QTL mapping and candidate gene mining for sheath blight resistance in rice (Oryza sativa L.) using QTLseqr approach

QTL mapping and candidate gene mining for sheath blight resistance in rice (Oryza sativa L.) using QTLseqr approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article QTL mapping and candidate gene mining for sheath blight resistance in rice (Oryza sativa L.) using QTLseqr approach Mayur R. Thesiya, Jagjeet Singh Lore, Dharminder Bhatia, Sanjay Kumar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4374976/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Sheath blight (ShB) caused by Rhizoctonia solani is a devastating disease that poses a major threat to rice ( Oryza sativa L.) production worldwide. In this study, next generation sequencing assisted bulk segregant analysis (BSA) integrated with R package i.e. QTLseqr was utilized to identify QTL regions controlling the sheath blight resistance trait. F 3 mapping progenies for ShB resistance trait was derived from the cross between susceptible rice cultivar PR121 and resistant donor IET 22769. Based on sheath blight screening of F 3 progenies under artificial inoculation conditions, fifteen resistant (20-30 cm lesion height) and fifteen highly susceptible (70-85 cm lesion height) progenies were selected. DNA of the selected progenies were extracted and bulked respectively to constitute ShB-R and ShB-S bulks respectively. The two bulks along with parents were sequenced at > 20 X read depth. A total of 11,45,820 high-quality single nucleotide polymorphism (SNPs) were used for QTL-seq analysis using QTLseqr package. QTL analysis identified five QTLs namely qShB1, qShB3, qShB5.1, qShB5.2 and qShB6 on chromosome 1, 3, 5 and 6, respectively for resistance to ShB. A total of 69 candidate genes were identified within the QTL regions including leucine-rich repeat receptor-like kinase, coiled-coil nucleotide-binding and transcription factor protein etc. which might play a significant role in defense mechanism against R . solani . The identified QTLs and candidate genes can be further studied to understand genetics of ShB resistance in rice and to develop ShB resistant varieties. Oryza sativa QTLseqr QTL mapping Rhizoctonia solani Sheath blight Figures Figure 1 Figure 2 Figure 3 Introduction Sheath blight (ShB) caused by Rhizoctonia solani Kühn [Teleomorph: Thanatephorus cucumeris (Frank) Donk], is one of the most prevalent soil-borne necrotrophic pathogens of rice worldwide. Since its first report in Japan in 1910 (Rush 1992), the disease has significantly increased after 2000 in the rice growing areas of India (Dey et al 2016). ShB incidence during flowering and panicle initiation stage significantly impacts the quality of rice grains, reduction in total grain weight and lead to losses ranging from 20–70% (Abbas et al. 2023 ). A range of 20.1–27.3% losses have been reported in mega rice varieties of South Asia (Lore et al. 2021). Presently, the predominant method for controlling this disease mainly depends on the use of chemical fungicides (Singh et al. 2019). Excessive use of pesticides has resulted in the emergence of novel pathotypes that exhibit resistance to these fungicides (Zeng et al. 2011) besides causing environmental pollution. Host plant resistance is economical and environmentally safe method for imparting resistance against ShB. Resistant sources with varying levels of tolerance to ShB have been reported to date (Khush 1977; Guo et al. 1985; Groth and Novick 1992; Lore et al. 2013 ; Aggarwal et al. 2019,; Molla et al. 2020). Based on genetic studies in such sources, resistance to sheath blight has been reported as a complex quantitative trait controlled possibly by polygenes (Sha and Zhu 1990; Li et al. 1995; Pinson et al. 2005). However, few researchers have proposed it to be controlled by only a few major genes (Xie et al. 1992; Pan et al. 1999). The quantitative trait loci (QTLs) for sheath blight have been identified in promising rice lines, germplasm, cultivars, land races and wild relatives by various workers viz ., qSB-7 in Teqing (Pinson et al. 2005), qShB3-1 in Jasmine 85 (Liu et al. 2009 ), qSBR 1–1 in Tetep (Channamallikarjuna et al. 2010 ), qShB6 in O. nivara (Eizenga et al. 2013 ), qshb8.1 in ARC10531 (Yadav et al. 2015), and qShB9 in O. nivara (Eizenga et al. 2022), qsbr10 .1 and few other QTLs in O. nivara accessions (Bhatia et al. 2024). Over 110 genetic loci have been identified governing resistance against ShB (Aggarwal et al. 2022) using biparental mapping and genome wide association studies. ShB resistant genes underlying major QTLs have been identified in rice as well as maize. In rice, OsWAK91 , a wall associated kinase 1 gene present in the major sheath blight resistance QTL region on chromosome 9, has been identified against resistance to ShB (Al-Bader et al. 2023). In maize, ZmFBL41 encoding F-box protein has been uncovered for resistance against banded sheath blight (Li et al. 2019) caused by R. solani . These studies are raising opportunities to identify major genes underlying other major QTLs. Thus, there is a need to identify other potential QTLs for resistance to sheath blight, which in turn could help in the development of rice varieties having durable resistance to sheath blight. Next Generation Sequencing based Bulked Segregant Analysis (NGS-BSA) is an efficient method in identifying QTLs that have significant impacts (Michelmore et al. 1991; Magwene et al. 2011 ; Vikram, et al. 2012). This is achieved by genotyping just the extreme phenotypes using NGS based high-throughput genotyping approaches, rather than the full mapping population. QTL-seq is one of the version of NGS-BSA, created by Takagi et al. ( 2013 ) and has been extensively used to identify major effect QTLs in many crops governing number of traits (Das et al. 2014; Lu et al. 2014; Win et al. 2016). QTLseq has been widely used in many crops such as rice (Bommisetty, et al. 2020; Lei et al. 2020), squash (Ramos et al. 2020), Korean cucumber (Zhang et al. 2021 a), tomato (Wen et al. 2019), spring wheat (Wang et al. 2021), melon (Qiao et al. 2021) and cotton (Zhang et al. 2021 b). QTL-seq has also been used in peanut (Korani et al. 2021) to identify regions in the genome controlling specific traits, such as disease resistance (Luo et al. 2019a) and physiological traits (Kumar et al. 2020). The QTLseqr package offers a quick and easy-to-use tool for researchers to conduct NGS-BSA using different analysis methods (Mansfeld and Grumet 2018 ). So present study was planned to map QTLs using the QTLseqr approach, derived from a cross between the resistant donor IET 22769 and the susceptible cultivar PR121. Material and Methods Experimental material PR121 is a high yielding, semi-dwarf rice variety released by Punjab Agricultural University (PAU), India in 2013, but shows susceptible reaction to ShB (Bharaj et al. 2014). The IET22769 is advanced breeding line with moderately resistant reaction to ShB that has been identified based on screening for more than five years (J S Lore, Personnel communication). F 1 seeds of cross PR121× IET22769 were selfed at ICAR-National Rice Research Institute, Cuttack, India to generate F 2 seeds during off-season 2021. Individual F 2 seeds were planted and selfed to generate F 3 progenies. A total of 347 F 3 progenies along with parents, susceptible check D-6766 (mutants of IR 64) (Lore et al. 2013 ) and standard resistant check Tetep (Channamallikarjuna et al. 2010 ) were raised in nursery and thirty-day-old seedlings were transplanted at 15 × 20 cm spacing using one seedling per hill during crop season 2022 at PAU, India. Ten plants of each progeny along with parents and checks were screened against sheath blight at maximum tillering stage. Sheath blight inoculation and disease assessment The Rhizoctonia solani was mass multiplied on maize meal-sand (1:3) medium. Glass flasks containing media were inoculated with pure culture of R. solani and incubated at 25 ± 2°C for 10 days. Five gram of inoculum was transferred to center of hill of each plant to be evaluated (Thind et al. 2008 ). Disease assessment was made 21 days after inoculation (DAI) under field conditions. The total lesion height (cm) was recorded from base of the plant to the topmost lesions on the stem. The plant height was also measured from bottom to top leaf height. Relative lesion height (RLH) was calculated using the following formula: lesion height / plant height X 100 (Sharma et al. 1990 ). Scoring was done based on Standard Evaluation System (SES) for rice sheath blight using scale 0–9 (IRRI, 2014 ). DNA extraction, bulks generation and whole genome re‑sequencing Genomic DNA was isolated from young leaves of selected F 3 progenies and parental line using cetyl trimethyl ammonium bromide (CTAB) method (Murray and Thompson 1980 ). The quality and quantity of genomic DNA was analyzed using NanodropTM 8000 spectrophotometer according to manufacturer’s manual (Thermo Fisher Scientific, USA). DNA was extracted from each selected line and was normalized to 1 µg/microliter concentration. Equal quantity of DNA of each individual F 3 progeny was then pooled to form ShB-resistant bulk (ShB-R) and ShB susceptible bulk (ShB-S). Both the parental lines and two bulks, i.e., ShB-R and ShB-S, were whole genome sequenced at 20X using pair-end sequencing on Illumina NovaSeq 6000 sequencer which was outsourced to NGB Diagnostics, New Delhi, India. Variant identification The short raw reads obtained from sequencing were initially trimmed with minimum phred Q score 30 at minimum 90% of reads using FASTQC version 0.11.8. FASTQ format sequences were then processed using Trimmomatic version 0.39 to filter low quality and adaptor sequences. The filtered reads aligned with the reference genome sequences of Oryza sativa IRGSP-1.0 using Burrows-Wheeler Aligner 0.7.17-r1188. The resulting mapped SAM (sequence alignment/map format) files were converted to BAM (binary version of SAM files) using SAMTools version 1.15. Further, picard tool ( https://broadinstitute.github.io/picard ) was used to modify read groups to make them compatible for variant calling followed by sorting the BAM files using Samtools. Variant calling was performed using Genome Analysis Toolkit (GATK, McKenna et al. 2010 ) to obtain single nucleotide polymorphism (SNP) and insertion and deletions (InDels). The variant file was filtered based on missing data ≥ 0.9 and minimum allele frequency ≥ 0.05 using VCFtools (Danecek et al. 2011 ). The filtered variants including SNPs and InDels were converted to table file using VariantsToTable format. QTL analysis using QTLseqr QTL(s) controlling resistance to ShB in rice was identified through QTLseqr (Mansfeld and Grumet 2018 ). QTLseqr apply the both statistical approaches such as G statistic (Magwene et al. 2011 ) and QTLseq (Takagi et al. 2013 ) in programming language R version 4.2.2 ( https://www.r-project.org/ ) to rapidly identify the significant QTL(s) contributing to traits of interest. The filtered SNP data of both bulks exported from VariantsToTable function of GATK was imported to QTLseqr using import from GATK function. Variants were again filtered at reference allele frequency = 0.10, minimum total depth = 40, minimum sample depth = 15 and 1.5e6 window size to minimize the noise and improve results. QTLseqr script was used to make plots and QTLseq analysis files. It calculates the total reference allele frequency for both bulks together, the SNP-index for each SNP in each bulk and the Δ (SNP- index) as follows; Reference allele frequency is equal to Ref allele depth High Bulk + Ref allele depth Low Bulk / total read depth for both bulks. SNP-index per bulk is equal to alternate allele depth / total read depth. Δ(SNP-index) is equal to SNP-index High Bulk − SNP-index Low Bulk . The analysis in QTLseqr is an execution of both pipelines for bulk segregant analysis, Δ(SNP-index) and G statistic/G' described by Takagi et al. ( 2013 ) and Magwene et al. ( 2011 ), respectively. The following analysis was performed: A tricube-smoothed Δ(SNP-index) A tricube-smoothed Δ(SNP index) was calculated within 1 Mb window size using the function, runQTLseqAnalysis for each SNP from the allele depths in comparison to uniform or rectangular window used by Takagi et al. ( 2013 ). It reduces the noise at the time of accounting linkage disequilibrium between SNPs. Δ(SNP-index) was simulated over 10,000 replications for both the bulks at each read depth. The extreme quantiles were used as confidence intervals (p < 0.05 and p = 0.3 SNP-index in both simulated bulks. A tricube-smoothed G statistic/G' A tricube-smoothed G statistic or G‟ was calculated for each SNP based on the observed and expected allele depths and smoothing this value using a tricube smoothing kernel within 1 Mb window size using the function runGprimeAnalysis. Initially it calculates the G statistic for each SNP and then it counts the number of SNPs and finally estimates the tricube-smoothed G‟ and ∆(SNP-index) values of each SNP within 1 Mb window size. A tricube-smoothed G‟ was computed by constant local regression within each chromosome using tricubeStat function. It allows noise reduction associated to SNP calling errors while computing linkage disequilibrium. P-values are computed using the non-parametric method given by Magwene et al. ( 2011 ) using function getPvals. G‟ and adjusted p-values were estimated with respect to the false discovery rate (FDR), q = 0.01 with the null distribution assuming there is no QTL linked to the SNP, respectively. Graphical representation of SNPs/window, the tricube-smoothed ∆ (SNP-index) and G‟ values, or the –log10(p-value) was done using plotQTLStats function. The statistically significant QTL(s) controlling resistance to sheath blight were identified from these graphs, as described below: a) Genomic regions that have tricube smoothed ∆ (SNP-index) surpassing the threshold level of simulated confidence interval and are putative QTL(s); b) A tricube smoothed G‟ and their estimated p -values at which genomic region surpass the threshold level of 0.01 FDR that contains the QTL(s) Extracting QTL(s) data and candidate gene mining After identification of putative QTL regions from plotted graphs, getSigRegions and getQTLTable functions were used to extract and summarize QTL(s) data. Candidate defense responsive genes present in the QTL regions associated with ShB resistance, were retrieved using BioMart of Ensembl Plants with Oryza sativa subsp. japonica genome (IRGSP-1.0) as reference. Results Screening of parents and F 3 progenies against sheath blight A total of 347 F 3 progenies along with parents and checks were screened against sheath blight. The susceptible (PR121) and resistant (IET22769) parents showed clear phenotypic differences with mean ShB score of 7.3 and 3.7 respectively (Fig. 1 ). Mean ShB score of resistant check “Tetep” was 4.9 while of susceptible check was 9.0. Mean ShB disease scores of F 3 progenies ranged from 2.1 to 9.0, with majority of lines ranged from 3.1 to 7.0 (Fig. 2 a). Further all the F 3 progenies were categorized into different groups based on the type of disease reaction. The lines that showed mean disease score of 3.0 to 4.9 were categorized as moderately resistant, 5.0 to 6.9 as moderately susceptible, 7.0 to 8.9 as susceptible and 9.0 as highly susceptible. The frequency distribution curve of F 3 population was positively skewed with skewness of 0.407 and showed platykurtic distribution as compared to normal distribution (Fig. 2 a). Based on the screening of F 3 progenies, fifteen moderately resistant (showing higher level of resistance as compared to rest of the population) and fifteen highly susceptible progenies (Suppl. Table 1) were selected to constitute ShB-R and ShB-S bulks respectively (Fig. 2 b). Identification of genome wide SNPs and QTLseqr analysis A total of 107.69 GB data with 346.84 million clean reads with QC > 30 were obtained from re-sequencing of two bulks (ShB-R, ShB-S) and two parental genotypes (Table 1 ) that represented 90% of the original sequencing data. The short reads were mapped to the reference genome using Bowtie and GATK to identify SNPs. A total of 11,45,820 high-quality SNPs were identified after filtering. The highest number of SNPs were identified on chromosome 1 followed by chromosome number 2 and it was the lowest on chromosome 9 (Supply Table 2 ). Table 1 Number of reads and total GB data obtained from whole genome re-sequencing of bulks and parents after quality control (QC) Samples No. of Reads GC% Total data in GB Resistant bulk (ShB-R) 79188477 41 24.59 Susceptible bulk (ShB-S) 95167740 42 29.55 Susceptible parent (PR121) 97790249 42 30.36 Resistant parent (IET22769) 74697050 41 23.19 Table 2 Quantitative trait loci (QTLs) regions associated with sheath blight resistance obtained through QTLseqr Chromosome (Ch) QTL name Start (Mb) End (Mb) Length (Mb) No. of SNPs in the QTL region Mean No. SNP/Mb Mean ∆(SNP- index) Mean G’ Mean p - value Mean q-value List of identified ShB-QTLs co-localized with previous report Ch 1 qShB1 34.89 40.05 5.16 25489 4941 0.262 14.66 8.22E-05 0.0016 Liu et al. ( 2009 ), Sharma et al. ( 2009 ), Gaihre et al. (2011), Eizenga et al. ( 2013 ), Zeng et al. ( 2015 ), Bal et al. ( 2020 ) Ch 3 qShB3 33.34 36.41 3.07 11223 3658 -0.006 16.26 9.29E-05 0.0015 Taguchi-Shiobara et al. ( 2013 ), Zeng et al. ( 2015 ) Ch 5 qShB5 .1 0.95 10.46 9.50 55335 5822 -0.101 12.93 22.02E-05 0.0028 Liu et al. ( 2009 ) Ch 5 qShB5 .2 13.91 21.05 7.15 27048 3784 -0.127 15.60 24.65E-05 0.0027 Gaihre et al. (2011) Ch 6 qShB6 0.81 4.27 3.46 16570 4789 -0.163 13.69 21.52E-05 0.0026 - Max G ’ – the max G‟ score in the region, Mean G‟ – the average G‟ score of that region, G‟ Std. dev – the standard deviation of G‟ within the region, Mean p- value – the average p- value s in the region. Identification of QTLs governing resistance to sheath blight using QTLseqr To infer the QTL region conferring ShB resistance, the genome-wide comparison SNP-index of ShB-R and ShB-S bulk over the entire length of genome was performed using QTLseqr. SNPs were further filtered based on the following parameters: the reference allele frequency 0.10, minimum total sample read depth, DP > = 50, maximum total sample read depth, DP = 20 and genotype quality, GQ > = 30. SNP-index was calculated as proportion of short reads harboring distinct SNPs to the total short reads covering the particular genomic position (Abe et al. 2012 ). If SNP-index is 0.5, we assume equal contribution from both parents to bulked progeny. If SNP-index is 0 means entire short reads represent genome of parent that is used as reference sequence and 1 means short reads harbors genomic fragments from another parent. For individual SNPs, the SNP index and G statistics were calculated as reported by Takagi et al. ( 2013 ) and Magwene et al. ( 2011 ), respectively using QTLseqr pipeline developed by Mansfeld and Grumet ( 2018 ). This approach identified the statistically significant QTL(s) on the basis of Δ(SNP index) and G statistic/ G prime with p- value analysis. Within a window size of 1.0 Mb genomic area, the tricube-smoothed delta SNP index and G value (G‟ value) were computed and plotted against all the twelve chromosomes of rice. Significant thresholds i.e., confidence intervals ( P < 0.05 and P < 0.01) and false discovery rate, FDR (q = 0.01) (Benjamini and Hochberg 1995) were estimated while calculating Δ(SNP index) and G statistic along with p- values from the null distribution assuming that there was no QTL linked to the SNP, respectively (Magwene et al. 2011 , Yang et al. 2013 ). Significant spikes have been observed on genomic regions of chromosome 1, 3, 5 and 6 on both tricube smoothed ∆(SNP-index) and G‟ plots surpassing the significant threshold i.e., confidence intervals ( P < 0.05, red and P < 0.01, blue) and FDR (q) of 0.01 respectively, indicating that these QTL regions are linked with sheath blight resistance (Fig. 3 ). The QTLs identified on chromosome 1, 3, 5, and 6 were named as qShB1 , qShB3 , qShB5.1 , qShB5.2 and qShB6 respectively (Table 2 ). Genomic regions locating QTLs varied from 3.07 Mb ( qShB3 ) on chromosome 3 to 9.50 Mb ( qShB5.1 ) on chromosome 5. The qShB1 spanned from 34.89 to 40.05 Mb on rice genome; qShB3 from 33.34 to 36.41 Mb, while qShB5.1 and qShB5.2 lies between 0.95 to 10.46 Mb and 13.91 to 21.05 Mb on rice genome, respectively. The QTL identified on chromosome 6 ( qShB6 ) spanned the genomic region from 0.81 to 4.27 Mb on rice genome (Table 2 ). A tricube smoothed Δ(SNP-index) distribution revealed the significant peak near to 95 and 99% confidence intervals on chromosome 1, 3, 5 and 6. Remaining chromosomes exhibited Δ(SNP-index) peaks under confidence intervals. Further, the tricube smoothed G' analysis also displayed the significant peak among all identified QTLs (Table 2 ) on chromosome 1, 3, 5 and 6 that surpass the threshold of FDR, q = 0.01 indicating the high likelihood of containing QTL(s) for sheath blight resistance (Fig. 3 ). All the other G' peaks scattered throughout the genome were apparently under the threshold level of FDR. Similar significant peaks were observed in log 10 ( p -values) analysis. Identification of putative candidate genes governing sheath blight resistance A total of 69 genes were predicted within identified QTL region on chromosome 1, 3, 5 and 6 (Suppl. Table 3). Candidate genes were selected based on gene stable ID, gene description, go name/ domain and its putative role in resistance against pathogens. The 69 genes included leucine-rich repeat receptor-like kinase (LRR-RLK), serine/threonine protein kinase, chitinase, gibberellin signal transduction and NBS-LRR encoding genes, abscisic acid hormone signal transduction and phenylpropanoid biosynthesis pathways, WRKY transcription factor 4, mitogen-activated protein kinase (MAPK), receptor-like kinases, zinc finger protein indicating that these enzymes/proteins might play important roles in rice defense pathway against R. solani. Discussion Sheath blight of rice is an emerging disease that has potential to do significant losses in rice growing areas. In the absence of true genetic resistance (Lore et al. 2013 ) and polygenic nature, breeding for ShB resistance poses significant challenges till date (Pinson et al. 2005). The previous reports indicated the existence of diverse sources of resistance in the rice germplasm and various QTLs associated with ShB resistance have also been identified (Jia et al. 2012; Yadav et al. 2015; Eizenga et al. 2022). Identification of diverse QTLs governing resistance to ShB and associated molecular marker will accelerate development of ShB resistant cultivars; combining of these QTLs in single background will build understanding of QTL X QTL interaction and development of broad-spectrum resistance against ShB. Further identification of OsWAK91 , a wall associated kinase 1 gene governing ShB resistance in rice (Al-Bader et al. 2023) has inspired identification of other putative QTLs and underlying genes that can be used in breeding programme. The IET22769 was identified as a promising donor showing moderately resistant reaction to ShB based on artificial screening for more than five years however it was even better than widely used resistant check “Tetep” in case of sheath blight of rice. The frequency distribution of F 3 progenies derived from the cross of PR121 and IET22769 for ShB disease reaction was continuous and fitted into normal distribution as expected from a quantitative trait. However, the two extremes with significant number of plants in each could be identified to make two contrasting bulks for further QTL analysis. Identification of QTLs through conventional methods is laborious and tedious because every individual of the mapping population needs to be genotyped. Bulked sergeant analysis is an efficient strategy for identifying the DNA markers linked to the gene of interest (Michelmore et al.1991). In bulked DNA, all loci are randomized; except for the region containing the gene of interest and polymorphic markers may represent markers that are linked to the gene or QTL of interest (Collard and Mackill 2005). Combination of NGS based BSA with QTLseqr is quick in identifying QTLs from large segregating population with more statistical significance. QTLseqr in the current study identified five different QTLs on four different chromosomes depending upon the selected confidence interval and FDR rate as the margins of each region. The FDR rate of 0.01 utilized in the G' method was more stringent than using a confidence interval of 99% with the QTLseqr method (Mansfeld and Grumet 2018 ). Similar observations were recorded in the data analysis involved in identification of QTLs controlling cold tolerance in rice seedlings. In this regard, Yang et al. ( 2013 ) reported the putative QTLs at chromosome 1, 2, 8 and 10 along with two small peaks at chromosome 2 and 5. Whereas, Mansfeld and Grumet ( 2018 ) identified the major QTLs on chromosomes 1, 2, 8 and 10 only uses the similar data. QTLseqr provides multiple testing options for the identification of statistically significant QTL(s). Dayanand (2021) used QTLseqr pipeline to identify four QTLs on chromosomes 4, 6, 8 and 12 for resistance against brown spot disease in rice. Zhang et al. ( 2021 ) identified four QTLs for heading date on chromosomes 3, 6, 9 and 10, three QTLs for plant height on chromosomes 1, 8 and 10 and two QTLs for panicle length on chromosomes 1 and 5 using QTLseqr approach. Four of the ShB-QTLs detected in this study were co-localized in same chromosome segments with the ShB-QTLs reported in previous studies against sheath blight disease of rice indicating that these QTL regions are important to study further for resistance against ShB. Candidate gene mining analysis revealed 69 different genes which were involved in defense mechanism against plant pathogens. The qShB1 interval had 19 genes that included defense responsive gene, coded for abscisic acid hormone signal transduction pathways (Feng et al. 2023 ), leucine-rich repeat receptor-like kinase (LRR-RLK) (Acharya et al. 2022 ) involved in the response to R. solani. The qShB3 QTL region spanned 13 genes including gene for phenylpropanoid biosynthesis (Feng et al. 2022 ), serine/threonine protein kinase (Oreiro et al. 2020 ) that might play important roles in host defense against R. solani. Fourteen defense genes i.e. chitinase, gibberellin signal transduction and NBS-LRR encoding genes were present in the QTL regions qShB5 .1, qShB5 .2 that were reported to be responsible for ShB resistance in transgenic rice plant (Baisakh et al. 2001 ; Datta et al. 2001 ; Rostami et al. 2019 ; Zhang et al. 2019 ; Bal et al. 2020 ). The qShB6 QTL region had 23 genes including WRKY transcription factor 4 (Wang et al. 2015 ), mitogen-activated protein kinase (MAPK) (Zhang et al. 2018 ), receptor-like kinases (Acharya et al. 2022 ), zinc finger protein (Shamim et al. 2022 ). In conclusion, the study has identified the important QTLs and underlying candidate genes that might play important role in further understanding genetics of sheath blight resistance in rice and development of sheath blight resistant cultivars. Conclusion The present study identified five QTLs namely qShB1, qShB3, qShB5.1, qShB5.2 and qShB6 on chromosome 1, 3, 5 and 6, respectively and putative candidate genes in the corresponding QTL intervals governing resistance to sheath blight of rice in F 3 progenies derived from susceptible and resistant parent using QTLseqr approach. These QTLs and candidate genes can be studied further in details to understand genetics of sheath blight resistance in rice. Declarations Conflict of Interest The authors declare no conflict of interest in relation to this publication. Authors Contribution JSL, DB designed the research work and optimized the protocol of BSA and QTL-seq; MRT and JSL conducted the experiments and collected the phenotypic data; MRT and DB did QTLseqr analysis. MRT, JSL, SK and DB have written the manuscript; DB, MSH, JSL, SK and RK gave critical suggestions throughout the study and reviewed the manuscript. Acknowledgements Authors thankfully acknowledged Punjab Agricultural University, Ludhiana, India for providing the infrastructure and other facilities for conducting experiments. Financial assistance provided to Mayur R. Thesiya by the Science and Engineering Research Board (SERB)-DST, Government of India, New Delhi is especially acknowledged. Data Availability Statement The data generated and/or analysed during the present study are attached as supplementary information file and can also be obtained from the corresponding author on request. References Abbas A, Mubeen M, Iftikha, Y, Shakeel Q, Imran Arshad HM, Carmen Zuñiga Romano MD, Hussain S (2023) Rice Sheath Blight: A Comprehensive Review on the Disease and Recent Management Strategies. Sarhad Journal of Agriculture 39:111–125. Abbas A, Fu Y, Qu Z, Zhao H, Sun Y, Lin Y, Xie J, Cheng J, Jiang D (2021) Isolation and evaluation of the biocontrol potential of Talaromyces spp. against rice sheath blight guided by soil microbiome. Environmental Microbiology 23:5946–5961. Abe A, Takagi H, Fujibe T, Aya K, Kojima M, Sakakibara H, Uemura A, Matsuoka M, Terauchi R (2012) OsGA20ox1 , a candidate gene for a major QTL controlling seedling vigor in rice. Theoretical and Applied Genetics 125:647–657. Acharya U, Das T, Ghosh Z, Ghosh A (2022) Defense Surveillance System at the Interface: Response of Rice towards Rhizoctonia solani During Sheath Blight Infection. Molecular Plant-Microbe Interactions 35:1081–1095. Baisakh N, Datta K, Oliva N., Ona I, Rao GJN, Mew TW, Datta SK (2001) Rapid development of homozygous transgenic rice using anther culture harboring rice chitinase gene for enhanced sheath blight resistance. Plant Biotechnology 18:101–108. Bal A, Samal P, Chakraborti M, Mukherjee AK, Ray S, Molla KA, Behera L, Samal R, Sarangi S, Sahoo P, Behera M (2020) Stable quantitative trait locus (QTL) for sheath blight resistance from rice cultivar CR 1014. Euphytica 216:1–19. Benjamini Y, Hotchberg Y (1995) controlling the false discovery rate - A practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57: 289–300. Channamallikarjuna V, Sonah H, Prasad M, Rao GJN, Chand S, Upreti HC, Singh NK, Sharma TR (2010) Identification of major quantitative trait loci qSBR11-1 for sheath blight resistance in rice. Molecular Breeding 25:155–166. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158. Datta K, Tu J, Oliva N, Ona I, Velazhahan R, Mew TW, Muthukrishnan S, Datta SK (2001) Enhanced resistance to sheath blight by constitutive expression of infection-related rice chitinase in transgenic elite indica rice cultivars. Plant Science 160:405–414. Dayananda VP (2021) Molecular and biochemical basis of resistance to brown spot in rice ( Oryza sativa L.). Dissertation, Punjab Agricultural University, India. Dubey AK, Pandian RTP, Rajashekara H, Khanna A, Ellur RK, Sharma P, Kumar A, Singh AK, Gopalakrishnan S, Rathour R, Singh UD (2014) Molecular validation of blast and sheath blight resistance in improved rice genotypes and landraces. Indian phytopathology 67:216–221. Eizenga GC, Prasad B, Jackson AK, Jia MH (2013) Identification of rice sheath blight and blast quantitative trait loci in two different O. sativa / O. nivara advanced backcross populations. Molecular Breeding 31:889–907. Feng T, Zhang ZY, Gao P, Feng ZM, Zuo SM, Ouyang SQ (2023) Suppression of rice osa-miR444. 2 improves the resistance to sheath blight in rice mediating through the phytohormone pathway. International Journal of Molecular Medical Science 24:3653. Feng ZM, Peng GAO, Zhao JH, Wang GD, Zhang HM, Cao WL, Xiang XUE, Zhang YF, Rong HUA, Chen ZX, Chen XJ (2022) iTRAQ-based quantitative proteomics analysis of defense responses triggered by the pathogen Rhizoctonia solani infection in rice. Journal of Integrative Agriculture 21:139–152. Gaihre YR, Yamagata Y, Yoshimura A, Nose A (2015) Identification of QTLs involved in resistance to sheath blight disease in rice line 32R derived from Tetep. Tropical Agriculture and Development 59:154–160. IRRI (2014) Standard evaluation system for rice . International Rice Research Institute, Manila. Liu G, Jia Y, Correa-Victoria F J, Prado GA, Yeater KM, McClung A, Correll JC (2009) Mapping quantitative trait loci responsible for resistance to sheath blight in rice. Phytopathology 99:1078–1084. Lore JS, Hunjan MS, Singh P, Willocquet L, Sri S, Savary S (2013) Phenotyping of partial physiological resistance to rice sheath blight. Journal of Phytopathology 161:224–29. Magwene PM, Willis JH, Kelly JK (2011) The statistics of bulk segregant analysis using next generation sequencing. PLoS Computational Biology 7:1–9. Mansfeld BN, Grumet R (2018) QTLseqr: an R package for bulk segregant analysis with next-generation sequencing. Plant Genome 11:180006. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20:1297–1303. Murray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research 8:321–26. Oreiro EG, Grimares EK, Atienza-Grande, G, Quibod IL, Roman-Reyna V, Oliva R (2020) Genome-wide associations and transcriptional profiling reveal ROS regulation as one underlying mechanism of sheath blight resistance in rice. Molecular Plant-Microbe Interactions 33:212-222. Rostami M, Tarighi S, Taheri P, Rahimian H (2019) Genes expression of chitinase, β-1, 3-gluconase and peroxidase enzymes in rice treated with the causal agent of sheath blight diseases, antagonistic and inducer bacteria, and potassium silicate. Applied Entomology and Phytopathology. 87:129-146. Shamim M, Sharma D, Bisht D, Maurya R, Kaashyap M, Srivastava D, Mishra A, Kumar D, Kumar M, Juturu VN, Khan NA (2022) Proteo-Molecular Investigation of Cultivated Rice, Wild Rice, and Barley Provides Clues of Defense Responses against Rhizoctonia solani Infection. Biotechnology and Bioengineering 9:589. Sharma A, McClung AM, Pinson SR, Kepiro JL, Shank AR, Tabien RE, Fjellstrom R (2009) Genetic mapping of sheath blight resistance QTLs within tropical japonica rice cultivars. Crop Science 49:256–64. Sharma NR, Teng PS, Olivares PM (1990) Comparison of assessment methods for rice sheath blight disease. Philippines Phytopathology 26:20–24. Taguchi-Shiobara F, Ozaki H, Sato H, Maeda H, Kojima Y, Ebitani T, Yano M (2013) Mapping and validation of QTLs for rice sheath blight resistance. Breeding Science, 63:301-308. Takagi H, Abe A, Yoshida K, Kosugi S, Natsume S, Mitsuoka C, Uemura A, Utsushi H, Tamiru M, Takuno S, Innan H (2013) QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome re-sequencing of DNA from two bulked populations. Plant J 74:174–183 Thind TS, Mohan C, Sharma, VK, Raj P, Arora JK, Singh PP (2008) Functional relationship of sheath blight of rice with crop age and weather factors. Plant Disease Research 23:34–40. Wang H, Meng J, Peng X, Tang X, Zhou P, Xiang J, Deng X (2015) Rice WRKY4 acts as a transcriptional activator mediating defense responses toward Rhizoctonia solani , the causing agent of rice sheath blight. Plant Molecular Biology 89:157-171. Yang Z, Huang D, Tang W, Zheng Y, Liang K, Cutler AJ, Wu W (2013) Mapping of quantitative trait loci underlying cold tolerance in rice seedlings via high-throughput sequencing of pooled extremes. PLoS ONE 8: e68433. Zeng YX, Xia LZ, Wen ZH, Ji ZJ, Zeng DL, Qian QI, Yang CD (2015) Mapping resistant QTLs for rice sheath blight disease with a doubled haploid population. Journal of Integrative Agriculture 14:801–810. Zhang B, Qi F, Hu G, Yang Y, Zhang L, Meng J, Han Z, Zhou X, Liu H, Ayaad M, Xing Y (2021) BSA-seq-based identification of a major additive plant height QTL with an effect equivalent to that of Semi-dwarf 1 in a large rice F2 population. Crop Journal 9:1428–37. Zhang C, Huang M, Sang X, Li P, Ling Y, Zhao F, Du D, Li Y, Yang Z, He G (2019) Association between sheath blight resistance and chitinase activity in transgenic rice plants expressing McCHIT1 from bitter melon. Transgenic Research 28:381-390. Zhang H, Feng Z, Wang Y, Shan W, Dong Y, Zeng W, Cao W, Zhang Y, Chen Z, Chen X, Pan X (2018) Expression pattern of rice MAPK genes in response to sheath blight pathogen and hormones. Journal of the Zhejiang University - Agriculture and Life Science 39:72-78. Zheng A, Lin R, Zhang D, Qin P, Xu L, Ai P, Ding L, Wang Y, Chen Y, Liu Y, Sun Z (2013) The evolution and pathogenic mechanisms of the rice sheath blight pathogen. Nature Communications 4:1424. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 01 Aug, 2024 Reviewers agreed at journal 01 Jun, 2024 Reviewers invited by journal 31 May, 2024 Editor invited by journal 16 May, 2024 Editor assigned by journal 16 May, 2024 First submitted to journal 05 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4374976","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309241275,"identity":"c4836121-64ca-4648-93a5-d54ad5938536","order_by":0,"name":"Mayur R. Thesiya","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mayur","middleName":"R.","lastName":"Thesiya","suffix":""},{"id":309241276,"identity":"5e81a766-db59-4cd9-9a38-98f4183b7efb","order_by":1,"name":"Jagjeet Singh Lore","email":"data:image/png;base64,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","orcid":"","institution":"Punjab Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Jagjeet","middleName":"Singh","lastName":"Lore","suffix":""},{"id":309241277,"identity":"55c3a0a5-a9e0-4209-ad6c-0f1056e4395d","order_by":2,"name":"Dharminder Bhatia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dharminder","middleName":"","lastName":"Bhatia","suffix":""},{"id":309241278,"identity":"415f8c0d-4305-4145-a344-1b7113ed4766","order_by":3,"name":"Sanjay Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sanjay","middleName":"","lastName":"Kumar","suffix":""},{"id":309241279,"identity":"eaf5bbd5-97fe-45fa-b248-3e3b6e9df3e6","order_by":4,"name":"Mandeep Singh Hunjan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mandeep","middleName":"Singh","lastName":"Hunjan","suffix":""},{"id":309241280,"identity":"763b190b-b496-49be-9d0a-26c1e7d6d885","order_by":5,"name":"Jyoti Jain","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jyoti","middleName":"","lastName":"Jain","suffix":""},{"id":309241281,"identity":"9d74e3a1-bd56-4cd6-a554-9de4835d165c","order_by":6,"name":"Rupinder Kaur","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rupinder","middleName":"","lastName":"Kaur","suffix":""}],"badges":[],"createdAt":"2024-05-06 07:49:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4374976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4374976/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58268728,"identity":"6bd369e4-064a-44e7-a858-3ff4254aaf71","added_by":"auto","created_at":"2024-06-13 08:02:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7925929,"visible":true,"origin":"","legend":"\u003cp\u003eSheath blight resistant donor parent (IET22769) showed lowest disease score (3.7) with few lesions on sheath (left, yellow circle), (B) susceptible recipient parent PR121 showed high disease score (7.3) with dark brown lesions up to flag leaves and panicles (right, yellow circle).\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4374976/v1/420ed8f08b49f94e341da478.jpg"},{"id":58269359,"identity":"fca76bc7-23de-4c94-9ae8-14d520d68b8c","added_by":"auto","created_at":"2024-06-13 08:10:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2995887,"visible":true,"origin":"","legend":"\u003cp\u003e2a)\u003cstrong\u003e \u003c/strong\u003eFrequency distribution of mean disease score of F\u003csub\u003e3\u003c/sub\u003e population lines against sheath blight disease. 2b) Mean lesion height (cm) of rice sheath blight among 15 moderately resistant and 15 highly susceptible bulk (derived from PR121×ShB1) screened against \u003cem\u003eR. solani\u003c/em\u003e during main crop season 2022\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4374976/v1/eba7c58e0c6492a2661788f1.jpg"},{"id":58268727,"identity":"90d9db24-60c0-4189-9398-2c9dc81b4d65","added_by":"auto","created_at":"2024-06-13 08:02:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1992969,"visible":true,"origin":"","legend":"\u003cp\u003eQuantitative trait loci for sheath blight resistance in rice on chromosome 1, 3, 5 \u0026amp; 6 identified in F\u003csub\u003e3\u003c/sub\u003e progenies (PR121× IET22769) using NGS-based BSA. Distribution of the G′ value calculated with a 1.0 Mb sliding window using tricube smoothing kernel. The\u0026nbsp;\u003cem\u003eY\u003c/em\u003e-axis represents G′ value in figure subsection a, b, c and d, respectively. The\u0026nbsp;\u003cem\u003eX\u003c/em\u003e-axis represents the position of chromosomes in Mb based on the \u003cem\u003eOryza sativa\u003c/em\u003e (Japonica Group) genome assembly IRGSP-1.0. The red line indicates the significant threshold for FDR\u0026nbsp;=\u0026nbsp;0.001 and genomic region where the G′ crosses the threshold value was considered as significant QTL. Only chromosomes with significant QTL regions are shown.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4374976/v1/fdb3180f8457ef543e9f0309.jpg"},{"id":58270092,"identity":"5e142982-2aa6-41e6-95f3-20122e7bcbaa","added_by":"auto","created_at":"2024-06-13 08:18:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13591663,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4374976/v1/6eb57641-da56-4486-b11d-15dd62ad5d32.pdf"},{"id":58268729,"identity":"bd76d095-3911-4e89-be82-e2bf4814487e","added_by":"auto","created_at":"2024-06-13 08:02:02","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":325439,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4374976/v1/79745f875d049f1915b91722.docx"}],"financialInterests":"","formattedTitle":"QTL mapping and candidate gene mining for sheath blight resistance in rice (Oryza sativa L.) using QTLseqr approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSheath blight (ShB) caused by \u003cem\u003eRhizoctonia solani\u003c/em\u003e K\u0026uuml;hn [Teleomorph: \u003cem\u003eThanatephorus cucumeris\u003c/em\u003e (Frank) Donk], is one of the most prevalent soil-borne necrotrophic pathogens of rice worldwide. Since its first report in Japan in 1910 (Rush 1992), the disease has significantly increased after 2000 in the rice growing areas of India (Dey et al 2016). ShB incidence during flowering and panicle initiation stage significantly impacts the quality of rice grains, reduction in total grain weight and lead to losses ranging from 20\u0026ndash;70% (Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A range of 20.1\u0026ndash;27.3% losses have been reported in mega rice varieties of South Asia (Lore et al. 2021). Presently, the predominant method for controlling this disease mainly depends on the use of chemical fungicides (Singh et al. 2019). Excessive use of pesticides has resulted in the emergence of novel pathotypes that exhibit resistance to these fungicides (Zeng et al. 2011) besides causing environmental pollution.\u003c/p\u003e \u003cp\u003eHost plant resistance is economical and environmentally safe method for imparting resistance against ShB. Resistant sources with varying levels of tolerance to ShB have been reported to date (Khush 1977; Guo et al. 1985; Groth and Novick 1992; Lore et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Aggarwal et al. 2019,; Molla et al. 2020). Based on genetic studies in such sources, resistance to sheath blight has been reported as a complex quantitative trait controlled possibly by polygenes (Sha and Zhu 1990; Li et al. 1995; Pinson et al. 2005). However, few researchers have proposed it to be controlled by only a few major genes (Xie et al. 1992; Pan et al. 1999).\u003c/p\u003e \u003cp\u003eThe quantitative trait loci (QTLs) for sheath blight have been identified in promising rice lines, germplasm, cultivars, land races and wild relatives by various workers \u003cem\u003eviz\u003c/em\u003e., \u003cem\u003eqSB-7\u003c/em\u003e in Teqing (Pinson et al. 2005), \u003cem\u003eqShB3-1\u003c/em\u003e in Jasmine 85 (Liu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), \u003cem\u003eqSBR 1\u0026ndash;1\u003c/em\u003e in Tetep (Channamallikarjuna et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), \u003cem\u003eqShB6\u003c/em\u003e in \u003cem\u003eO. nivara\u003c/em\u003e (Eizenga et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003eqshb8.1\u003c/em\u003e in ARC10531 (Yadav et al. 2015), and \u003cem\u003eqShB9\u003c/em\u003e in \u003cem\u003eO. nivara\u003c/em\u003e (Eizenga et al. 2022), \u003cem\u003eqsbr10\u003c/em\u003e.1 and few other QTLs in \u003cem\u003eO. nivara\u003c/em\u003e accessions (Bhatia et al. 2024). Over 110 genetic loci have been identified governing resistance against ShB (Aggarwal et al. 2022) using biparental mapping and genome wide association studies. ShB resistant genes underlying major QTLs have been identified in rice as well as maize. In rice, \u003cem\u003eOsWAK91\u003c/em\u003e, a wall associated kinase 1 gene present in the major sheath blight resistance QTL region on chromosome 9, has been identified against resistance to ShB (Al-Bader et al. 2023). In maize, \u003cem\u003eZmFBL41\u003c/em\u003e encoding F-box protein has been uncovered for resistance against banded sheath blight (Li et al. 2019) caused by \u003cem\u003eR. solani\u003c/em\u003e. These studies are raising opportunities to identify major genes underlying other major QTLs. Thus, there is a need to identify other potential QTLs for resistance to sheath blight, which in turn could help in the development of rice varieties having durable resistance to sheath blight.\u003c/p\u003e \u003cp\u003eNext Generation Sequencing based Bulked Segregant Analysis (NGS-BSA) is an efficient method in identifying QTLs that have significant impacts (Michelmore et al. 1991; Magwene et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vikram, et al. 2012). This is achieved by genotyping just the extreme phenotypes using NGS based high-throughput genotyping approaches, rather than the full mapping population. QTL-seq is one of the version of NGS-BSA, created by Takagi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and has been extensively used to identify major effect QTLs in many crops governing number of traits (Das et al. 2014; Lu et al. 2014; Win et al. 2016). QTLseq has been widely used in many crops such as rice (Bommisetty, et al. 2020; Lei et al. 2020), squash (Ramos et al. 2020), Korean cucumber (Zhang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003ea), tomato (Wen et al. 2019), spring wheat (Wang et al. 2021), melon (Qiao et al. 2021) and cotton (Zhang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003eb). QTL-seq has also been used in peanut (Korani et al. 2021) to identify regions in the genome controlling specific traits, such as disease resistance (Luo et al. 2019a) and physiological traits (Kumar et al. 2020). The QTLseqr package offers a quick and easy-to-use tool for researchers to conduct NGS-BSA using different analysis methods (Mansfeld and Grumet \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). So present study was planned to map QTLs using the QTLseqr approach, derived from a cross between the resistant donor IET 22769 and the susceptible cultivar PR121.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental material\u003c/h2\u003e \u003cp\u003ePR121 is a high yielding, semi-dwarf rice variety released by Punjab Agricultural University (PAU), India in 2013, but shows susceptible reaction to ShB (Bharaj et al. 2014). The IET22769 is advanced breeding line with moderately resistant reaction to ShB that has been identified based on screening for more than five years (J S Lore, Personnel communication). F\u003csub\u003e1\u003c/sub\u003e seeds of cross PR121\u0026times; IET22769 were selfed at ICAR-National Rice Research Institute, Cuttack, India to generate F\u003csub\u003e2\u003c/sub\u003e seeds during off-season 2021. Individual F\u003csub\u003e2\u003c/sub\u003e seeds were planted and selfed to generate F\u003csub\u003e3\u003c/sub\u003e progenies. A total of 347 F\u003csub\u003e3\u003c/sub\u003e progenies along with parents, susceptible check D-6766 (mutants of IR 64) (Lore et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and standard resistant check Tetep (Channamallikarjuna et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) were raised in nursery and thirty-day-old seedlings were transplanted at 15 \u0026times; 20 cm spacing using one seedling per hill during crop season 2022 at PAU, India. Ten plants of each progeny along with parents and checks were screened against sheath blight at maximum tillering stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSheath blight inoculation and disease assessment\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eRhizoctonia solani\u003c/em\u003e was mass multiplied on maize meal-sand (1:3) medium. Glass flasks containing media were inoculated with pure culture of \u003cem\u003eR. solani\u003c/em\u003e and incubated at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C for 10 days. Five gram of inoculum was transferred to center of hill of each plant to be evaluated (Thind et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Disease assessment was made 21 days after inoculation (DAI) under field conditions. The total lesion height (cm) was recorded from base of the plant to the topmost lesions on the stem. The plant height was also measured from bottom to top leaf height. Relative lesion height (RLH) was calculated using the following formula: lesion height / plant height X 100 (Sharma et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Scoring was done based on Standard Evaluation System (SES) for rice sheath blight using scale 0\u0026ndash;9 (IRRI, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, bulks generation and whole genome re‑sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNA was isolated from young leaves of selected F\u003csub\u003e3\u003c/sub\u003e progenies and parental line using cetyl trimethyl ammonium bromide (CTAB) method (Murray and Thompson \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). The quality and quantity of genomic DNA was analyzed using NanodropTM 8000 spectrophotometer according to manufacturer\u0026rsquo;s manual (Thermo Fisher Scientific, USA). DNA was extracted from each selected line and was normalized to 1 \u0026micro;g/microliter concentration. Equal quantity of DNA of each individual F\u003csub\u003e3\u003c/sub\u003e progeny was then pooled to form ShB-resistant bulk (ShB-R) and ShB susceptible bulk (ShB-S). Both the parental lines and two bulks, i.e., ShB-R and ShB-S, were whole genome sequenced at 20X using pair-end sequencing on Illumina NovaSeq 6000 sequencer which was outsourced to NGB Diagnostics, New Delhi, India.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVariant identification\u003c/h2\u003e \u003cp\u003eThe short raw reads obtained from sequencing were initially trimmed with minimum \u003cem\u003ephred\u003c/em\u003e Q score 30 at minimum 90% of reads using FASTQC version 0.11.8. FASTQ format sequences were then processed using Trimmomatic version 0.39 to filter low quality and adaptor sequences. The filtered reads aligned with the reference genome sequences of \u003cem\u003eOryza sativa\u003c/em\u003e IRGSP-1.0 using Burrows-Wheeler Aligner 0.7.17-r1188. The resulting mapped SAM (sequence alignment/map format) files were converted to BAM (binary version of SAM files) using SAMTools version 1.15. Further, picard tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://broadinstitute.github.io/picard\u003c/span\u003e\u003cspan address=\"https://broadinstitute.github.io/picard\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to modify read groups to make them compatible for variant calling followed by sorting the BAM files using Samtools. Variant calling was performed using Genome Analysis Toolkit (GATK, McKenna et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to obtain single nucleotide polymorphism (SNP) and insertion and deletions (InDels). The variant file was filtered based on missing data\u0026thinsp;\u0026ge;\u0026thinsp;0.9 and minimum allele frequency\u0026thinsp;\u0026ge;\u0026thinsp;0.05 using VCFtools (Danecek et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The filtered variants including SNPs and InDels were converted to table file using VariantsToTable format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQTL analysis using QTLseqr\u003c/h2\u003e \u003cp\u003eQTL(s) controlling resistance to ShB in rice was identified through QTLseqr (Mansfeld and Grumet \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). QTLseqr apply the both statistical approaches such as G statistic (Magwene et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and QTLseq (Takagi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in programming language R version 4.2.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to rapidly identify the significant QTL(s) contributing to traits of interest. The filtered SNP data of both bulks exported from VariantsToTable function of GATK was imported to QTLseqr using import from GATK function. Variants were again filtered at reference allele frequency\u0026thinsp;=\u0026thinsp;0.10, minimum total depth\u0026thinsp;=\u0026thinsp;40, minimum sample depth\u0026thinsp;=\u0026thinsp;15 and 1.5e6 window size to minimize the noise and improve results. QTLseqr script was used to make plots and QTLseq analysis files. It calculates the total reference allele frequency for both bulks together, the SNP-index for each SNP in each bulk and the Δ (SNP- index) as follows; Reference allele frequency is equal to Ref allele depth \u003csub\u003eHigh Bulk\u003c/sub\u003e + Ref allele depth \u003csub\u003eLow Bulk\u003c/sub\u003e / total read depth for both bulks. SNP-index per bulk is equal to alternate allele depth / total read depth. Δ(SNP-index) is equal to SNP-index \u003csub\u003eHigh Bulk\u003c/sub\u003e \u0026minus; SNP-index \u003csub\u003eLow Bulk\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe analysis in QTLseqr is an execution of both pipelines for bulk segregant analysis, Δ(SNP-index) and G statistic/G' described by Takagi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Magwene et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), respectively. The following analysis was performed:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eA tricube-smoothed Δ(SNP-index)\u003c/h2\u003e \u003cp\u003eA tricube-smoothed Δ(SNP index) was calculated within 1 Mb window size using the function, runQTLseqAnalysis for each SNP from the allele depths in comparison to uniform or rectangular window used by Takagi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It reduces the noise at the time of accounting linkage disequilibrium between SNPs. Δ(SNP-index) was simulated over 10,000 replications for both the bulks at each read depth. The extreme quantiles were used as confidence intervals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with the null distribution assuming there is no QTL linked to the SNP. The data was averaged over the given sliding window and kept the SNPs with \u0026gt;\u0026thinsp;=\u0026thinsp;0.3 SNP-index in both simulated bulks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eA tricube-smoothed G statistic/G'\u003c/h2\u003e \u003cp\u003eA tricube-smoothed G statistic or G‟ was calculated for each SNP based on the observed and expected allele depths and smoothing this value using a tricube smoothing kernel within 1 Mb window size using the function runGprimeAnalysis. Initially it calculates the G statistic for each SNP and then it counts the number of SNPs and finally estimates the tricube-smoothed G‟ and ∆(SNP-index) values of each SNP within 1 Mb window size. A tricube-smoothed G‟ was computed by constant local regression within each chromosome using tricubeStat function. It allows noise reduction associated to SNP calling errors while computing linkage disequilibrium. P-values are computed using the non-parametric method given by Magwene et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) using function getPvals. G‟ and adjusted p-values were estimated with respect to the false discovery rate (FDR), q\u0026thinsp;=\u0026thinsp;0.01 with the null distribution assuming there is no QTL linked to the SNP, respectively. Graphical representation of SNPs/window, the tricube-smoothed ∆ (SNP-index) and G‟ values, or the \u0026ndash;log10(p-value) was done using plotQTLStats function. The statistically significant QTL(s) controlling resistance to sheath blight were identified from these graphs, as described below: a) Genomic regions that have tricube smoothed ∆ (SNP-index) surpassing the threshold level of simulated confidence interval and are putative QTL(s); b) A tricube smoothed G‟ and their estimated \u003cem\u003ep\u003c/em\u003e-values at which genomic region surpass the threshold level of 0.01 FDR that contains the QTL(s)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eExtracting QTL(s) data and candidate gene mining\u003c/h2\u003e \u003cp\u003eAfter identification of putative QTL regions from plotted graphs, getSigRegions and getQTLTable functions were used to extract and summarize QTL(s) data. Candidate defense responsive genes present in the QTL regions associated with ShB resistance, were retrieved using BioMart of Ensembl Plants with \u003cem\u003eOryza sativa\u003c/em\u003e subsp. \u003cem\u003ejaponica\u003c/em\u003e genome (IRGSP-1.0) as reference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eScreening of parents and F\u003csub\u003e3\u003c/sub\u003e progenies against sheath blight\u003c/h2\u003e \u003cp\u003eA total of 347 F\u003csub\u003e3\u003c/sub\u003e progenies along with parents and checks were screened against sheath blight. The susceptible (PR121) and resistant (IET22769) parents showed clear phenotypic differences with mean ShB score of 7.3 and 3.7 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mean ShB score of resistant check \u0026ldquo;Tetep\u0026rdquo; was 4.9 while of susceptible check was 9.0. Mean ShB disease scores of F\u003csub\u003e3\u003c/sub\u003e progenies ranged from 2.1 to 9.0, with majority of lines ranged from 3.1 to 7.0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Further all the F\u003csub\u003e3\u003c/sub\u003e progenies were categorized into different groups based on the type of disease reaction. The lines that showed mean disease score of 3.0 to 4.9 were categorized as moderately resistant, 5.0 to 6.9 as moderately susceptible, 7.0 to 8.9 as susceptible and 9.0 as highly susceptible. The frequency distribution curve of F\u003csub\u003e3\u003c/sub\u003e population was positively skewed with skewness of 0.407 and showed platykurtic distribution as compared to normal distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Based on the screening of F\u003csub\u003e3\u003c/sub\u003e progenies, fifteen moderately resistant (showing higher level of resistance as compared to rest of the population) and fifteen highly susceptible progenies (Suppl. Table\u0026nbsp;1) were selected to constitute ShB-R and ShB-S bulks respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of genome wide SNPs and QTLseqr analysis\u003c/h2\u003e \u003cp\u003eA total of 107.69 GB data with 346.84\u0026nbsp;million clean reads with QC\u0026thinsp;\u0026gt;\u0026thinsp;30 were obtained from re-sequencing of two bulks (ShB-R, ShB-S) and two parental genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that represented 90% of the original sequencing data. The short reads were mapped to the reference genome using Bowtie and GATK to identify SNPs. A total of 11,45,820 high-quality SNPs were identified after filtering. The highest number of SNPs were identified on chromosome 1 followed by chromosome number 2 and it was the lowest on chromosome 9 (Supply Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of reads and total GB data obtained from whole genome re-sequencing of bulks and parents after quality control (QC)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGC%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal data in GB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistant bulk (ShB-R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79188477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusceptible bulk (ShB-S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95167740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusceptible parent (PR121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97790249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistant parent (IET22769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74697050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantitative trait loci (QTLs) regions associated with sheath blight resistance obtained through QTLseqr\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome (Ch)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003cp\u003ename\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStart (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnd (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLength (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo. of SNPs in the QTL region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean No. SNP/Mb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e∆(SNP-\u003c/p\u003e \u003cp\u003eindex)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean G\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean \u003cem\u003ep\u003c/em\u003e- value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMean q-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eList of identified \u003cem\u003eShB-QTLs\u003c/em\u003e co-localized with previous report\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqShB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.22E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLiu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Sharma et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Gaihre et al. (2011), Eizenga et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Zeng et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Bal et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqShB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.29E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTaguchi-Shiobara et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Zeng et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqShB5\u003c/em\u003e.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.02E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLiu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqShB5\u003c/em\u003e.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.65E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eGaihre et al. (2011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqShB6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.52E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cb\u003eMax G\u003c/b\u003e\u003cb\u003e\u0026rsquo;\u003c/b\u003e \u003cb\u003e\u0026ndash; the max G‟ score in the region, Mean G‟ \u0026ndash; the average G‟ score of that region, G‟ Std. dev \u0026ndash; the standard deviation of G‟ within the region, Mean\u003c/b\u003e \u003cb\u003ep-\u003c/b\u003e\u003cb\u003evalue \u0026ndash; the average\u003c/b\u003e \u003cb\u003ep-\u003c/b\u003e\u003cb\u003evalue\u003c/b\u003e\u003cb\u003es\u003c/b\u003e \u003cb\u003ein the region.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of QTLs governing resistance to sheath blight using QTLseqr\u003c/h2\u003e \u003cp\u003eTo infer the QTL region conferring ShB resistance, the genome-wide comparison SNP-index of ShB-R and ShB-S bulk over the entire length of genome was performed using QTLseqr. SNPs were further filtered based on the following parameters: the reference allele frequency 0.10, minimum total sample read depth, DP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50, maximum total sample read depth, DP\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;400, minimum sample read depth, DP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;20 and genotype quality, GQ\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30. SNP-index was calculated as proportion of short reads harboring distinct SNPs to the total short reads covering the particular genomic position (Abe et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). If SNP-index is 0.5, we assume equal contribution from both parents to bulked progeny. If SNP-index is 0 means entire short reads represent genome of parent that is used as reference sequence and 1 means short reads harbors genomic fragments from another parent. For individual SNPs, the SNP index and G statistics were calculated as reported by Takagi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Magwene et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), respectively using QTLseqr pipeline developed by Mansfeld and Grumet (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This approach identified the statistically significant QTL(s) on the basis of Δ(SNP index) and G statistic/ G prime with \u003cem\u003ep-\u003c/em\u003evalue analysis. Within a window size of 1.0 Mb genomic area, the tricube-smoothed delta SNP index and G value (G‟ value) were computed and plotted against all the twelve chromosomes of rice. Significant thresholds i.e., confidence intervals (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and false discovery rate, FDR (q\u0026thinsp;=\u0026thinsp;0.01) (Benjamini and Hochberg 1995) were estimated while calculating Δ(SNP index) and G statistic along with \u003cem\u003ep-\u003c/em\u003evalues from the null distribution assuming that there was no QTL linked to the SNP, respectively (Magwene et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Yang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSignificant spikes have been observed on genomic regions of chromosome 1, 3, 5 and 6 on both tricube smoothed ∆(SNP-index) and G‟ plots surpassing the significant threshold i.e., confidence intervals (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, red and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, blue) and FDR (q) of 0.01 respectively, indicating that these QTL regions are linked with sheath blight resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The QTLs identified on chromosome 1, 3, 5, and 6 were named as \u003cem\u003eqShB1\u003c/em\u003e, \u003cem\u003eqShB3\u003c/em\u003e, \u003cem\u003eqShB5.1\u003c/em\u003e, \u003cem\u003eqShB5.2\u003c/em\u003e and \u003cem\u003eqShB6\u003c/em\u003e respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Genomic regions locating QTLs varied from 3.07 Mb (\u003cem\u003eqShB3\u003c/em\u003e) on chromosome 3 to 9.50 Mb (\u003cem\u003eqShB5.1\u003c/em\u003e) on chromosome 5. The \u003cem\u003eqShB1\u003c/em\u003e spanned from 34.89 to 40.05 Mb on rice genome; \u003cem\u003eqShB3\u003c/em\u003e from 33.34 to 36.41 Mb, while \u003cem\u003eqShB5.1\u003c/em\u003e and \u003cem\u003eqShB5.2\u003c/em\u003e lies between 0.95 to 10.46 Mb and 13.91 to 21.05 Mb on rice genome, respectively. The QTL identified on chromosome 6 (\u003cem\u003eqShB6\u003c/em\u003e) spanned the genomic region from 0.81 to 4.27 Mb on rice genome (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA tricube smoothed Δ(SNP-index) distribution revealed the significant peak near to 95 and 99% confidence intervals on chromosome 1, 3, 5 and 6. Remaining chromosomes exhibited Δ(SNP-index) peaks under confidence intervals. Further, the tricube smoothed G' analysis also displayed the significant peak among all identified QTLs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) on chromosome 1, 3, 5 and 6 that surpass the threshold of FDR, q\u0026thinsp;=\u0026thinsp;0.01 indicating the high likelihood of containing QTL(s) for sheath blight resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All the other G' peaks scattered throughout the genome were apparently under the threshold level of FDR. Similar significant peaks were observed in log\u003csub\u003e10\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e-values) analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of putative candidate genes governing sheath blight resistance\u003c/h2\u003e \u003cp\u003eA total of 69 genes were predicted within identified QTL region on chromosome 1, 3, 5 and 6 (Suppl. Table\u0026nbsp;3). Candidate genes were selected based on gene stable ID, gene description, go name/ domain and its putative role in resistance against pathogens. The 69 genes included leucine-rich repeat receptor-like kinase (LRR-RLK), serine/threonine protein kinase, chitinase, gibberellin signal transduction and NBS-LRR encoding genes, abscisic acid hormone signal transduction and phenylpropanoid biosynthesis pathways, WRKY transcription factor 4, mitogen-activated protein kinase (MAPK), receptor-like kinases, zinc finger protein indicating that these enzymes/proteins might play important roles in rice defense pathway against \u003cem\u003eR. solani.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSheath blight of rice is an emerging disease that has potential to do significant losses in rice growing areas. In the absence of true genetic resistance (Lore et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and polygenic nature, breeding for ShB resistance poses significant challenges till date (Pinson et al. 2005). The previous reports indicated the existence of diverse sources of resistance in the rice germplasm and various QTLs associated with ShB resistance have also been identified (Jia et al. 2012; Yadav et al. 2015; Eizenga et al. 2022). Identification of diverse QTLs governing resistance to ShB and associated molecular marker will accelerate development of ShB resistant cultivars; combining of these QTLs in single background will build understanding of QTL X QTL interaction and development of broad-spectrum resistance against ShB. Further identification of \u003cem\u003eOsWAK91\u003c/em\u003e, a wall associated kinase 1 gene governing ShB resistance in rice (Al-Bader et al. 2023) has inspired identification of other putative QTLs and underlying genes that can be used in breeding programme. The IET22769 was identified as a promising donor showing moderately resistant reaction to ShB based on artificial screening for more than five years however it was even better than widely used resistant check \u0026ldquo;Tetep\u0026rdquo; in case of sheath blight of rice. The frequency distribution of F\u003csub\u003e3\u003c/sub\u003e progenies derived from the cross of PR121 and IET22769 for ShB disease reaction was continuous and fitted into normal distribution as expected from a quantitative trait. However, the two extremes with significant number of plants in each could be identified to make two contrasting bulks for further QTL analysis.\u003c/p\u003e \u003cp\u003eIdentification of QTLs through conventional methods is laborious and tedious because every individual of the mapping population needs to be genotyped. Bulked sergeant analysis is an efficient strategy for identifying the DNA markers linked to the gene of interest (Michelmore et al.1991). In bulked DNA, all loci are randomized; except for the region containing the gene of interest and polymorphic markers may represent markers that are linked to the gene or QTL of interest (Collard and Mackill 2005). Combination of NGS based BSA with QTLseqr is quick in identifying QTLs from large segregating population with more statistical significance. QTLseqr in the current study identified five different QTLs on four different chromosomes depending upon the selected confidence interval and FDR rate as the margins of each region. The FDR rate of 0.01 utilized in the G' method was more stringent than using a confidence interval of 99% with the QTLseqr method (Mansfeld and Grumet \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similar observations were recorded in the data analysis involved in identification of QTLs controlling cold tolerance in rice seedlings. In this regard, Yang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reported the putative QTLs at chromosome 1, 2, 8 and 10 along with two small peaks at chromosome 2 and 5. Whereas, Mansfeld and Grumet (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) identified the major QTLs on chromosomes 1, 2, 8 and 10 only uses the similar data. QTLseqr provides multiple testing options for the identification of statistically significant QTL(s). Dayanand (2021) used QTLseqr pipeline to identify four QTLs on chromosomes 4, 6, 8 and 12 for resistance against brown spot disease in rice. Zhang et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified four QTLs for heading date on chromosomes 3, 6, 9 and 10, three QTLs for plant height on chromosomes 1, 8 and 10 and two QTLs for panicle length on chromosomes 1 and 5 using QTLseqr approach.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFour of the ShB-QTLs detected in this study were co-localized in same chromosome segments with the ShB-QTLs reported in previous studies against sheath blight disease of rice indicating that these QTL regions are important to study further for resistance against ShB.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCandidate gene mining analysis revealed 69 different genes which were involved in defense mechanism against plant pathogens. The \u003cem\u003eqShB1\u003c/em\u003e interval had 19 genes that included defense responsive gene, coded for abscisic acid hormone signal transduction pathways (Feng et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), leucine-rich repeat receptor-like kinase (LRR-RLK) (Acharya et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) involved in the response to \u003cem\u003eR. solani.\u003c/em\u003e The \u003cem\u003eqShB3\u003c/em\u003e QTL region spanned 13 genes including gene for phenylpropanoid biosynthesis (Feng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), serine/threonine protein kinase (Oreiro et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) that might play important roles in host defense against \u003cem\u003eR. solani.\u003c/em\u003e Fourteen defense genes i.e. chitinase, gibberellin signal transduction and NBS-LRR encoding genes were present in the QTL regions \u003cem\u003eqShB5\u003c/em\u003e.1, \u003cem\u003eqShB5\u003c/em\u003e.2 that were reported to be responsible for ShB resistance in transgenic rice plant (Baisakh et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Datta et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rostami et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bal et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The \u003cem\u003eqShB6\u003c/em\u003e QTL region had 23 genes including WRKY transcription factor 4 (Wang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), mitogen-activated protein kinase (MAPK) (Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), receptor-like kinases (Acharya et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), zinc finger protein (Shamim et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In conclusion, the study has identified the important QTLs and underlying candidate genes that might play important role in further understanding genetics of sheath blight resistance in rice and development of sheath blight resistant cultivars.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study identified five QTLs namely \u003cem\u003eqShB1, qShB3, qShB5.1, qShB5.2\u003c/em\u003e and \u003cem\u003eqShB6\u003c/em\u003e on chromosome 1, 3, 5 and 6, respectively and putative candidate genes in the corresponding QTL intervals governing resistance to sheath blight of rice in F\u003csub\u003e3\u003c/sub\u003e progenies derived from susceptible and resistant parent using QTLseqr approach. These QTLs and candidate genes can be studied further in details to understand genetics of sheath blight resistance in rice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest in relation to this publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthors Contribution\u003c/h2\u003e \u003cp\u003eJSL, DB designed the research work and optimized the protocol of BSA and QTL-seq; MRT and JSL conducted the experiments and collected the phenotypic data; MRT and DB did QTLseqr analysis. MRT, JSL, SK and DB have written the manuscript; DB, MSH, JSL, SK and RK gave critical suggestions throughout the study and reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eAuthors thankfully acknowledged Punjab Agricultural University, Ludhiana, India for providing the infrastructure and other facilities for conducting experiments. Financial assistance provided to Mayur R. Thesiya by the Science and Engineering Research Board (SERB)-DST, Government of India, New Delhi is especially acknowledged.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe data generated and/or analysed during the present study are attached as supplementary information file and can also be obtained from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas A, Mubeen M, Iftikha, Y, Shakeel Q, Imran Arshad HM, Carmen Zu\u0026ntilde;iga Romano MD, Hussain S (2023) Rice Sheath Blight: A Comprehensive Review on the Disease and Recent Management Strategies. Sarhad Journal of Agriculture 39:111\u0026ndash;125.\u003c/li\u003e\n\u003cli\u003eAbbas A, Fu Y, Qu Z, Zhao H, Sun Y, Lin Y, Xie J, Cheng J, Jiang D (2021) Isolation and evaluation of the biocontrol potential of \u003cem\u003eTalaromyces\u003c/em\u003e spp. against rice sheath blight guided by soil microbiome. Environmental Microbiology 23:5946\u0026ndash;5961.\u003c/li\u003e\n\u003cli\u003eAbe A, Takagi H, Fujibe T, Aya K, Kojima M, Sakakibara H, Uemura A, Matsuoka M, Terauchi R (2012) \u003cem\u003eOsGA20ox1\u003c/em\u003e, a candidate gene for a major QTL controlling seedling vigor in rice. Theoretical and Applied Genetics 125:647\u0026ndash;657.\u003c/li\u003e\n\u003cli\u003eAcharya U, Das T, Ghosh Z, Ghosh A (2022) Defense Surveillance System at the Interface: Response of Rice towards \u003cem\u003eRhizoctonia solani\u003c/em\u003e During Sheath Blight Infection. Molecular Plant-Microbe Interactions 35:1081\u0026ndash;1095.\u003c/li\u003e\n\u003cli\u003eBaisakh N, Datta K, Oliva N., Ona I, Rao GJN, Mew TW, Datta SK (2001) Rapid development of homozygous transgenic rice using anther culture harboring rice chitinase gene for enhanced sheath blight resistance. Plant Biotechnology 18:101\u0026ndash;108.\u003c/li\u003e\n\u003cli\u003eBal A, Samal P, Chakraborti M, Mukherjee AK, Ray S, Molla KA, Behera L, Samal R, Sarangi S, Sahoo P, Behera M (2020) Stable quantitative trait locus (QTL) for sheath blight resistance from rice cultivar CR 1014. Euphytica 216:1\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eBenjamini Y, Hotchberg Y (1995) controlling the false discovery rate - A practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57: 289\u0026ndash;300.\u003c/li\u003e\n\u003cli\u003eChannamallikarjuna V, Sonah H, Prasad M, Rao GJN, Chand S, Upreti HC, Singh NK, Sharma TR (2010) Identification of major quantitative trait loci \u003cem\u003eqSBR11-1\u003c/em\u003e for sheath blight resistance in rice. Molecular Breeding 25:155\u0026ndash;166.\u003c/li\u003e\n\u003cli\u003eDanecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G (2011) The variant call format and VCFtools. Bioinformatics 27:2156\u0026ndash;2158.\u003c/li\u003e\n\u003cli\u003eDatta K, Tu J, Oliva N, Ona I, Velazhahan R, Mew TW, Muthukrishnan S, Datta SK (2001) Enhanced resistance to sheath blight by constitutive expression of infection-related rice chitinase in transgenic elite indica rice cultivars. Plant Science 160:405\u0026ndash;414.\u003c/li\u003e\n\u003cli\u003eDayananda VP (2021) Molecular and biochemical basis of resistance to brown spot in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.). Dissertation, Punjab Agricultural University, India.\u003c/li\u003e\n\u003cli\u003eDubey AK, Pandian RTP, Rajashekara H, Khanna A, Ellur RK, Sharma P, Kumar A, Singh AK, Gopalakrishnan S, Rathour R, Singh UD (2014) Molecular validation of blast and sheath blight resistance in improved rice genotypes and landraces. Indian phytopathology 67:216\u0026ndash;221.\u003c/li\u003e\n\u003cli\u003eEizenga GC, Prasad B, Jackson AK, Jia MH (2013) Identification of rice sheath blight and blast quantitative trait loci in two different \u003cem\u003eO. sativa\u003c/em\u003e/\u003cem\u003eO. nivara\u003c/em\u003e advanced backcross populations. Molecular Breeding 31:889\u0026ndash;907.\u003c/li\u003e\n\u003cli\u003eFeng T, Zhang ZY, Gao P, Feng ZM, Zuo SM, Ouyang SQ (2023) Suppression of rice osa-miR444. 2 improves the resistance to sheath blight in rice mediating through the phytohormone pathway. International Journal of Molecular Medical Science 24:3653.\u003c/li\u003e\n\u003cli\u003eFeng ZM, Peng GAO, Zhao JH, Wang GD, Zhang HM, Cao WL, Xiang XUE, Zhang YF, Rong HUA, Chen ZX, Chen XJ (2022) iTRAQ-based quantitative proteomics analysis of defense responses triggered by the pathogen \u003cem\u003eRhizoctonia solani\u003c/em\u003e infection in rice. Journal of Integrative Agriculture 21:139\u0026ndash;152.\u003c/li\u003e\n\u003cli\u003eGaihre YR, Yamagata Y, Yoshimura A, Nose A (2015) Identification of QTLs involved in resistance to sheath blight disease in rice line 32R derived from Tetep. Tropical Agriculture and Development 59:154\u0026ndash;160.\u003c/li\u003e\n\u003cli\u003eIRRI (2014) \u003cem\u003eStandard evaluation system for rice\u003c/em\u003e. International Rice Research Institute, Manila.\u003c/li\u003e\n\u003cli\u003eLiu G, Jia Y, Correa-Victoria F J, Prado GA, Yeater KM, McClung A, Correll JC (2009) Mapping quantitative trait loci responsible for resistance to sheath blight in rice. Phytopathology 99:1078\u0026ndash;1084.\u003c/li\u003e\n\u003cli\u003eLore JS, Hunjan MS, Singh P, Willocquet L, Sri S, Savary S (2013) Phenotyping of partial physiological resistance to rice sheath blight. Journal of Phytopathology 161:224\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eMagwene PM, Willis JH, Kelly JK (2011) The statistics of bulk segregant analysis using next generation sequencing. PLoS Computational Biology 7:1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMansfeld BN, Grumet R (2018) QTLseqr: an R package for bulk segregant analysis with next-generation sequencing. Plant Genome 11:180006.\u003c/li\u003e\n\u003cli\u003eMcKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20:1297\u0026ndash;1303. \u003c/li\u003e\n\u003cli\u003eMurray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research 8:321\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eOreiro EG, Grimares EK, Atienza-Grande, G, Quibod IL, Roman-Reyna V, Oliva R (2020) Genome-wide associations and transcriptional profiling reveal ROS regulation as one underlying mechanism of sheath blight resistance in rice. Molecular Plant-Microbe Interactions 33:212-222.\u003c/li\u003e\n\u003cli\u003eRostami M, Tarighi S, Taheri P, Rahimian H (2019) Genes expression of chitinase, \u0026beta;-1, 3-gluconase and peroxidase enzymes in rice treated with the causal agent of sheath blight diseases, antagonistic and inducer bacteria, and potassium silicate. Applied Entomology and Phytopathology. 87:129-146.\u003c/li\u003e\n\u003cli\u003eShamim M, Sharma D, Bisht D, Maurya R, Kaashyap M, Srivastava D, Mishra A, Kumar D, Kumar M, Juturu VN, Khan NA (2022) Proteo-Molecular Investigation of Cultivated Rice, Wild Rice, and Barley Provides Clues of Defense Responses against \u003cem\u003eRhizoctonia\u003c/em\u003e \u003cem\u003esolani\u003c/em\u003e Infection. Biotechnology and Bioengineering 9:589.\u003c/li\u003e\n\u003cli\u003eSharma A, McClung AM, Pinson SR, Kepiro JL, Shank AR, Tabien RE, Fjellstrom R (2009) Genetic mapping of sheath blight resistance QTLs within tropical japonica rice cultivars. Crop Science 49:256\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eSharma NR, Teng PS, Olivares PM (1990) Comparison of assessment methods for rice sheath blight disease. Philippines Phytopathology 26:20\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eTaguchi-Shiobara F, Ozaki H, Sato H, Maeda H, Kojima Y, Ebitani T, Yano M (2013) Mapping and validation of QTLs for rice sheath blight resistance. Breeding Science, 63:301-308.\u003c/li\u003e\n\u003cli\u003eTakagi H, Abe A, Yoshida K, Kosugi S, Natsume S, Mitsuoka C, Uemura A, Utsushi H, Tamiru M, Takuno S, Innan H (2013) QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome re-sequencing of DNA from two bulked populations. Plant J 74:174\u0026ndash;183\u003c/li\u003e\n\u003cli\u003eThind TS, Mohan C, Sharma, VK, Raj P, Arora JK, Singh PP (2008) Functional relationship of sheath blight of rice with crop age and weather factors. Plant Disease Research 23:34\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eWang H, Meng J, Peng X, Tang X, Zhou P, Xiang J, Deng X (2015) Rice WRKY4 acts as a transcriptional activator mediating defense responses toward \u003cem\u003eRhizoctonia solani\u003c/em\u003e, the causing agent of rice sheath blight. Plant Molecular Biology 89:157-171.\u003c/li\u003e\n\u003cli\u003eYang Z, Huang D, Tang W, Zheng Y, Liang K, Cutler AJ, Wu W (2013) Mapping of quantitative trait loci underlying cold tolerance in rice seedlings via high-throughput sequencing of pooled extremes. PLoS ONE 8: e68433.\u003c/li\u003e\n\u003cli\u003eZeng YX, Xia LZ, Wen ZH, Ji ZJ, Zeng DL, Qian QI, Yang CD (2015) Mapping resistant QTLs for rice sheath blight disease with a doubled haploid population. Journal of Integrative Agriculture 14:801\u0026ndash;810.\u003c/li\u003e\n\u003cli\u003eZhang B, Qi F, Hu G, Yang Y, Zhang L, Meng J, Han Z, Zhou X, Liu H, Ayaad M, Xing Y (2021) BSA-seq-based identification of a major additive plant height QTL with an effect equivalent to that of Semi-dwarf 1 in a large rice F2 population. Crop Journal 9:1428\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eZhang C, Huang M, Sang X, Li P, Ling Y, Zhao F, Du D, Li Y, Yang Z, He G (2019) Association between sheath blight resistance and chitinase activity in transgenic rice plants expressing McCHIT1 from bitter melon. Transgenic Research 28:381-390.\u003c/li\u003e\n\u003cli\u003eZhang H, Feng Z, Wang Y, Shan W, Dong Y, Zeng W, Cao W, Zhang Y, Chen Z, Chen X, Pan X (2018) Expression pattern of rice MAPK genes in response to sheath blight pathogen and hormones. Journal of the Zhejiang University - Agriculture and Life Science 39:72-78.\u003c/li\u003e\n\u003cli\u003eZheng A, Lin R, Zhang D, Qin P, Xu L, Ai P, Ding L, Wang Y, Chen Y, Liu Y, Sun Z (2013) The evolution and pathogenic mechanisms of the rice sheath blight pathogen. Nature Communications 4:1424.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tppa","sideBox":"Learn more about [Tropical Plant Pathology](https://www.springer.com/journal/40858)","snPcode":"40858","submissionUrl":"https://www.editorialmanager.com/tppa","title":"Tropical Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Oryza sativa, QTLseqr, QTL mapping, Rhizoctonia solani, Sheath blight ","lastPublishedDoi":"10.21203/rs.3.rs-4374976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4374976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSheath blight (ShB) caused by \u003cem\u003eRhizoctonia solani\u003c/em\u003e is a devastating disease that poses a major threat to rice (\u003cem\u003eOryza\u003c/em\u003e \u003cem\u003esativa\u003c/em\u003e L.) production worldwide. In this study, next generation sequencing assisted bulk segregant analysis (BSA) integrated with R package i.e. QTLseqr was utilized to identify QTL regions controlling the sheath blight resistance trait. F\u003csub\u003e3\u003c/sub\u003e mapping progenies for ShB resistance trait was derived from the cross between susceptible rice cultivar PR121 and resistant donor IET 22769. Based on sheath blight screening of F\u003csub\u003e3\u003c/sub\u003e progenies under artificial inoculation conditions, fifteen resistant (20-30 cm lesion height) and fifteen highly susceptible (70-85 cm lesion height) progenies were selected. DNA of the selected progenies were extracted and bulked respectively to constitute ShB-R and ShB-S bulks respectively. The two bulks along with parents were sequenced at \u0026gt; 20 X read depth. A total of 11,45,820 high-quality single nucleotide polymorphism (SNPs) were used for QTL-seq analysis using QTLseqr package. QTL analysis identified five QTLs namely \u003cem\u003eqShB1, qShB3, qShB5.1, qShB5.2 \u003c/em\u003eand \u003cem\u003eqShB6 \u003c/em\u003eon chromosome 1, 3, 5 and 6, respectively for resistance to ShB. A total of 69 candidate genes were identified within the QTL regions including leucine-rich repeat receptor-like kinase, coiled-coil nucleotide-binding and transcription factor protein etc. which might play a significant role in defense mechanism against \u003cem\u003eR\u003c/em\u003e. \u003cem\u003esolani\u003c/em\u003e. The identified QTLs and candidate genes can be further studied to understand genetics of ShB resistance in rice and to develop ShB resistant varieties.\u003c/p\u003e","manuscriptTitle":"QTL mapping and candidate gene mining for sheath blight resistance in rice (Oryza sativa L.) using QTLseqr approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 08:01:57","doi":"10.21203/rs.3.rs-4374976/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-08-01T16:47:54+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-06-01T04:22:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-31T20:33:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Tropical Plant Pathology","date":"2024-05-16T11:31:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-16T07:28:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Plant Pathology","date":"2024-05-06T03:49:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tppa","sideBox":"Learn more about [Tropical Plant Pathology](https://www.springer.com/journal/40858)","snPcode":"40858","submissionUrl":"https://www.editorialmanager.com/tppa","title":"Tropical Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a1475541-d82b-4d70-95f7-82815d4dd68f","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T16:43:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 08:01:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4374976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4374976","identity":"rs-4374976","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}