大豆粒形性状QTL的精细定位

在溧水中子黄豆×南农493-1衍生的504个F_2:6家系中选择Satt331~Satt592目标区间7个杂合单株和168个重组单株,衍生成356株RHL-F₂个体(群体I)和168个重组体家系(群体II)。群体II来自142个F_2:6家系,若每个F_2:6家系只保留1个重组家系则构成群体III。采用lasso和复合区间作图(CIM)法检测3个群体粒形性状2种指标的QTL。结果表明, lasso法检测到的粒长关联标记是O19和S21/Satt331,而CIM检测到的QTL区间是S21~S22和O23~O19;lasso法检测到的粒宽关联标记是O19/O21,而CIM检测到的QTL区间是O23~O19/O19~O21;长宽比与S21~S22关联是由于粒长QTL引起的,与O23~O19 /O19~O21关联是由于粒长和粒宽QTL引起的。将原Satt331~Satt592目标区间的粒长QTL剖分为与标记S21~S22和O23~O19/O19~O21关联的2个多效性QTL。根据大豆基因注释数据库,Glyma10g35240和Glyma10g34980可能是控制粒形性状发育的候选基因。     

Identification and confirmation of quantitative trait loci for stachyose content in soybean seed

Stachyose is an unfavorable sugar in soybean meal that causes flatulence for non‐ruminant animals. Understanding the genetic control of stachyose in soybean will facilitate the modification of stachyose content at the molecular level. The objective of this study was to identify quantitative trait loci (QTL) associated with seed stachyose content using simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers. A normal stachyose cultivar, ‘Osage’, was crossed with a low stachyose line, V99‐5089, to develop a QTL mapping population. Two parents were screened with 33 SSR and 37 SNP markers randomly distributed on chromosome 10, and 20 SSR and 19 SNP markers surrounding a previously reported stachyose QTL region on chromosome 11. Of these, 5 SSR and 16 SNP markers were used to screen the F_3:4 lines derived from ‘Osage’ x V99‐5089. Seed samples from F_3:5 and F_3:6 lines were analyzed for stachyose content using high‐performance liquid chromatography (HPLC). Composite interval mapping analysis indicated that two stachyose QTL were mapped to chromosome 10 and 11, explaining 11% and 79% of phenotypic variation for stachyose content, respectively. The SSR/SNP markers linked to stachyose QTL could be used in breeding soybean lines with desired stachyose contents. Chi‐square tests further indicated that these two QTL probably represent two independent genes for stachyose content. Therefore, a major QTL was confirmed on chromosome 11 and a novel QTL was found on chromosome 10 for stachyose content.     

Quantitative trait loci mapping of seed colour,hairy leaf,seedling anthocyanin,leaf chlorosis and days to flowering in F2 population of Brassica rapa L.

A diversity arrays technology (DArT) map was constructed to identify quantitative trait loci (QTL) affecting seed colour, hairy leaf, seedling anthocyanin, leaf chlorosis and days to flowering in Brassica rapa using a F₂ population from a cross between two parents with contrasting traits. Two genes with dominant epistatic interaction were responsible for seed colour. One major dominant gene controls the hairy leaf trait. Seedling anthocyanin was controlled by a major single dominant gene. The parents did not exhibit leaf chlorosis; however, 32% F₂ plants showed leaf chlorosis in the population. A distorted segregation was observed for days to flowering in the F₂ population. A linkage map was constructed with 376 DArT markers distributed over 12 linkage groups covering 579.7 cM. The DArT markers were assigned on different chromosomes of B. rapa using B. rapa genome sequences and DArT consensus map of B. napus. Two QTL (RSC1‐2 and RSC12‐56) located on chromosome A8 and chromosome A9 were identified for seed colour, which explained 19.4% and 18.2% of the phenotypic variation, respectively. The seed colour marker located in the ortholog to Arabidopsis thaliana Transparent Testa2 (AtTT2). Two QTL RLH6‐0 and RLH9‐16 were identified for hairy leaf, which explained 31.6% and 20.7% phenotypic variation, respectively. A single QTL (RSAn‐12‐157) on chromosome A7, which explained 12.8% of phenotypic variation was detected for seedling anthocyanin. The seedling anthocyanin marker is found within the A. thaliana Transparent Testa12 (AtTT12) ortholog. A QTL (RLC6‐04) for leaf chlorosis was identified, which explained 55.3% of phenotypic variation. QTL for hairy leaf and leaf chlorosis were located 0–4 cM apart on the same chromosome A1. A single QTL (RDF‐10‐0) for days to flowering was identified, which explained 21.4% phenotypic variation.     

A SNP genetic linkage map based on the ‘Hamilton’ by ‘Spencer’ recombinant inbred line population identified QTL for seed isoflavone contents in soybean

Soybean is one of the most important crops worldwide for its protein and oil as well as the health beneficial phytoestrogens or isoflavone. This study reports a relatively dense single nucleotide polymorphism (SNP)‐based genetic map based on ‘Hamilton’ by ‘Spencer’ recombinant inbred line population and quantitative trait loci (QTL) for seed isoflavone contents. The genetic map is composed of 1502 SNP markers and covers about 1423.72 cM of the soybean genome. Two QTL for seed isoflavone contents have been identified in this population. One major QTL that controlled both daidzein (qDZ1) and total isoflavone contents (qTI1) was found on LG C2 (Chr 6). And a second QTL for glycitein content (qGT1) was identified on the LG G (Chr 18). These two QTL in addition to others identified in soybean could be used in soybean breeding to optimize isoflavone content. This newly assembled soybean linkage map is a useful tool to identify and map QTL for important agronomic traits and enhance the identification of the genes involved in these traits.     

Genetics and mapping of quantitative traits for nodule number, weight, and size in soybean (Glycine max L.[Merr.])

Soybean research has found that nodule traits, especially nodule biomass, are associated with N₂ fixation ability. Two genotypes, differing in nodule number per plant and individual nodule weight, KS4895 and Jackson, were mated to create 17 F₃- and 80 F₅-derived RILs. The population was mapped with 664 informative markers with an average distance of less than 20 cM between adjacent markers. Nodule traits were evaluated in 3-year field trials. Broad-sense heritability for nodule number (no. plant^?1), individual nodule dry weight (mg nodule^?1), individual nodule size (mm nodule^?1), and total nodule dry weight (g plant^?1) was 0.41, 0.42, 0.45, and 0.27, respectively. Nodule number was negatively correlated with individual nodule weight and size. Nodule number, individual nodule weight, and size are major components which likely contributed to increased total nodule weight per plant. Composite interval mapping (CIM) identified eight QTLs for nodule number with R² values ranging from 0.14 to 0.20. Multiple interval mapping (MIM) identified two QTLs for nodule number, one of which was located close to the QTL identified with CIM. Six QTLs for individual nodule weight were detected with CIM, and one QTL was identified with MIM. For nodule size, CIM identified seven QTLs with R² values ranging from 0.14 to 0.27. Five QTLs for total nodule weight were detected with CIM, one of which was located close to a QTL identified with MIM. These results document the first QTL information on nodule traits in soybean from field experiments utilizing a dense, complete linkage map.     

Identification of quantitative trait loci for seed isoflavone concentration in soybean (Glycine max) against soybean cyst nematode stress

Isoflavones are plant secondary metabolites produced in soybean (Glycine max), which provide plant defense against pathogens and are beneficial to human health. Soybean cyst nematode (SCN) is a major yield‐limiting pest in most soybean‐producing area across the world. Traits, seed isoflavones and SCN resistance are quantitative in nature, and their phenotypic evaluations are expensive. Quantitative trait loci (QTL) underlying the two traits will be helpful to develop SCN‐resistant lines with elevated isoflavones using marker‐assisted‐selection (MAS). This study aims to identify isoflavones and SCN‐related QTL in a soybean population consisting of 109 RILs, which was developed from a cross between two commercial soybean cultivars viz. ‘RCAT1004’ and ‘DH4202’ and grown in four non‐SCN and SCN‐infested fields during 2015 and 2016. While single marker ANOVA identified 10 QTL for isoflavones and five for SCN (p < 0.01), simple interval and multiple QTL mappings identified four QTL associated with isoflavones (LOD ≥ 2.2). These results contribute to a better understanding of the genetics of the two traits and provide molecular markers that can be used in MAS to facilitate developing SCN‐resistant soybeans with increased isoflavones.     

Identification of quantitative trait loci for leaf‐related traits in an IBM Syn10 DH maize population across three environments

Leaf‐related traits (leaf length, leaf width, leaf area and leaf angle) are very important for the yield of maize (Zea mays L) due to their influence on plant type. Therefore, it is necessary to identify quantitative trait loci (QTLs) for leaf‐related traits. In this report, 221 doubled haploid lines (DHLs) of an IBM Syn10 DH population were provided for QTL mapping. In total, 54 QTLs were detected for leaf‐related traits in single environments using a high‐density genetic linkage map. Among them, only eight common QTLs were identified across two or three environments, and the common QTLs for the four traits explained 4.38%–19.99% of the phenotypic variation. qLL‐2‐1 (bin 2.09), qLW‐2‐2 (bin 2.09), qLW‐6‐3 (bin 6.07) and qLA‐5‐2 (bin 2.09) were detected in previous studies, and qLL‐1‐1, qLAr‐1‐1, qLAr‐2‐1 and qLA‐7‐1 may be new QTLs. Notably, qLW‐6‐3 and qLA‐5‐2 were found to be major QTLs explaining 19.99% and 10.96% of the phenotypic variation, respectively. Interestingly, we found three pairs of QTLs (qLW‐2‐2 and qLAr‐2‐1, qLW‐8‐1 and qLL‐8‐2, qLL‐3‐3 and qLAr‐3‐3) that control different traits and that were located on the same chromosome or in a nearby location. Moreover, nine pairs of loci with epistatic effects were identified for the four traits. These results may provide the foundation for QTL fine mapping and for an understanding of the genetic basis of variation in leaf‐related traits.     

Quantitative trait loci mapping of freezing tolerance and photosynthetic acclimation to cold in winter two‐ and six‐rowed barley

Photoacclimation (PA) and freezing tolerance (FT) have been identified as closely related traits, due to common mechanisms of environmental control. In this study, diversity array technology (DArT) was used for identification of the quantitative trait loci (QTL) of FT and PA in winter barley. Simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers were subsequently used to saturate QTL regions. Two F₂ mapping populations were created, for two‐rowed (P44) and six‐rowed barley (CaP). Different regions of the genome were responsible for differences in traits between parents in these two populations. Eleven QTLs were identified in the P44 population, including five typical for FT and PA, on chromosomes 2H, 3H and 7H. In the CaP population, only one QTL connected with PA and 10 connected with FT were found on all chromosomes except 2H. Our results demonstrate that different sets of markers should be applied in marker‐assisted selection for FT in two‐ and six‐rowed barley, as several loci determine FT at the level of biparental crosses.     

Selection of soybean elite cultivars based on phenotypic and genomic characters related to lodging tolerance

Soybean lodging can result in serious yield reduction. Detecting the quantitative trait loci (QTL) associated with lodging tolerance for their further application in marker‐assisted selection (MAS) has the potential to enhance soybean breeding efficiency. In this study, a genome‐wide association analysis (GWAS) was performed to identify soybean accessions that could potentially be used to produce lodging‐tolerant varieties, based on the comprehensive evaluation of lodging scores (LS) obtained for the parental cultivar “Tokachi nagaha” and its 137 derived cultivars. Results showed that genotype, environment and genotype × environment interaction significantly influenced LS. Of the 31 significant SNPs identified, 22 were consistently detected in two or more environments and 27 SNPs were located in or close to agronomically important QTL mapped by linkage analysis. Best linear unbiased predictors (BLUPs) of LS tend to decrease with the elite alleles contained by accessions increasing. Some excellent accessions, with lower BLUPs and D_i (stability coefficients) values and more elite alleles, were selected. This study contributed to understand the genetic mechanism of lodging, providing genetic and phenotypic information for MAS.     

Detection of QTLs for salt tolerance in Asian barley (Hordeum vulgare L.) by association analysis with SNP markers

Two hundred ninety-six Asian barley (Hordeum vulgare L.) accessions were assessed to detect QTLs underlying salt tolerance by association analysis using a 384 single nucleotide polymorphism (SNP) marker system. The experiment was laid out at the seedling stage in a hydroponic solution under control and 250 mM NaCl solution with three replications of four plants each. Salt tolerance was assessed by leaf injury score (LIS) and salt tolerance indices (STIs) of the number of leaves (NL), shoot length (SL), root length (RL), shoot dry weight (SDW) and root dry weight (RDW). LIS was scored from 1 to 5 according to the severity of necrosis and chlorosis observed on leaves. There was a wide variation in salt tolerance among Asian barley accessions. LIS and STI (SDW) were the most suitable traits for screening salt tolerance. Association was estimated between markers and traits to detect QTLs for LIS and STI (SDW). Seven significant QTLs were located on chromosomes 1H (2 QTLs), 2H (2 QTLs), 3H (1 QTL), 4H (1 QTL) and 5H (1 QTL). Five QTLs were associated with LIS and 2 QTLs with STI (SDW). Two QTLs associated with LIS were newly identified on chromosomes 3H and 4H.     

Detection of epistatic and environmental interaction QTLs for leaf orientation‐related traits in maize

Leaf architecture traits in maize are quantitative and have been studied by quantitative trait loci (QTLs) mapping. However, additional QTLs for these traits require mapping and the interactions between mapped QTLs require studying because of the complicated genetic nature of these traits. To detect common QTLs and to find new ones, we investigated the maize traits of leaf angle, leaf flagging‐point length, leaf length and leaf orientation value using a set of recombinant inbred line populations and single nucleotide polymorphism markers. In total, 19 QTLs contributed 4.13–13.52% of the phenotypic effects to the corresponding traits that were mapped, and their candidate genes are provided. Common and major QTLs have also been detected. All of the QTLs showed significant additive effects and non‐significant additive × environment effects in combined environments. The majority showed additive × additive epistasis effects and non‐significant QTL × environment effects under single environments. Common and major QTLs provided information for fine mapping and gene cloning, and SNP markers can be used for marker‐assisted selection breeding.     

Confirmation of quantitative trait loci for resistance to multiple-HG types of soybean cyst nematode (<Emphasis Type="Italic">Heterodera glycines</Emphasis> Ichinohe)

Genetic analysis of resistance of plant introduction (PI) 438489B to soybean cyst nematode (SCN) have shown that this PI is highly resistant to many SCN HG types. However, validation of the previously detected quantitative trait loci (QTL) has not been done. In this study, 250 F_2:3 progeny of a Magellan (susceptible) × PI 438489B (resistant) cross were used for primary genetic mapping to detect putative QTL for resistance to five SCN HG types. QTL confirmation study was subsequently conducted using F_6:7 recombinant inbred lines (RILs) derived from the same cross. Simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers were employed for molecular genotyping. Interval mapping (IM), permutation tests, cofactor selection, and composite interval mapping (CIM) were performed to identify and map QTL. Results showed that five QTL intervals were associated with resistance to either multiple- or single-HG types of SCN. Among these, two major QTL for resistance to multiple-SCN HG types were mapped to chromosomes (Chr.) 8 and 18, consistent with the known rhg1 and Rhg4 locations. The other QTL were mapped to Chr. 4. The results of our study confirmed earlier reported SCN resistance QTL in this PI. Moreover, SSR and SNP molecular markers tightly linked to these QTL can be useful for the near-isogenic lines (NILs) development aiming to fine-mapping of these QTL regions and map-based cloning of SCN resistance candidate genes.     

Genomewide association analysis of salt tolerance in soybean [Glycine max (L.) Merr.]

Salinity is a common abiotic stress causing soybean [Glycine max (L.) Merr.] yield loss worldwide. The use of tolerant cultivars is an effective and economic approach to coping with this stress. Towards this, research is needed to identify salt‐tolerant germplasm and better understand the genetic and molecular basis of salt tolerance in soybean. The objectives of this study were to identify salt‐tolerant genotypes, to search for single‐nucleotide polymorphisms (SNPs) and QTLs associated with salt tolerance. A total of 192 diverse soybean lines and cultivars were screened for salt tolerance in the glasshouse based on visual leaf scorch scores after 15–18 days of 120 mM NaCl stress. These genotypes were further genotyped using the SoySNP50K iSelect BeadChip. Genomewide association mapping showed that 62 SNP markers representing six genomic regions on chromosomes (Chr.) 2, 3, 5, 6, 8 and 18, respectively, were significantly associated with salt tolerance (p < 0.001). A total of 52 SNP markers on Chr. 3 are mapped at or near the major salt tolerance QTL previously identified in S‐100 (Lee et al., 2014). Three SNPs on Chr. 18 map near the salt tolerance QTL previously identified in Nannong1138‐2 (Chen, Cui, Fu, Gai, & Yu, 2008). The other significant SNPs represent four putative minor QTLs for salt tolerance, newly identified in this study. The results above lay the foundation for fine mapping, cloning and molecular breeding for soybean salt tolerance.     

Quantitative trait locus mapping of floral and related traits using an F2 population of Aquilegia

The objective of this study was to determine quantitative trait loci (QTL) underlying ten floral and related traits in Aquilegia. The traits assessed were calyx diameter, corolla diameter, petal length, petal blade length, sepal length, sepal width, spur length, spur width, plant height and flower number. These are important traits for ornamental value and reproductive isolation of Aquilegia. QTL analysis of these traits was conducted using single‐marker analysis and composite interval mapping (CIM). We used an F₂ population consisting of 148 individuals derived from a cross between the Chinese wild species Aquilegia oxysepala and the cultivar Aquilegia flabellata ‘pumila’. Resulting CIM analysis identified 39 QTLs associated with these traits, which were mapped on seven linkage groups. These QTLs could explain 1.22–53.28% of the phenotypic variance. Thirty‐one QTLs, which explained more than 10% of the phenotypic variation, were classified as major QTLs. Graphical representations of the QTLs on seven linkage groups were made. Our research provides the potential for future molecular assisted selection breeding programmes and the cloning of target genes through fine mapping.     

QTL mapping of resistance in lentil (Lens culinaris ssp. culinaris) to ascochyta blight (Ascochyta lentis)

Quantitative trait locus (QTL) analysis of ascochyta blight resistance in lentil was conducted using genomic maps developed from two F₂ populations, viz. ILL5588/ILL7537 and ILL7537/ILL6002. Five QTLs for ascochyta blight resistance were identified by composite interval mapping (CIM) across four linkage groups (LG) in population ILL5588/ILL7537. Three QTLs were identified by CIM in population ILL7537/ILL6002 (two in close proximity on LGI and one on LGII). Two of these coincided with regions identified using multiple interval mapping (MIM) and were shown to be conditioned by dominant and partial dominant gene action. Together, they accounted for approximately 50% of the phenotypic variance of disease severity. Comparison between the two populations revealed a potentially common QTL and several common regions that contained markers significantly associated with resistance. This study demonstrated the transferability of QTLs among populations and identified markers closely linked to the major QTL that may be useful for future marker‐assisted selection for disease resistance.     

QTL mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (Zea mays L.)

Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought‐stressed and unstressed conditions, which explained 0.71%–20.62% of phenotypic variation in single watering condition. Forty‐five of the 100 QTLs were identified under water‐stressed conditions, and 29 stable QTLs (sQTLs) were identified under water‐stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via QTL pyramiding. We further integrated 27 independent QTL studies in a meta‐analysis to identify 21 meta‐QTLs (mQTLs). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding mQTLs. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker‐assisted selection to generate high‐yielding maize varieties.     

Association mapping of leaf traits in spinach (Spinacia oleracea L.)

Spinach (Spinacia oleracea L.) is an important leafy vegetable crop grown worldwide. Leaf traits, surface texture (smooth, savoy or semi‐savoy), petiole colour (different shades of green vs. purple) and edge shape (serrate vs. entire), are important commercial traits of spinach. Association mapping for the three traits was conducted on 323 USDA spinach germplasm accessions, originally collected from 33 countries and representing the entire USDA spinach germplasm collection. The majority of accessions were from Europe (36.3%), Asia (25.3%) and North America (15.8%). The majority of the spinach accessions (82.0%) were smooth (unwrinkled types), whereas the savoy and semi‐savoy types (wrinkled types) accounted for 18.0%. The collection contained 74.9% green petiole types, while the purple petioles consisted of 25.1%. The collection consisted of 27.2% serrated leaf types and 72.8% entire leaf edge types. Genotyping‐by‐sequencing (GBS) was used for single nucleotide polymorphism (SNP) discovery, and SNPs were used as genotypic data to conduct genetic diversity and association mapping of the three leaf traits. Five genetic subpopulations and principal components (PCs) were postulated by structure 2 and JMP Genomics 7 for this association panel. Five, seven and 14 SNPs were identified to be associated with surface texture, edge shape and petiole colour, respectively. This study provides us an approach to identify SNP markers through association analysis in spinach and thus leads to select these three leaf traits through marker‐assisted selection in spinach breeding programme.     

Grouping patterns of rainfed winter wheat test locations and the role of climatic variables

Soybean mosaic virus (SMV) is a member of genus Potyvirus, which causes worldwide soybean [Glycine max (L.) Merr.] yield loss and seed quality deterioration. It is of great significance to find new resistance loci and genes for cultivation of soybean variety. In the present study, a recombinant inbred line (RIL) population and a genome-wide association study (GWAS) panel, which contained 193 lines and 379 germplasms, respectively, were used for QTL mapping of resistance to SMV. Linkage mapping identified a major QTL, qSMV13, on chromosome 13, conferring resistance to SMV SC3 and SC7 strains, explaining phenotypic variations 71.21 and 76.59?%, respectively. The QTL qSMV13 was located close to the known SMV resistance loci Rsv1-h. GWAS analysis revealed five single nucleotide polymorphisms (SNPs) significantly associated with resistance to SC3 on chromosomes 2, 11, 13, 14 and 16. One of the SNP markers, ss715614844, was the right flanking marker of qSMV13. Combining linkage mapping and GWAS analysis enabled us to delimit qSMV13 in a 97.2-kb genomic region containing seven genes. A LRR-RLK protein was proposed as the candidate gene of qSMV13. These results provided selection markers and candidate genes for SMV resistance in soybean molecular breeding programs.

     

Identification and mapping of quantitative resistance to late blight (<Emphasis Type="Italic">Phytophthora infestans</Emphasis>) in <Emphasis Type="Italic">Solanum habrochaites</Emphasis> LA1777

Late blight (Phytophthora infestans) can have devastating effects on tomato production over the whole world. Most of the commercial cultivars of tomato, Solanum lycopersicum, are susceptible. Qualitative and quantitative resistance has been described in wild relatives of tomato. In general qualitative resistance can more easily be overcome by newly evolved isolates. Screening of three S. habrochaites accessions (LA1033, LA2099 and LA1777) through a whole plant assay showed that accession LA1777 had a good level of resistance to several isolates of P. infestans. To explore the potential in this wild species, an introgression line (IL) population of S. habrochaites LA1777 was used to screen individual chromosome regions of the wild species by a detached leaf assay. Two major isolates (T_1,2 and T_1,2,4) were used and two parameters were measured: lesion size (LS), and disease incidence (DI). Substantial variation was observed between the individual lines. QTLs were identified for LS but not for DI. The presence of five QTLs derived from LA1777 (Rlbq4a, Rlbq4b, Rlbq7, Rlbq8 and Rlbq12) results in unambiguous higher levels of resistance. All QTLs co-localized with previously described QTLs from S. habrochaites LA2099 except QTL Rlbq4b, which is therefore a novel QTL.     

Identification and marker‐assisted introgression of QTL conferring resistance to bacterial leaf blight in cowpea (Vigna unguiculata (L.) Walp.)

Bacterial leaf blight (BLB), caused by Xanthomonas axonopodis pv. vignicola (Xav), is widespread in major cowpea [Vigna unguiculata (L.) Walp.] growing regions of the world. Considering the resource poor nature of cowpea farmers, development and introduction of cultivars resistant to the disease is the best option. Identification of DNA markers and marker‐assisted selection will increase precision of breeding for resistance to diseases like bacterial leaf blight. Hence, an attempt was made to detect QTL for resistance to BLB using 194 F_2 : 3 progeny derived from the cross ‘C‐152’ (susceptible parent) × ‘V‐16’ (resistant parent). These progeny were screened for resistance to bacterial blight by the leaf inoculation method. Platykurtic distribution of per cent disease index scores indicated quantitative inheritance of resistance to bacterial leaf blight. A genetic map with 96 markers (79 SSR and 17 CISP) constructed from the 194 F₂ individuals was used to perform QTL analysis. Out of three major QTL identified, one was on LG 8 (qtlblb‐1) and two on LG 11 (qtlblb‐2 and qtlblb‐3). The PCR product generated by the primer VuMt337 encoded for RIN2‐like mRNA that positively regulate RPM1‐ and RPS2‐dependent hypersensitive response. The QTL qtlblb‐1 explained 30.58% phenotypic variation followed by qtlblb‐2 and qtlblb‐3 with 10.77% and 10.63%, respectively. The major QTL region on LG 8 was introgressed from cultivar V‐16 into the bacterial leaf blight susceptible variety C‐152 through marker‐assisted backcrossing (MABC).