Mapping Novel QTLs Associated with Grain Number and Primary Branching in Rice using New plant type derived RILs

Mapping Novel QTLs Associated with Grain Number and Primary Branching in Rice using New plant type derived RILs | 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 Mapping Novel QTLs Associated with Grain Number and Primary Branching in Rice using New plant type derived RILs Shekharappa Nandakumar, Sonu Shekhawat, Vikram Jeet Singh, Kunnummal Kurungara Vinod, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7133316/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in BMC Plant Biology → Version 1 posted 22 You are reading this latest preprint version Abstract Background Grain number per panicle, thousand-grain weight, panicle branching and productive tiller number per unit area are the major yield-attributing traits in rice. The yield plateau for more than two decades, demands for the identification of novel genes or QTLs (genome) from diverse germplasm. The current study aims to identify the genes/QTLs for grain number and primary branch number using yield-attributing traits. Results RIL population consisting of 175 lines derived from PR126 and Pusa NPT34. Total of 25 QTLs were identified for different traits, among which 18 were found distributed in five hotspots. Seven QTLs were having major effects and the remaining showed minor effects on the respective target traits. QTLs, qFGN9.1 for grain number lying at the marker interval, RM444-RM6920 and qPBN11.1 for primary branches lying at the marker interval of RM144-RM6965 on chromosome 9 and 11, were found novel respectively. A largest QTL hotspot on chromosome 6, harboured QTLs for GN, PH, PL, PBN, and YLD. 17 putative candidate gene models were identified through in silico analysis which involves in inflorescence development. The major gene models include APETALA genes, MADS box family proteins and WD40 which directly controls panicle branching, spikelet development. Therefore, further fine mapping of marker intervals can help in narrowing the genomic region and trait specific marker development. It enables more precise introgression of QTLs, for primary branches, grain number and other panicle architecture related traits into elite cultivars. Conclusion The scope of future study may involve the fine mapping of the major QTLs to identify the more closely linked marker, development of gene based or functional markers and cloning to understand the molecular mechanism of the gene/QTLs. It also helps us to use these genes in breeding program for more precise marker assisted selection and transfer. Rice QTL mapping panicle branching grain number Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. BACKGROUND Rice is cultivated in more than 114 countries spread over six continents [ 1 ], where India’s contribution is more than 20% of the world's rice production [ 2 ]. In India, rice production accounts for 47% [ 3 ] of the total cereal production and is considered one of the most important dietary sources. Recent estimates suggest that to meet the food demand of the growing world population, which is predicted to increase by 9.7% in 2050, the production of rice must go up by 40% [ 4 ]. The current phase of rice cultivation, productivity and production, will not be sufficient enough to feed the future population [ 2 , 5 , 6 , 7 ]. The most essential but difficult objective for breeders has been to increase the yield of staple crops [ 8 ]. The rice panicle is a raceme with no apical growth and consists of predetermined primary and secondary branches as well as number of spikelets. The genetic mechanism of panicle development is very complex, that begins with the transformation of shoot apical meristem (SAM) and ends with the formation of spiekelets. The branches and their differentiated spikelet meristems will eventually form the basic structure of a rice panicle and determine the spikelet number [ 9 ]. The spikelet number is influenced by endogenous factors such as genetic makeup and plant physiology. Therefore, to study the genetics of grain number, it is essential to identify the genomic regions controlling this trait. Identification of QTLs for yield and yield attributing traits through QTL mapping is an effective strategy to break the yield plateau in rice. With the advancement in the molecular marker technology many QTLs have been identified for grain number, panicle branching, panicle length, plant height, tiller number, flowering. Among the several quantitative trait loci (QTLs) which are reported for grain yield namely, Gn1a , NOG1 , qGN4-1 , DEP1 , LAX1 , IPA1 , APO1 , in rice are seen to control the grain number primarily [ 10 ]. The first QTL identified for grain number in rice was Gn1a on chromosome 1, encodes cytokinin degradation enzyme. Reduced expression of Gn1a allele increases cytokinin content in the inflorescence meristem thereby increases the grain number per panicle. Similarly, NUMBER OF GRAINS 1 ( NOG1 ) is a gene was, mapped using an Oryza rufipogon introgression line SIL176 in a high-yielding indica background, Guichao 2. This gene is located on chromosome 1, encodes for an enoyl-CoA hydratase/isomerase (ECH) enzyme, bearing a primary role in the β-oxidation of fatty acids, and regulates grain number and panicle branching [ 11 ]. Panicle branching is another important trait for increasing the grain yield by increasing the grain number. Identification of gene/QTLs for panicle branching and use in introgression along with other yield attributing gene/QTL is a good strategy to improve the total grain yield. DEP1 (Dense and Erect Panicle) is a dominant regulator of panicle branching mapped on chromosome 9. It encodes a Gγ that is reported to control and regulates the panicle branching, grain number. Therefor targeting grain number and panicle branching will be an attractive strategy to increase the grain yield to mitigate the food scarcity. DEP1 is one of the major QTL in japonica based super rice breeding pipeline in northern China. Similarly, QTL, qGN4.1 , was identified on chromosome 4, co-located with the other QTLs for panicle branching, tiller number, flag leaf length and width [ 12 ]. This QTL was introgressed in 12 mega varieties of rice as a result, in increase in grain number by 21.6 grains per panicle to 147.2 grains per panicle over the base verities. Currently, there are inclusion of more than 900 QTLs for grain number in the rice database at Gramene, but precise genic information underlying these QTLs remains largely unknown as only a few of them have been fine mapped/cloned [ 11 ]. The utility of the mapped QTLs has become straightforward in breeding, since the QTL linked markers, themselves can act as foreground markers in marker assisted selection (MAS). Parental line selection having wide phenotypic variation for the desired trait is the key to the success of a linkage-based mapping [ 13 ]. Despite the genetic divergence for grain number among indica and japonica , mapping the QTLs linked to this trait using indica / japonica cross combinations has remained scanty in the literature, which could be due to their poor cross compatibility [ 14 , 15 ]. To make this gap up, in our study, we have chosen one indica parental line PR126 having a low grain number and a japonica derived line, Pusa NPT34 having a high grain number for mapping QTLs using SSR markers. Further, we have attempted to validate the identified QTLs. 2. MATERIALS AND METHODS 2. 1 Plant material In the current study PR126 which matures early, having less total grain number (TGN), shorter panicle, less primary branches per panicle, was used as a female parent whereas Pusa NPT34 having contrasting phenotype for the above said traits was used as male parent to develop recombinant inbred lines. PR126 is a short duration indica rice variety matures in 120 days. Developed originally as Huanghuazhan (HHZ), PR126 was bred in China having the parentage of Fenghuazhan /Huangxinzhang. It was one among the most popular green super rice (GSR) varieties distributed across the world by the International Rice Research Institute. Being widely grown in the southern China, HHZ shows wide adaptation, high yielding potential, profuse tillering and tolerance to multiple stresses. The other parent, Pusa NPT 34 was an advanced breeding line, derived from new plant type (NPT) breeding, having high grain number, good panicle architecture, and panicle density. Hybridization between PR126 and Pusa NPT34 was attempted and thirty-three F 1 were grown at IARI-Regional Breeding and Genetics Research Centre (IARI-RBGRC), Aduthurai, Tamilnadu. After checking the hybridity using RM8094 marker, single true F 1 plant was selected to generate F 2 population. The F 2 was advanced till F 6 without any selection bias during the Recombinant inbred line (RILs) development. A total of 175 RILs along with parental lines and checks were used generate phenotypic data at different locations. The population was raised and tested under normal conditions by following recommended package of practice. 2.2 Phenotyping of recombinant inbred lines Three sites, spread across India namely, DEL (New Delhi; 28°64’N; 77°15’ E; 220m), KAR (Karnal; 29°70’ N; 76°99’ E; 200m) and ADT (Aduthurai; 11°08’N; 79°47’ E; 200m) were selected for the evaluation of RILs. These sites are the part of the shuttle breeding chain of ICAR-IARI exclusively used for rice crop improvement, and represented diverse agroecology. Sowing was taken up on raised nursery bed and 21 days old seedlings were transplanted on puddled soil. Augmented RCBD was used to test the genotypes with five checks namely PR126 (Female parent), Pusa NPT34 (Male parent), Pusa Basmati 1509, Rasi and PKF 8 -218. Experiment unit was divided into 8 blocks; the test genotypes were randomised among eight blocks. Five checks were replicated across all the blocks. At physiological maturity, five plants were randomly tagged from each family and data was recorded on the targeted traits. The traits observed were plant height (PH), panicle length (PL), number of primary branches (PBN), total spikelet number per panicle (TGN), Tiller number (TN), number of fully matured grains per panicle (FGN) and five plant grain yield (YLD). The data on unfilled grains (UFG) and spikelet fertility (SF) were derived from above observations. The data on the grains were recorded after harvest of the tagged plants. The mean of five plants of each family is considered for further data analysis. Border plants were excluded to reduce the error. 2.4 Linkage map construction Genomic DNA was isolated from each RIL using freshly collected leaves from the field using CTAB method [ 4 , 16 ]. After DNA isolation, 1083 SSR markers were used for parental polymorphism and the product were separated using 3.5% agarose gel electrophoresis. The amplified and resolved PCR amplicons on gel electrophoresis were classified as PR126 type (A), Pusa NPT34 type (B) and heterozygotes(H). The genotypic data was subjected to chi-square test, and the marker which shows segregation distortion, low-rate amplification was deleted. A linkage map was constructed using the software QTL ICIM v4.2 by multipoint analysis using the Kosambi mapping function and a LOD value of 3.0. 2.5 Statistical testing Analysis of variance was performed for all the phenotypic data collected in the current study location wise as well as across the locations. Adjusted mean value (BLUPs) for each trait was calculated using PBTools (IRRI, 2014). The phenotypic distribution of all the traits was visualised through box plots using SR Plots tool. The genetic advance (GA) was calculated using multi-location phenotypic data using traitstat package in R. Correlation coefficients were calculated and graphically visualised using the corrplot package. Principal component analysis of the nine yield related traits was performed using prcomp in R base. Biplots were drawn for major principal components using the GGEBiplot package. Principal components and its contributing traits were graphically visualised using Circos. 2.6 QTL Mapping QTL mapping was carried out to identify the genomic regions underlying the nine traits studied in the current experiment. QTL mapping was performed using ICIMapping v3.2. Threshold LOD values was calculated by running permutation test of 1000 iterations at alpha value 0.05. Forward regression was used with walk distance of 1 cM and probabilityof inclusion at 0.01. QTLs were classified as major QTLs having more than 10% PVE and as minor QTLs having less than 10% PVE. A genomic region or marker interval possess more than one QTL for different traits are considered as QTL hotspots. 2.7 In-silico analysis of consistent QTLs In-silico analysis was performed for major QTL hotspots to identify the probable putative candidate genes. The marker sequence information was used to know the physical position of the markers on the chromosomes. The probably expressed genes present between the marker positions were downloaded from the Rice Annotation Project Database (RAP-DB). Annotated candidate genes were shortlisted that have already known functions with the target traits based on previous reports. The interrelationship between the putative candidate genes in the QTL hotspot region and their association with the traits was established using the knetminer ( https://knetminer.com ). 3. RESULTS 3.1 Morphological evaluation and analysis of variance Phenotypic evaluation in the field and ANOVA revealed that all the traits exhibited highly significant variation across sites ( Supplementary table 1 ). The variation presents for TGN and other panicle related traits among the RILs can be seen in Fig. 1 (a) . The pooled ANOVA was performed to check for the presence of GE interactions and GxE interaction was also found highly significant ( Supplementary table 2 ). The distribution pattern in box plot confirms the normal distribution pattern for all the traits and revealed that the traits PH, TN, PL, PBN, FGN, and TGN exhibited the highest mean performance at Delhi, while the lowest performance was observed at Aduthurai, except for TGN, which was low in Karnal (Table 1 and Fig. 1 (b) ). The adjusted mean value of all the genotypes is given in Supplementary Table 3 . The heritability (Broad sense, H 2 ) was varied from as minimum as 11.66% and as high as 92.23%. The range confirms the wides range of heritability and presence of highly heritable as well as low heritable traits. The H 2 of SF % at Delhi was high whereas the lowest was observed for PL at Karnal. Higher genetic advance was found for FGN, UFG, and TGN while, PL and TN had the lowest (Table 1 ). Ranking based on phenotype of the genotypes across the location indicated that mean performance of most of the traits of the RILs was higher at Delhi, followed by Karnal and Aduthurai. Delhi was found to be the best for PBN, PH, TGN with a high mean performance of the RILs. The population mean performance for TN, PL and YLD was observed to be good at Karnal, followed by Delhi and Aduthurai. (Fig. 2 ). Table 1 Descriptive statistics of PR126, Pusa NPT34 and RIL population across sites during kharif 2022 Traits PR126 Pusa NPT34 Delhi Karnal Aduthurai Range Mean ± SE CV Range Mean ± SE CV Range Mean ± SE CV PH (cm) 90.02 100.58 74.73-125.09 104.58 ± 0.69 4.44 65.24-129.37 99.93 ± 1.04 6.44 62.53–107 87.40 ± 0.52 7.06 TN 18.59 12.59 6.65–31.79 13.18 ± 0.30 15.74 4.88–22.4 10.46 ± 0.26 13.09 3.95–14.95 7.06 ± 0.13 26.65 PL (cm) 22.07 18.96 17.05–29.97 23.79 ± 0.19 9.94 16.70-32.22 23.70 ± 0.26 7.90 16.21-29.00 21.97 ± 0.19 10.12 PBN 11.16 15.17 8.54–19.71 15.32 ± 0.12 5.29 9.29–20.45 14.43 ± 0.12 7.78 9.02–18.97 13.43 ± 0.17 7.61 FGN 176.75 301.53 98.51-385.13 233.64 ± 4.22 9.80 72.21-355.36 208.98 ± 3.90 24.09 24.54-355.31 176.98 ± 4.71 16.10 UFG 40.48 112.75 9.70–175.00 71.39 ± 2.39 25.58 4.63-140.68 51.56 ± 1.92 52.22 0-219.42 87.05 ± 3.30 24.02 TGN 209.63 433.06 181.45-538.31 305.02 ± 4.78 10.22 121.49-413.74 260.54 ± 3.99 20.91 114.21-447.78 264.03 ± 4.93 11.64 SF (%) 80.47 73.88 42.84–96.07 76.59 ± 0.67 4.10 45.17–95.25 79.93 ± 0.70 7.11 21.94–100 66.63 ± 1.19 6.25 YLD (gm) 142.22 160.23 80.43-218.47 162.11 ± 4.26 10.63 97.08-168.43 118.44 ± 0.87 14.74 24.83-113.63 47.25 ± 0.93 28.02 Genetic Parameter BS h 2 (%) GA (%) Delhi Karnal Aduthurai PH (cm) 71.58 74.66 78.38 8.11 TN 75.00 74.33 70.70 0.91 PL (cm) 34.90 11.66 72.31 0.73 PB 81.23 76.03 56.81 1.22 FGN 78.23 83.91 58.00 20.87 UFG 79.89 76.10 55.00 20.21 TGN 78.06 78.62 65.00 30.00 SF (%) 92.23 88.67 63.55 5.50 YLD (gm) 35.02 43.79 52.91 5.90 NPT, New Plant Type, CV, Coefficient of variation; SE, Standard error; FG, Filled grain; PB, Primary branches; PH, Plant height; PL, Panicle length; SF, Spikelet fertility; TGN, Total grain, number; TN, Tiller number; UFG, Unfilled grain; YLD, Yield; BS h 2 , Broad sense heritability; GA, Genetic advance 3.2 Interrelationship between the panicle architecture related traits The correlogram showed that FGN, PBN, PH, PL, and TN were positively correlated with the TGN. Similarly, PH was positively correlated with PBN, FGN, SF, PL, but negatively correlated with the TN (Fig. 3 ). PBN was found positively correlated with TGN and PL. The principal component analysis showed four major principal components which explain a cumulative variance of 83% ( Supplementary table 4 ). The data visualisation using Circos Table Viewer v0.63-10 depicts the contribution of various traits on principal components (Fig. 4 ). The first principal component explains 33% of total variance followed by PC2 (21%), PC3 (16%), PC4 (13%). The major contributor to the total variance of PC1 was TGN, followed by FG (Filled grain), PB (Primary branch number), UFG (Unfilled grain number), PH (Plant height) similarly in PC2, major contributor was SF followed by FG and PH, in PC3, major contributor was PB followed by TN (Tiller number), PH, in PC4, major contributor to total variance was TGN, FGN, UFG. The TGN was highly variable followed by FGN, PBN and PH in the current study. 3.3 Linkage map construction and QTL mapping Among the 1083 markers used for polymorphism survey, 11.63% of marker i.e. 126 markers were polymorphic. Out of 126 markers, 23 were removed as they were distorted. The list of polymorphic markers along with the physical position, forward and reverse primer details is given in ( Supplementary table 5 ). These 103 markers record the genome diversity of 9.51% and the distribution of markers was uniform throughout the genome. The cumulative length of the complete genome was 2415.07 cM and average marker interval was 23.44 cM. The polymorphic markers distribution was ranged 5 on chromosome 7 & 10 while 14 markers on chromosome 11 ( Supplementary table 6 ). A total of 25 major and minor QTLs associated with all the traits and distributed on various chromosomes were identified ( Table 2 ) . Among them, seven QTLs were major QTLs and eighteen were minor QTLs (Fig. 5 (a) ). Table 2 QTLs mapped for grain number and yield attributing traits in the RIL population of ‘PR126/PusaNPT34’ TQL name Trait Name Chromosome Position (cM) Left Marker Right Marker LOD** PVE (%) PVE (QTL x E) Additive effect # qFGN3.1 FG/Kar 3 32 OSR13 RM7 3.10 (2.87) 7.47 3.22 15.66 qFGN3.2* FG/ADT 3 126 RM168 RM520 3.14 (2.89) 4.64 0.53 21.49 FG/Delhi 3 127 RM168 RM520 3.69 (2.93) 10.38 20.02 qFGN6.1* FG/Delhi 6 158 RM204 RGNMS2221 4.50 (2.93) 10.07 5.98 18.79 FG/Kar 6 157 RM204 RGNMS2221 4.06 (2.87) 11.12 17.35 qFGN9.1 FG/Delhi 9 144 RM444 RM6920 3.09(2.93) 5.67 2.87 14.82 qTGN3.1 TGN/Kar 3 33 OSR13 RM7 3.45 (2.85) 4.61 16.47 qTGN3.2* TGN/Delhi 3 127 RM168 RM520 4.82 (2.90) 11.67 1.13 28.71 qTGN6.1* TGN/Delhi 6 156 RM190 RM204 6.94 (2.90) 10.58 3.90 27.44 TGN/Kar 6 156 RM190 RM204 3.68 (2.85) 5.06 17.31 qTGN11.1 TGN/Kar 11 201 RM6965 RM27150 3.84(2.85) 5.56 18.03 qPL2.1 PL/Delhi 2 163 RM13672 RM6 3.58 (2.86) 5.80 0.28 0.75 qPL3.1* PL/Delhi 3 108 HvSSR03-82 RM168 5.54 (2.86) 10.32 0.34 1.00 qPL6.1 PL/ADT 6 156 RM190 RM204 4.84 (2.89) 9.12 0.85 qPL11.1 PL/Delhi 11 43 RM332 HvSSR11-13 3.69 (2.86) 9.40 0.58 0.97 qPH2.1 PH/ADT 2 94 RM2634 HvSSR02-59 3.28 (2.97) 6.83 0.28 1.96 qPH3.1 PH/Delhi 3 144 RM520 RM1230 4.26 (2.92) 7.19 0.84 3.27 qPH6.1* PH/Kar 6 159 RM204 RGNMS2221 4.86 (3.19) 12.77 1.49 5.21 PH/ADT 6 157 RM204 RGNMS2221 4.66 (2.97) 9.90 2.36 qPBN3.1 PB/Kar 3 115 HvSSR03-82 RM168 4.03 (2.75) 6.73 0.54 qPBN4.1 PB/Delhi 4 175 RM567 nksssr04-11 4.20 (2.91) 5.18 0.53 qPBN6.1* PB/ADT 6 152 RM190 RM204 7.47 (2.84) 18.27 1.17 qPBN11.1 PB/Delhi 11 176 RM144 RM6965 3.84(2.91) 7.34 0.26 0.61 qTN2.1 TN/Delhi 2 103 RM2634 HvSSR02-59 3.26 (2.61) 8.52 1.72 1.51 TN/Kar 2 110 RM2634 HvSSR02-59 2.94 (2.85) 5.67 0.89 qTN4.1 TN/Kar 4 183 nksssr04-11 RM567 3.19 (2.85) 8.75 0.76 1.14 qYLD4.1 YPP/Kar 4 47 RM127 RM16775 2.92 (2.81) 6.38 0.62 7.23 qYLD6.1 YPP/Delhi 6 161 RM204 RGNMS2221 3.04 (2.95) 6.97 23.92 qUFG6.1 UFG/Delhi 6 155 RM190 RM204 3.52 (2.57) 5.20 1.18 12.93 qSF1.1 SF/Kar 1 147 RM12230 RM10217 2.89 (2.83) 7.34 0.03 6.25 LOD, Logarithm of the odds; RM, Rice microsatellite; HvSSR, highly variable simple sequence repeats; *, major QTLs; **, Threshold LOD; # Bold values = P1 and Unbold = P2. 3.3.1 Filled grain number per panicle (FGN) Four QTLs were mapped for FGN namely, qFGN3.1, qFGN3.2, qFGN6.1 , and qFGN9.1 , on chromosomes 3, 6, and 9, respectively. The marker interval RM204 and RGNMS2221 possessed, qFGN6.1 , mapped both at Delhi and Karnal with the LOD values ranged between 4.06 to 4.50 with PVE of 10.07% at Delhi, 11.12% at Karnal and additive effect of 18.79, 17.35 due to Pusa NPT34 respectively. Another QTL, qFGN3.2 , which remained consistent at Delhi and Aduthurai was mapped between the markers RM168 and RM520 on chromosome 3. The PVE of the QTLs was ranged from 4.64–10.38%, with the additive effect of 20.02 and 21.49 due to Pusa NPT34 respectively. The remaining QTL for FGN namely qFGN3.1 , and qFGN9.1 were mapped on chromosomes 3 and 9 between OSR13-RM7 and RM444-RM6920, respectively. The genomic region flanked between the markers RM444 and RM6920 possessed novel QTL, qFGN9.1 , for FGN. The QTL had LOD value of 3.09 with the PVE of 5.67% and additive effect of 14.82 due to PR126 (Table 2 ). Among four QTLs identified for TGN, qTGN3.1 and qTGN3.2 falls in the same marker interval as that of qFGN3.1 and qFGN3.2 (Table 2 ). The study was able to identify a consistent QTL, qTGN6.1 across two locations. 3.3.2 Panicle length (PL) Four QTLs namely, qPL2.1, qPL3.1, qPL6.1 and qPL11.1 were mapped on chromosomes 2, 3, 6 and 11 respectively. A genomic region flanked between the markers HvSSR03-82 and RM168 possessed a major QTL, qPL3.1 . The QTL had LOD value of 5.54 with PVE of 10.32% and additive effect 1 due PR126. The remaining QTLs were minor QTLs namely, qPL2.1, qPL6.1, qPL11.1 , mapped between flanking markers, RM13672-RM6, RM190-RM204, RM332-HvSSR11-13, respectively (Table 2 ). 3.3.3 Plant height (PH) Three QTL namely, qPH2.1 , qPH3.1 , and qPH6.1 were mapped on chromosomes 2, 3 and 6 respectively. The markers RM204 and RGNMS2221 possessed a major QTL, qPH6.1 , and it was mapped at both Karnal and Aduthurai. The QTL had the LOD values ranged between 4.66 to 4.86, with a PVE of 9.90 at Aduthurai, 12.77% at Karnal and additive effect of 2.36, 5.21 due to Pusa NPT34 respectively. The other QTLs namely, qPH2.1 and qPH3.1 , were minor and site specific. The QTL, qPH2.1 , was mapped between RM2634-HvSSR02-59 and qPH3.1 , was mapped between RM520-RM1230 (Table 2 ) . 3.3.4 Primary branches number (PBN) Four QTLs were mapped, qPBN3.1, qPBN4.1, qPBN6.1 , and qPBN11.1 on chromosomes 3, 4, 6, and 11 respectively. A novel genomic region flanked between the markers RM144-RM6965 possessed a QTL, qPBN11.1 . The LOD of the QTL was 3.84 with a phenotypic variance of 7.34% and additive effect 0.61 due to PR126. The other marker interval between RM190 and RM204 possessed a major QTL, qPBN6.1 and qPBN3.1 , was mapped between HvSSR03-82-RM168 (Table 2 ). 3.3.5 Tiller number (TN) QTL mapping for TN have detected two QTLs namely qTN2.1 and qTN4.1 on chromosome 2 and 4 respectively. The qTN2.1 was mapped between the markers RM2634 and HvSSR02-59 both at Delhi and Karnal. The LOD of the QTL was 2.94, 3.26 with PVE of 5.67% at Karnal, 8.52% at Delhi and additive effect 0.89 and 1.51 due to PR126 respectively. The other minor site-specific QTL, qTN4.1 was mapped between the markers, nksssr04-11-RM567 at Karnal with the LOD of 3.19 (Table 2 ). 3.3.6 Yield (YLD) Two site specific minor QTLs namely, qYLD4.1 and qYLD6.1 , were mapped for YLD on chromosome 4 and 6 respectively. The QTL, qYPP4.1 , was mapped between the flanking markers RM127 and RM16775 at Karnal. The QTL had LOD value of 2.92 with PVE of 6.38% and additive effect of 7.23 due to PR126. The other QTL, qYPP6.1 , was mapped between the flanking markers, RM204 and RGNMS2221 at Delhi. The QTL had LOD value of 3.04 with PVE of 6.97% and additive effect of 23.92 due to PR126 (Table 2 ). The other QTL for unfilled grains and SF includes qUFG6.1 and qSF1.1 . 3.4 QTL hotspots The current study was able to identify four QTL hotspots, where among 25 QTLs, 16 were distributed on different chromosome within these QTL hotspots (Table 3 ). Chromosome 2 reported to possess a QTL hotspot of size 1.65 Mb bracketed between RM2634 and HvSSR02-59 (Cluster I). The Second hotspot had four QTLs viz., qTGN3.2, qPBN3.1, qPL3.1, qPH3.1 (Cluster II) mapped on the short arm of third chromosome. This region was bracketed between the markers HvSSR03-82-RM1230 with marker interval length of 2.40 Mb. The long arm of chromosome 4 possesses a QTL hotspot bracketed between nksrssr04-11 and RM567 with total genomic size of 3.81 Mb. This QTL hotspot possesses two QTLs namely, qPBN4.1 and qTN4.1 (Cluster III). Fourth hotspot was identified on chromosome 6 which carries five QTLs namely, qTGN6.1, qPL6.1, qPBN6.1, qPH6.1, qYLD6.1 (Cluster IV) and was found flanked between markers RM190 and RGNMS2221 with a span of 3.47 Mb. Table 3 QTL hotspots identified in PR126 x Pusa NPT34 derived RILs during kharif 2020 Cluster number Chromosome Marker interval Interval length (Mb) Number of QTLs Name of the QTLs I 2 RM2634-HvSSR02-59 1.65 2 qPH2.1, qTN2.1 II 3 HvSSR03-82-RM1230 2.40 4 qTGN3.2, qPBN3.1, qPL3.1, qPH3.1 III 4 RM567-nksssr04-11 3.81 2 qPBN4.1, qTN4.1 IV 6 RM190-RGNMS2221 3.47 5 qTGN6.1, qPL6.1, qPBN6.1, qPH6.1, qYLD6.1 3.5 In-silico analysis QTL hotspot on chromosome 6 possess five QTLs for different traits and the total size of the marker interval is 3.47 Mb. The in-silico study of the QTL hotspot on chromosome 6 has showed that this region comprises 530 gene models. Among 530 genes, few genes which may have direct or indirect role in the biosynthetic pathway of the QTLs were identified. A total of 17 genes were putative candidates which may play a key role in regulation of grain number, plant panicle length and panicle branching (Table 4 ). Four of which putative genes encode AP2 domain containing protein, two coded zinc finger proteins, two auxin responsive factors, and one gene each coding an AP1 complex subunit, OsMAPK6, OsDP1 , F-box containing domains, MADS box family protein, helix loop helix protein, WD-40 , G-protein coupled receptors and ethylene responsive elements. Table 4 The predicted gene models in the QTL hotspot on chromosome 6 Locus ID Start (bp) Stop (bp) Putative function LOC/Os06g04540 1962306 1963580 OsDepressed palea1 , expressed LOC/Os06g06090 2806668 2812929 OsMAPK6 , expressed LOC/Os06g06750 3162801 3169415 OsMADS5 - MADS-box family gene with MIKCc type-box, expressed LOC/Os06g06870 3250462 3261364 zinc finger protein, putative, expressed LOC/Os06g06900 3273373 3276890 helix-loop-helix DNA-binding domain containing protein, expressed LOC/Os06g06970 3310866 3311822 AP2 domain containing protein, expressed LOC/Os06g07000 3319111 3322612 OsFBL28 -F-box domain and LRR containing protein, expressed LOC/Os06g07030 3337083 3338612 AP2 domain containing protein, expressed LOC/Os06g07040 3342423 3346318 OsIAA20 - Auxin-responsive Aux/IAA gene family member, expressed LOC/Os06g07090 3376566 3386549 AP-1 complex subunit gamma-1, putative, expressed LOC/Os06g07540 3632663 3639659 WD-40 repeat family protein, putative, expressed LOC/Os06g08340 4040908 4041551 AP2 domain containing protein, expressed LOC/Os06g08360 4062304 4063540 ethylene-responsive element-binding protein, putative, expressed LOC/Os06g09310 4678838 4680332 zinc finger, C 3 HC 4 type domain containing protein, expressed LOC/Os06g09390 4731330 4733911 AP2 domain containing protein, expressed LOC/Os06g09660 4926492 4932177 auxin response factor, putative, expressed LOC/Os06g09930 5060660 5065027 G protein coupled receptor, putative, expressed 4. DISCUSSION During the 1990s, researchers at IRRI were propounding a novel model for rice plants known as the new plant type (NPT). Pusa NPT34, used in this study is a high yielding breeding line derived from the NPT linage and characteristically possessed high number of grains. In the next decade, the idea of breeding for green super rice (GSR) was initially floated in China in 2005, for assimilating the ‘green’ traits - traits that are environment friendly, impart resource use efficiency and multiple stress tolerance [ 17 ]. Several multiparent inter-cross lines were bred integrating semi-dwarf trait with super yield and grain quality. One among these was HHZ, a high yielding, widely adapted, early maturing and lodging resistant variety. Soon HHZ found itself a part of the GSR project and released as PR126 in Punjab, it proved highly adaptable and early maturing. Soon PR126 started replacing long-duration popular variety, Pusa 44 [ 17 ]. The biparental population of PR126/Pusa NPT34 that was stabilised and was in the F 6 generation, we could demonstrate that the distribution of traits was normal and predominantly quantitative, enabling a perfect condition for QTL mapping. Traits having more heritability shows that they are more easily transferable. Higher heritability of few traits such as grain number and primary branching in the current study indicates that they can be transferable through breeding into the elite varietal background to enhance the productivity [ 18 ]. Agronomic performance of breeding lines in one site does not assure success in other sites, because of GEI, especially when the locations are far apart. The conglomeration of genomic regions at different marker interval/hotspots indicated that these hotspots are the region possesses closely related genes involved in the biosynthetic pathway of grain number and its associated traits. It makes pyramiding of the haplotypes carrying the hotspot region relatively easier. The QTL hotspot possesses a greater number of QTLs (five QTLs) for different traits was identified on chromosome 6. QTLs associated were qTGN6.1 , qPL6.1 , qPBN6.1 , qPH6.1 , and qYLD6.1 . There were also two functionally associated QTLs of TGN, namely, qFGN6.1 and qUFG6.1 found at this hotspot. It was further interesting to observe that the most desirable QTLs for TGN, PL, PBN and YLD came from Pusa NPT34, except for PH. This eventually showed that this hotspot associated with taller plants that are higher yielding with a greater number of grains. QTL hotspot with positive alleles for yield attributing traits will be an important genomic region for marker assisted introgression to improve the yield barrier. During 2017, identified QTL hotspot on chromosome 3 where qnt3.1 , and qnt3.2 , qph3.1 , qTGW3.4 for tiller number, plant height, and thousand grain weight were mapped [ 19 ]. Similarly on chromosome 4, QTL hotspot possessing qTGW4.1 and qPPP4.2 for thousand grain weight and panicles per plant were mapped. Another study during 2020, identified QTLs for DFF ( qFD6.1 ), PH ( qPHT6.1 ), TN ( qTL6.1 ), PL ( qPL6.1 ), on chromosome 6 [ 20 ]. The currently identified genomic regions are away from the report by Donde et al., 2020 but this hotspot region was reported to possess genes/QTLs for FGN, TGN, PL, PH and PBN from the previous reports [ 21 ]. The hotspot on the short arm of chromosome 3 (Cluster II) having four QTLs was the next interesting candidate genomic region. Spanned 2.4 Mb long, harboured qTGN3.2 , qPBN3.1 , qPL3.1 and qPH3.1 along with one functionally associated QTL of TGN, namely, qFGN3.2 . The most desirable traits contributing QTLs for TGN, PBN and PH came from Pusa NPT34 except for PL. Although the integration of both these hotspots can improve grain number, a chance of increased PH and reducing the PL cannot be avoided. In order to use the QTL hotspot on chromosome 3, the genomic region must be fine mapped to identify the closely associated marker with no linkage drag of increased plant height. The marker linked to qTGN3.2 was also associated with two earlier reported QTLs, gpp3.1 and gpp3.2 describing grains per panicle [ 22 ]. There are earlier reports of QTLs associated with PL, PBN, and PH co-localised in this hotspot region [ 23 ]. A recent study on marker trait association for filled grain, have identified an MTA on chromosome 3 for filled grain number at 3.85 Mb [ 24 ]. Another study has also identified a QTL on chromosome 3 for yield per plant at 7.68 Mb along with association for grain length, width and thousand grain weight on the same chromosome [ 25 ]. The hotspot on chromosome 2 (Cluster I) harboured QTLs for PH and TN, and was not a direct contributor to grain number. The QTL for both PH and TN in this hotspot was also reported by previous reports [ 26 ]. A recent report has identified MTAs for GNP (Grain number per panicle), PBN (Primary branch number), PL (Panicle length) and TN (Tiller number) [ 25 ]. The MTA for TN falls significantly away from the QTL detected in the present study. Similarly, another study was able to map a QTL for plant height and internode length on chromosome 3 at 12.5 Mb whereas the QTL in the current study was mapped at 20 Mb region [ 27 ]. The remaining hotspot on chromosome 4 (Cluster III) carried QTLs for PBN and TN, of which qPBN4.1 was derived from PR126 and qTN4.1 from Pusa NPT34. The size of this region is 3.81 Mb and, therefore, we need to pick out recombinants of this hotspot to get the desirable effects of both the traits together. The QTLs identified in this hotspot for PBN and TN were reported earlier [ 12 ]. During 2023, it was confirmed that the results of previous report by Deshmukh et al for the presence of QTL for tiller number on chromosome 4 [ 28 ]. Our study also confirms the presence of QTL in the same genomic region on chromosome 4. The QTLs on chromosome 3 were considered as a single QTL obtained from Pusa NPT34, as it was found associated only with the grain number traits, TGN and FGN. This location was previously reported to carry QTLs for grains per panicle, gpp3.1 [ 22 ] or qGn3.1 [ 12 ]. There were also report that identified the presence of MTAs for grain number on chromosome 3 at 0.5 Mb which is near telomere end [ 29 ]. Of the nine QTLs that lay outside the hotspot regions, qFGN9.1 and qPBN11.1 are novel as there are no previous reports of a QTL affecting these traits. The QTL, qFGN9.1 , is mapped between a 5.92 to 7.00 Mb on the long arm of chromosome 11. Although there are past studies reporting QTLs for grain number on chromosome 9, such as gpp9.1 falling near the 21.18 Mb region [ 30 ] and GP9.1 falling near the 17.74 Mb region [ 31 ], these QTLs are reasonably far placed from the currently discovered ones. Similarly, in an earlier report, the genomic region which harbours qPBN11.1 was reported associated with genes for panicle neck diameter [ 32 ], however, no reports were found on its role in primary branch number in rice. Previous studies have provided reports on the remaining five QTLs. The QTL locus qTGN11.1 was identified in studies conducted by [ 33 ] and [ 34 ], while qSF1.1 was documented by Mei [ 26 ], qYLD4.1 by Lian et al., [ 35 ], qPL2.1 by Zhang et al., [ 36 ], and qPL11.1 by Cui et al., [ 37 ], each associated with specific traits. Identification of putative candidate genes and landing closer to the gene of interest in many of the genetic study become easy with the advent of genome sequencing and gene annotation in rice [ 38 ]. The QTL hotspot identified on chromosome 6, possessed 17 putative candidate genes display significant functional attributes. This region was selected as it harboured QTLs for TGN, PH, PL, PBN, and YLD, particularly coming from Pusa NPT34. The 17 putative candidate gene models encode various proteins which play direct role in inflorescence and spikelet development. Notably, APETALA genes (AP1 and AP2) zinc finger proteins, MADS box family proteins, helix loop helix proteins, WD40, G-protein coupled receptors and ethylene receptors play pivotal roles in rice spikelet development. APETALA proteins and MADS box family proteins reported to play major role inflorescence development. A recessive mutant, reported on chromosome 3 is a homeotic mutant of MADS box gene, can affect inflorescence development [ 39 ]. Similarly, many of the MADS box genes come under SEPALLATA -like gene and are associated with floral organ identity, inflorescence and spikelet development [ 40 ]. Apart from AP and MADS box family genes, WD40 protein reported to play an important role in inflorescence development through the regulating the panicle branching. The ASP1 ( aberrant spikelet ) encodes a protein called, WD40. This protein is homologous to TPL/TPR ( topless/topless related ) of Arabidopsis and REL2 ( romosa enhancer loci ) of maize regulates the panicle branching and spikelet development in rice [ 41 ]. One of the major QTL for branching DEP1 (Dense and Erect Panicle) acts through G-protein to regulate the panicle branching, length of the panicle and grain number [ 42 ]. Most of the putative candidate genes present in the QTL hotspot region plays key role in regulating the panicle architecture through the control of panicle branching, length of the panicle, grain number and through the regulation of the plant hormone production. A functional interconnectivity of the putative genes present in the QTL hotspot was found with HD1 being linked to the PBN, as well as AHL22 and DP1 , which affect FGN. The interrelationship between these genes and traits can be attributed to the colocalization of QTLs on chromosome 6 (Fig. 7). 5. CONCLUSION In the current study, the QTLs were mapped for grain number, panicle branching and yield attributed traits and identified two novel genomic regions for grain number and panicle branching. Out of 25 QTLs identified, 16 QTLs were distributed in 4 marker intervals (hotspots) on different chromosomes in the current study. The scope of future study may involve the fine mapping of the major QTLs to identify the more closely linked marker, development of gene based or functional markers and cloning to understand the molecular mechanism of the gene/QTLs. It also helps us to use these genes in breeding program for more precise marker assisted selection and transfer. Declarations No Clinical trial is involved. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. Supplementary Information: Supplementary figures and tables are given Author contributions PKB and AKS conceived the idea and formulated the research plan. NS carried out the research work and prepared the manuscript. NS, VJS, SS and BKD generated the mapping population. MN, NS, VJS, HBS and S assisted in the execution of field trials. KKV, RKE, HB and GKS assisted in data analysis. PKB and KKV improved the manuscript. This work forms a part of the doctoral research work of NS. All authors have read the article and approved the final version. Funding No funding was available Conflicts of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data availability The data sets supporting the results of this article are included in the supplementary file. Competing interests The authors declare no competing interests. Acknowledgement The first author, NS is grateful to CSIR for CSIR JRF fellowship References Singh VJ, Vinod KK, Krishnan SG, Singh AK. 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Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sonu","middleName":"","lastName":"Shekhawat","suffix":""},{"id":495943486,"identity":"61cb6825-fd5b-44f4-a706-fd4a4a5a9f3d","order_by":2,"name":"Vikram Jeet Singh","email":"","orcid":"","institution":"Acharya Narendra Deva University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Vikram","middleName":"Jeet","lastName":"Singh","suffix":""},{"id":495943487,"identity":"7605edf0-43fb-4dc7-84d3-8a0ce38c464d","order_by":3,"name":"Kunnummal Kurungara Vinod","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kunnummal","middleName":"Kurungara","lastName":"Vinod","suffix":""},{"id":495943489,"identity":"a9bddd66-5a0e-47a7-9c60-f5e49a8fc9eb","order_by":4,"name":"Bheemapura Shivakumar Harshitha","email":"","orcid":"","institution":"CSB-Institute for Seri-Biotechnological Research","correspondingAuthor":false,"prefix":"","firstName":"Bheemapura","middleName":"Shivakumar","lastName":"Harshitha","suffix":""},{"id":495943490,"identity":"d5722bcb-778e-4042-93cf-9f4893e3d269","order_by":5,"name":"Subbaiyan Gopala Krishnan","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Subbaiyan","middleName":"Gopala","lastName":"Krishnan","suffix":""},{"id":495943491,"identity":"95aff74e-49ab-4554-8f4e-6064c3c1a006","order_by":6,"name":"Brijesh Kumar Dixit","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Brijesh","middleName":"Kumar","lastName":"Dixit","suffix":""},{"id":495943492,"identity":"a102274a-ce89-4516-992c-d1ce5f583f45","order_by":7,"name":"Shridhar Ragi","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shridhar","middleName":"","lastName":"Ragi","suffix":""},{"id":495943493,"identity":"5ad14bc0-327d-4ec3-bd9c-5c2d00c1972b","order_by":8,"name":"Ranjith Kumar Ellur","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ranjith","middleName":"Kumar","lastName":"Ellur","suffix":""},{"id":495943494,"identity":"8cc97ad5-d2f6-408c-acf7-37b0fcc60a67","order_by":9,"name":"Haritha Bollinedi","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Haritha","middleName":"","lastName":"Bollinedi","suffix":""},{"id":495943495,"identity":"55b85a49-b578-4d4c-afe8-277361a6d045","order_by":10,"name":"Mariappan Nagarajan","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mariappan","middleName":"","lastName":"Nagarajan","suffix":""},{"id":495943496,"identity":"362132ac-8ab7-4c96-ae58-77cab34c1dc2","order_by":11,"name":"Nanjappa Shivakumar","email":"","orcid":"","institution":"University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nanjappa","middleName":"","lastName":"Shivakumar","suffix":""},{"id":495943497,"identity":"fa414d3f-7901-4801-ba5f-5669711d2a53","order_by":12,"name":"Tapan Kumar Mondal","email":"","orcid":"","institution":"ICAR-National Institute of Plant Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Tapan","middleName":"Kumar","lastName":"Mondal","suffix":""},{"id":495943498,"identity":"a8fb93b8-099d-4474-8178-f7c09cd2aeb1","order_by":13,"name":"Ashok Kumar Singh","email":"","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ashok","middleName":"Kumar","lastName":"Singh","suffix":""},{"id":495943499,"identity":"16412ee7-a632-4487-9ab2-bf8c09f412f7","order_by":14,"name":"Prolay Kumar Bhowmick","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACxgMMDAcSQKwDDAdsQAKNBwjpQdaSBtLSQLwWIH0YJoIbmEsfPnDgZ9udPH6J7MQDP86ct1vbfhhoS41NNC4tln1pCQd7254VS87I3XCw58bt5G1nEoFajqXlNuDQYnCGx+AAb9vhxA23czccZvhwO9nsAFALY8NhvFoO/kVoOZdsdv4hYS2HEbbcOGBndoOALZY9bAmHZc49S5w5/y3QL2eSE8xuAG1JwOMXcx7mgw/flN1J7Oc5u/nDj2N29mbn0x8++FBjg9thIIKRDSGQCFaZgEM5XAvDH4SAPR7Fo2AUjIJRMEIBAMC3eK2fCJlsAAAAAElFTkSuQmCC","orcid":"","institution":"ICAR-Indian Agricultural Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Prolay","middleName":"Kumar","lastName":"Bhowmick","suffix":""}],"badges":[],"createdAt":"2025-07-15 18:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7133316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7133316/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-026-08481-2","type":"published","date":"2026-03-10T15:58:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88438162,"identity":"e01a106a-405a-4c1a-87c0-b0542b6bd757","added_by":"auto","created_at":"2025-08-06 12:13:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":499575,"visible":true,"origin":"","legend":"\u003cp\u003eA). Phenotypic variability for grain number and panicle related traits among the RILs during \u003cem\u003ekharif\u003c/em\u003e2020 at ICAR-IARI. B) Box plot depicting the phenotypic distribution of grain number and yield attributing traits across three locations during \u003cem\u003ekharif\u003c/em\u003e 2020.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7133316/v1/e9b187fa5b77399c0ef1933e.jpeg"},{"id":88438745,"identity":"1b6e0aa2-5042-40e3-a9b1-2c53498e6058","added_by":"auto","created_at":"2025-08-06 12:21:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":359764,"visible":true,"origin":"","legend":"\u003cp\u003eRanking of the site performance for grain number and yield attributing traits during kharif 2020\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7133316/v1/c4c6ee07dcb3aa5ab0e4d754.jpeg"},{"id":88438174,"identity":"38c5db94-d4a6-4a9f-93bb-167c0578877e","added_by":"auto","created_at":"2025-08-06 12:13:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":369524,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficient among the grain number and yield attributing traits using combined BLUP\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7133316/v1/e7859e6def78ae79e73eb3bb.jpeg"},{"id":88438169,"identity":"91b2014f-6372-4628-ae5d-c99214652bb4","added_by":"auto","created_at":"2025-08-06 12:13:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":680213,"visible":true,"origin":"","legend":"\u003cp\u003eThe contribution of different traits to principal components using combined BLUP\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7133316/v1/f3c19e5f27f18adbf63e1834.jpeg"},{"id":88438170,"identity":"739fddf8-fc4b-44c8-bd4f-836be616fcde","added_by":"auto","created_at":"2025-08-06 12:13:27","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":352770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA). \u003c/strong\u003eGraphical representation of QTLs identified for various traits across the location. \u003cstrong\u003eB).\u003c/strong\u003eAssociation of a putative candidate genes underlying the QTL hotspot on chromosome 6 with plant height, grain number and primary branch number in rice.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7133316/v1/550076c1eaec48ab3fe16178.jpeg"},{"id":104739642,"identity":"69c0c66a-f2cf-405b-ac7a-65f3e90c8711","added_by":"auto","created_at":"2026-03-16 16:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3815658,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7133316/v1/872e0b41-b1e0-4585-b372-672a6ab64ff1.pdf"},{"id":88438161,"identity":"3666d228-04cd-46bf-9f6c-045fc3b0fd7b","added_by":"auto","created_at":"2025-08-06 12:13:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170130,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7133316/v1/109665f3014eb16af6aa1d17.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Novel QTLs Associated with Grain Number and Primary Branching in Rice using New plant type derived RILs","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eRice is cultivated in more than 114 countries spread over six continents [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], where India\u0026rsquo;s contribution is more than 20% of the world's rice production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In India, rice production accounts for 47% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] of the total cereal production and is considered one of the most important dietary sources. Recent estimates suggest that to meet the food demand of the growing world population, which is predicted to increase by 9.7% in 2050, the production of rice must go up by 40% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The current phase of rice cultivation, productivity and production, will not be sufficient enough to feed the future population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The most essential but difficult objective for breeders has been to increase the yield of staple crops [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe rice panicle is a raceme with no apical growth and consists of predetermined primary and secondary branches as well as number of spikelets. The genetic mechanism of panicle development is very complex, that begins with the transformation of shoot apical meristem (SAM) and ends with the formation of spiekelets. The branches and their differentiated spikelet meristems will eventually form the basic structure of a rice panicle and determine the spikelet number [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The spikelet number is influenced by endogenous factors such as genetic makeup and plant physiology. Therefore, to study the genetics of grain number, it is essential to identify the genomic regions controlling this trait.\u003c/p\u003e\u003cp\u003eIdentification of QTLs for yield and yield attributing traits through QTL mapping is an effective strategy to break the yield plateau in rice. With the advancement in the molecular marker technology many QTLs have been identified for grain number, panicle branching, panicle length, plant height, tiller number, flowering. Among the several quantitative trait loci (QTLs) which are reported for grain yield namely, \u003cem\u003eGn1a\u003c/em\u003e, \u003cem\u003eNOG1\u003c/em\u003e, \u003cem\u003eqGN4-1\u003c/em\u003e, \u003cem\u003eDEP1\u003c/em\u003e, \u003cem\u003eLAX1\u003c/em\u003e, \u003cem\u003eIPA1\u003c/em\u003e, \u003cem\u003eAPO1\u003c/em\u003e, in rice are seen to control the grain number primarily [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The first QTL identified for grain number in rice was \u003cem\u003eGn1a\u003c/em\u003e on chromosome 1, encodes cytokinin degradation enzyme. Reduced expression of \u003cem\u003eGn1a\u003c/em\u003e allele increases cytokinin content in the inflorescence meristem thereby increases the grain number per panicle. Similarly, \u003cem\u003eNUMBER OF GRAINS 1\u003c/em\u003e (\u003cem\u003eNOG1\u003c/em\u003e) is a gene was, mapped using an \u003cem\u003eOryza rufipogon\u003c/em\u003e introgression line SIL176 in a high-yielding \u003cem\u003eindica\u003c/em\u003e background, Guichao 2. This gene is located on chromosome 1, encodes for an enoyl-CoA hydratase/isomerase (ECH) enzyme, bearing a primary role in the β-oxidation of fatty acids, and regulates grain number and panicle branching [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Panicle branching is another important trait for increasing the grain yield by increasing the grain number. Identification of gene/QTLs for panicle branching and use in introgression along with other yield attributing gene/QTL is a good strategy to improve the total grain yield. \u003cem\u003eDEP1\u003c/em\u003e (Dense and Erect Panicle) is a dominant regulator of panicle branching mapped on chromosome 9. It encodes a \u003cem\u003eGγ\u003c/em\u003e that is reported to control and regulates the panicle branching, grain number. Therefor targeting grain number and panicle branching will be an attractive strategy to increase the grain yield to mitigate the food scarcity. \u003cem\u003eDEP1\u003c/em\u003e is one of the major QTL in \u003cem\u003ejaponica\u003c/em\u003e based super rice breeding pipeline in northern China. Similarly, QTL, \u003cem\u003eqGN4.1\u003c/em\u003e, was identified on chromosome 4, co-located with the other QTLs for panicle branching, tiller number, flag leaf length and width [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This QTL was introgressed in 12 mega varieties of rice as a result, in increase in grain number by 21.6 grains per panicle to 147.2 grains per panicle over the base verities. Currently, there are inclusion of more than 900 QTLs for grain number in the rice database at Gramene, but precise genic information underlying these QTLs remains largely unknown as only a few of them have been fine mapped/cloned [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe utility of the mapped QTLs has become straightforward in breeding, since the QTL linked markers, themselves can act as foreground markers in marker assisted selection (MAS). Parental line selection having wide phenotypic variation for the desired trait is the key to the success of a linkage-based mapping [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite the genetic divergence for grain number among \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003e, mapping the QTLs linked to this trait using \u003cem\u003eindica\u003c/em\u003e/\u003cem\u003ejaponica\u003c/em\u003e cross combinations has remained scanty in the literature, which could be due to their poor cross compatibility [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To make this gap up, in our study, we have chosen one \u003cem\u003eindica\u003c/em\u003e parental line PR126 having a low grain number and a \u003cem\u003ejaponica\u003c/em\u003e derived line, Pusa NPT34 having a high grain number for mapping QTLs using SSR markers. Further, we have attempted to validate the identified QTLs.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e\u003cb\u003e2. 1 Plant material\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the current study PR126 which matures early, having less total grain number (TGN), shorter panicle, less primary branches per panicle, was used as a female parent whereas Pusa NPT34 having contrasting phenotype for the above said traits was used as male parent to develop recombinant inbred lines. PR126 is a short duration \u003cem\u003eindica\u003c/em\u003e rice variety matures in 120 days. Developed originally as Huanghuazhan (HHZ), PR126 was bred in China having the parentage of Fenghuazhan /Huangxinzhang. It was one among the most popular green super rice (GSR) varieties distributed across the world by the International Rice Research Institute. Being widely grown in the southern China, HHZ shows wide adaptation, high yielding potential, profuse tillering and tolerance to multiple stresses. The other parent, Pusa NPT 34 was an advanced breeding line, derived from new plant type (NPT) breeding, having high grain number, good panicle architecture, and panicle density.\u003c/p\u003e\u003cp\u003eHybridization between PR126 and Pusa NPT34 was attempted and thirty-three F\u003csub\u003e1\u003c/sub\u003e were grown at IARI-Regional Breeding and Genetics Research Centre (IARI-RBGRC), Aduthurai, Tamilnadu. After checking the hybridity using RM8094 marker, single true F\u003csub\u003e1\u003c/sub\u003e plant was selected to generate F\u003csub\u003e2\u003c/sub\u003e population. The F\u003csub\u003e2\u003c/sub\u003e was advanced till F\u003csub\u003e6\u003c/sub\u003e without any selection bias during the Recombinant inbred line (RILs) development. A total of 175 RILs along with parental lines and checks were used generate phenotypic data at different locations. The population was raised and tested under normal conditions by following recommended package of practice.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Phenotyping of recombinant inbred lines\u003c/h2\u003e\u003cp\u003eThree sites, spread across India namely, DEL (New Delhi; 28\u0026deg;64\u0026rsquo;N; 77\u0026deg;15\u0026rsquo; E; 220m), KAR (Karnal; 29\u0026deg;70\u0026rsquo; N; 76\u0026deg;99\u0026rsquo; E; 200m) and ADT (Aduthurai; 11\u0026deg;08\u0026rsquo;N; 79\u0026deg;47\u0026rsquo; E; 200m) were selected for the evaluation of RILs. These sites are the part of the shuttle breeding chain of ICAR-IARI exclusively used for rice crop improvement, and represented diverse agroecology. Sowing was taken up on raised nursery bed and 21 days old seedlings were transplanted on puddled soil. Augmented RCBD was used to test the genotypes with five checks namely PR126 (Female parent), Pusa NPT34 (Male parent), Pusa Basmati 1509, Rasi and PKF\u003csub\u003e8\u003c/sub\u003e-218. Experiment unit was divided into 8 blocks; the test genotypes were randomised among eight blocks. Five checks were replicated across all the blocks. At physiological maturity, five plants were randomly tagged from each family and data was recorded on the targeted traits. The traits observed were plant height (PH), panicle length (PL), number of primary branches (PBN), total spikelet number per panicle (TGN), Tiller number (TN), number of fully matured grains per panicle (FGN) and five plant grain yield (YLD). The data on unfilled grains (UFG) and spikelet fertility (SF) were derived from above observations. The data on the grains were recorded after harvest of the tagged plants. The mean of five plants of each family is considered for further data analysis. Border plants were excluded to reduce the error.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Linkage map construction\u003c/h2\u003e\u003cp\u003eGenomic DNA was isolated from each RIL using freshly collected leaves from the field using CTAB method [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. After DNA isolation, 1083 SSR markers were used for parental polymorphism and the product were separated using 3.5% agarose gel electrophoresis. The amplified and resolved PCR amplicons on gel electrophoresis were classified as PR126 type (A), Pusa NPT34 type (B) and heterozygotes(H). The genotypic data was subjected to chi-square test, and the marker which shows segregation distortion, low-rate amplification was deleted. A linkage map was constructed using the software QTL ICIM v4.2 by multipoint analysis using the Kosambi mapping function and a LOD value of 3.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical testing\u003c/h2\u003e\u003cp\u003eAnalysis of variance was performed for all the phenotypic data collected in the current study location wise as well as across the locations. Adjusted mean value (BLUPs) for each trait was calculated using PBTools (IRRI, 2014). The phenotypic distribution of all the traits was visualised through box plots using SR Plots tool. The genetic advance (GA) was calculated using multi-location phenotypic data using \u003cem\u003etraitstat\u003c/em\u003e package in R. Correlation coefficients were calculated and graphically visualised using the \u003cem\u003ecorrplot\u003c/em\u003e package. Principal component analysis of the nine yield related traits was performed using \u003cem\u003eprcomp\u003c/em\u003e in R base. Biplots were drawn for major principal components using the \u003cem\u003eGGEBiplot\u003c/em\u003e package. Principal components and its contributing traits were graphically visualised using Circos.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.6 QTL Mapping\u003c/h2\u003e\u003cp\u003eQTL mapping was carried out to identify the genomic regions underlying the nine traits studied in the current experiment. QTL mapping was performed using ICIMapping v3.2. Threshold LOD values was calculated by running permutation test of 1000 iterations at alpha value 0.05. Forward regression was used with walk distance of 1 cM and probabilityof inclusion at 0.01. QTLs were classified as major QTLs having more than 10% PVE and as minor QTLs having less than 10% PVE. A genomic region or marker interval possess more than one QTL for different traits are considered as QTL hotspots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.7 In-silico analysis of consistent QTLs\u003c/h2\u003e\u003cp\u003eIn-silico analysis was performed for major QTL hotspots to identify the probable putative candidate genes. The marker sequence information was used to know the physical position of the markers on the chromosomes. The probably expressed genes present between the marker positions were downloaded from the Rice Annotation Project Database (RAP-DB). Annotated candidate genes were shortlisted that have already known functions with the target traits based on previous reports. The interrelationship between the putative candidate genes in the QTL hotspot region and their association with the traits was established using the \u003cem\u003eknetminer\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://knetminer.com\u003c/span\u003e\u003cspan address=\"https://knetminer.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Morphological evaluation and analysis of variance\u003c/h2\u003e\u003cp\u003ePhenotypic evaluation in the field and ANOVA revealed that all the traits exhibited highly significant variation across sites (\u003cb\u003eSupplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/b\u003e). The variation presents for TGN and other panicle related traits among the RILs can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(a)\u003c/b\u003e. The pooled ANOVA was performed to check for the presence of GE interactions and GxE interaction was also found highly significant (\u003cb\u003eSupplementary table 2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe distribution pattern in box plot confirms the normal distribution pattern for all the traits and revealed that the traits PH, TN, PL, PBN, FGN, and TGN exhibited the highest mean performance at Delhi, while the lowest performance was observed at Aduthurai, except for TGN, which was low in Karnal (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(b)\u003c/b\u003e). The adjusted mean value of all the genotypes is given in \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e. The heritability (Broad sense, \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was varied from as minimum as 11.66% and as high as 92.23%. The range confirms the wides range of heritability and presence of highly heritable as well as low heritable traits. The \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of SF % at Delhi was high whereas the lowest was observed for PL at Karnal. Higher genetic advance was found for FGN, UFG, and TGN while, PL and TN had the lowest (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ranking based on phenotype of the genotypes across the location indicated that mean performance of most of the traits of the RILs was higher at Delhi, followed by Karnal and Aduthurai. Delhi was found to be the best for PBN, PH, TGN with a high mean performance of the RILs. The population mean performance for TN, PL and YLD was observed to be good at Karnal, followed by Delhi and Aduthurai. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" 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\u003eDescriptive statistics of PR126, Pusa NPT34 and RIL population across sites during \u003cem\u003ekharif\u003c/em\u003e 2022\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePR126\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePusa NPT34\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eKarnal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eAduthurai\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.73-125.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65.24-129.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e99.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e62.53\u0026ndash;107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e87.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.65\u0026ndash;31.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.88\u0026ndash;22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.95\u0026ndash;14.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e26.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.05\u0026ndash;29.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.70-32.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16.21-29.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e21.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePBN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.54\u0026ndash;19.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.29\u0026ndash;20.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.02\u0026ndash;18.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFGN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e301.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.51-385.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e233.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.21-355.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e208.98\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e24.54-355.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e176.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e16.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUFG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.70\u0026ndash;175.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.63-140.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0-219.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e87.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e24.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTGN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e209.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e433.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181.45-538.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e305.02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e121.49-413.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e260.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e114.21-447.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e264.03\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.84\u0026ndash;96.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.17\u0026ndash;95.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e79.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.94\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e66.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLD (gm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.43-218.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e162.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.08-168.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e118.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e24.83-113.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e47.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e28.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eGenetic Parameter\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eBS h\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c12\" namest=\"c10\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eGA (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eDelhi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eKarnal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003eAduthurai\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePH (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e71.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e74.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e78.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e8.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e75.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e74.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e70.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePL (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e34.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e11.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e72.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e81.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e76.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e56.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFGN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e78.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e83.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e58.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e20.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUFG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e79.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e76.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e20.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTGN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e78.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e78.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e65.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e92.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e88.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e63.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYLD (gm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e35.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e43.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e52.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e5.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eNPT, New Plant Type, CV, Coefficient of variation; SE, Standard error; FG, Filled grain; PB, Primary branches; PH, Plant height; PL, Panicle length; SF, Spikelet fertility; TGN, Total grain, number; TN, Tiller number; UFG, Unfilled grain; YLD, Yield; BS h\u003csup\u003e2\u003c/sup\u003e, Broad sense heritability; GA, Genetic advance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Interrelationship between the panicle architecture related traits\u003c/h2\u003e\u003cp\u003eThe correlogram showed that FGN, PBN, PH, PL, and TN were positively correlated with the TGN. Similarly, PH was positively correlated with PBN, FGN, SF, PL, but negatively correlated with the TN (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PBN was found positively correlated with TGN and PL. The principal component analysis showed four major principal components which explain a cumulative variance of 83% (\u003cb\u003eSupplementary table 4\u003c/b\u003e). The data visualisation using Circos Table Viewer v0.63-10 depicts the contribution of various traits on principal components (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The first principal component explains 33% of total variance followed by PC2 (21%), PC3 (16%), PC4 (13%). The major contributor to the total variance of PC1 was TGN, followed by FG (Filled grain), PB (Primary branch number), UFG (Unfilled grain number), PH (Plant height) similarly in PC2, major contributor was SF followed by FG and PH, in PC3, major contributor was PB followed by TN (Tiller number), PH, in PC4, major contributor to total variance was TGN, FGN, UFG. The TGN was highly variable followed by FGN, PBN and PH in the current study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Linkage map construction and QTL mapping\u003c/h2\u003e\u003cp\u003eAmong the 1083 markers used for polymorphism survey, 11.63% of marker i.e. 126 markers were polymorphic. Out of 126 markers, 23 were removed as they were distorted. The list of polymorphic markers along with the physical position, forward and reverse primer details is given in (\u003cb\u003eSupplementary table 5\u003c/b\u003e). These 103 markers record the genome diversity of 9.51% and the distribution of markers was uniform throughout the genome. The cumulative length of the complete genome was 2415.07 cM and average marker interval was 23.44 cM. The polymorphic markers distribution was ranged 5 on chromosome 7 \u0026amp; 10 while 14 markers on chromosome 11 (\u003cb\u003eSupplementary table 6\u003c/b\u003e). A total of 25 major and minor QTLs associated with all the traits and distributed on various chromosomes were identified \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Among them, seven QTLs were major QTLs and eighteen were minor QTLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(a)\u003c/b\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\u003eQTLs mapped for grain number and yield attributing traits in the RIL population of \u0026lsquo;PR126/PusaNPT34\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTQL name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrait Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChromosome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePosition (cM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeft Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRight Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLOD**\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePVE (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePVE (QTL x E)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAdditive effect\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqFGN3.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFG/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOSR13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.10 (2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqFGN3.2*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFG/ADT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.14 (2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFG/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.69 (2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqFGN6.1*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFG/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRGNMS2221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.50 (2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFG/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRGNMS2221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.06 (2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqFGN9.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFG/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM6920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.09(2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e14.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqTGN3.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGN/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOSR13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.45 (2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqTGN3.2*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGN/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.82 (2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqTGN6.1*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGN/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.94 (2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e27.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGN/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.68 (2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqTGN11.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGN/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM6965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c6\"\u003e\u003cp\u003eRM6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.58 (2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqPL3.1*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePL/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHvSSR03-82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c6\"\u003e\u003cp\u003eHvSSR02-59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.26 (2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTN/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM2634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHvSSR02-59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.94 (2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqTN4.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTN/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003enksssr04-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.19 (2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqYLD4.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYPP/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM16775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.92 (2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e7.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqYLD6.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYPP/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRGNMS2221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.04 (2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e23.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqUFG6.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUFG/Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.52 (2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqSF1.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSF/Kar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRM12230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRM10217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.89 (2.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eLOD, Logarithm of the odds; RM, Rice microsatellite; HvSSR, highly variable simple sequence repeats; *, major QTLs; **, Threshold LOD; \u003csup\u003e#\u003c/sup\u003e Bold values\u0026thinsp;=\u0026thinsp;P1 and Unbold\u0026thinsp;=\u0026thinsp;P2.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Filled grain number per panicle (FGN)\u003c/h2\u003e\u003cp\u003eFour QTLs were mapped for FGN namely, \u003cem\u003eqFGN3.1, qFGN3.2, qFGN6.1\u003c/em\u003e, and \u003cem\u003eqFGN9.1\u003c/em\u003e, on chromosomes 3, 6, and 9, respectively. The marker interval RM204 and RGNMS2221 possessed, \u003cem\u003eqFGN6.1\u003c/em\u003e, mapped both at Delhi and Karnal with the LOD values ranged between 4.06 to 4.50 with PVE of 10.07% at Delhi, 11.12% at Karnal and additive effect of 18.79, 17.35 due to Pusa NPT34 respectively. Another QTL, \u003cem\u003eqFGN3.2\u003c/em\u003e, which remained consistent at Delhi and Aduthurai was mapped between the markers RM168 and RM520 on chromosome 3. The PVE of the QTLs was ranged from 4.64\u0026ndash;10.38%, with the additive effect of 20.02 and 21.49 due to Pusa NPT34 respectively. The remaining QTL for FGN namely \u003cem\u003eqFGN3.1\u003c/em\u003e, and \u003cem\u003eqFGN9.1\u003c/em\u003e were mapped on chromosomes 3 and 9 between OSR13-RM7 and RM444-RM6920, respectively. The genomic region flanked between the markers RM444 and RM6920 possessed novel QTL, \u003cem\u003eqFGN9.1\u003c/em\u003e, for FGN. The QTL had LOD value of 3.09 with the PVE of 5.67% and additive effect of 14.82 due to PR126 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among four QTLs identified for TGN, \u003cem\u003eqTGN3.1\u003c/em\u003e and \u003cem\u003eqTGN3.2\u003c/em\u003e falls in the same marker interval as that of \u003cem\u003eqFGN3.1\u003c/em\u003e and \u003cem\u003eqFGN3.2\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The study was able to identify a consistent QTL, \u003cem\u003eqTGN6.1\u003c/em\u003e across two locations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Panicle length (PL)\u003c/h2\u003e\u003cp\u003eFour QTLs namely, \u003cem\u003eqPL2.1, qPL3.1, qPL6.1\u003c/em\u003e and \u003cem\u003eqPL11.1\u003c/em\u003e were mapped on chromosomes 2, 3, 6 and 11 respectively. A genomic region flanked between the markers HvSSR03-82 and RM168 possessed a major QTL, \u003cem\u003eqPL3.1\u003c/em\u003e. The QTL had LOD value of 5.54 with PVE of 10.32% and additive effect 1 due PR126. The remaining QTLs were minor QTLs namely, \u003cem\u003eqPL2.1, qPL6.1, qPL11.1\u003c/em\u003e, mapped between flanking markers, RM13672-RM6, RM190-RM204, RM332-HvSSR11-13, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3 Plant height (PH)\u003c/h2\u003e\u003cp\u003eThree QTL namely, \u003cem\u003eqPH2.1\u003c/em\u003e, \u003cem\u003eqPH3.1\u003c/em\u003e, and \u003cem\u003eqPH6.1\u003c/em\u003e were mapped on chromosomes 2, 3 and 6 respectively. The markers RM204 and RGNMS2221 possessed a major QTL, \u003cem\u003eqPH6.1\u003c/em\u003e, and it was mapped at both Karnal and Aduthurai. The QTL had the LOD values ranged between 4.66 to 4.86, with a PVE of 9.90 at Aduthurai, 12.77% at Karnal and additive effect of 2.36, 5.21 due to Pusa NPT34 respectively. The other QTLs namely, \u003cem\u003eqPH2.1\u003c/em\u003e and \u003cem\u003eqPH3.1\u003c/em\u003e, were minor and site specific. The QTL, \u003cem\u003eqPH2.1\u003c/em\u003e, was mapped between RM2634-HvSSR02-59 and \u003cem\u003eqPH3.1\u003c/em\u003e, was mapped between RM520-RM1230 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.3.4 Primary branches number (PBN)\u003c/h2\u003e\u003cp\u003eFour QTLs were mapped, \u003cem\u003eqPBN3.1, qPBN4.1, qPBN6.1\u003c/em\u003e, and \u003cem\u003eqPBN11.1\u003c/em\u003e on chromosomes 3, 4, 6, and 11 respectively. A novel genomic region flanked between the markers RM144-RM6965 possessed a QTL, \u003cem\u003eqPBN11.1\u003c/em\u003e. The LOD of the QTL was 3.84 with a phenotypic variance of 7.34% and additive effect 0.61 due to PR126. The other marker interval between RM190 and RM204 possessed a major QTL, \u003cem\u003eqPBN6.1\u003c/em\u003e and \u003cem\u003eqPBN3.1\u003c/em\u003e, was mapped between HvSSR03-82-RM168 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.5 Tiller number (TN)\u003c/h2\u003e\u003cp\u003eQTL mapping for TN have detected two QTLs namely \u003cem\u003eqTN2.1\u003c/em\u003e and \u003cem\u003eqTN4.1\u003c/em\u003e on chromosome 2 and 4 respectively. The \u003cem\u003eqTN2.1\u003c/em\u003e was mapped between the markers RM2634 and HvSSR02-59 both at Delhi and Karnal. The LOD of the QTL was 2.94, 3.26 with PVE of 5.67% at Karnal, 8.52% at Delhi and additive effect 0.89 and 1.51 due to PR126 respectively. The other minor site-specific QTL, \u003cem\u003eqTN4.1\u003c/em\u003e was mapped between the markers, nksssr04-11-RM567 at Karnal with the LOD of 3.19 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.6 Yield (YLD)\u003c/h2\u003e\u003cp\u003eTwo site specific minor QTLs namely, \u003cem\u003eqYLD4.1\u003c/em\u003e and \u003cem\u003eqYLD6.1\u003c/em\u003e, were mapped for YLD on chromosome 4 and 6 respectively. The QTL, \u003cem\u003eqYPP4.1\u003c/em\u003e, was mapped between the flanking markers RM127 and RM16775 at Karnal. The QTL had LOD value of 2.92 with PVE of 6.38% and additive effect of 7.23 due to PR126. The other QTL, \u003cem\u003eqYPP6.1\u003c/em\u003e, was mapped between the flanking markers, RM204 and RGNMS2221 at Delhi. The QTL had LOD value of 3.04 with PVE of 6.97% and additive effect of 23.92 due to PR126 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The other QTL for unfilled grains and SF includes \u003cem\u003eqUFG6.1\u003c/em\u003e and \u003cem\u003eqSF1.1\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 QTL hotspots\u003c/h2\u003e\u003cp\u003eThe current study was able to identify four QTL hotspots, where among 25 QTLs, 16 were distributed on different chromosome within these QTL hotspots (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Chromosome 2 reported to possess a QTL hotspot of size 1.65 Mb bracketed between RM2634 and HvSSR02-59 (Cluster I). The Second hotspot had four QTLs viz., \u003cem\u003eqTGN3.2, qPBN3.1, qPL3.1, qPH3.1\u003c/em\u003e (Cluster II) mapped on the short arm of third chromosome. This region was bracketed between the markers HvSSR03-82-RM1230 with marker interval length of 2.40 Mb. The long arm of chromosome 4 possesses a QTL hotspot bracketed between nksrssr04-11 and RM567 with total genomic size of 3.81 Mb. This QTL hotspot possesses two QTLs namely, \u003cem\u003eqPBN4.1\u003c/em\u003e and \u003cem\u003eqTN4.1\u003c/em\u003e (Cluster III). Fourth hotspot was identified on chromosome 6 which carries five QTLs namely, \u003cem\u003eqTGN6.1, qPL6.1, qPBN6.1, qPH6.1, qYLD6.1\u003c/em\u003e (Cluster IV) and was found flanked between markers RM190 and RGNMS2221 with a span of 3.47 Mb.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQTL hotspots identified in PR126 x Pusa NPT34 derived RILs during \u003cem\u003ekharif\u003c/em\u003e 2020\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChromosome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarker interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterval length (Mb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNumber of QTLs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eName of the QTLs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRM2634-HvSSR02-59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eqPH2.1, qTN2.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHvSSR03-82-RM1230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eqTGN3.2, qPBN3.1, qPL3.1, qPH3.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRM567-nksssr04-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eqPBN4.1, qTN4.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRM190-RGNMS2221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eqTGN6.1, qPL6.1, qPBN6.1, qPH6.1, qYLD6.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 In-silico analysis\u003c/h2\u003e\u003cp\u003eQTL hotspot on chromosome 6 possess five QTLs for different traits and the total size of the marker interval is 3.47 Mb. The in-silico study of the QTL hotspot on chromosome 6 has showed that this region comprises 530 gene models. Among 530 genes, few genes which may have direct or indirect role in the biosynthetic pathway of the QTLs were identified. A total of 17 genes were putative candidates which may play a key role in regulation of grain number, plant panicle length and panicle branching (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Four of which putative genes encode AP2 domain containing protein, two coded zinc finger proteins, two auxin responsive factors, and one gene each coding an AP1 complex subunit, \u003cem\u003eOsMAPK6, OsDP1\u003c/em\u003e, F-box containing domains, MADS box family protein, helix loop helix protein, \u003cem\u003eWD-40\u003c/em\u003e, G-protein coupled receptors and ethylene responsive elements.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe predicted gene models in the \u003cem\u003eQTL\u003c/em\u003e hotspot on chromosome 6\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocus ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStart (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStop (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePutative function\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g04540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1962306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1963580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eOsDepressed palea1\u003c/em\u003e, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g06090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2806668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2812929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eOsMAPK6\u003c/em\u003e, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g06750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3162801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3169415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eOsMADS5\u003c/em\u003e - MADS-box family gene with MIKCc type-box, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g06870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3250462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3261364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ezinc finger protein, putative, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g06900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3273373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3276890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehelix-loop-helix DNA-binding domain containing protein, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g06970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3310866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3311822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAP2\u003c/em\u003e domain containing protein, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g07000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3319111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3322612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eOsFBL28\u003c/em\u003e-F-box domain and LRR containing protein, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g07030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3337083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3338612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAP2 domain containing protein, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g07040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3342423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3346318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eOsIAA20\u003c/em\u003e - Auxin-responsive Aux/IAA gene family member, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g07090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3376566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3386549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAP-1\u003c/em\u003e complex subunit gamma-1, putative, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g07540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3632663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3639659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWD-40 repeat family protein, putative, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g08340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4040908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4041551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAP2\u003c/em\u003e domain containing protein, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g08360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4062304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4063540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eethylene-responsive element-binding protein, putative, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g09310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4678838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4680332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ezinc finger, C\u003csub\u003e3\u003c/sub\u003eHC\u003csub\u003e4\u003c/sub\u003e type domain containing protein, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g09390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4731330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4733911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAP2\u003c/em\u003e domain containing protein, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g09660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4926492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4932177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eauxin response factor, putative, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOC/Os06g09930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5060660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5065027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG protein coupled receptor, putative, expressed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eDuring the 1990s, researchers at IRRI were propounding a novel model for rice plants known as the new plant type (NPT). Pusa NPT34, used in this study is a high yielding breeding line derived from the NPT linage and characteristically possessed high number of grains. In the next decade, the idea of breeding for green super rice (GSR) was initially floated in China in 2005, for assimilating the \u0026lsquo;green\u0026rsquo; traits - traits that are environment friendly, impart resource use efficiency and multiple stress tolerance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Several multiparent inter-cross lines were bred integrating semi-dwarf trait with super yield and grain quality. One among these was HHZ, a high yielding, widely adapted, early maturing and lodging resistant variety. Soon HHZ found itself a part of the GSR project and released as PR126 in Punjab, it proved highly adaptable and early maturing. Soon PR126 started replacing long-duration popular variety, Pusa 44 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe biparental population of PR126/Pusa NPT34 that was stabilised and was in the F\u003csub\u003e6\u003c/sub\u003e generation, we could demonstrate that the distribution of traits was normal and predominantly quantitative, enabling a perfect condition for QTL mapping. Traits having more heritability shows that they are more easily transferable. Higher heritability of few traits such as grain number and primary branching in the current study indicates that they can be transferable through breeding into the elite varietal background to enhance the productivity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Agronomic performance of breeding lines in one site does not assure success in other sites, because of GEI, especially when the locations are far apart.\u003c/p\u003e\u003cp\u003eThe conglomeration of genomic regions at different marker interval/hotspots indicated that these hotspots are the region possesses closely related genes involved in the biosynthetic pathway of grain number and its associated traits. It makes pyramiding of the haplotypes carrying the hotspot region relatively easier. The QTL hotspot possesses a greater number of QTLs (five QTLs) for different traits was identified on chromosome 6. QTLs associated were \u003cem\u003eqTGN6.1\u003c/em\u003e, \u003cem\u003eqPL6.1\u003c/em\u003e, \u003cem\u003eqPBN6.1\u003c/em\u003e, \u003cem\u003eqPH6.1\u003c/em\u003e, and \u003cem\u003eqYLD6.1\u003c/em\u003e. There were also two functionally associated QTLs of TGN, namely, \u003cem\u003eqFGN6.1\u003c/em\u003e and \u003cem\u003eqUFG6.1\u003c/em\u003e found at this hotspot. It was further interesting to observe that the most desirable QTLs for TGN, PL, PBN and YLD came from Pusa NPT34, except for PH. This eventually showed that this hotspot associated with taller plants that are higher yielding with a greater number of grains. QTL hotspot with positive alleles for yield attributing traits will be an important genomic region for marker assisted introgression to improve the yield barrier. During 2017, identified QTL hotspot on chromosome 3 where \u003cem\u003eqnt3.1\u003c/em\u003e, and \u003cem\u003eqnt3.2\u003c/em\u003e, \u003cem\u003eqph3.1\u003c/em\u003e, \u003cem\u003eqTGW3.4\u003c/em\u003e for tiller number, plant height, and thousand grain weight were mapped [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly on chromosome 4, QTL hotspot possessing \u003cem\u003eqTGW4.1\u003c/em\u003e and \u003cem\u003eqPPP4.2\u003c/em\u003e for thousand grain weight and panicles per plant were mapped. Another study during 2020, identified QTLs for DFF (\u003cem\u003eqFD6.1\u003c/em\u003e), PH (\u003cem\u003eqPHT6.1\u003c/em\u003e), TN (\u003cem\u003eqTL6.1\u003c/em\u003e), PL (\u003cem\u003eqPL6.1\u003c/em\u003e), on chromosome 6 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The currently identified genomic regions are away from the report by Donde et al., 2020 but this hotspot region was reported to possess genes/QTLs for FGN, TGN, PL, PH and PBN from the previous reports [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe hotspot on the short arm of chromosome 3 (Cluster II) having four QTLs was the next interesting candidate genomic region. Spanned 2.4 Mb long, harboured \u003cem\u003eqTGN3.2\u003c/em\u003e, \u003cem\u003eqPBN3.1\u003c/em\u003e, \u003cem\u003eqPL3.1\u003c/em\u003e and \u003cem\u003eqPH3.1\u003c/em\u003e along with one functionally associated QTL of TGN, namely, \u003cem\u003eqFGN3.2\u003c/em\u003e. The most desirable traits contributing QTLs for TGN, PBN and PH came from Pusa NPT34 except for PL. Although the integration of both these hotspots can improve grain number, a chance of increased PH and reducing the PL cannot be avoided. In order to use the QTL hotspot on chromosome 3, the genomic region must be fine mapped to identify the closely associated marker with no linkage drag of increased plant height. The marker linked to \u003cem\u003eqTGN3.2\u003c/em\u003e was also associated with two earlier reported QTLs, \u003cem\u003egpp3.1\u003c/em\u003e and \u003cem\u003egpp3.2\u003c/em\u003e describing grains per panicle [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. There are earlier reports of QTLs associated with PL, PBN, and PH co-localised in this hotspot region [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A recent study on marker trait association for filled grain, have identified an MTA on chromosome 3 for filled grain number at 3.85 Mb [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Another study has also identified a QTL on chromosome 3 for yield per plant at 7.68 Mb along with association for grain length, width and thousand grain weight on the same chromosome [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe hotspot on chromosome 2 (Cluster I) harboured QTLs for PH and TN, and was not a direct contributor to grain number. The QTL for both PH and TN in this hotspot was also reported by previous reports [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A recent report has identified MTAs for GNP (Grain number per panicle), PBN (Primary branch number), PL (Panicle length) and TN (Tiller number) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The MTA for TN falls significantly away from the QTL detected in the present study. Similarly, another study was able to map a QTL for plant height and internode length on chromosome 3 at 12.5 Mb whereas the QTL in the current study was mapped at 20 Mb region [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The remaining hotspot on chromosome 4 (Cluster III) carried QTLs for PBN and TN, of which \u003cem\u003eqPBN4.1\u003c/em\u003e was derived from PR126 and \u003cem\u003eqTN4.1\u003c/em\u003e from Pusa NPT34. The size of this region is 3.81 Mb and, therefore, we need to pick out recombinants of this hotspot to get the desirable effects of both the traits together. The QTLs identified in this hotspot for PBN and TN were reported earlier [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. During 2023, it was confirmed that the results of previous report by Deshmukh et al for the presence of QTL for tiller number on chromosome 4 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our study also confirms the presence of QTL in the same genomic region on chromosome 4. The QTLs on chromosome 3 were considered as a single QTL obtained from Pusa NPT34, as it was found associated only with the grain number traits, TGN and FGN. This location was previously reported to carry QTLs for grains per panicle, \u003cem\u003egpp3.1\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] or \u003cem\u003eqGn3.1\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. There were also report that identified the presence of MTAs for grain number on chromosome 3 at 0.5 Mb which is near telomere end [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOf the nine QTLs that lay outside the hotspot regions, \u003cem\u003eqFGN9.1\u003c/em\u003e and \u003cem\u003eqPBN11.1\u003c/em\u003e are novel as there are no previous reports of a QTL affecting these traits. The QTL, \u003cem\u003eqFGN9.1\u003c/em\u003e, is mapped between a 5.92 to 7.00 Mb on the long arm of chromosome 11. Although there are past studies reporting QTLs for grain number on chromosome 9, such as \u003cem\u003egpp9.1\u003c/em\u003e falling near the 21.18 Mb region [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and \u003cem\u003eGP9.1\u003c/em\u003e falling near the 17.74 Mb region [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], these QTLs are reasonably far placed from the currently discovered ones. Similarly, in an earlier report, the genomic region which harbours \u003cem\u003eqPBN11.1\u003c/em\u003e was reported associated with genes for panicle neck diameter [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], however, no reports were found on its role in primary branch number in rice. Previous studies have provided reports on the remaining five QTLs. The QTL locus \u003cem\u003eqTGN11.1\u003c/em\u003e was identified in studies conducted by [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], while \u003cem\u003eqSF1.1\u003c/em\u003e was documented by Mei [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], \u003cem\u003eqYLD4.1\u003c/em\u003e by Lian et al., [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], \u003cem\u003eqPL2.1\u003c/em\u003e by Zhang et al., [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and \u003cem\u003eqPL11.1\u003c/em\u003e by Cui et al., [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], each associated with specific traits.\u003c/p\u003e\u003cp\u003eIdentification of putative candidate genes and landing closer to the gene of interest in many of the genetic study become easy with the advent of genome sequencing and gene annotation in rice [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The QTL hotspot identified on chromosome 6, possessed 17 putative candidate genes display significant functional attributes. This region was selected as it harboured QTLs for TGN, PH, PL, PBN, and YLD, particularly coming from Pusa NPT34. The 17 putative candidate gene models encode various proteins which play direct role in inflorescence and spikelet development. Notably, \u003cem\u003eAPETALA\u003c/em\u003e genes (AP1 and AP2) zinc finger proteins, MADS box family proteins, helix loop helix proteins, WD40, G-protein coupled receptors and ethylene receptors play pivotal roles in rice spikelet development. APETALA proteins and MADS box family proteins reported to play major role inflorescence development. A recessive mutant, reported on chromosome 3 is a homeotic mutant of MADS box gene, can affect inflorescence development [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Similarly, many of the MADS box genes come under \u003cem\u003eSEPALLATA\u003c/em\u003e-like gene and are associated with floral organ identity, inflorescence and spikelet development [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Apart from AP and MADS box family genes, WD40 protein reported to play an important role in inflorescence development through the regulating the panicle branching. The \u003cem\u003eASP1\u003c/em\u003e (\u003cem\u003eaberrant spikelet\u003c/em\u003e) encodes a protein called, WD40. This protein is homologous to \u003cem\u003eTPL/TPR\u003c/em\u003e (\u003cem\u003etopless/topless related\u003c/em\u003e) of \u003cem\u003eArabidopsis\u003c/em\u003e and \u003cem\u003eREL2\u003c/em\u003e (\u003cem\u003eromosa enhancer loci\u003c/em\u003e) of maize regulates the panicle branching and spikelet development in rice [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. One of the major QTL for branching \u003cem\u003eDEP1\u003c/em\u003e (Dense and Erect Panicle) acts through G-protein to regulate the panicle branching, length of the panicle and grain number [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Most of the putative candidate genes present in the QTL hotspot region plays key role in regulating the panicle architecture through the control of panicle branching, length of the panicle, grain number and through the regulation of the plant hormone production. A functional interconnectivity of the putative genes present in the QTL hotspot was found with \u003cem\u003eHD1\u003c/em\u003e being linked to the PBN, as well as \u003cem\u003eAHL22\u003c/em\u003e and \u003cem\u003eDP1\u003c/em\u003e, which affect FGN. The interrelationship between these genes and traits can be attributed to the colocalization of QTLs on chromosome 6 (Fig.\u0026nbsp;7).\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eIn the current study, the QTLs were mapped for grain number, panicle branching and yield attributed traits and identified two novel genomic regions for grain number and panicle branching. Out of 25 QTLs identified, 16 QTLs were distributed in 4 marker intervals (hotspots) on different chromosomes in the current study. The scope of future study may involve the fine mapping of the major QTLs to identify the more closely linked marker, development of gene based or functional markers and cloning to understand the molecular mechanism of the gene/QTLs. It also helps us to use these genes in breeding program for more precise marker assisted selection and transfer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eNo Clinical trial is involved.\u003c/p\u003e\n\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary figures and tables are given\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePKB and AKS conceived the idea and formulated the research plan. NS carried out the research work and prepared the manuscript. NS, VJS, SS and BKD generated the mapping population. MN, NS, VJS, HBS and S assisted in the execution of field trials. KKV, RKE, HB and GKS assisted in data analysis. PKB and KKV improved the manuscript. This work forms a part of the doctoral research work of NS. All authors have read the article and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be\u0026nbsp;construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets supporting the results of this article are included in the supplementary file.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author, NS is grateful to CSIR for CSIR JRF fellowship\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingh VJ, Vinod KK, Krishnan SG, Singh AK. 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ABERRANT SPIKELET AND PANICLE1, encoding a TOPLESS‐related transcriptional co‐repressor, is involved in the regulation of meristem fate in rice. Plant J. 2012;70(2):327\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.1365-313X.2011.04872.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-313X.2011.04872.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang X, Qian Q, Zhengbin L, Hongying S, Shuyuan He, Da L, et al. Natural variation at the DEP1 locus enhances grain yield in rice. Nat Genet. 2009;41(4):494\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1038/ng.352\u003c/span\u003e\u003cspan address=\"10.1038/ng.352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rice, QTL mapping, panicle branching, grain number","lastPublishedDoi":"10.21203/rs.3.rs-7133316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7133316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrain number per panicle, thousand-grain weight, panicle branching and productive tiller number per unit area are the major yield-attributing traits in rice. The yield plateau for more than two decades, demands for the identification of novel genes or QTLs (genome) from diverse germplasm. The current study aims to identify the genes/QTLs for grain number and primary branch number using yield-attributing traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRIL population consisting of 175 lines derived from PR126 and Pusa NPT34. Total of 25 QTLs were identified for different traits, among which 18 were found distributed in five hotspots. Seven QTLs were having major effects and the remaining showed minor effects on the respective target traits. QTLs, \u003cem\u003eqFGN9.1 \u003c/em\u003efor grain number lying at the marker interval, RM444-RM6920 and \u003cem\u003eqPBN11.1 \u003c/em\u003efor primary branches lying at the marker interval of RM144-RM6965 on chromosome 9 and 11, were found novel respectively. A largest QTL hotspot on chromosome 6, harboured QTLs for GN, PH, PL, PBN, and YLD. 17 putative candidate gene models were identified through in silico analysis which involves in inflorescence development. The major gene models include \u003cem\u003eAPETALA\u003c/em\u003e genes, MADS box family proteins and WD40 which directly controls panicle branching, spikelet development. Therefore, further fine mapping of marker intervals can help in narrowing the genomic region and trait specific marker development. It enables more precise introgression of QTLs, for primary branches, grain number and other panicle architecture related traits into elite cultivars.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scope of future study may involve the fine mapping of the major QTLs to identify the more closely linked marker, development of gene based or functional markers and cloning to understand the molecular mechanism of the gene/QTLs. It also helps us to use these genes in breeding program for more precise marker assisted selection and transfer.\u003c/p\u003e","manuscriptTitle":"Mapping Novel QTLs Associated with Grain Number and Primary Branching in Rice using New plant type derived RILs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 12:13:22","doi":"10.21203/rs.3.rs-7133316/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T12:48:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T08:59:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-17T18:20:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-17T13:49:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T06:41:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T23:14:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T12:36:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T05:29:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180437952185293152673244656631820423351","date":"2025-08-08T06:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43307391884449204357107749911531333701","date":"2025-08-07T05:52:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30899738053517259855578081355422271256","date":"2025-08-07T04:36:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247071471961537136292704176775213518194","date":"2025-08-07T02:18:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213746350584042385657915234063048616190","date":"2025-08-05T10:21:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238367125975470329566801611493662639628","date":"2025-08-05T07:03:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22534222169242032038123926902871969529","date":"2025-08-05T06:24:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127025471441010787246102017363422598365","date":"2025-08-05T02:40:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3255771285560695375145756880093396832","date":"2025-08-05T02:32:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42399639486264715141265914729099437457","date":"2025-08-05T02:13:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T01:54:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-21T12:43:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-21T12:40:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-07-15T18:09:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed33acfb-34fe-4ad0-91ec-498a38ad903c","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:06:50+00:00","versionOfRecord":{"articleIdentity":"rs-7133316","link":"https://doi.org/10.1186/s12870-026-08481-2","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2026-03-10 15:58:43","publishedOnDateReadable":"March 10th, 2026"},"versionCreatedAt":"2025-08-06 12:13:22","video":"","vorDoi":"10.1186/s12870-026-08481-2","vorDoiUrl":"https://doi.org/10.1186/s12870-026-08481-2","workflowStages":[]},"version":"v1","identity":"rs-7133316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7133316","identity":"rs-7133316","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}