Discovering chromosomal regions related to spot blotch disease in wheat (Triticum aestivum L.) by QTL analysis


 Spot blotch (SB) is a major constraint to wheat (Triticum aestivum L.) production in South Asia's warmer plains. SB, also known as leaf blight, is caused by Bipolaris sorokiniana and causes significant yield losses in India's Eastern Gangetic Plain Zone. The aim of this study was to map SB resistance via composite interval mapping (CIM) in the PBW343/IC252874 population, which comprised of 165 doubled haploid lines (DHLs), across two years in India. The phenotypic analysis of these lines revealed a constant variance in disease severity, implying that SB resistance is most likely polygenic. DHLs' phenotypic data was also used to map QTLs using SSR markers. The presence of quantitative inheritance with transgressive segregation for SB resistance in the population was also revealed. The QTLs were discovered on 12 chromosomes i.e. 1B, 1D, 2A, 2B, 2D, 3B, 4A, 4D, 5A, 5B, 6A, and 7A. A new QTL was detected on chromosome 4D being linked to SB in both years. We have also found two consistent QTLs on the chromosomes, 2B and 5B with the average PVE of 17.9% and 19.9% respectively. These findings reveal new genomic areas linked to spot blotch disease, which could be used in disease resistance breeding strategies in wheat advancement with further validation.


Introduction
For nearly three-quarters of the world's population, wheat (Triticum aestivum L.) is their primary source of nutrition (FAO, 2018). It provides edible grain which forms staple food for billions of people worldwide (Verma et al. 2020). It is necessary to expedite wheat improvement in order to ful l estimated world food demand by

The latest United
Nations projections indicate that world population will reach 10 billion in the year 2056 (Prakash et al. 2019).
During this heavily populated region of the world, which encompasses India, Pakistan, Nepal, and Bangladesh, the Green Revolution played a key role in ensuring food security (Joshi et al., 2007b).Wheat yields, on the other hand, are under threat from a range of disease kinds and pests, as well as from increased climate unpredictability, all of which threaten crop growth and yields (Gurung et al., 2012). Climate resilient, diseaseresistant, and high-agronomic-value wheat has the potential to greatly boost wheat yields and meet future food needs (Mondal et al., 2016). This devastating wheat disease, which affects 9 million hectares of crops farmed by primarily smallholder farmers in the Eastern Gangetic Plains (Fig. 1), is the most common in the region (CIMMYT, 2013). Shrestha et al. (1998) reported a yield loss of up to 23.8%, Mehta (1998) asserted that spot blotch resulted in high yield losses ranging from 20 percent to 100 percent. For example, Siddique et al. (2006) revealed that productivity losses in Bangladesh could be as high as 22 percent. It was observed by Duveiller et al. 2005, that the combined effect of spot blotch and tan spot HLB complex resulted in a yield drop of around 30 percent on average. In order to combat the devastating effects of spot blotch, researchers are concentrating their efforts on developing disease-resistant strains. Several regions of India's North Eastern Plains Zone (NEPZ), Nepal's Tarai region, and Bangladesh's North Western region have been ravaged by the illness. Spot blotch is estimated to cause yield losses ranging between 15.5 and 19.6 percent, with losses exceeding 100 percent in the most severe cases of infection.
There have been ranges of management measures proposed and applied to prevent the disease including timely sowing, appropriate fertilisation, chemical control, crop rotation, and tillage system (Wolf et al., 1998).
Although host resistance has long been recognised as a vital component of the disease management system, it has only recently been recognised as such. Natural SB resistance is widespread and is determined by a combination of factors including genetics and environment (Singh et al., 2010). In early marker-trait association studies, a number of genes, including the simple satellite repeat (SSR) genes gwm67, gwm570, and gwm469, were found to be associated with SB resistance (Andrie et al., 2007), but newer QTL mapping studies have provided more detailed information on the chromosome locations and effects of SB resistance QTL. In the Chinese resistant cultivar "Yangmai#6," Kumar et al. 2009 found four quantitative trait loci (QTL) on chromosomes 2AL, 2BS, 5BL, and 6DL. Same research group discovered SB resistance QTL on chromosomes 2AS, 2BS, 5BL, and 7DS in the cultivar "Ning 8201," and also on chromosomes 2AS and 2DS on chromosomes 2BS, 3BS, 7BS, and 7DS in the cultivar "Chirya#3" (Rudd et al., 2017). In a mapping population derived from the cross Avocet/Saar, Lillemo et al. 2013 discovered that the pleiotropic multi-fungal resistance gene Lr34, located on chromosome 7DL, had a stronger effect on SB resistance than the gene Lr46, located on chromosome 1BL, in a mapping population derived from the cross Avocet Saar, with the former gene designated Sb1. Using the SYN1 CIMMYT synthetic wheat derived line, Zhu et al. 2014 found QTL for SB resistance in the SYN1 CIMMYT on chromosomes 1B, 3BS, and 5AL. Kumar et al. 2010 discovered a big QTL on chromosome 5BL, which they named Sb2 (Kumar et al., 2015). Sb3 is the name of a QTL on chromosome 3BS that has been ne-mapped and found to be important (Lu et al., 2016). Adhikari et al. 2012

Plant Materials And Experimental Set Up
Resistance was investigated in two-year trials at the Rajendra Prasad Central Agricultural University's research farm in Pusa, Bihar, India (25° 57′ 08′′ N; 85° 40′ 13′′ E). A total of 165 DH lines, including the parents PBW343 and IC252874 were sown during the 2017-2018 and 2018-2019 growth seasons. The third week of December was chosen for seeding to coincide with the post-anthesis period and the warmer temperatures favorable to spot blotch. Three replications of 165 doubled haploid lines and their parents were sowed in an alpha lattice design in each experiment. Two 2 m long beds with four rows each made up the experimental plots. Because it displayed very sensitive reactions (IR = 7-9 on a 0-9 scale) and a 99 score (double-digit score) of SB severity at various stages of crop in the eld.

Irrigation and management practises
Throughout the two years, the required agronomic methods for irrigated and normal fertility (125 kg N; 55 kg P2O5 and 35 kg K2O ha-1) conditions were followed. At the time of seeding, full doses of K2O and P2O5 were administered. Split application of nitrogen was used, with 55 kilogramme N ha-1 applied at sowing, 25 kg N ha-1 applied at rst irrigation (20 days after sowing), and 35 kg N ha-1 used at second irrigation (42 days after sowing). To ensure optimum soil moisture, irrigation was applied to the crop at ve critical growth stages (at crown root initiation Zadok, GS 21; tiller completion Zadok, GS 29; late jointing Zadok, GS 36; owering Zadok, GS 61; and milk stage Zadok, GS 75). Weeds were manually plucked out.
Disease Scoring for spot blotch resistance Single digit scoring After the completion of heading in all genotypes, we noted symptoms in the ag leaf (F) and penultimate leaf (F-1) for single digit scoring. In each plot of three replications, single digit scoring was performed using the CIMMYT standard diagram (Muzeeb -Kaazi et al., 1996).
(%) Disease severity = (sum of numeric rating/ total numbers of plant observed) ×100 Double digit scoring SB severity (Fig 4) was measured at the GS63, GS69, and GS77 stages of plant development, using Saari and Prescott's double-digit (00-99) severity scale, with the rst (D1) and second (D2) digits representing disease progression vertically from the ground and percentage of leaf area infected with SB, respectively.
Using the algorithm provided, the severity of disease was determined for each DHL (Duveiller et al., 2005).

Estimating Area under Disease Progress Curve (AUDPC)
The area under the disease progress curve (AUDPC) value for each genotype was calculated using the disease severity. The area under the disease progression curve was developed to determine disease progression. AUDPC was calculated independently for ag leaf (F), penultimate leaf (F-1), and double digit scoring (Das et al. 1992).
Where, i Yi = Disease severity in the ith date ti = Date on which the disease was scored n = number of dates on which disease was recorded

Genotyping
The wheat Coleoptiles were used to extract DNA (http://www.triticarte.com.au/content/DNA-prepa ration.html). A consensus map was used to do this (Somers et al. 2004). In Thermal Cycler22, a 3 minute denaturation at 94°C was followed by 30 second cycles at 50/65°C, 30 second cycles at 72°C, and a nal 2 minute cycle at 72°C (Sharma et al. 2016). On 541 SSRs, the parents' SSR markers were evaluated for polymorphism. Between parental genotypes, were polymorphic (Fig 5)). The 130 SSRs (gwm, wmc, swm, barc, and cfd) were utilised to create wheat chromosome linkage maps. The visual evaluation of polymorphic SSR pro les was done by coding PBW343 alleles as "A" and IC252874 alleles as "B." The letters "H" and "NA" stand for heterozygote and missing bands, respectively. On 3 percent agarose gels, allele bands were seen.

Linkage map construction and QTL mapping
Using the software MapDisto 2.1.7.1, the genotyped data from the DH population was utilised to create twelve linkage maps. For the conversion of recombination frequency to genetic distance, the Kosambi mapping function and interval position type were utilised. QTL Cartographer v2.5 was used to conduct the analyses (Wang et al., 2010). In the composite interval mapping (CIM) approach, the software Windows QTL Cartographer 2.5 was used to perform forward regression using ve backdrop markers, a window size of 10.0 cM, and a walking speed of 2 cM. Model 6 was used to set the trait for Composite interval mapping (CIM), which was utilised to determine plausible QTL sites and a 1,000 permutation test threshold of P = 0.05. Two or more linked markers associated with a characteristic with LOD > 3.0 were identi ed as putative QTLs.
Suggestion QTLs were classi ed as QTLs with two or more connected markers found at a LOD of 2.0 to 3.0. (McIntyre et al. 2010). The LOD value was set at a minimum of 2.0 in order to account for both suggestive and minor QTL.

Statistical analysis
The disease severity scores across different years were used to obtain best linear unbiased estimates (BLUEs) and predictions (BLUPs) by tting linear mixed effects models in R v4.0.3 (R Core Team, 2019) using where Yik is the trait of interest, m is the mean effect, Yeari is the effect of the ith year, Linek is the effect of the kth line, and +ik is the error associated with the kth line. The genotypes were treated as xed effects in the BLUEs model, while all of the effects in the BLUPs model were treated as random effects. When genotypes are treated as random effects, the impact of screening time and other environmental factors on SB severity is reduced (Tomar et al., 2021). To explore the distribution across the DHLs, the disease severity scores generated by tting the BLUPs and BLUEs models were displayed using ggplot2 v3.3.3 (Wickham, 2016) and ggpubr v0.4.0 (Kassambara, 2020) in R v4.0.3.

Result Phenotypic Evaluation of DHLs
The analysis of variance for AUDPC values revealed a signi cant variation for genotypes and genotype-by-year interaction (Table 1). Large variation in disease severity was observed across the different growth stages with disease pressure increasing from GS63, GS69 and GS77 (Table 2). Across the environments, overall disease pressures were the lowest in E1 and highest in E2. Within the same year, both populations responded similarly as observed from their comparable disease severity/AUDPC score ranges. To enhance the accuracy and map stable QTLs across the environments, linear mixed-effects models were used to obtain tted values of disease severity, accounting for G X E effect. These values are termed as BLUPs (genotypes as random effects) and BLUEs (genotypes as xed effects) from here onward. The BLUPs showed lower variance than the BLUEs which meant BLUPs were able to reduce the environmental variance across the years to a larger extent.
Mean spot blotch severity (%) of the resistant parent (PBW343) and the susceptible parent (IC252874) parents at GS 77 (Zadoks scale, Zadoks et al. 1974) ranged from 80% (2017-2018) to 87% (2018-2019) ( genotypes, DH10, DH125, DH80 and DH134 were found highly resistant across all the growth stages. Overall, less than 5% of lines were categorized under highly resistant category while 25 and 30% of genotypes showed moderate to high susceptibility, respectively. The rest of the lines fell under resistant to moderately resistant category. The data from different years were used separately for QTL mapping.

Linkage map and QTL detection
The anchored markers helped to form 12 linkage groups representing wheat chromosomes in the DH mapping population. A total no. of eighteen QTLs was detected for spot blotch AUDPC over the two years (Table 3). We found the QTLs for spot blotch resistance on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 4A, 4D, 5A, 5B, 6A, and 7A over the course of two years (Fig 3). The LOD values ranged from 2.61 to 15.39 (Fig 6) and the corresponding R2 ranged from 7.43 to 21.12 in the individual years. Individual QTLs explained between 11.31 and 39.15 % of phenotypic variance in the composite interval mapping. Using composite interval mapping, two most consistent QTLs mapped on the chromosome 2B and 5B anked by the marker, Wmc109-gwm312 and Cfd71-

Discussion
Spot blotch is one of the major constraints to the global wheat production, especially in areas with hot and humid climate (Tomar et al., 2021). To counter the constraints from foliar diseases like SB, there is a need for constantly identifying and introgressing new sources of resistance. The DHLs used in the present study showed wide range of variation for different traits and has already been reported to possess various QTLs for heat tolerance (Pankaj et al., 2022;Pankaj et al., 2022). In the present study, during phenotypic evaluation of disease severity for SB, four DH viz. DH10, DH125, DH80 and DH134 were identi ed to be highly resistant against SB.
Because no wheat cultivar presently grown in North-Eestern plains of India possesses resistance to SB, these lines become an important resource for transfer of SB resistance. The phenotypic evaluation of the segregating population showed a wide range of SB severity scores from highly resistant to highly susceptible, which indicated that more than one locus for resistance was segregating in the population. The continuous distribution of spot blotch AUDPC and the test of normality using revealed that the DHLs data t a normal distribution (Fig. 7). The distribution of 165 DHLs for spot blotch AUDPC suggested that spot blotch resistance is polygenic and not controlled by a single gene in the PBW343 and IC252874 cross. Earlier studies on the inheritance of resistance to spot blotch (Joshi et al. 2004b) also suggested a polygenic control. The resistant parent, PBW343 have two genes for disease resistance and resistance was found to be dominant over susceptibility. It revealed that, for spot blotch resistance, duplicate and complementary gene effects are contributing signi cantly along with additive gene effects (Goel et al., 2005) December led the post-anthesis stages to coincide with relatively higher temperature that favoured disease development. It has been reported that spot blotch disease becomes more severe when the mean temperature exceeds 26°C (Chaurasia et al. 2000). The phenotypic evaluation of the segregating population showed a wide range of SB severity scores from highly resistant to highly susceptible, which indicated that more than one locus for resistance was segregating in the population. AUDPC was calculated using the disease severity (%) data, recorded at three growth stages (GS63, GS69, GS77). Kaur et al., 2021 also conducted disease scoring at three different growth stages (GS) on Zadoks' scale (Zadoks et al., 1974), which are GS55 ( owering stage or FS), GS75 (medium milk/dough stage or DS), and GS87 (hard dough stage or HDS).
In this study, DHLs were classi ed into four groups based on days to heading and disease severity was recorded at speci c growth stages when days to headings were synchronized by differential sowing of the DHLs. Hence, the problem of variation in earliness was overcome. Similarly, RILs were classi ed into three groups based on days to heading and disease severity was recorded at speci c growth stages when days to headings were synchronized by differential sowing of the RILs (Kumar et al., 2009).
Following two years of disease recording at different growth stages, an accurate evaluation of the population for resistance to spot blotch was obtained under eld conditions. The results suggest that accuracy and reproducibility of experimental conditions and of the scoring method used for spot blotch evaluation is reliable.
Most of the recent studies on spot blotch (Joshi et al. 2007a) are also based on AUDPC. We deployed approximately 10-20 microsatellite markers covering wheat chromosomes. The ratio of polymorphic markers of nearly 30% was consistent with results of Prasad et al. (1999) and Roy et al. (1999). The order and orientation of the mapped microsatellite markers in our study was in agreement with those in the map of the ITMI population (Ganal and Ro¨der 2007). There were ve loci that did not segregate in 1:1 mendelian ratio showed segregation distortion. These loci were randomly distributed throughout the genome. However, the linkage map was not affected by the distortion and we did not include these loci in the linkage map. Since only 35% of the markers were polymorphic, gaps in certain region were not covered. Across two years, we identi ed eighteen Kaur et al., 2021 discovered a QTL, QSb.pau-6A and it was mapped 53 Mb from QTL SNP_3021829 (Bainsla et al., 2020) mapped in the same genomic region. A gene for Ubiquitin family protein was found anking the QTL.
Ubiquitin, which is a part of the ubiquitin-proteasome system (UPS), controls various pathways including response to biotic and abiotic stresses (Sadanandom et al., 2012), and acts as one of the major systems in plant immunity (Üstün et al., 2016).The QTL mapping achieved in this study should therefore serve as a starting point for developing a more detailed map and initiating a marker assisted selection approach. Successful MAS and cloning of the key resistance QTL will critically depend on the introduction of novel anking markers and high-resolution mapping populations in the future. Thus, it is necessary to investigate additional marker systems, such as genotyping by sequencing, in order to develop markers for marker-assisted transfer of additional QTLs found in this work.

Declarations
Tomar V, Singh D, Dhillon GS, Singh RP, Poland J, Joshi AK, Singh PK, Bhati PK, Kumar S, Rahman M, Tiwari BS  Page 17/18 Disease severity/symptoms for spot blotch on wheat leaves  An integrated wheat genetic map displaying the locations of various QTL and genes for spot blotch resistance/sensitivity on wheat chromosomes.