and were well distributed along all the chromosomes of the reference wheat genome (Fig. 2a and 2b). Maintaining SNPs with higher call rate (>70%) and MAF >0.05 resulted in 8353 SNP markers, among which 78% (6524) had a known chromosome position, of which 2,501 were distributed on the A genome, 3,034 on the B genome, and 989 on the D genome. These 8,353 SNPs were used in downstream analyses. Among the 21 wheat chromosomes, the highest and the lowest numbers of SNPs were mapped to chromosomes 2B (594) and 4D (47), respectively (Fig. 2c).

The SAMP was divided into two sub-populations, as confirmed by hierarchical clustering and constilation plot (Fig. S1a and S1b). The phenotyping division of the population based on STB AUDPC is shown in a hierarchical cluster. The wheat lines were randomly distributed across two groups, splitting into nine sub-clusters. Group I contained 51 wheat genotypes, while Group II comprised 130 accessions. All five resistant genotypes having low AUDPC scores for STB (BGD52, BGD54, IND92, IND8, IND75) were reported from Group I, and four of them fell under the same sub-cluster (BGD52, BGD54, IND92, IND8-light green). The Kinship and neighbour-joining cluster analysis also verified the presence of two clusters (Fig. 3) The kinship (Fig. 3C) showed a large proportion of yellow, representing greater diversity among genotypes.

Linkage disequilibrium varied considerably along individual chromosomes, among chromosomes and sub-genomes (Table S4). Genome A showed a higher proportion of significant marker pairs (38.25%) in comparison to genome B (37.15%) and genome D (23.86%). However, the SNPs on the A genome showed the strongest LD, with a mean value of r²=0.14. The marker pairs for chromosomes 4D (r²=0.04%) and 2D (r²=0.24%) showed the weakest and strongest LD values, respectively. Genome-wide LD decayed to its half at 4.91 Mb, and for A genome at 4.38 Mb, B genome at 5.08 Mb, and D genome at 7.13 Mb (Fig. S2).

Marker-trait association for STB, Pleiotropic regions, and stable QTNs

Across all experiments, a total of 99 quantitative traits SNP's (QTNs) were identified with significant -log10(p) value of ≥3 (Fig. 4, Table S5). Among them, 21 QTNs showing repeatability across years were located on all wheat chromosomes except 2D, 3A, 3D, 4A, 4D, 5D, 6B, 6D and 7A (Table 3). The most stable QTNs include Q.STB.5A.1 on 5AS associated with SNP 995502, Q.STB.5B.1 on 5BS associated with SNP 1122319, and Q.STB.5B.3 on 5BL associated with SNP 3222429, they were significant in all experiments as well as the overall mean (Table 3).

In addition to QTNs for STB only, there were a few QTNs associated with both STB and DH, like Q.STB_DH.2A.1 and Q.STB_DH.3B.2 that were significant in two years, and Q.STB_DH.5A.3 and Q.STB_DH.5D.4 that were significant in one year only (Table S6, Fig. S3). The same applies to Q.STB_DH.UN.5, but its chromosome location is unknown. No common QTN between STB and PH was observed.

In silico analysis

Most SNPs were close to transcripts or candidate genes (CGs) associated with disease resistance. For repetitive QTNs, 42 CGs (Table 3) were sorted, while for unique QTNs, 62 CGs (Table S7) were sorted in the IWGSC RefSeq v1.1 reference genome. The linked genes were associated with pathogen resistance in wheat and other crops, encoding ADP binding, protein-coding/binding, metal ion binding, chromosome binding or lipid binding activities etc. (Table 3).

Haplotype analysis and staking of R alleles

The 21 significant QTNs in Table 3 were used to understand the frequency of favourable alleles and their effects on STB resistance. The grand mean frequency of favourable alleles in the panel was 43.88%, with the highest per cent of favourable alleles identified in BGD52 (78.57%), followed by IND 75 (71.43) (Table S8). Haplotype analysis conducted on different allele combinations showed a significant reduction in STB severity with increased resistant alleles (Fig. 5). For example, the accession BGD 52 has 11 resistant alleles and exhibited the lowest mean AUDPC score of 356.58, and IND 75, BGD 53, BGD55, and IND 72, having 10 resistant alleles, had low values of 484.16, 579.01, 567.39, 495.88 respectively (Table S8). On the contrary, the most susceptible genotypes, like IND 51 with an AUDPC score of 1384.84, were associated with only one resistant allele.

Seven significant QTNs i.e., Q.STB.2A.3, Q.STB.2B.4, Q.STB_DH.3B.2, Q.STB.5A.1, Q.STB.5B.1, Q.STB.5B.3, and Q.STB.6A.1, were used in haplotype analysis, and as expected, accessions with the resistance alleles exhibited significant lower AUDPC than those with the susceptibility alleles (Fig. 6). The alleles C, G and T alleles of SNP markers 995502, 1122319 and 3222429 associated with QTNs- Q.STB.5A.1, Q.STB.5B.1 and Q.STB.5B.3 displayed a significant reduction in STB severity (Fig. 6). The allele G associated with the SNP 1122319 on chromosome 5B (Q.STB.5B.1) had the highest allele frequency (94.77%), followed by allele A associated with SNP 1059080 (88.70%) on chromosome 3B (Q.STB_DH.3B.2) and contributed significantly for reduction in the pycnidia development (Table S8).

Finding resistance sources and genetic loci for marker-assisted breeding is crucial because most temperate wheat cultivars are susceptible to STB. Bread wheat accessions from India and Bangladesh have not been screened for STB, so this study identifies new sources of STB resistance. Our findings revealed complex genotypic and environmental interactions, quantitative STB resistance inheritance, and novel QTNs for STB breeding program.

Genetic variation for STB resistance

This study found polygenic control of STB severity due to its near-normal distribution over three years. For future wheat breeding and improvement, phenotypic evaluation showed significant genetic variability among tested wheat genotypes for STB resistance. The broad sense heritability estimated across environments was high, as suggested by previous studies. [18,22,23,24,25]. However, ANOVA showed significant year- and genotype-by-year effects, supporting the need for multiple-year germplasm evaluation to identify stable STB-resistant genotypes [25,26].

Many lines from the cross CHIR7/ANB//CHIR1 also showed increased resistance, highlighting the significance of the parents in this cross for the STB resistance breeding programme. STB resistance is negatively correlated with PH and DH, which are among the most confounding factors. Several studies reported increased disease severity in earlier heading and dwarf genotypes [22,27]. The relationship suggests that genotypes with tall and late phenology may escape STB with reduced infection, or that their genes may have pleiotropism or be tightly linked to STB genes. Many GWAS and QTL mapping studies have found genetic linkage as the cause of the relationship [28,29]. We found genetic factors supporting the association between STB and DH in QTNs Q.STB_DH.2A.1, Q.STB_DH.3B2, Q.STB_DH.5A.3, and Q.STB_DH.5D.4.

Association analysis and candidate genes for STB resistance

In the current study, the ‘year’ effect exerted a significant influence on disease expression, which was also observed in other studies on STB resistance, justifying the importance of multiple field trials. Of the 99 identified QTNs, only 21 were significant in more than one environment and most were of minor effects. This agrees well with the fact that resistance against STB in field experiments is quantitatively inherited trait [19,30]. Stacking favourable alleles from multiple loci, a prerequisite to developing STB resistance germplasm, is possible via introgressing the alleles from their respective donors in the present SAMP.

Of the 21 repeatable STB QTNs detected, allele A on Q.STB.2A.3, Q.STB.2B.4, Q.STB_DH.3B.2, Q.STB.6A.; C, G and T alleles on QTNs- Q.STB.5A.1, Q.STB.5B.1 and Q.STB.5B.3 were associated decrease in STB severity and displayed a significant reduction in STB severity. The allele G associated with the SNP 1122319 on chromosome 5B (Q.STB.5B.1) had the highest allele frequency (94.77%), followed by allele A associated with SNP 1059080 (88.70%) on chromosome 3B (Q.STB_DH.3B.2) and contributed significantly for reduction in the pycnidia development (Table S8). The above seven QTNs lies in the previously reported QTLs for STB resistance confounding to regions of MQTL14 (QTL identified at seedling and adult stage) on 3B and QTL10 (QTL identified at seedling stage) on 5B [19].

Some tagged QTNs concurred with the known mapping locations for STB resistance genes (Fig.7). For example, the markers of Q.STB.1D.1 was positioned at similar chromosomal region as STB resistance genes QStb.ipk-1D and close to Stb10; Q.STB_DH.3B.2 and Q.STB.3B.1 was positioned at MQTL14; Q.STB.5B.1, Q.STB.5B.3 at QTL10; Q.STB.6A.1, Q.STB.6A.3 at MQTL20; and Q.STB.7D.5 at MQTL26 region of a previously published QTLs [19]. The QTNs, Q.STB.1B.2 and Q.STB.1B3 on chromosome 1B were found close to SSR wmc206, which was linked to MQTL3. Similarly, QTNs, Q.STB.2A.6 and Q.STB.2A.7 were close (3-4Mb) to marker gwm294, conferring MQTL5 for STB resistance. The Q.STB_DH2A.1 was located close to MQTL4 and is responsible for adult plant resistance [19]. On chromosome 5A, we detected QTNs, Q.STB.5A.5 positioned at 417.22Mb and Q.STB_DH.5A.3 positioned at 588.45Mb, which are in the region of a previously published MQTL19 and QTL9 [3]respectively, conferring resistance to STB. However, both QTL9 and MQTL19 provide STB resistance at the seedling stage, while in our study, the QTNs were associated with adult plant resistance. Moreover, the QTN, Q.STB_DH.5A.3 also coincided with the Stb17 gene having a quantitative effect on disease at the adult plants stage [31]. Stb10 in chromosome 1D is a qualitative resistance gene imparting durable resistance to STB [32]. The QTL10 and MQTL9 are responsible for seedling resistance, while MQTL14, MQTL20 and MQTL19 are effective at seedling and adult plant stages. Interestingly four SNPs synchronizing with QTLs: MQTL3 at 1B chromosome, MQTL5 and MQTL4 on 2A chromosome and MQTL26 at 7D chromosome imparting adult plant resistance were also mapped. Brown et al. [19] also reported the involvement of QTLs on chromosomes 1B, 2A and 7D, imparting quantitative resistance to STB, which is true in our findings also. Hence, strategic use of the identified major and minor genes/QTNs can be used to deploy STB-resistant varieties.

Q.STB.1A.1, Q.STB_DH 2A.1, Q.STB.2A.3, Q.STB.2B.4, Q.STB.5A.1, Q.STB.5A.2 and Q.STB.7B.2 didn't coincide the location on the previously reported STB resistance loci in respective chromosomes. At the 7B chromosome, the Q.STB.7B.2 didn't coincide with Stb13, Stb3 or Stb 8 genes and any of the previously reported QTLs (MQTL25, MQTL26). Similar was the case with other QTNs. We have found novel QTNs on chromosomes 1A, 2A, 2B,5A and 7B which can be a good candidate for MAS for the STB resistance wheat breeding programme.

Examining the putative CG associated with significant SNP markers and STB resistance is crucial. Q.STB_DH 2A.1, 2A.3, 2A.6, and 2A.7 were mapped on chromosome 2A. SNP 3955868 at 106.81 Mb is associated with the putative candidate gene linked to this marker, TraesCS2A02G159300 (glycoside hydrolase superfamily). The loci covered glycosyl group manipulation genes. Molecular modifications by glycosylation change protein properties, activity, and target location. Glycosylation of metabolites and hormones occurs during biotic and abiotic stress. He et al. [33] found that wheat Glycosyl transferases convert Fusarium graminearum toxin DON into non-toxic DON-3-glucoside. QTNs Q.STB.2A.3 encodes CG TraesCS2A02G307700, Q.STB.2A.6 encodes TraesCS2A02G561400 and TraesCS2A02G561300, and Q.STB.2A.7 encodes TraesCS2A02G563700 and TraesCS2A02G563900, which are associated with metal ion binding, zinc ion binding, serine/threonine kinase activity. These proteins aid plant-pathogen interactions [34]. Saintenac et al. [11] cloned a major STB resistance gene (Stb6) that also encodes a conserved wall-associated receptor kinase-like protein. In wheat, inactivating serine/threonine kinase gene TaPsIPK1 confers broad-spectrum resistance to Chinese Pst races endemic in 2020 and 2021 [35]. Additionally, we found that chromosome 5A harboured protein binding, protein-coding and protein kinase domain (Peak marker 995502 of Q.STB.5A.1; 1228444of Q.STB.5A.2; 1023146 of Q.STB.5A.5 and Q.STB_DH.5A.3 (5411867). In wheat, Wang et al. [35] reported that the inactivation of a wheat protein kinase gene, TaPsIPK1 confers broad-spectrum resistance to rust fungi.

Further, TraesCS3B02G440700 (Q.STB_DH.3B.2) encodes protein kinase activity, while TraesCS3B02G442100 encodes aldose 1-epimerase and TraesCS3B02G442400 encodes cysteine-kinase. The major STB resistance gene Stb16q encodes a cysteine-rich receptor-like kinase and resists Z. tritici broadly [14]. Most R genes already identified have the ATPase domain, which is involved in biotic stress response and found in our CGs. The present study outlined the CGs encoded proteins, most of which confer plant disease resistance (Table 3).

Overlapping regions for STB resistance and disease -escape traits

One of the most perplexing factors affecting the selection for STB resistance could be the reported interaction between resistance and plant height (PH) or heading date (DH) [36,37]. In the present study, a few loci were identified to be associated with both STB and DH on chromosome 2A, 3B, 5A, 5D, and unknown location Q.STB_DH.UN.5. Louriki et al. [30] also reported QTLs for heading days in wheat on chromosome 2A. Previous studies confirmed chromosome 5 for flowering and the presence of the vernalization gene, VRN-1 having three homoeologous loci VRN-A1, VRN-B1, and VRN-D1, which are reported on the long arm of homologous chromosomes 5A, 5B, and 5D, respectively [38,39]. The identified QTN in our study at 5A was close to VRN-A1. Also, for flower induction, the photoperiod response in wheat is mainly controlled by the PHOTOPERIOD1 (PPD1) loci located on the short arms of chromosomes 2A, 2B, and 2D. PPD1 genes identified in wheat are members of the pseudo-response regulator family [40]. The fact that in our panel, days to heading provide putative chromosome locations with QTNs for disease resistance at 2A, 5A and 5D chromosomes hypothesizes the presence of a common genetic base for plant structure, phenology, and disease susceptibility in the studied material. The present study also revealed that some of the significant QTNs for DH, co-mapped with previously identified STB-resistant regions like MQTL4 (Q.STB_DH.2A.1/3955868), MQTL14 (Q.STB_DH.3B.2/1059080), QTL9 and Stb17 gene (Q.STB_DH.5A.3/5411867) [19] thereby showed strong pleiotropism. QTN such as Q.STB_DH.3B.2/1059080 located near transcript TraesCS3B02G440800 related to magnesium ion transmembrane transporter activity has been reported to play a significant role in powdery mildew and stripe rust resistance in wheat [41]. The putative-associated regions identified in the present study need further study for confounding indirect selection for STB resistance via late heading.

Septoria tritici blotch resistance breeding will improve wheat yield. Thus, genetic dissection of the STB-resistant genomic region is highly desired. The study found STB resistance sources, genomic regions, QTLs, haplotypes, pleiotropic SNPs, and candidate genes in Asian bread wheat genotypes. The SAMP had many STB resistance alleles for wheat improvement. BGD52, BGD54, IND92, IND8, and IND75, which are more resistant to "Huirivis#1", can be used to generate STB-resistant varieties and mapping populations to find STB resistance genes/QTLs. This study revealed 21 high-confidence STB resistance markers. Many MTAs were near the candidate gene and protein-coding transcript that may affect desirable traits. Five of the discovered QTNs were potentially unique, one on each chromosome 1B (Q.STB.1A), 2A (Q.STB_DH2A.1, Q.STB.2A.3), 2B.4, 5A, and 7B. Our findings demonstrate the value of Asian bread wheat as a source of STB resistance alleles and novel stable QTNs for wheat breeding initiatives to generate durable and broad-spectrum Z. tritici-resistant wheat cultivars.

Association Mapping Panel

The Septoria Association Mapping Panel (SAMP) was constituted of 181 bread wheat (T. aestivum L.) accessions (Table S1) from wheat breeding programs in India (88 genotypes) and Bangladesh (93 genotypes).

Adult plant screening for STB resistance

The field screening experiments were conducted at CIMMYT’s Sanajaya Rajaram Experimental Station, Toluca, Mexico. This location experiences warm and humid wheat seasons from May to September, with frequent rainfall, which is ideal for creating both artificial and natural epiphytotic conditions for STB infection. The SAMP panel was evaluated during the planting seasons of 2019, 2020, and 2021, and the corresponding experiments were designated STB19, STB20, and STB21, respectively. In accordance with a randomized complete block design (RCBD), the accessions were planted in two replications using double rows of 1 m spaced 25 cm apart. Wheat genotypes "Murga" and "Huirivis#1" were sown as resistant and susceptible checks, respectively. A combination of six aggressive isolates of Z. tritici, namely St1 (B1), St2 (P8), St5 (OT), St6 (KK), 64 (St 81.1), and 86 (St 133.4), was sprayed at a concentration of 1 × 10⁷ spores/ml for artificial inoculation in the field [42]. The first inoculation was done at the Zadoks growth stage (ZGS) 29 [43] using an ultra-low volume applicator, succeeded by two more applications at weekly intervals. STB severity was evaluated on leaves with necrotic lesions bearing pycnidia, using a double-digit scale (00 to 99) [44], wherein the first digit (D1) represents the relative height of the disease spread vertically, and the second digit (D2) represents the severity based on the diseased leaf area using the following formula:

Disease evaluations were conducted five times at weekly interval, starting from approximately ZGS 60. The STB severity values were subsequently used to calculate the area under the disease progress curve (AUDPC) using the following equation [45]:

Where STB_i = STB severity on i^th day, t_i = time at i^th observation, and n is the total number of observations.

Agronomic Data Scoring

Plant height (PH) and days to heading (DH) were scored in all the field experiments. PH was measured at physiological maturity as the height from the ground to the average spike tips excluding awns. DH was scored as days from sowing to the date when approximately 75% of the spikes emerged.

DNA Extraction and Genotyping

Genomic DNA was extracted from two-week-old seedlings employing the CTAB method [46]. After that, extracted DNA quality and quantity were inspected on a Thermo Scientific™ NanoDrop™ 2000 Spectrophotometer (Thermo Scientific™, USA). Single nucleotide polymorphism (SNP) genotyping was carried out using DArTSeq genotyping-by-sequencing platform [47]. Markers were filtered for minor allele frequency (MAF) higher than 5%, missing data less than 30% and heterozygosity less than 10% for further analysis.

Population Structure and Linkage Disequilibrium

Population structure was visualized by principal component analysis (PCA) using the KDCompute system version 1.0.1 (https://kdcompute.seqart.net/kdcompute/plugins), and kinship among the individuals was computed using the centred identity-by-state method. PCA over the genotypic data was conducted by plotting the PC1 over PC2 using the R software [48]. The Linkage disequilibrium (LD) decay at the half-life (R=0.5) analysis was performed separately for each sub-genome (A, B and D) and across the genome. The LD was calculated through TASSEL software version 5.2.53 [49] using SNP markers with known physical positions, and the LD decay plots were produced by plotting the R² values against physical distance (bp).

Genome-Wide Association Mapping

Association mapping was conducted with the mixed linear model (MLM) in the TASSEL software (v 5.2.53). The analysis for STB resistance was executed for individual years and the BLUE means across years. In addition, GWAS on PH and DH was also conducted to investigate their potential association with STB resistance.

All marker-trait associations (MTAs) with LOD ≥ 3 (-log₁₀ of P value) were declared to be significant for STB resistance. The Manhattan and Quantile-Quantile plots were recreated in R-studio using the CMplot-R tool. The physical position of the significant markers was further confirmed with the BLAST search tool of the Wheat@URGI portal (http://www.plants.ensembl.org/Triticum_aestivum/Tools/Blast) using marker sequences against the IWGSC RefSeq v1.0 genome [50].

For comparison, QTNs identified in the present study and those already catalogued STB genes/QTLs [19] were projected onto a linkage map developed using MapChart software version 2.3 [51]. A QTN was considered potentially new if the genetic distance was ≥ 50 Mb away from the reported STB gene or QTL.

Putative Candidate Gene Identification

SNP markers for the detected QTLs were used to identify candidate genes in the genomic regions encompassing the SNPs in the PGSB (Plant Genome and Systems Biology) database; the search was focused on genes or domains that are functionally related to disease resistance mechanisms. The SNP sequences were BLASTed against the wheat reference genome sequence IWGSC (RefSeq v1.0) in "Plant Ensembl" (https://plants.ensembl.org/Triticum_aestivum/) to retrieve the corresponding genes and their functional descriptions [52,53]. For precise identification of candidate genes, the physical starting point of the marker and the chromosome name were entered into Ensembl Plants. An additional 300 bp was added before and after the SNP to increase accuracy. Some markers had SNPs within gene sequences, classified as direct gene hits. However, for markers without SNPs within a gene, potential candidate genes were selected 2 Mb upstream and downstream of the SNPs. These genes were related to the pathogenic process or known to regulate the induction of genes related to pathogenesis. In cases where annotations were unavailable in the Triticum aestivum genome, orthologous genes in related species with known predicted functions were screened using the comparative genomics tool in Ensembl.

Staking Resistance alleles

The BLUP-corrected mean disease severity data was used to assess the effects of pyramiding different numbers of resistance alleles at the significant loci detected in the current study. Wilcoxon-test and t-test (p<0.05) were used to examine the significance of phenotypic differences across groups in the R package 'multcom View' and ‘ggpubr’ [54,55].

Haplotype Analysis

Stable MTAs on chromosomes 2A, 2B, 3B, 5A, 5B, and 6A were chosen for haplotype analysis, for which seven markers that met the criterion of being significant in all environments were selected for analysis: 2A:1091069, 2B:3026452, 3B:1059080, 5A:995502, 5B:1122319, 5B:3222429 and 6A:2258509. Using the R package, the Wilcoxon test was used to assess the corrected disease severities between haplotypes [55].

Statistical Analysis

Data analysis was performed using R software. Analysis of variation (ANOVA) was employed to assess variability in disease severity, and least significant difference (LSD) was used to compare the significant means. Phenotypic and genotypic coefficients of variation and broad-sense heritability were estimated using a formula described by Singh and Chaudhary [56]:

Where σ²_g stands for genetic variance, σ²_gy for variance associated with genotype-by-year interaction, σ²_e the experimental error, and y and r indicate the number of years and replication, respectively. Using SAS software version 9.2 [57], the Pearson's correlation between STB and agronomic variables was determined.

Acknowledgements

The authors wish to thank Francisco Lopez and Nerida Lozano for their technical support in field trials and inoculum preparation, respectively.

Funding Declaration

Financial support from the Indian Council of Agriculture Research (ICAR), India, Accelerating Genetic Gain (AGG) in Maize and Wheat Project Grant INV-003439 funded by the Bill and Melinda Gates Foundation (BMGF), the Foreign and Commonwealth Development Office (FCDO) and Foundation for Food and Agriculture Research (FFAR), USAID, and One CGIAR Initiatives for conducting this research is gratefully acknowledged.

Data availability statement

The original data presented in the study are included in the supplementary materials (phenotypic data) and are accessible via https://hdl.handle.net/11529/10548634 (genotypic data). Further inquiries can be directed to the corresponding author.

Author Information

Authors and affiliations

Madhu Patial¹^ϯ, Xinyao He²^ϯ, Sudhir Navathe³^ϯ, Umesh Kamble⁴, Manjeet Kumar⁵,Arun Kumar Joshi⁶, Pawan Kumar Singh²^✉

¹ ICAR-Indian Agricultural Research Institute, Shimla, India

² International Maize and Wheat Improvement Centre (CIMMYT), Texcoco, Mexico

³Agharkar Research Institute, Pune, India

⁴ICAR-Indian Institute of Wheat and Barley Research, Karnal, India

⁵ ICAR-Indian Agricultural Research Institute, New Delhi, India

⁶Borlaug Institute for South Asia, NASC Complex, New Delhi, India

Author Contributions

Madhu Patial, Xinyao He and Sudhir Navathe contributed to manuscript writing and data analysis. Xinyao He contributed to conceptualization, disease scoring and data analysis, and Umesh Kamble, Manjeet Kumar and Madhu Patial helped in data analysis and manuscript writing. Pawan Kumar Singh and Arun Kumar Joshi contributed to the conceptualization, project monitoring, fund acquisition and revising. All authors contributed to the article write-up and approved the submitted version.

^✉Corresponding author: Pawan Kumar Singh, email: [email protected]

Additional information

Competing interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics

No human/animal studies are presented. No potentially identifiable human images or data is presented in this study.

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Table 1. Analysis of variance, genotypic variance, and broad-sense heritability (h²) for Septoria tritici blotch severity (STB), days to heading (DH) and plant height (PH) in 181 wheat genotypes combined over three years (2019-2021) of testing.

Source of variation	DF	Mean Square
		STB
Year	2	600414.96**
Replication within environment	3	95842.58**
Genotype	180	243067.14**
Genotype x year	360	29387.83**
Genotypic variance		35613.20**
h²		0.88
Phenotypic variance		44301.21**
Environment variance		1353.32***
Genotype x year interaction variance		7334.69**
**Significant at p<0.01

Table 2. Pearson correlation coefficients among Septoria tritici blotch (STB), days to heading (DH) and plant height (PH).

Environment	Trait	DH	PH
STB 19	PH	0.030**
STB 19	STB	-0.60**	-0.40**
STB 20	PH	0.17**
STB 20	STB	-0.47**	-0.40**
STB 21	PH	0.06**
STB 21	STB	-0.53**	-0.26**
Overall (pooled of three years)	PH	0.13**
Overall (pooled of three years)	STB	-0.61**	-0.46**
Significant at p<0.01 STB19=year 2019; STB 20= year 2020; STB 21=year 2021**

Table 3. The repetitive quantitative trait SNPs (QTNs) controlling the Septoria tritici blotch resistance and the putative candidate genes covering the flanking region of repetitive QTNs.

Sr	QTSs	SNP ID	Position	P. Value	Marker R²	Experiment	Transcript ID	Biological Function

1	Q.STB.1A.1	997942	370228607	2.25E-04 to 3.52E-04	0.08291 to 0.07845	STB19 and STBm	TraesCS1A02G208700	Guanyl-nucleotide exchange factor activity

							TraesCS1A02G208500	L-valine transaminase activity, L-isoleucine transaminase activity, catalytic activity, branched-chain-amino-acid transaminase activity, catalytic activity
2	Q.STB.1B.2	1093490	572985684	6.30E-04 to 0.000721	0.07875 to 0.07677	STB19 and STBm	TraesCS1B02G344600	Protein binding

							TraesCS1B02G344900	Inositol tetrakisphosphate 1-kinase activity, metal ion binding, magnesium ion binding
3	Q.STB.1B3	1090302	579824305	0.000358 to 9.47E-04	0.06594 to 0.07557	STB20 and STBm	TraesCS1B02G349300	Translation initiation factor activity, GTPase activity,hydrolase activity, tRNA binding
							TraesCS1B02G349200	Regulation of transcription, positive regulation of long-day photoperiodism, flowering
							TraesCS1B02G349300	hydrolase activity, tRNA binding, nucleotide binding, translation initiation factor activity, GTPase activity,
4	Q.STB.1D.1	3064683	44775232	0.00146 to 0.00049	0.11093 to 0.11724	STB19 and STBm	TraesCS1D02G064200	ADP binding

							TraesCS1D02G064000	Zinc ion binding
5	*Q.STB_DH.2A.1*	3955868	106815147	2.07E-07 to 8.76E-04	0.06042-0.14214	STB19, STB20 and STBm	TraesCS2A02G159300	4-alpha-glucanotransferase, Glycoside hydrolase superfamily
6	Q.STB.2A.3	1091069	528700315	6.45E-05 to 8.10E-05	0.08697 to 0.0898	STB19, STB20 and STBm	TraesCS2A02G307700	Metal ion binding
							TraesCS2A02G307700	Metal ion binding
7	Q.STB.2A.6	1060943	763087785	0.000352 to 9.92E-04	0.07873 to 0.09192	STB19 and STBm	TraesCS2A02G561400	Zinc ion binding,sequence-specific DNA binding


							TraesCS2A02G561300	Transcription factor activity, zinc ion binding, sequence-specific DNA binding
8	Q.STB.2A.7	1101257	764311151	0.00109 to 0.000283	0.07464 to 0.09122	STB19 and STBm	TraesCS2A02G563900	Serine/threonine kinase activity, protein kinase activity, protein, protein binding, ATP binding

							TraesCS2A02G563700	Carbohydrate binding
9	Q.STB.2B.4	3026452	812292519	0.00108 to 0.000000655	0.07663 to 0.17215	STB19, STB21 and STBm	TraesCS2B02G630000	Protein kinase activity, ATP binding, protein phosphorylation

							TraesCS2B02G630100	Protein binding, protein ubiquitination
							TraesCS2B02G629800	ADP binding
10	*Q.STB_DH.3B.2*	1059080	680375512	0.000397 to 0.000793	0.0631 to 0.07068	STB19 and STB21	TraesCS3B02G440800
							TraesCS3B02G440700	Protein kinase activity, protein tyrosine kinase activity, transmembrane receptor protein tyrosine kinase activity, ATP binding
11	Q.STB.3B.1	1037234	681714448	0.000598 to 0.0000782	0.06694 to 0.08853	STB19 and STB21	TraesCS3B02G442303	Zinc ion binding, transferase activity, metal ion binding
							TraesCS3B02G442400	Cysteine-kinase, deubiquitinase activity, ubiquitin-dependent protein catabolic process, protein deubiquitination
							TraesCS3B02G442600	Protein coding
							TraesCS3B02G442100	Catalytic activity, aldose 1-epimerase activity, isomerase activity, carbohydrate binding, carbohydrate metabolic process, glucose metabolic process, hexose metabolic process, galactose catabolic process via UDP-galactose
12	Q.STB.4B.1	1092528	22704276	0.00143 to 0.00149	0.06384 to 0.06498	STB 21 and STBm	TraesCS4B02G030300	DNA binding
13	*Q.STB.5A.1*	995502	36538241	0.0000209 to 0.000551	0.07149 to 0.10462	STB19, STB20, STB21 and STBm	TraesCS5A02G040600	Protein binding


							TraesCS5A02G040500	Protein coding

14	Q.STB.5A.2	1228444	37896699	3.32E-04 to 0.0000691	0.07483 to 0.09046	STB20 and STBm	TraesCS5A02G042200	Protein coding

15	Q.STB.5A.5	1023146	417225903	2.21E-04 to 3.95E-05	0.15921 to 0.20859	STB21 and STBm	TraesCS5A02G206700	Protein kinase activity, ATP binding
16	*Q.STB.5B.1*	1122319	49925060	0.00111 to 5.07E0.5	0.06974 to 0.11234	STB19, STM20, STN21 and STBm	TraesCS5B02G044500	Protein coding

17	*Q.STB.5B.3*	3222429	244491166	0.000176 to 7.75E-05	0.07687 to 0.10041	STB19, STM20, STN21 and STBm	TraesCS5B02G131500	Protein coding

18	Q.STB.6A.1	2258509	600737360	0.00015 to 0.00000698	0.09489 to 0.14017	STB19, STB21 and STBm	TraesCS6A02G380300	Metal ion binding
							TraesCS6A02G380200	Chromatin binding, methyltransferase activity

							TraesCS6A02G380500	Protein binding
19	Q.STB.6A.3	1089240	614688143	0.00103 to 0.000314	0.06105 to 0.07382	STB21 and STBm	TraesCS6A02G41400	ADP binding
							TraesCS6A02G414200	ADP binding

							TraesCS6A02G413700	Lipid binding
20	Q.STB.7B.2	1228174	68307597	0.00102 to 0.00038	0.06196 to 0.07229	STB19 and STBm	TraesCS7B02G063400	Sucrose synthase activity, transferase activity, glycosyltransferase activity

							TraesCS7B02G063500	Phosphatase activity, phosphoric ester hydrolase activity, phosphatidylinositol-3,5-bisphosphate 5-phosphatase activity

							TraesCS7B02G063500	Phosphatase activity, phosphoric ester hydrolase activity, phosphatidylinositol-3,5-bisphosphate 5-phosphatase activity
21	Q.STB.7D.5	2273491	556224991	0.000414	0.09115	STB19 and STBm	TraesCS7D02G437000	Lipid binding

							TraesCS7D02G436800	DNA binding, protein binding

*Bold red colour represents QTNs associated with STB and DH

*Bold black colour represents stable QTNs

No competing interests reported.

Novel resistance loci for quantitative resistance to Septoria tritici blotch in Asian wheat (Triticum aestivum) via genome-wide association study

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Resistance spectra of wheat accessions to Zymoseptoria tritici

Population structure, diversity, and linkage disequilibrium (LD) analysis

Discussion

Conclusion

Materials and Methods

Agronomic Data Scoring

Genome-Wide Association Mapping

Putative Candidate Gene Identification

Statistical Analysis

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1