Genome-Wide Dissection of Novel QTLs and Genes Associated with Weed Competitiveness Traits Using a Population of Early-Backcross Selective Introgression Lines of Rice (Oryza sativa L.)

Genome-Wide Dissection of Novel QTLs and Genes Associated with Weed Competitiveness Traits Using a Population of Early-Backcross Selective Introgression Lines of Rice (Oryza sativa L.) | 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 Genome-Wide Dissection of Novel QTLs and Genes Associated with Weed Competitiveness Traits Using a Population of Early-Backcross Selective Introgression Lines of Rice (Oryza sativa L.) Kim Diane Nocito, Varunseelan Murugaiyan, Jauhar Ali, Ambika Pandey, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4505794/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Direct-seeded rice (DSR) system is anticipated to become the dominant rice cultivation method in the coming years due to its advantages, such as reduced water usage and labor requirements, less greenhouse gas emission, and adaptation to climate change risks. However, weeds are a significant constraint in the DSR system due to the yield losses incurred by weed infestations. Developing rice cultivars that are competitive against weeds through selective breeding has great potential to solve this challenge. Early seed germination (ESG) and seedling vigor (ESV) are important characteristics for the competitive ability of rice against weeds. This study used 181 early-backcross selective introgression-breeding lines (EB-SILs) that were generated by the process of backcrossing Weed Tolerant Rice 1 (WTR 1) with three specific donor parents (Haoannong, Cheng Hui 448, and Y134). Using the tunable genotyping-by-sequencing (tGBS®) method, we obtained 3,971 single nucleotide polymorphisms (SNPs). These SNPs were then utilized to identify and map quantitative trait loci (QTLs) associated with ESG and ESV features using their precise physical positions. Results High phenotypic variations among the EB-SILs and parental lines in different ESG- and ESV-related traits were observed. The association between the phenotypic and genotypic data detected 18 QTLs governing weed competitiveness, with eight and ten QTLs associated with the ESG and ESV traits, respectively. All detected QTLs were novel, except qRPH 1, associated with relative plant height at 14 and 21 days after sowing (DAS). Four ESG-related QTLs ( qRL 2, qTDWG 2, qSVI2 .1, and qSVI2 .1) were detected on chromosome 2, with two more QTLs governing germination rate ( qGR 12) and seed vigor index ( qSVI 12) identified on chromosome 12, and additional QTLs for seed vigor index found on chromosome 3 ( qSVI 3) and 6 ( qSVI 6). For ESV traits, qRPH 1 detected on chromosome 1 was linked with relative plant height at 14 DAS and 28 DAS, while chromosome 10 harbored four QTLs ( qRLC10 .1, qRLC10 .2, qRTN 10, and qRRL 10) associated with relative leaf count, relative tiller number, and relative root length at 28 DAS. Additionally, QTLs were detected for relative plant height at 21 DAS on chromosomes 5 and 9 ( qRPH 5 and qRPH 9), relative leaf count at 28 DAS on chromosome 4 ( qRLC 4), relative tiller number at 28 DAS on chromosome 3 ( qRTN 3), and relative root length at 28 DAS on chromosome 8 ( qRRL 8). Candidate genes discovered within the identified QTLs were responsible for the plant’s response to various abiotic and biotic stresses. Conclusion This study provides a more profound comprehension of the genetic foundation of ESG and ESV traits, which are essential characteristics for the weed competitiveness of rice. The novel QTLs and candidate genes found have the potential to aid in marker-assisted and genomic selection approaches for breeding rice varieties with enhanced weed competitiveness. Simultaneously, the potential genes might be further examined to determine their expression patterns when subjected to intense weed pressure. The findings of this research will contribute to the development of rice varieties capable of competing with weeds. These varieties will be crucial in integrated weed management within the DSR system. Direct-Seeded Rice Weed competitiveness Seedling vigor Single nucleotide polymorphisms Quantitative trait loci Candidate genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Rice is the primary food supply and a significant economic driver throughout most of Asia, providing essential employment and financial stability for rural communities (Ali et al. 2021a ; Yu et al. 2022 ). Rice consumption is projected to rise by 30% by 2050, driven by growing populations, urbanization, and consumer preferences, particularly in sub-Saharan Africa, where rice consumption is increasing by over 6% yearly (Arouna et al. 2021 ; Samal et al. 2022 ). The availability of resources necessary for rice cultivation, such as land, water, fertilizers, and labor, is diminishing. Additionally, the impact of climate change, marked by higher temperatures and frequent floods and droughts, has led to significant loss to rice production globally (McNally and Henry 2023 ). Water is the primary and crucial element for ensuring rice production, with one-third of the world's freshwater resources utilized for irrigating lowland rice farming systems (Padma et al. 2023 ). Diminished investments in irrigation infrastructure compromise the sustainability of rice production under current farming systems, heightened water competition, substantial withdrawals of subsurface water, and non-adoptability of suitable technologies (Zhang et al. 2013a ; Panda et al. 2021 ). Direct-seeded rice (DSR) farming system is anticipated to emerge as the dominant rice cultivation method, boasting a 15.3% reduction in water usage compared to traditional transplanted-flooded rice fields (Marasini et al. 2018 ; Peramaiyan et al. 2023 ). Furthermore, when combined with short-duration high-yielding inbred and hybrid rice varieties, DSR offers additional benefits such as reduced labor needs, higher yields per unit area, less greenhouse gas emissions, fewer energy inputs, and increased resilience to climate change risks (Connor et al. 2022 ). Despite being viewed as a potential alternative to unsustainable water-intensive transplanted rice systems, the heavy infestation of various weed species limits the large-scale adoption of DSR technology (He et al. 2022 ). Weed infestation is one of the most significant constraints in DSR that can lead to severe yield losses. Weed diversity in DSR exhibits a greater diversity of plant species and is more abundant than transplanted rice. As a result, the DSR farming system experiences much higher levels of weed pressure (Ahmed et al. 2021 ). A global assessment conducted in many rice-producing nations revealed that weed infestation posed a significant biological limitation, impeding rice production (Chauhan et al. 2015 ). The extent of yield loss in DSR due to weed infestation can vary widely based on several factors, including the weed species present, weed density, timing of weed competition, crop management practices, and environmental conditions. The percentage of yield loss can range from 20–90%, depending on the severity of weed infestation and how well weed management practices are implemented (Xu et al. 2019 ). If weeds are not effectively controlled, they can out-compete rice plants for resources such as sunlight, water, and nutrients, leading to stunted growth, yield losses, and poor grain quality (Nazir et al. 2023 ). Farmers and agricultural extension services often need to assess local conditions and adopt a combination of cultural, chemical, and mechanical weed control methods to optimize yield and ensure the success of DSR cultivation. Effective weed control in DSR farming systems requires regular monitoring, timely interventions, breeding and adoption of weed-competitive rice varieties (Mahajan et al. 2017 ). In DSR farming systems, weed diversity can encompass various species such as barnyardgrass ( Echinochloa crus-galli ), junglerice ( Echinochloa colona ), broadleaf signalgrass ( Brachiaria platyphylla ), late watergrass ( Echinochloa phyllopogon ), ammannia ( Ammannia spp .), eclipta ( Eclipta prostrata ), fringerush ( Fimbristylis miliacea ), and other sedges ( Cyperus spp. ). Managing this diversity is crucial for successful DSR establishment, particularly during the rice plant’s most vulnerable early growth stages. Rice varieties that are competitive against weeds are vital in reducing the amount of crop output lost in the DSR farming system (Shekhawat et al. 2020 ; Shrestha et al. 2021 ). These varieties possess innate characteristics that enable plants to surpass weeds in the competition for vital resources, including sunlight, water and nutrients. Weed-competitive rice varieties possess robust initial growth, rapid development of a dense canopy, and effective use of resources, which aids in inhibiting weed growth and establishing a competitive edge. This minimizes the need for excessive herbicides or handweeding, promoting sustainable and cost-efficient weed control in DSR systems. Consequently, farmers may successfully establish their crops without yield penalty, which raises the total productivity of DSR farming (Bhandari et al. 2020 ). Echinochloa colona (L.), commonly known as junglerice, is a C4 annual summer grass that poses a significant challenge as a weed species, impacting rice production systems worldwide (Narayana Rao 2021 ). Its invasive nature is attributed to its vigorous growth and high seed production, with each plant capable of producing up to 42,000 seeds (Wu et al. 2022 ). Its pervasive presence has led to substantial grain yield losses, ranging from 27–62%, emphasizing its profound impact on rice production. By out-competing rice crops for essential resources such as water, nutrients, space, and sunlight, E. colona imposes significant constraints on crop growth and productivity (Narayana Rao 2021 ). Consequently, E. colona poses a significant threat to rice yields, leading to substantial economic losses and jeopardizing food security in regions where rice is a staple crop. E. colona , widely adopted in rainfed rice agroecosystems, owes its prevalence to its remarkable adaptability across diverse environments, facilitated by its plasticity in morphology, phenology, rapid growth, prolific seed production, and seed dormancy (Awan et al. 2014 ). By elucidating the molecular mechanisms underlying weed competitiveness, our study provides valuable insights for breeding programs to develop rice varieties with improved weed competitiveness. Weed-competitive rice varieties are distinguished by a suite of traits that collectively confer a competitive advantage against weed pressure (Saito and Futakuchi 2014 ; Dimaano et al. 2017 ). These traits encompass early vigor, enabling rapid germination and robust seedling growth, and prolific tillering, resulting in a dense canopy that shades out competing weeds. Tall and erect canopy architecture and broad leaves enhance the shading effect, limiting weed access to sunlight (Bharamappanavara et al. 2020 ). Favorable root systems, characterized by depth and spread, enable efficient nutrient and water uptake, contributing to the overall vigor of the rice plants. Some varieties also exhibit allelopathic compounds, suppressing weed germination and growth (Schumaker et al. 2021 ). Genetic factors, including specific genes and quantitative trait loci (QTLs), play a crucial role in defining these traits, facilitating the development of rice varieties with enhanced weed competitiveness (Schumaker et al. 2019 ). Additionally, traits such as tolerance to weed competition, early maturity, and resistance to diseases further fortify the resilience of these varieties. However, the effectiveness of weed-competitive rice varieties is context-dependent and influenced by regional weed species and environmental conditions (Swanton et al. 2015 ). A holistic approach that integrates genetic traits with sound agronomic practices and targeted weed management strategies is essential for optimizing the success of weed-competitive rice cultivation, ultimately mitigating yield loss in rice production (Chaudhary et al. 2023 ). QTLs associated with weed-competitive traits in rice are pivotal in enhancing the crop's ability to suppress weed growth. Our group’s previous work has successfully identified QTLs associated with weed competitiveness in 167 selective introgression lines from a cross between Weed Tolerant Rice 1 (WTR1) and Y134. Among the QTLs identified, q1st GC12.1 found on chromosome 12 is linked to SAP domain-containing protein, which is functionally related to stress-associated proteins and are responsible for abiotic stress response by regulating GA and ABA signaling (Dimaano et al. 2020 ). Another notable QTL identified from our prior research, qTFGS12 , is associated to a genomic region responsible for flavin monooxygenases, which has a crucial function in the tryptophan (Trp)-dependent indole-acetic acid synthesis for auxin biosynthetic pathways, enhancing the rapid response to early seedling growth and development of root tips (Dimaano et al. 2017 , 2020 ). Other specific key genes reported for weed-competitive rice varieties include OsPAL 4 (Phenylalanine Ammonia Lyase 4), which plays a crucial function in allelopathy by synthesizing compounds that act as natural herbicides, thus creating an unfavorable environment for weed growth (Tonnessen et al. 2015 ). Another gene, OsERF 3 (Ethylene Response Factor 3) modulates ethylene responses, influencing root architecture and elongation, enhancing rice's competitive ability against weeds for below-ground resources (Zhang et al. 2013b ). ATP-binding cassette transporters like OsABC 1-13 contribute to herbicide resistance, providing weed-competitive rice varieties with resilience against standard weed control measures (Goldberg-Cavalleri et al. 2023 ). The DEP 1 (Dense and Erect Panicle 1) gene, associated with increased grain yield and altered plant architecture, influences weed competitiveness by conferring a more compact growth habit that limits space for weed establishment (Lu et al. 2022 ). Our current study unveils additional QTLs and key candidate genes, providing valuable insights for targeted breeding programs to create tailored weed-competitive rice varieties adaptable to DSR farming systems. The Green Super Rice (GSR) project, led by the International Rice Research Institute (IRRI) and the Chinese Academy of Agricultural Sciences (CAAS), successfully developed and released high-yielding and multiple stress-tolerant rice varieties to overcome climate change, resource constraints, and disease pressures (Yu et al. 2020 ; Ali et al. 2021). One of the GSR project's primary focuses was enhancing yield and resilience, including weed competitiveness, which is crucial to sustainable rice cultivation. GSR varieties are tailored to exhibit multiple stress tolerance and competitiveness against weeds through traits like early vigor, prolific tillering, efficient canopy development, and high yielding. Integrating weed-resistant characteristics into elite GSR breeding materials and using DSR technology will ensure rice production's long-term sustainability and productivity (Ali et al. 2021b ; Yu et al. 2022 ). Our current study investigated the genetic and molecular mechanisms underpinning weed competitiveness in rice to expedite the development of weed-competitive varieties for sustainable DSR farming systems. The study's objectives were to screen for weed-competitiveness in early GSR backcross selective introgression populations of rice and to identify QTLs and candidate genes associated with weed-competitiveness using high-density genome-wide SNP markers. Methods Plant Materials A population of 181 BC 1 F 6 Early-Backcross Selective Introgression Lines (EB-SILs), developed through a cross between the common recipient parent WTR1 ( indica ) and three donor parents: Haoannong ( japonica ), ChengHui448 ( indica ), and Y134 ( indica ), was produced using single seed descent at the International Rice Research Institute (IRRI), Los Baños, Philippines. Additional comprehensive information on this breeding process and the development of the population was elucidated by Ali et al. ( 2018 ). Seeds of junglerice ( Echinochloa colona ) were harvested from rice-fallow fields at IRRI during the dry season of 2023. The seeds were then kept in a refrigerator at 4°C before being used in the weed competitiveness screening experiment. Phenotypic screening for weed competitiveness Rice seeds were dried in an oven at 60°C for seven days to disrupt residual seed dormancy. The dormancy of junglerice seeds was removed by subjecting the seeds to a 24-hour immersion in sterilized water, followed by an additional 24-hour incubation period at 40°C in an oven. The phenotyping of early seed germination (ESG) traits involved the placement of 25 seeds per line in a 9-cm diameter Petri dish with wet filter paper, maintaining optimal moisture conditions through regular watering in a germination chamber set at 30°C with a 12-hour photoperiod for 14 days. The set-up was arranged in a complete randomized design (CRD) with two replications. Germination parameters were meticulously measured throughout the experiment, including second-day and seventh-day germination counts, germination rate, shoot length, root length, total dry weight, average dry weight, and seed vigor index. Concurrently, the phenotyping of early seedling vigor (ESV) traits occurred in a greenhouse with an average night and day temperature of 24℃ and 32℃, respectively. Rice lines were exposed to weedy and non-weedy conditions in metal trays (150 cm×90 cm×10 cm) filled with sterilized Maahas clay-loam soil in a randomized complete block design (RCBD) with two replications. Weed prevention, disease control, and pest management were ensured through soil sterilization. Basal fertilizer was applied following local recommendation (60-30-30 kg NPK ha − 1 ). The weedy condition involved direct seeding with 40 plants/0.1 m 2 density alongside junglerice seeds sown randomly, following a 1:1 ratio for rice and junglerice seeds to simulate high weed pressure. In contrast, the non-weedy condition was maintained weed-free throughout the experiment. Both set-ups were watered daily and kept unsaturated to simulate DSR conditions. Data collection involved five randomly tagged plants per line, evaluating parameters such as plant height, leaf count, and tiller number at 14, 21, and 28 DAS, and seedling vigor index, shoot dry weight, root dry weight, total dry weight, and root length at 28 DAS (Supplemental Table 1) . Non-segregating parents (WTR1, Haoannong, ChengHui448, and Y134) were grown as checks in ESG and ESV experiments. Any positional effect within the germination chamber and greenhouse was minimized by altering the position of the Petri dish and metal trays every second day. SNP extraction and physical map construction From the previous publication (Ali et al. 2018 ), all 181 lines and their source tunable genotyping-by-sequencing (tGBS®) sequences utilizing 10 Ion Proton runs were downloaded. Concurrently, the rice reference genome, precisely the Osativa_204_v7.0.fa sequence, was obtained from the Phytozome repository ( https://phytozome.jgi.doe.gov/pz/portal.html ). After acquiring raw sequencing reads, a preprocessing step was undertaken utilizing the Lucy software ( http://www.tigr.org/softlab ) to trim sequences, effectively discarding bases exhibiting a PHRED quality score inferior to 15. For SNP extraction, the GSNAP algorithm developed by (Ott et al. 2017 ) for dealing with the tGBS® dataset was employed to align the trimmed reads against the reference genome, adopting stringent criteria that limited mismatches to two per 36 base pairs and unaligned bases to less than five per 75 base pairs. The criteria for distinguishing homozygous from heterozygous SNPs were rigorously defined; homozygous SNPs necessitated a PHRED quality score of 20 or above and support from a minimum of three reads. Conversely, heterozygous SNPs required support from at least two reads for each allele, with each allele constituting more than 20% of supporting reads, cumulatively surpassing 90%. SNP selection was subjected to a series of filters, including a missing data rate not exceeding 80%, a stipulation for biallelic SNPs, a minimum of two genotypes, a minor allele frequency threshold of 0.1, and a heterozygosity range between 0 and 10%. The SNP dataset, designated as Low Missing Data 50 (LMD50), underwent further refinement to ensure a 50% or less missing data rate. Subsequent analyses facilitated the determination of major and minor alleles for each SNP, predicated on sub-population-specific ratios of reference to alternative alleles, with ratios greater than one signifying the major allele and those less than one denoting the minor allele. Leveraging the precise physical loci of the SNPs, a physical map was constructed to underpin QTLs associated with weed competitiveness. Statistical analysis and QTL mapping A one-way analysis of variance (ANOVA) at a one percent significance level was used to analyze the phenotypic data for ESG. In contrast, two-way ANOVA was used for ESV to observe the effects of genotypes and the growing conditions. Pearson’s correlation analysis was conducted to correlate the ESG-related traits among each other, as well as to correlate the ESV-related traits. ANOVA and Pearson’s correlation were performed in R version 4.3 studio ( http://www.rstudio.com/ ) , and heat maps were generated using the hmsic package. Principal component analysis of correlations was also done to create biplots using JMP® ( https://www.jmp.com/en_ph/home.html ). The QTL analysis for ESG used the mean values of phenotypic data gathered, while relative phenotypic values were used for the analysis of ESV. The mapping of QTLs was performed by single-marker regression analysis using the single-marker analysis (SMA) function in IciMapping software v4.1 (Meng et al. 2015 ). The association of a phenotypic trait and a QTL was declared significant once it had a threshold level (− log p(F) ≥ 4.15) based on a permutation test (1000 permutations, P = 0.01). To further delimit the confidence interval of each QTL, the 1-LOD drop method from the estimated QTL position was followed (Pang et al. 2017 ; Murugaiyan et al. 2019 ). Candidate Gene Identification The gene models lying within the QTL intervals for the discovered QTLs controlling ESG and ESV-related phenotypes were extracted from the MSU7 Rice Genome Annotation Database ( http://rapdb.dna.affrc.go.jp/ ). Polymorphisms within the candidate genes of the parental lines were obtained from the Rice SNP-Seek Database ( https://snp-seek.irri.org/ ). Further genotyping data on the parents was acquired from the previous Tunable Genotyping-By-Sequencing (tGBS®) study, which included 10 ion proton runs and yielded 794,297 polymorphic SNPs (Ali et al. 2018 ). Subsequently, we identified gene models within the candidate loci that exhibited non-synonymous polymorphism between the parents and were evaluated as the most likely candidates for weed competitiveness. Results ESG Performance of Parental Lines and EB-SILs Traits involved in ESG are associated with crop competitiveness against weeds (Additional file 1: Sheet 1). The results showed high phenotypic variation among the lines. Among the 181 EB-SILs and four parents, all ESG-related traits had significant differences ( Table 1 ). Seven out of eight traits had a p-value of less than 0.001. The mean ESG performance of the parental lines and top-performing EB-SILs under germination assay are shown in Fig. 1 . No significant differences were observed among the parental lines and EB-SILs in second-day germination count, seventh-day germination count, total dry weight, seed vigor index, and shoot length. The second-day and seventh-day germination count ranged from 18 to 25 and 23 to 25 in all lines, respectively, while the total dry weight ranged from 0.38 to 0.54 g. The lowest and highest values for shoot length were 6.28 and 8.18 cm, respectively, while the lowest and highest values for root length were 3.83 and 5.28 cm, respectively. On the other hand, significant differences were observed in germination rate and root length. EB-SILs generally exhibited the highest germination rate, along with the parental lines Haoannong, ChengHui448, Y134, and WTR1. Additionally, the EB-SILs recorded the highest increase in root length among all the lines, ranging from 8.33 to 9.19 cm. Table 1 Descriptive statistics of early seed germination (ESG) traits. Traits MIN. MAX. MEAN SUM OF SQUARES MEAN SQUARE F-VALUE P-VALUE Second-day Germination Count 3 25 19.01 5671 30.821 7.082 0.0000*** Seventh-day Germination Count 19 25 23.77 356.9 1.940 1.553 0.00147** Germination Rate (%) 15 100 79.77 88891 483.1 8.34 0.0000*** Shoot Length (cm) 3.16 9.79 6.536 876.7 4.765 8.927 0.0000*** Root Length (cm) 1.12 15.4 5.24 3274 17.80 5.614 0.0000*** Total Dry Weight of Germinated Seeds (g) 0.13 0.69 0.3953 0.6862 0. .0037 2.145 0.0000*** Average Dry Weight of Germinated Seeds (g) 0.0054 0.03 0.0166 0.0010 5.597e-06 2.032 0.0000*** Seed Vigor Index 5.25 63 31.68 21560 117.2 4.986 0.0000*** Significant codes: *0.05 ≥ P-value ≥ 0.01; **0.01 ≥ P-value ≥ 0.001; ***P-value ≤ 0.001 ESV Performance of Parental Lines and EB-SILs Another associated trait with the weed competitive ability of crops is ESV ( Additional file 1: Sheet 2) . High phenotypic variations were observed in all 12 ESV-related traits ( Table 2 ) . There were significant differences among the parental lines and EB-SILs with p-values of less than 0.001 in all traits except root dry weight at 28 DAS with a p-value of 0.005. On the other hand, all traits had significant differences between the weedy and non-weedy treatments. All ESV-related traits had p-values of less than 0.001. A similar high phenotypic variance was also observed in the interaction of lines and conditions. Only root dry weight at 28 DAS resulted in a p-value of 0.008, while the remaining traits have less than 0.001. Weed interference caused reductions in the mean performance of all lines for all ESV-related traits, as shown in the box-plot distribution (Supplementary Fig. 1) . For further identification of the effect of weed pressure on the parental lines and EB-SILs, mean values of phenotypes were plotted in a graph ( Fig. 2 ) . As early as 14 DAS, all parental lines exhibited tolerance to weeds in terms of plant height. There were no significant differences in mean values between the treatments, except for WTR1, which recorded a higher plant height in the weedy condition than in the non-weedy condition. There was also no significant difference between the mean performance of parental lines under non-weedy and weedy conditions at 21 DAS, except for Y134, which had a higher plant height at 21 DAS in the weedy condition. EB-SILs and ChengHui448 had the highest recorded plant height at 21 DAS under weedy conditions. Significant reductions in tiller numbers were recorded in all lines upon subjecting to weedy conditions. However, it was observed that the top-performing EB-SILs had significantly different mean performances in all parameters. The EB-SILs performed the best among all lines across all measured characteristics. Table 2 Descriptive statistics of early seedling vigor (ESV) traits. TRAITS NON-WEEDY WEEDY ANOVA RESULT Min. Max. Mean Min. Max. Mean G T G*T Plant Height at 14 DAS (cm) 13.2 41.2 25.89 13 36.5 24.79 *** *** *** Plant Height at 21 DAS (cm) 22.2 54.9 36.68 17.2 51.4 34.1 *** *** *** Plant Height at 28 DAS (cm) 23 65.6 45.79 20 90 42.88 *** *** *** Leaf Count at 14 DAS 2 5 3 1 5 2.86 *** *** *** Leaf Count at 21 DAS 2 10 5.87 2 8 4.44 *** *** *** Leaf Count at 28 DAS 3 17 9.5 3 16 6.56 *** *** *** Number of Tiller at 28 das 1 6 2.99 1 5 1.71 *** *** *** Seedling Vigor Index 141 655 352.37 26 419 149.44 *** *** *** Shoot Dry Weight (g) 1.2 4.87 2.85 0.13 3.15 1.29 *** *** *** Root Dry Weight (g) 0.06 1.68 0.67 0.01 1.04 0.21 ** *** ** Total Dry Weight (g) 1.41 6.55 3.52 0.26 4.19 1.5 *** *** *** Root Length (cm) 4.5 37 16.33 2.8 24.9 9.64 *** *** *** Significant codes: *0.05 ≥ P-value ≥ 0.01; **0.01 ≥ P-value ≥ 0.001; ***P-value ≤ 0.001. Abbreviations: G, genotype; T, treatment; G*T, genotype × treatment interaction. Correlation analysis among measured traits A Pearson pairwise comparison was conducted to understand the relationship between ESG related traits ( Fig. 3 -A ). Second-day germination count showed a strong positive correlation with germination rate (r = 0.94, p < 0.001) and seed vigor index (r = 0.90, p < 0.001). Similarly, germination rate was positively correlated with seed vigor index (r = 0.85, p < 0.001). Total and average dry weights exhibited a significant positive association (r = 0.59, p < 0.001). Conversely, root length displayed negative and significant associations with second-day germination count (r = -0.40, p < 0.001), seventh-day germination count (r = -0.29, p < 0.001), germination rate (r = -0.37, p < 0.001), and seed vigor index (r = -0.37, p < 0.001). Furthermore, correlation analysis among all ESV-related traits was conducted. Relative phenotypic values of non-weedy and weedy treatments were used to assess the response of parental lines and EB-SILs to weed pressure. All traits showed significant positive correlations with each other ( Fig. 3 -B ) . The strongest correlation was observed between the relative seedling vigor index at 28 DAS and relative total dry weight at 28 DAS (r = 1.00, p < 0.001). Additionally, a high positive correlation was found between relative shoot dry weight at 28 DAS and relative total dry weight at 28 DAS (r = 0.98, p < 0.001), as well as between relative root dry weight at 28 DAS and relative total dry weight at 28 DAS (r = 0.86, p < 0.001). Conversely, the weakest positive correlation was noted between relative plant height at 14 DAS, and relative tiller number at 28 DAS (r = 0.15, p < 0.001), and another low positive correlation was found between relative plant height at 21 DAS and relative leaf count at 28 DAS (r = 0.18, p < 0.001). Principal component analysis among measured traits The principal component analysis (PCA) was conducted to identify the relationship between ESG and ESV-related traits. The PCA accounted for 63.3% of the overall variance in eight early germination parameters, as shown in Fig. 4 -A. PC1 explained 46.9% of the variance, whereas PC2 explained 20.5%. The PCA accounted for 63.3% of the overall variance in 12 early vigor variables for ESV traits ( Fig. 4 -B ) . PC1 explained 50.1% of the variance, whereas PC2 explained 13.2%. The 15 rice lines that exhibited strong performance were identified by analyzing the main component and loading plot matrix of ESG traits (Supplementary Fig. 2-A; Supplementary Table 2) . The lines with excellent ESG features were chosen based on the EB-SILs identified within the region of seed vigor index and germination rate. These lines showed a favorable correlation between significant ESG traits, such as second-day germination count, seventh-day germination count, germination rate, and seed vigor index. As measured by ESV-related features, fifteen lines with superior weed competitiveness were identified using principal component and loading plot matrix analysis. These lines have the potential to produce significant yields. Supplementary Table 3 summarizes the 15 selected best performing EB-SILs depending upon their location in the biplot matrix (Supplementary Fig. 2-B) . EB-SILs located the nearest with the relative tiller number at 28 DAS were considered the 15 lines with favorable ESV-related traits. The five top-performing EB-SILs (Fig. 5 ) , together with their donor and recipient parents, show ESV traits in both weedy and non-weedy environments. SNP markers generated by (tGBS®) sequences for QTL mapping A total of 943.4M raw tGBS® sequencing reads were acquired by extracting the raw tGBS® sequencing reads. These reads were produced using 10 Ion Proton runs from the initial population of 564 lines (Ali et al. 2018 ). Following the removal of low-quality bases, a total of 881.6M reads were obtained, with 87.8% of the base pairs being retained. By analyzing the sequencing results from the 564 lines that matched primarily to the reference genome ( Oryza sativa ssp. japonica cv. Nipponbare ), we discovered a total of 794,297 polymorphic sites. These sites were found after analyzing 2,679,180 bases that had at least 5 reads in at least 50% of the 564 samples. The 181 samples used in the current study were selected from a subset of 564 samples taken from three populations including the common recipient parent WTR1 and three donor parents Haoannong, ChengHui448, and Y134. Low-Missing Dataset (LMD50) was filtered, resulting in the identification of the total number of LMD50 SNPs found in all three sub-populations by analyzing the distinct alignments of every read from the 181 samples in relation to the publicly available reference genome. Sub-population 1, which included 112 samples (110 Introgression Lines and the two parental lines), was denoted as WTR1 X Haoannong, and 4,669 LMD50 SNPs were detected. Sub-population 2, which was formed by crossing WTR1 and ChengHui448, had a total of 41 samples. Among these samples, 39 introgression lines (ILs) and the two parental lines were detected, and 5,968 LMD50 SNPs were detected. Sub-population 3, also known as WTR1 X Y134, included 28 samples. This included 26 ILs and the two parental lines, and we were able to identify a total of 4,435 LMD50 SNPs. By integrating all common LMD50 SNPs across the groups, 3,791 LMD SNPs were obtained. Subsequently, these SNPs were used to construct the physical map required for QTL analysis ( Fig. 6 ). Identification of QTLs for Weed Competitive Traits Nineteen SNPs showed a significant marker-trait association with ESG and ESV-related traits. QTLs were defined by assuming that closely linked significant markers are in the QTL region ( Table 3 and Fig. 6 ) . Four of these eight major QTLs (R 2 ≥ 10) associated with ESG traits were located on chromosome 2, while two QTLs were on chromosome 12, and one each on chromosomes 3 and 6. The first QTL identified on chromosome 2 was for root length, qRL2 , with a phenotypic variance of 15.25%. On chromosome 2, qTDWG2 , responsible for the total dry weight during the germination stage of rice (R 2 = 13.98%), was also detected. Two QTLs for seedling vigor index were also found on chromosome 2, where qSVI2.1 and qSVI2.1 can be explained by their phenotypic variances of 15.97% and 16.26%, respectively. In addition, another major QTL was detected on chromosome 3, responsible for the seedling vigor index ( qSVI3 ), with a phenotypic variance of 11.08%. Another major QTL identified was located on chromosome 6 ( qSVI6 ) for early seedling vigor, explained by a phenotypic variance of 12.20%. On the other hand, two major QTLs were on chromosome 12, qGR12 associated with germination rate (R 2 = 10.57%) and qSVI12 associated with seedling vigor index (R 2 = 13.29%). For ESV-related traits, four QTLs were found on chromosome 10 and one QTL each on chromosomes 1, 3, 4, 5, 8, and 9 (Table 3 and Fig. 5 ). On chromosome 1, qRPH1 was detected to be related to two ESV-related traits, relative plant height at 14 and 28 DAS, which have a phenotypic variance of 11.29% and 9.94%, respectively. A major QTL was found on chromosome 3 with a phenotypic variance of 14.80% that was associated with the relative tiller number of rice ( qRTN3 ), while qRLC4 identified on chromosome 4 with a phenotypic variance of 11.34% was a QTL for relative leaf count at 28 DAS. Two QTLs linked with relative plant height at 21 DAS were also identified, namely qRPH5 on chromosome 5 (R 2 = 10.10%) and qRPH 9 on chromosome 9 (R 2 = 10.87%). For relative root length at 28 DAS, the major QTL ( qRRL8) detected was on chromosome 8 (R 2 = 10.60%). Moreover, another QTL ( qRRL10) linked with root length at 28 DAS was on chromosome 10 (R 2 = 10.34%), along with qRTN10 (R 2 = 10.24%), qRLC10.1 (R 2 = 10.15%), and qRLC10.2 (R 2 = 10.04%), QTLs responsible for relative tiller number at 28 DAS and relative leaf count at 28 DAS, respectively. Table 3 Quantitative trait loci (QTLs) associated with weed competitiveness at early germination and early vegetative stages in Early-Backcross Selective Introgression Lines (EB-SILs) breeding population by marker trait association analysis (SMA). No. QTL a Trait Chr. Position b Associated Marker c _ LOG P(F) d R 2 (%) e Additive Effect f Early Seed Germination 1 qGR12 Germination Rate 12 5720211–6950257 S12_6548722 4.51 10.57 -6.15 2 qRL2 Root Length 2 34379631–34576493 S2_34576493 6.68 15.25 0.55 3 qTDWG2 Total Dry Weight 2 8474520–8752801 S2_8699045 6.08 13.98 -0.02 4 qSVI2.1 Seed Vigor Index 2 8474495–8752801 S2_8474495 7.03 15.97 -4.04 5 qSVI2.2 2 30478421–30791659 S2_30791659 7.17 16.26 -3.62 6 qSVI3 3 25401607–25452773 S3_25401672 4.74 11.08 -3.25 7 qSVI6 6 1542513–1877725 S6_1698496 5.26 12.2 -2.81 8 qSVI12 12 6950207–7011126 S12_6950257 5.76 13.29 -3.45 Early Seedling Vigor 9 qRPH1 Relative Plant Height at 14 DAS 1 42171596–42617013 S1_42549502 4.84 11.29 -4.27 10 qRPH 5 Relative Plant Height at 21 DAS 5 28497478–28567356 S5_28525048 4.29 10.10 -4.61 11 qRPH9 9 14626500–14826499 S9_14725794 4.64 10.87 -5.19 12 qRPH1 Relative Plant Height at 28 DAS 1 42171596–42617013 S1_42171596 4.23 9.94 -3.47 13 qRLC10.1 Relative Leaf Count at 28 DAS 10 416500–516499 S10_466091 4.32 10.15 -4.32 14 qRLC10.2 10 1393500–1493499 S10_1441265 4.27 10.04 -4.61 15 qRLC 4 4 1753300–1753386 S4_1753338 4.86 11.34 4.42 16 qRTN 3 Relative Tiller Number at 28 DAS 3 35948030–35965394 S3_35948030 6.47 14.80 -6.18 17 qRTN10 10 18051000–18164000 S10_18105284 4.36 10.24 -5.46 18 qRRL8 Relative Root Length at 28 DAS 8 7492455–7777214 S8_7541070 4.52 10.60 -4.42 19 qRRL10 10 18060329–18452140 S10_18228061 4.41 10.34 -4.88 a Closely linked markers are assumed as the same QTL, b Physical position of markers on chromosomes, c Marker associated with QTL. d F-statistical analysis indicates association between markers and trait, e Proportion of phenotypic variance explained. f Positive/negative values indicate that additive effect that can increase trait values. Candidate Genes Associated with ESG and ESV Traits Among the 18 QTLs identified in this study, there were 480 gene models present within the QTL interval (Additional file 2: Sheet 1–17) . Out of these, 27 gene models were found to be directly associated with biotic and abiotic stress-tolerant genes, thus considered the most likely candidate genes (Supplemental Table 4) . These genes were selected for further SNP analysis to find the most promising candidate genes. Fourteen of the most promising candidate gene models were associated with ESG traits, and another 13 were related to ESV traits. In the most likely candidate gene models, 5,286 SNPs were identified between the parents in the Rice SNP-Seek Database. Among those identified SNPs, 24% showed polymorphism between the parents in 27 genes, and most of these polymorphisms (87.5%) were synonymous mutations, with non-synonymous mutations (12.4%) between the parents in 27 genes. Out of the 27 gene models, 19 genes that contain SNPs resulting in non-synonymous mutations were identified as the most promising candidate genes associated with weed competitiveness in rice ( Table 4 ) . Discussion In this study, we investigated the genetic basis of weed competitiveness in rice through QTL analysis using a mapping population of 181 EB-SILs derived from the cross between the common recipient parent WTR1 and three donor parents Haoannong, ChengHui448, and Y134. Also, by employing high-density tGBS® SNPs, our study identified nineteen QTL regions associated with weed competitiveness and the specific candidate genes within these regions. Weed competitiveness is an untargeted trait that was not considered during the population development of EB-SILs by assuming no correlation exists between weed competitiveness and the selected traits used in population development, yield under different conditions like irrigated, rainfed, drought, salinity, submergence, and low input conditions. Previously, similar approaches were employed in elite breeding lines to identify QTLs and simultaneously enhance the elite lines for various biotic and abiotic stresses (Pang et al. 2017 ; Ali et al. 2021b ; Zhang et al. 2023 ). The EB-SILs mapping population could be employed as a randomly segregating population to map weed-competitiveness. Gaining a comprehensive understanding of the competitive advantage of rice over weeds is crucial for implementing sustainable farming practices and maintaining food security. Despite weedy pressure, the pronounced phenotypic variation observed among EB-SILs suggests a solid genetic basis underlying these traits. This variability indicates a high likelihood of detecting QTLs governing these traits, underscoring the potential for genetic improvement in rice competitiveness against weeds. Furthermore, the observed phenotypic variations reflect gene segregation upon backcrossing of genetically distant donor and recipient parents, suggesting that both parental lines contribute favorable traits to the elite EB-SILs (Ali et al. 2021b ; Zhang et al. 2021 ; Yu et al. 2022 ). The EB-SILs exhibited similar mean performances to parental lines across several ESG traits. This parity suggests that desirable ESG-related traits are inherited from both parents and remain expressed in EB-SILs despite genetic recombination. The seventh-day germination count is of particular significance, which is critical for establishing a competitive advantage over weeds by ensuring timely seedling emergence and higher crop density, thus reducing weed biomass accumulation. Rapid and uniform seedling emergence, measured by the seed vigor index, is crucial for rice competitiveness. Shoot and root length are also essential for resource acquisition and crop competitiveness. Seed vigor, indicating rapid and uniform germination, is pivotal for plant establishment (Manangkil et al. 2013 ). The second-day germination count and germination rate positively correlated with the seed vigor index, supporting their importance for rice competitiveness against weeds. These associations align with previous findings highlighting the importance of these ESG traits in determining rice competitiveness against weeds. However, inconsistencies arise regarding root length correlations with ESG traits, contrasting with previous reports (Teixeira et al. 2021 ). A fundamental principle of crop-weed competition underscores the advantage of early establishment, emphasizing the critical role of emergence time in field competitiveness (Swanton et al. 2015 ). Accordingly, second-day germination count, seventh-day germination count, germination rate, and seed vigor index emerge as pivotal ESG traits influencing rice competitiveness against weeds. The mean performances of EB-SILs and parental lines in ESV traits were notably diminished under weedy conditions. Significant differences were observed in plant height, leaf count, and tiller number at 28 DAS among all lines. Conversely, no significant differences were noted in root length, shoot dry weight, root dry weight, total dry weight, and seedling vigor at 28 DAS under non-weedy conditions, consistent even in weedy conditions. This reduction in EB-SILs performance in weedy conditions can be attributed to the competitive pressure exerted by E. colona , leading to interspecific competition and, subsequently, lower rice performance compared to non-weedy conditions. This observation aligns with previous studies reporting decreased values for plant height, tillering ability, and chlorophyll content under weedy conditions (Dimaano et al. 2017 ). A decrease in rice tillers was evident with increasing weed density. In contrast, a negative correlation between Echinochloa spp., weed dry weight, and rice root dry weight suggested a direct impact of weeds on root development (Mahajan and Chauhan 2013 ; Narayana Rao 2021 ). Similarly, a decrease in seedling vigor index correlated with higher weed density, implying the detrimental effects of weed competition on rice seedling vigor. These findings underscore the negative impacts of weeds on rice growth and development, emphasizing the importance of effective weed management strategies and adoption of weed competitive varieties (Mahender et al. 2015 ). Notably, no significant differences in plant height between weedy and non-weedy treatments for parental lines suggest genetic stability under both conditions, indicating the resilience of genotypes to weed competition. All ESV-related traits exhibited significant and positive correlations with each other in this study. These ESV-related traits were also significantly and positively correlated in the study conducted by Dimaano et al. ( 2017 ). A positive correlation between plant height and dried vegetative crop biomass under non-weedy and weedy conditions was also observed by (Mennan et al. 2012 ). Additionally, dried vegetative crop biomass was closely related to tiller number, vigor ratings, and canopy ground cover (Saito et al. 2010 ). The results observed in the study, as well as in other studies, revealed that the ability of the seedlings to emerge and have vigorous growth, also known as seedling vigor, is governed by different ESV-related traits, where it is the sum of various properties of a plant associated with the rate and uniformity of seedling growth (Manangkil et al. 2013 ). Weed tolerance pertains to the ability of the crop to have a high yield despite weeds in the field. In this study, the tiller number at 28 DAS was used to associate with the grain-yielding capacity of rice plants, as the tiller number is positively correlated with grain yield (Dimaano et al. 2017 ). During the vegetative growth stage, tillering number is a trait highly associated with panicle number, which is a critical yield component of rice (Fageria 2007 ). Identifying top-performing EB-SILs based on seed vigor index, germination rate, and tiller number at 28 DAS provides insights into selecting lines with desirable ESG and ESV traits. The top-ranking lines exhibit promising correlations among key ESG traits and between ESV-related traits and tiller number at 28 DAS, suggesting their potential for high yield and weed competitiveness. In the current weed competitiveness screening, the population was advanced using the backcross breeding approach, retrieving only a small number of genomic introgression fragments from a donor parent. This limited the number of genomic introgression fragments present in the EB-SILs. However, tunable genotyping-by-sequencing (tGBS®) for genotyping yielded a substantial number of polymorphic markers, enabling a clear distinction of genomic introgression fragments (Ott et al. 2017 ; Ali et al. 2018 ). These markers have the potential to provide a comprehensive understanding of genetic variation in the population. QTL mapping for weed competitiveness involved using 3,791 LMD SNP markers, resulting in the mapping of 19 significant SNPs linked to QTLs associated with this trait through marker-trait association. To assess the novelty of our findings, the putative QTL regions identified for weed competitiveness traits were compared with previously reported QTLs. This comparison was based on the physical positions of the associated markers in the Nipponbare genome, using information from the International Rice Genome Sequencing Project ( http://rgp.dna.affrc.go.jp/IRGSP/ ). In ESG-related traits, a total of eight QTLs were identified. Four QTLs were detected on chromosome 2, linked with the root length ( qRL 2), total dry weight ( qTDWG 2), and seed vigor index ( qSVI 2.1 and qSVI 2.1) of the rice plants. Two were identified on chromosome 12, governing germination rate ( qGR 12) and seed vigor index ( qSVI 12). Two other QTLs related to the seed vigor index were found on chromosome 3 ( qSVI 3) and 6 ( qSVI 6). The QTLs detected for ESG-related traits in this study differed from those reported QTLs associated with the said traits (Yang et al. 2019 ; Xu et al. 2023 ). There were 11 QTLs associated with ESV-related traits. On chromosome 1, a QTL ( qRPH 1) was detected that was linked with the relative plant height trait at 14 DAS and relative plant height at 28 DAS. Then, four QTLs were located on chromosome 10, which were associated with relative leaf count at 28 DAS ( qRLC 10.1 and qRLC 10.2), relative tiller number at 28 DAS ( qRTN 10), and relative root length at 28 DAS (qRRL10) were consistent with previous studies (Singh et al. 2017 ; Yang et al. 2019 ; Xu et al. 2023 ). Moreover, there were also QTLs detected for relative plant height at 21 DAS on chromosomes 5 and 9 ( qRPH 5 and qRPH 9), relative leaf count at 28 DAS on chromosome 4 ( qRLC 4), relative tiller number at 28 DAS on chromosome 3 ( qRTN 3), and relative root length at 28 DAS on chromosome 8 ( qRRL 8). The detected QTL was linked with plant height at 14 and 28 DAS ( qRPH 1) and co-localized with qPH-14.1, identified for rice plant height at 14 DAS by (Dimaano et al. 2020 ). However, other QTLs found on chromosome 1 in the study of (Dimaano et al. 2020 ), which are associated with plant height at 21 and 28 DAS, were not detected in the current study. Instead, novel QTLs for relative plant height at 21 DAS were determined on chromosomes 5 and 9, qRPH 5 and qRPH 9, respectively. Moreover, the rest of the identified ESV trait-related QTLs were all novel. To enhance the precision of our QTL analysis, we utilized a whole-genome sequencing strategy for the parental lines. This approach aimed to reduce the number of candidate genes within the QTL intervals. Since QTLs mapped in bi-parental populations are confined to loci present in the gene pool of the founder parents, we focused on analyzing non-synonymous mutations between the parental sequences within the QTL interval (Pang et al. 2017 ; Murugaiyan et al. 2019 ). This analysis successfully narrowed down the initial pool of 480 candidate genes to a more manageable 18 candidate genes ( Table 4 ) . Overall, the whole-genome sequence of parents and gene expression analysis proved to be an effective strategy to narrow down the candidate genes in the QTL intervals. Among these, ASR 4 (Abscisic acid-stress-ripening-inducible4 protein) encoded by LOC_Os01g73250 is associated with abscisic acid (ABA) response, which can regulate seed dormancy and germination. ABA influences weed competitiveness by affecting seed germination timing, potentially giving rice seedlings an advantage over weeds (Park et al. 2020 ). Among these genes, LOC_Os09g24560 and LOC_Os02g15340 , both encoding putative no apical meristem proteins and belonging to the NAC family of plant transcription factors, are implicated in meristem regulation, potentially influencing weed competition through growth modulation (Marques et al. 2017 ). Additionally, LOC_Os09g24800 , a putative MYB family transcription factor, and LOC_Os02g15350 , encoding a dof zinc finger domain-containing protein, underscore the regulatory mechanisms involved in response to weed pressure (Zou and Sun 2023 ). The presence of genes like LOC_Os10g33940 , encoding an auxin response factor 18 (ARF22), and LOC_Os10g33960 , expressing a START domain-containing protein (OSHB2), highlights the involvement of hormonal signaling pathways and transcriptional regulation in weed competitiveness (Schrick et al. 2014 ; Zhai et al. 2020 ). Furthermore, genes such as LOC_Os10g34020 and LOC_Os10g34430 , encoding putative glutathione S-transferase and Dicer proteins, respectively, suggest the importance of stress response mechanisms in weed competition (Kumar and Trivedi 2018 ). Notably, genes like LOC_Os12g10720 and LOC_Os12g10730 , both encoding glutathione S-transferases, and LOC_Os12g12580 , encoding an NADP-dependent oxidoreductase, indicate the role of detoxification processes in enhancing rice competitiveness against weeds (Dasari et al. 2018 ). Moreover, genes like LOC_Os02g50240 , encoding glutamine synthetase 1;1, LOC_Os02g50330 , encoding an RNA-dependent RNA polymerase, LOC_Os06g04070 , encoding an arginine decarboxylase, and LOC_Os06g04200 , encoding a starch synthase, further contribute to the multifaceted response of rice plants to weed competition, emphasizing the intricate interplay between various molecular pathways in shaping weed competitiveness in rice (Kusano et al. 2020 ). The ZF-HD protein encoded by LOC_Os09g24820 is a transcription factor that regulates multiple developmental processes in rice plants. While its specific role in weed competitiveness is not well understood, transcription factors like ZF-HD are often associated with stress responses and growth regulation. It is plausible that ZF-HD may indirectly influence weed competitiveness by modulating the expression of genes involved in stress tolerance or developmental pathways that affect plant vigor. ZF-HD might regulate the expression of genes involved in root architecture or nutrient uptake, traits crucial for outcompeting weeds (Todaka et al. 2012 ). On the other hand, LEA15 (Late embryogenesis abundant protein 15) encoded by LOC_Os02g15250 is a protein known for protecting plants from various stresses, including drought and salinity. Early seedling vigor and germination in rice are closely linked to stress tolerance during the critical early stages of growth. LEA proteins like LEA15 are involved in maintaining cellular hydration and stabilizing proteins and membranes under stress conditions, which can contribute to improved seedling vigor and germination rates. Enhancing stress tolerance during germination and early seedling growth, LEA15 may indirectly enhance competitiveness against weeds by ensuring a robust start for rice plants (Hundertmark and Hincha 2008 ; Dirk et al. 2020 ). Interestingly, ZF-HD and LEA proteins are part of the complex regulatory network governing plant responses to environmental stimuli. While ZF-HD proteins may be more directly involved in transcriptional regulation, LEA proteins act as molecular chaperones, safeguarding cellular components during stress. Despite their distinct roles, both types of proteins ultimately contribute to the overall fitness and competitiveness of rice plants by ensuring proper growth and development, especially during the early stages when plants are most vulnerable to weed competition and environmental stresses (Hundertmark and Hincha 2008 ; Todaka et al. 2012 ; Dirk et al. 2020 ). Limited QTL and candidate gene information is available for weed competitiveness in rice, reflecting a research area still in its early stages. While some studies have identified QTL associated with traits related to weed competitiveness, such as early vigor, root architecture, and allelopathy, the number of known QTLs remains relatively small. Additionally, identifying candidate genes underlying these QTLs has been challenging, further limiting our understanding of the genetic basis of weed competitiveness in rice. The QTLs and candidate genes identified through this study will hold significant implications for sustainable agriculture, as enhancing weed competitiveness in rice can reduce herbicide usage, increase yield stability, and promote resource use efficiency, ultimately contributing to DSR production systems' economic and environmental sustainability. Conclusion Our work investigated the genetic factors that contribute to the ability of rice plants to compete with weeds. We used QTL analysis on a mapping population of 181 EB-SILs created by crossing the recipient parent WTR1 with three different donor parents. We used high-density tGBS® SNPs to detect nineteen QTL areas linked to weed competitiveness. Seventeen out of the identified 19 QTLs associated with the ESG- and ESV-related traits were distinct from the previously reported QTLs. Hence, this study detected 17 novel QTLs that could aid in breeding rice with improved weed competitive ability during its early vegetative stage. Our analysis also highlights the negative impacts of weeds on rice growth and development, emphasizing the importance of effective weed management strategies. The identified QTLs and candidate genes provide valuable insights into the genetic mechanisms underlying weed competitiveness in rice, with implications for sustainable agriculture. Enhancing weed competitiveness in rice can reduce herbicide usage, increase yield stability, and promote resource use efficiency, contributing to the economic and environmental sustainability of the rice production system. Overall, these findings would assist rice breeders in developing rice varieties with a competitive advantage against weeds suitable for DSR systems. In addition, the candidate genes associated with ESG and ESV traits could be further studied to understand better the expression of these genes under high weed pressure. Abbreviations DSR Dry-Seeded Rice EB-SILs Early backcross selective-introgression lines ESG Early seed germination ESV Early seedling vigor QTL Quantitative trait locus SNP Single nucleotide Polymorphism Declarations Acknowledgments The authors wish to express their gratitude to the individuals at IRRI who took part in the internal review of the manuscript. Their valuable suggestions and significant efforts have contributed significantly to its improvement. Author contributions Experiment concept and design, JA, NGD, and VM; JA and VM contributed to the development of breeding materials, Weed Seed material collection, KDN, VM, NGD, CLC; Phenotyping, KDN; QTL analysis, KDN, VM, and NGD; Statistical Analysis and Writing, KDN, VM, NGD, EJDA, AP and JA. The final version of the manuscript received approval from all authors. The work reported in this manuscript is part of KDN’s undergraduate thesis. Funding The Bill & Melinda Gates Foundation (BMGF) is responsible for providing a research grant to the Green Super Rice Project under ID OPP1130530. Availability of data and materials This research article offers extensive data supporting its conclusions, presented in figures, tables, and additional supplementary tables. Ethics approval and consent to participate The authors assert that this research review was carried out without affiliations with commercial or economic entities that could be interpreted as potential conflicts of interest. Consent for publication Not applicable. Competing interests No conflicts of interest have been declared. References Ahmed S, Jahangir Alam M, Hossain A et al (2021) Interactive effect of weeding regimes, rice cultivars, and seeding rates influence the rice-weed competition under dry direct-seeded condition. Sustain 13:1–15. https://doi.org/10.3390/su13010317 Ali J, Anumalla M, Murugaiyan V, Li Z (2021a) Green Super Rice (GSR) Traits: Breeding and Genetics for Multiple Biotic and Abiotic Stress Tolerance in Rice. Rice Improvement. Springer International Publishing, Cham, pp 59–97 Ali J, Anumalla M, Murugaiyan V, Li Z (2021b) Green Super Rice (GSR) Traits: Breeding and Genetics for Multiple Biotic and Abiotic Stress Tolerance in Rice. Rice Improvement. Springer, Cham, pp 59–97 Ali J, Aslam UM, Tariq R et al (2018) Exploiting the genomic diversity of rice (Oryza sativa L.): SNP-typing in 11 early-backcross introgression-breeding populations. Front Plant Sci 9:315127. https://doi.org/10.3389/fpls.2018.00849 Arouna A, Fatognon IA, Saito K, Futakuchi K (2021) Moving toward rice self-sufficiency in sub-Saharan Africa by 2030: Lessons learned from 10 years of the Coalition for African Rice Development. https://doi.org/10.1016/j.wdp.2021.100291 . World Dev Perspect 21: Awan TH, Chauhan BS, Sta. Cruz PC (2014) Growth Plasticity of Junglerice (Echinochloa colona) for Resource Use When Grown with Different Rice (Oryza sativa) Planting Densities and Nitrogen Rates in Dry-Seeded Conditions. Weed Sci 62:571–587. https://doi.org/10.1614/ws-d-14-00054.1 Bhandari S, Khanal S, Dhakal S (2020) Adoption of Direct Seeded Rice (Dsr) Over Puddled-Transplanted Rice (Tpr) for Resource Conservation and Increasing Wheat Yield. Rev Food Agric 1:59–66. https://doi.org/10.26480/rfna.02.2020.59.66 Bharamappanavara M, Siddaiah AM, Ponnuvel S et al (2020) Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud. Sci Rep 10:22103. https://doi.org/10.1038/s41598-020-78675-7 Chaudhary A, Venkatramanan V, Kumar Mishra A, Sharma S (2023) Agronomic and Environmental Determinants of Direct Seeded Rice in South Asia. Circ Econ Sustain 3:253–290 Chauhan BS, Awan TH, Abugho SB et al (2015) Effect of crop establishment methods and weed control treatments on weed management, and rice yield. F Crop Res 172:72–84. https://doi.org/10.1016/j.fcr.2014.12.011 Connor M, Cuong OQ, Demont M et al (2022) The influence of climate change knowledge on consumer valuation of sustainably produced rice in Vietnam. Sustain Prod Consum 31:1–12. https://doi.org/10.1016/j.spc.2022.01.034 Dasari S, Ganjayi MS, Yellanurkonda P et al (2018) Role of glutathione S-transferases in detoxification of a polycyclic aromatic hydrocarbon, methylcholanthrene. Chem Biol Interact 294:81–90. https://doi.org/10.1016/j.cbi.2018.08.023 Dimaano NGB, Ali J, Cruz PCS et al (2017) Performance of Newly Developed Weed-Competitive Rice Cultivars under Lowland and Upland Weedy Conditions. Weed Sci 65:798–817. https://doi.org/10.1017/wsc.2017.57 Dimaano NGB, Ali J, Mahender A et al (2020) Identification of quantitative trait loci governing early germination and seedling vigor traits related to weed competitive ability in rice. Euphytica 216:1–20. https://doi.org/10.1007/s10681-020-02694-8 Dirk LMA, Abdel CG, Ahmad I et al (2020) Late embryogenesis abundant protein–client protein interactions. Plants 9:1–35. https://doi.org/10.3390/plants9070814 Fageria NK (2007) Yield Physiology of Rice. J Plant Nutr 30:843–879. https://doi.org/10.1080/15226510701374831 Goldberg-Cavalleri A, Onkokesung N, Franco-Ortega S, Edwards R (2023) ABC transporters linked to multiple herbicide resistance in blackgrass (Alopecurus myosuroides). Front Plant Sci 14:1082761. https://doi.org/10.3389/FPLS.2023.1082761/BIBTEX He A, Jiang M, Nie L et al (2022) A preliminary study of ‘Tidy Field Technology’ to assess growth, development and weed control in direct-seeded rice. F Crop Res 277. https://doi.org/10.1016/j.fcr.2021.108408 Hundertmark M, Hincha DK (2008) LEA (Late Embryogenesis Abundant) proteins and their encoding genes in Arabidopsis thaliana. BMC Genomics 9:118. https://doi.org/10.1186/1471-2164-9-118 Kumar S, Trivedi PK (2018) Glutathione S-transferases: Role in combating abiotic stresses including arsenic detoxification in plants. Front Plant Sci 9:1–9. https://doi.org/10.3389/fpls.2018.00751 Kusano M, Fukushima A, Tabuchi-Kobayashi M et al (2020) Cytosolic GLUTAMINE SYNTHETASE1;1 Modulates Metabolism and Chloroplast Development in Roots. Plant Physiol 182:1894–1909. https://doi.org/10.1104/pp.19.01118 Lu Y, Chuan M, Wang H et al (2022) Genetic and molecular factors in determining grain number per panicle of rice. Front. Plant Sci 13:964246 Mahajan G, Chauhan BS (2013) The role of cultivars in managing weeds in dry-seeded rice production systems. Crop Prot 49:52–57. https://doi.org/10.1016/J.CROPRO.2013.03.008 Mahajan G, Kaur G, Chauhan BS (2017) Seeding rate and genotype effects on weeds and yield of dry-seeded rice. Crop Prot 96:68–76. https://doi.org/10.1016/j.cropro.2017.01.008 Mahender A, Anandan A, Pradhan SK (2015) Early seedling vigour, an imperative trait for direct-seeded rice: an overview on physio-morphological parameters and molecular markers. Planta 241:1027–1050 Manangkil OE, Vu HTT, Mori N et al (2013) Mapping of quantitative trait loci controlling seedling vigor in rice (Oryza sativa L.) under submergence. Euphytica 192:63–75. https://doi.org/10.1007/S10681-012-0857-Z/FIGURES/3 Marasini S, Joshi T, Amgain L (2018) Direct seeded rice cultivation method: a new technology for climate change and food security. J Agric Environ 17:30–38. https://doi.org/10.3126/aej.v17i0.19857 Marques DN, Reis SP dos, de Souza CRB (2017) Plant NAC transcription factors responsive to abiotic stresses. Plant Gene 11:170–179. https://doi.org/10.1016/j.plgene.2017.06.003 McNally KL, Henry A (2023) Tools for using the International Rice Genebank to breed for climate-resilient varieties. PLoS Biol 21. https://doi.org/10.1371/journal.pbio.3002215 Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3:269–283. https://doi.org/10.1016/J.CJ.2015.01.001 Mennan H, Ngouajio M, Sahin M et al (2012) Competitiveness of rice (Oryza sativa L.) cultivars against Echinochloa crus-galli (L.) Beauv. in water-seeded production systems. Crop Prot 41:1–9. https://doi.org/10.1016/j.cropro.2012.04.027 Murugaiyan V, Ali J, Mahender A et al (2019) Mapping of genomic regions associated with arsenic toxicity stress in a backcross breeding populations of rice (Oryza sativa L). Rice 12:1–14. https://doi.org/10.1186/S12284-019-0321-Y/FIGURES/5 Narayana Rao A (2021) Echinochloa colona and Echinochloa crus-galli. In: Biology and Management of Problematic Crop Weed Species, 1st Edition. Academic Press, pp 197–239 Nazir A, Bhat MA, Bhat TA et al (2023) Impact of crop establishment techniques and weed management practices on Oryza sativa L. growth and yield. Agron J 115:1812–1826. https://doi.org/10.1002/agj2.21363 Ott A, Liu S, Schnable JC et al (2017) tGBS® genotyping-by-sequencing enables reliable genotyping of heterozygous loci. Nucleic Acids Res 45:e178. https://doi.org/10.1093/NAR/GKX853 Padma S, Vijayakumar S, Venkatanna B et al (2023) Energy and water budget of rice under different establishment methods. Oryza-An Int J Rice 60:578–587. https://doi.org/10.35709/ory.2023.60.4.10 Panda S, Majhi PK, Anandan A et al (2021) Proofing direct-seeded rice with better root plasticity and architecture. Int J Mol Sci 22 Pang Y, Chen K, Wang X et al (2017) Simultaneous improvement and genetic dissection of salt tolerance of rice (Oryza sativa L.) by designed QTL pyramiding. Front Plant Sci 8:273909. https://doi.org/10.3389/fpls.2017.01275 Park SI, Kim JJ, Shin SY et al (2020) ASR Enhances Environmental Stress Tolerance and Improves Grain Yield by Modulating Stomatal Closure in Rice. Front Plant Sci 10. https://doi.org/10.3389/fpls.2019.01752 Peramaiyan P, Srivastava AK, Kumar V et al (2023) Crop establishment and diversification strategies for intensification of rice-based cropping systems in rice-fallow areas in Odisha. F Crop Res 302. https://doi.org/10.1016/j.fcr.2023.109078 Saito K, Azoma K, Rodenburg J (2010) Plant characteristics associated with weed competitiveness of rice under upland and lowland conditions in West Africa. F Crop Res 116:308–317. https://doi.org/10.1016/j.fcr.2010.01.008 Saito K, Futakuchi K (2014) Improving estimation of weed suppressive ability of upland rice varieties using substitute weeds. F Crop Res 162:1–5. https://doi.org/10.1016/j.fcr.2014.03.006 Samal P, Babu SC, Mondal B, Mishra SN (2022) The global rice agriculture towards 2050: An inter-continental perspective. Outlook Agric 51:164–172. https://doi.org/10.1177/00307270221088338 Schrick K, Bruno M, Khosla A et al (2014) Shared functions of plant and mammalian StAR-related lipid transfer (START) domains in modulating transcription factor activity. BMC Med 12:1–20. https://doi.org/10.1186/s12915-014-0070-8 Schumaker B, Stallworth S, Tucker A et al (2021) Phenotyping of Weedy Rice to Assess Root Characteristics Associated with Allelopathy. Am J Plant Sci 12:1210–1221. https://doi.org/10.4236/ajps.2021.128084 Schumaker BC, Stallworth S, De Castro E et al (2019) Repeatable stair-step assay to access the allelopathic potential of weedy rice (Oryza sativa ssp.). J Vis Exp 2020:. https://doi.org/10.3791/60764 Shekhawat K, Rathore SS, Chauhan BS (2020) Weed management in dry direct-seeded rice: A review on challenges and opportunities for sustainable rice production. Agronomy 10 Shrestha M, Baral B, Dulal PR (2021) a Review on Weed in Direct-Seeded Rice (Dsr). Sustain Food Agric 2:99–104. https://doi.org/10.26480/sfna.02.2021.99.104 Singh UM, Yadav S, Dixit S et al (2017) QTL hotspots for early vigor and related traits under dry direct-seeded system in rice (Oryza sativa L). Front Plant Sci 8:1–14. https://doi.org/10.3389/fpls.2017.00286 Swanton CJ, Nkoa R, Blackshaw RE (2015) Experimental Methods for Crop–Weed Competition Studies. Weed Sci 63:2–11. https://doi.org/10.1614/ws-d-13-00062.1 Teixeira SB, Pires SN, Ávila GE et al (2021) Application of vigor indexes to evaluate the cold tolerance in rice seeds germination conditioned in plant extract. Sci Rep 2021 111 11:1–8. https://doi.org/10.1038/s41598-021-90487-x Todaka D, Nakashima K, Shinozaki K, Yamaguchi-Shinozaki K (2012) Toward understanding transcriptional regulatory networks in abiotic stress responses and tolerance in rice. Rice 5:1–9. https://doi.org/10.1186/1939-8433-5-6 Tonnessen BW, Manosalva P, Lang JM et al (2015) Rice phenylalanine ammonia-lyase gene OsPAL4 is associated with broad spectrum disease resistance. Plant Mol Biol 87:273–286. https://doi.org/10.1007/s11103-014-0275-9 Wu D, Shen E, Jiang B et al (2022) Genomic insights into the evolution of Echinochloa species as weed and orphan crop. Nat Commun 13:1–16. https://doi.org/10.1038/s41467-022-28359-9 Xu L, Li X, Wang X et al (2019) Comparing the grain yields of direct-seeded and transplanted rice: A meta-analysis. Agronomy 9. https://doi.org/10.3390/agronomy9110767 Xu S, Fei Y, Wang Y et al (2023) Identification of a Seed Vigor–Related QTL Cluster Associated with Weed Competitive Ability in Direct–Seeded Rice (Oryza Sativa L). https://doi.org/10.1186/s12284-023-00664-x . Rice 16: Yang J, Yang G, Yang M et al (2019) Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice. Front Plant Sci 10. https://doi.org/10.3389/fpls.2019.01582 Yu S, Ali J, Zhang C et al (2020) Genomic Breeding of Green Super Rice Varieties and Their Deployment in Asia and Africa. Theor Appl Genet 133:1427–1442 Yu S, Ali J, Zhou S et al (2022) From Green Super Rice to green agriculture: Reaping the promise of functional genomics research. Mol Plant 15:9–26 Zhai R, Ye S, Zhu G et al (2020) Identification and integrated analysis of glyphosate stress-responsive microRNAs, lncRNAs, and mRNAs in rice using genome-wide high-throughput sequencing. BMC Genomics 21:238. https://doi.org/10.1186/s12864-020-6637-6 Zhang C, Li M, Rey JD et al (2023) Simultaneous improvement and genetic dissection of drought and submergence tolerances in rice (Oryza sativa L.) by selective introgression. Front Plant Sci 14:1134450. https://doi.org/10.3389/fpls.2023.1134450 Zhang F, Shi Y, Ali J et al (2021) Breeding by selective introgression: Theory, practices, and lessons learned from rice. Crop J 9:646–657 Zhang H, Zhang J, Quan R et al (2013a) EAR motif mutation of rice OsERF3 alters the regulation of ethylene biosynthesis and drought tolerance. Planta 237:1443–1451. https://doi.org/10.1007/s00425-013-1852-x Zhang H, Zhang J, Quan R et al (2013b) EAR motif mutation of rice OsERF3 alters the regulation of ethylene biosynthesis and drought tolerance. Planta 237:1443–1451. https://doi.org/10.1007/s00425-013-1852-x Zou X, Sun H (2023) DOF transcription factors: Specific regulators of plant biological processes. Front Plant Sci 14:1–13. https://doi.org/10.3389/fpls.2023.1044918 Tables Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additionalfile2.xlsx SupplementaryFigure1.tif Supplementary Figure 1.Box plots of early seedling vigor- (ESV-) related traits of the early backcross selective-introgression lines (EB-SILs) and parental lines. Non-weedy: weed-free condition, Weedy: weedy condition with 400 plants/m2 weed density that could potentially cause 100% yield loss. SupplementaryFigure2.tif Supplementary Figure 2. Loading plot showing the vector coefficients for the traits related to rice competitiveness against weeds variables for the first principal component vs. the coefficient for the second principal component. Loading plot showing the vector coefficients of Plot A shows the loading plot for the first two principal component (PC) scores, PC1 versus PC2, depicting the measurements for early seed germination (ESG) traits across all genotypes. Plot B displays the loading plot for PCA of early seedling vigor (ESV) traits, again showing PC1 versus PC2 for all genotypes. SupplementalTables.docx Table4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4505794","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314206434,"identity":"b5a786b5-9e94-4c46-8b50-3b0f2af4a73e","order_by":0,"name":"Kim Diane Nocito","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kim","middleName":"Diane","lastName":"Nocito","suffix":""},{"id":314206435,"identity":"aaea5f0f-2ba4-421b-8eaa-c43496af5075","order_by":1,"name":"Varunseelan Murugaiyan","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Varunseelan","middleName":"","lastName":"Murugaiyan","suffix":""},{"id":314206436,"identity":"07d08679-17be-4550-a593-6f8a5766c149","order_by":2,"name":"Jauhar Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYBACxgYGxgMJFRIMDMwkaGE4kHCGFC0gcICxjRTlzA3MDw48nGeRb97OwPi44hdRDmMzOJC4TcJyzmEGZsOzfURpYQBrMZBgZmCTbOwhSgv7hwOJc0jTwgO0pQGqpeEHMVqaeQoOJBwDaWFsNmxsIEKLYXv7xoc/auoMJPgPH3zY8IcYLc1IbmQgKoLkUbnE2DIKRsEoGAUjDgAALX0xNnM3AtwAAAAASUVORK5CYII=","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Jauhar","middleName":"","lastName":"Ali","suffix":""},{"id":314206437,"identity":"e56f1495-3993-43ac-980b-03b286c4cfc5","order_by":3,"name":"Ambika Pandey","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ambika","middleName":"","lastName":"Pandey","suffix":""},{"id":314206438,"identity":"37ad2db1-3a6b-46e7-bf31-809fde25d777","order_by":4,"name":"Carlos Casal","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Casal","suffix":""},{"id":314206439,"identity":"40ff9ba8-3b3e-4c55-886b-069bbfe30e57","order_by":5,"name":"Erik Jon de Asis","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"Jon","lastName":"de Asis","suffix":""},{"id":314206442,"identity":"9b60239e-e928-420a-866f-a4574452908c","order_by":6,"name":"Niña Gracel B. Dimaano","email":"","orcid":"","institution":"University of the Philippines Los Baños","correspondingAuthor":false,"prefix":"","firstName":"Niña","middleName":"Gracel B.","lastName":"Dimaano","suffix":""}],"badges":[],"createdAt":"2024-05-31 02:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4505794/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4505794/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58389920,"identity":"eea2b3ad-8c5e-4c42-a529-bfb0ccb14f0d","added_by":"auto","created_at":"2024-06-14 20:01:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237045,"visible":true,"origin":"","legend":"\u003cp\u003eMean performance of parental lines and Early Backcross Selective-Introgression Lines (EB-SILs) was assessed under germination assay for early seed germination (ESG) traits. The vertical bars in the graph represent the mean phenotypic performance of these lines. Traits with no letter above the bars indicate no significant difference among the lines, while letters above the bars indicate significant differences among the lines. Different letters denote significantly different means.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/339eb542a2acaf2401230091.png"},{"id":58389922,"identity":"7971f2a8-69db-4734-9a8a-142f4a452d99","added_by":"auto","created_at":"2024-06-14 20:01:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":561316,"visible":true,"origin":"","legend":"\u003cp\u003eMean performance of parental lines and Early Backcross Selective-Introgression Lines (EB-SILs) was assessed under both non-weedy and weedy conditions for early seedling vigor (ESV). The vertical bars in the graph represent the mean phenotypic performance of these lines. Traits with no letter above the bars indicate no significant difference among the lines, while letters above the bars indicate significant differences among the lines. Different letters denote significantly different means.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/11d296ac6f788b413a093a4b.png"},{"id":58389927,"identity":"f02d0f4e-54b4-4b2e-beb5-edaf22b2d082","added_by":"auto","created_at":"2024-06-14 20:01:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":465880,"visible":true,"origin":"","legend":"\u003cp\u003eThe heat map displays the correlation coefficients for traits related to weed competitiveness. Heat map A shows the correlation coefficients for early seed germination (ESG) traits, while heat map B illustrates the correlation coefficients for early seedling vigor (ESG) traits. Significant *0.05≥P-value ≥0.01; **0.01≥P-value≥0.001; ***P-value ≤0.001.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/f757c179c1751697d3828fb9.png"},{"id":58390658,"identity":"f6382c8a-9150-42d9-8da7-7ca5d13cbda8","added_by":"auto","created_at":"2024-06-14 20:17:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":131419,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) was conducted to determine traits related to rice competitiveness against weeds. Plot A shows the score plot for the first two principal component (PC) scores, PC1 versus PC2, depicting the measurements for early seed germination (ESG) traits across all genotypes. Plot B displays the score plot for PCA of early seedling vigor (ESV) traits, again showing PC1 versus PC2 for all genotypes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/23f15e2c9b27bb318530f5fe.png"},{"id":58389925,"identity":"f4f28520-6b25-4441-ab39-5d4ada023a57","added_by":"auto","created_at":"2024-06-14 20:01:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2860515,"visible":true,"origin":"","legend":"\u003cp\u003eFive selected early backcross selective-introgression lines (EB-SILs) along with their recipient and donor parents showing early seedling vigor (ESV) traits—abbreviations: NW, non-weedy condition; W, weedy condition.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/642d1a22ee3ae2bb18c85a8c.png"},{"id":58390266,"identity":"2af9a225-213d-47c9-8634-6aecf257b620","added_by":"auto","created_at":"2024-06-14 20:09:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":142883,"visible":true,"origin":"","legend":"\u003cp\u003eChromosomal distribution of Low Missing Data 50 (LMD50) single nucleotide polymorphisms (SNPs) and identified quantitative trait loci (QTLs) associated with early seed germination (ESG) and early seedling vigor (ESV) traits. QTLs were located on the chromosome based on the physical position of the SNP marker\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/3b4571ef25471240d5d03e7e.png"},{"id":65876766,"identity":"0c892ae8-d932-4720-93c0-bde6ec44ff68","added_by":"auto","created_at":"2024-10-04 00:01:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6256410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/e0c1fc0d-de89-4b80-b0bf-ecdf725d341f.pdf"},{"id":58389923,"identity":"57956258-2c96-4d69-815a-20059ef2f924","added_by":"auto","created_at":"2024-06-14 20:01:27","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":63443,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/68395e03ed946f627efb0b98.xlsx"},{"id":58389921,"identity":"1d63c13e-b6ad-4aca-82bb-81a8bd0191df","added_by":"auto","created_at":"2024-06-14 20:01:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":50886,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/7bfb4ddec3c5dcf65f638c97.xlsx"},{"id":58389931,"identity":"54ee6591-401d-4581-b63c-598fda311535","added_by":"auto","created_at":"2024-06-14 20:01:28","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1168646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003eBox plots of early seedling vigor- (ESV-) related traits of the early backcross selective-introgression lines (EB-SILs) and parental lines. Non-weedy: weed-free condition, Weedy: weedy condition with 400 plants/m2 weed density that could potentially cause 100% yield loss.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/3d9b02cf18a75578976af529.tif"},{"id":58389930,"identity":"63d67086-6838-43e9-89f9-92a969198574","added_by":"auto","created_at":"2024-06-14 20:01:28","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":884565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2.\u003c/strong\u003e Loading plot showing the vector coefficients for the traits related to rice competitiveness against weeds variables for the first principal component vs. the coefficient for the second principal component.\u003cstrong\u003e \u003c/strong\u003eLoading plot showing the vector coefficients of Plot A shows the loading plot for the first two principal component (PC) scores, PC1 versus PC2, depicting the measurements for early seed germination (ESG) traits across all genotypes. Plot B displays the loading plot for PCA of early seedling vigor (ESV) traits, again showing PC1 versus PC2 for all genotypes.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/0c6185a76502c8dbf711f17a.tif"},{"id":58390268,"identity":"b5a0dfb6-5648-4cdf-a1b3-ee17ade6aa6b","added_by":"auto","created_at":"2024-06-14 20:09:27","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":35555,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/2a0be9bfee79445406d9bf84.docx"},{"id":58389929,"identity":"37df7dc6-5a31-4d78-a542-80d73d5a825c","added_by":"auto","created_at":"2024-06-14 20:01:27","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":29592,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4505794/v1/a6fa4d4e9dfb7e8809458f9a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-Wide Dissection of Novel QTLs and Genes Associated with Weed Competitiveness Traits Using a Population of Early-Backcross Selective Introgression Lines of Rice (Oryza sativa L.)","fulltext":[{"header":"Background","content":"\u003cp\u003eRice is the primary food supply and a significant economic driver throughout most of Asia, providing essential employment and financial stability for rural communities (Ali et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rice consumption is projected to rise by 30% by 2050, driven by growing populations, urbanization, and consumer preferences, particularly in sub-Saharan Africa, where rice consumption is increasing by over 6% yearly (Arouna et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Samal et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The availability of resources necessary for rice cultivation, such as land, water, fertilizers, and labor, is diminishing. Additionally, the impact of climate change, marked by higher temperatures and frequent floods and droughts, has led to significant loss to rice production globally (McNally and Henry \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Water is the primary and crucial element for ensuring rice production, with one-third of the world's freshwater resources utilized for irrigating lowland rice farming systems (Padma et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Diminished investments in irrigation infrastructure compromise the sustainability of rice production under current farming systems, heightened water competition, substantial withdrawals of subsurface water, and non-adoptability of suitable technologies (Zhang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e; Panda et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Direct-seeded rice (DSR) farming system is anticipated to emerge as the dominant rice cultivation method, boasting a 15.3% reduction in water usage compared to traditional transplanted-flooded rice fields (Marasini et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peramaiyan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, when combined with short-duration high-yielding inbred and hybrid rice varieties, DSR offers additional benefits such as reduced labor needs, higher yields per unit area, less greenhouse gas emissions, fewer energy inputs, and increased resilience to climate change risks (Connor et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite being viewed as a potential alternative to unsustainable water-intensive transplanted rice systems, the heavy infestation of various weed species limits the large-scale adoption of DSR technology (He et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeed infestation is one of the most significant constraints in DSR that can lead to severe yield losses. Weed diversity in DSR exhibits a greater diversity of plant species and is more abundant than transplanted rice. As a result, the DSR farming system experiences much higher levels of weed pressure (Ahmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A global assessment conducted in many rice-producing nations revealed that weed infestation posed a significant biological limitation, impeding rice production (Chauhan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The extent of yield loss in DSR due to weed infestation can vary widely based on several factors, including the weed species present, weed density, timing of weed competition, crop management practices, and environmental conditions. The percentage of yield loss can range from 20\u0026ndash;90%, depending on the severity of weed infestation and how well weed management practices are implemented (Xu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). If weeds are not effectively controlled, they can out-compete rice plants for resources such as sunlight, water, and nutrients, leading to stunted growth, yield losses, and poor grain quality (Nazir et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Farmers and agricultural extension services often need to assess local conditions and adopt a combination of cultural, chemical, and mechanical weed control methods to optimize yield and ensure the success of DSR cultivation. Effective weed control in DSR farming systems requires regular monitoring, timely interventions, breeding and adoption of weed-competitive rice varieties (Mahajan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn DSR farming systems, weed diversity can encompass various species such as barnyardgrass (\u003cem\u003eEchinochloa crus-galli\u003c/em\u003e), junglerice (\u003cem\u003eEchinochloa colona\u003c/em\u003e), broadleaf signalgrass (\u003cem\u003eBrachiaria platyphylla\u003c/em\u003e), late watergrass (\u003cem\u003eEchinochloa phyllopogon\u003c/em\u003e), ammannia (\u003cem\u003eAmmannia spp\u003c/em\u003e.), eclipta (\u003cem\u003eEclipta prostrata\u003c/em\u003e), fringerush (\u003cem\u003eFimbristylis miliacea\u003c/em\u003e), and other sedges (\u003cem\u003eCyperus spp.\u003c/em\u003e). Managing this diversity is crucial for successful DSR establishment, particularly during the rice plant\u0026rsquo;s most vulnerable early growth stages. Rice varieties that are competitive against weeds are vital in reducing the amount of crop output lost in the DSR farming system (Shekhawat et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shrestha et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These varieties possess innate characteristics that enable plants to surpass weeds in the competition for vital resources, including sunlight, water and nutrients. Weed-competitive rice varieties possess robust initial growth, rapid development of a dense canopy, and effective use of resources, which aids in inhibiting weed growth and establishing a competitive edge. This minimizes the need for excessive herbicides or handweeding, promoting sustainable and cost-efficient weed control in DSR systems. Consequently, farmers may successfully establish their crops without yield penalty, which raises the total productivity of DSR farming (Bhandari et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eEchinochloa colona\u003c/em\u003e (L.), commonly known as junglerice, is a C4 annual summer grass that poses a significant challenge as a weed species, impacting rice production systems worldwide (Narayana Rao \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Its invasive nature is attributed to its vigorous growth and high seed production, with each plant capable of producing up to 42,000 seeds (Wu et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Its pervasive presence has led to substantial grain yield losses, ranging from 27\u0026ndash;62%, emphasizing its profound impact on rice production. By out-competing rice crops for essential resources such as water, nutrients, space, and sunlight, \u003cem\u003eE. colona\u003c/em\u003e imposes significant constraints on crop growth and productivity (Narayana Rao \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, \u003cem\u003eE. colona\u003c/em\u003e poses a significant threat to rice yields, leading to substantial economic losses and jeopardizing food security in regions where rice is a staple crop. \u003cem\u003eE. colona\u003c/em\u003e, widely adopted in rainfed rice agroecosystems, owes its prevalence to its remarkable adaptability across diverse environments, facilitated by its plasticity in morphology, phenology, rapid growth, prolific seed production, and seed dormancy (Awan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By elucidating the molecular mechanisms underlying weed competitiveness, our study provides valuable insights for breeding programs to develop rice varieties with improved weed competitiveness.\u003c/p\u003e \u003cp\u003eWeed-competitive rice varieties are distinguished by a suite of traits that collectively confer a competitive advantage against weed pressure (Saito and Futakuchi \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dimaano et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These traits encompass early vigor, enabling rapid germination and robust seedling growth, and prolific tillering, resulting in a dense canopy that shades out competing weeds. Tall and erect canopy architecture and broad leaves enhance the shading effect, limiting weed access to sunlight (Bharamappanavara et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Favorable root systems, characterized by depth and spread, enable efficient nutrient and water uptake, contributing to the overall vigor of the rice plants. Some varieties also exhibit allelopathic compounds, suppressing weed germination and growth (Schumaker et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Genetic factors, including specific genes and quantitative trait loci (QTLs), play a crucial role in defining these traits, facilitating the development of rice varieties with enhanced weed competitiveness (Schumaker et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, traits such as tolerance to weed competition, early maturity, and resistance to diseases further fortify the resilience of these varieties. However, the effectiveness of weed-competitive rice varieties is context-dependent and influenced by regional weed species and environmental conditions (Swanton et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A holistic approach that integrates genetic traits with sound agronomic practices and targeted weed management strategies is essential for optimizing the success of weed-competitive rice cultivation, ultimately mitigating yield loss in rice production (Chaudhary et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQTLs associated with weed-competitive traits in rice are pivotal in enhancing the crop's ability to suppress weed growth. Our group\u0026rsquo;s previous work has successfully identified QTLs associated with weed competitiveness in 167 selective introgression lines from a cross between Weed Tolerant Rice 1 (WTR1) and Y134. Among the QTLs identified, \u003cem\u003eq1st GC12.1\u003c/em\u003e found on chromosome 12 is linked to SAP domain-containing protein, which is functionally related to stress-associated proteins and are responsible for abiotic stress response by regulating GA and ABA signaling (Dimaano et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another notable QTL identified from our prior research, \u003cem\u003eqTFGS12\u003c/em\u003e, is associated to a genomic region responsible for flavin monooxygenases, which has a crucial function in the tryptophan (Trp)-dependent indole-acetic acid synthesis for auxin biosynthetic pathways, enhancing the rapid response to early seedling growth and development of root tips (Dimaano et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Other specific key genes reported for weed-competitive rice varieties include \u003cem\u003eOsPAL\u003c/em\u003e4 (Phenylalanine Ammonia Lyase 4), which plays a crucial function in allelopathy by synthesizing compounds that act as natural herbicides, thus creating an unfavorable environment for weed growth (Tonnessen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Another gene, \u003cem\u003eOsERF\u003c/em\u003e3 (Ethylene Response Factor 3) modulates ethylene responses, influencing root architecture and elongation, enhancing rice's competitive ability against weeds for below-ground resources (Zhang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e). ATP-binding cassette transporters like \u003cem\u003eOsABC\u003c/em\u003e1-13 contribute to herbicide resistance, providing weed-competitive rice varieties with resilience against standard weed control measures (Goldberg-Cavalleri et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The \u003cem\u003eDEP\u003c/em\u003e1 (Dense and Erect Panicle 1) gene, associated with increased grain yield and altered plant architecture, influences weed competitiveness by conferring a more compact growth habit that limits space for weed establishment (Lu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our current study unveils additional QTLs and key candidate genes, providing valuable insights for targeted breeding programs to create tailored weed-competitive rice varieties adaptable to DSR farming systems.\u003c/p\u003e \u003cp\u003eThe Green Super Rice (GSR) project, led by the International Rice Research Institute (IRRI) and the Chinese Academy of Agricultural Sciences (CAAS), successfully developed and released high-yielding and multiple stress-tolerant rice varieties to overcome climate change, resource constraints, and disease pressures (Yu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ali et al. 2021). One of the GSR project's primary focuses was enhancing yield and resilience, including weed competitiveness, which is crucial to sustainable rice cultivation. GSR varieties are tailored to exhibit multiple stress tolerance and competitiveness against weeds through traits like early vigor, prolific tillering, efficient canopy development, and high yielding. Integrating weed-resistant characteristics into elite GSR breeding materials and using DSR technology will ensure rice production's long-term sustainability and productivity (Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our current study investigated the genetic and molecular mechanisms underpinning weed competitiveness in rice to expedite the development of weed-competitive varieties for sustainable DSR farming systems. The study's objectives were to screen for weed-competitiveness in early GSR backcross selective introgression populations of rice and to identify QTLs and candidate genes associated with weed-competitiveness using high-density genome-wide SNP markers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant Materials\u003c/h2\u003e \u003cp\u003eA population of 181 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e6\u003c/sub\u003e Early-Backcross Selective Introgression Lines (EB-SILs), developed through a cross between the common recipient parent WTR1 (\u003cem\u003eindica\u003c/em\u003e) and three donor parents: Haoannong (\u003cem\u003ejaponica\u003c/em\u003e), ChengHui448 (\u003cem\u003eindica\u003c/em\u003e), and Y134 (\u003cem\u003eindica\u003c/em\u003e), was produced using single seed descent at the International Rice Research Institute (IRRI), Los Ba\u0026ntilde;os, Philippines. Additional comprehensive information on this breeding process and the development of the population was elucidated by Ali et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Seeds of junglerice (\u003cem\u003eEchinochloa colona\u003c/em\u003e) were harvested from rice-fallow fields at IRRI during the dry season of 2023. The seeds were then kept in a refrigerator at 4\u0026deg;C before being used in the weed competitiveness screening experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic screening for weed competitiveness\u003c/h2\u003e \u003cp\u003eRice seeds were dried in an oven at 60\u0026deg;C for seven days to disrupt residual seed dormancy. The dormancy of junglerice seeds was removed by subjecting the seeds to a 24-hour immersion in sterilized water, followed by an additional 24-hour incubation period at 40\u0026deg;C in an oven. The phenotyping of early seed germination (ESG) traits involved the placement of 25 seeds per line in a 9-cm diameter Petri dish with wet filter paper, maintaining optimal moisture conditions through regular watering in a germination chamber set at 30\u0026deg;C with a 12-hour photoperiod for 14 days. The set-up was arranged in a complete randomized design (CRD) with two replications. Germination parameters were meticulously measured throughout the experiment, including second-day and seventh-day germination counts, germination rate, shoot length, root length, total dry weight, average dry weight, and seed vigor index. Concurrently, the phenotyping of early seedling vigor (ESV) traits occurred in a greenhouse with an average night and day temperature of 24℃ and 32℃, respectively. Rice lines were exposed to weedy and non-weedy conditions in metal trays (150 cm\u0026times;90 cm\u0026times;10 cm) filled with sterilized Maahas clay-loam soil in a randomized complete block design (RCBD) with two replications. Weed prevention, disease control, and pest management were ensured through soil sterilization. Basal fertilizer was applied following local recommendation (60-30-30 kg NPK ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The weedy condition involved direct seeding with 40 plants/0.1 m\u003csup\u003e2\u003c/sup\u003e density alongside junglerice seeds sown randomly, following a 1:1 ratio for rice and junglerice seeds to simulate high weed pressure. In contrast, the non-weedy condition was maintained weed-free throughout the experiment. Both set-ups were watered daily and kept unsaturated to simulate DSR conditions. Data collection involved five randomly tagged plants per line, evaluating parameters such as plant height, leaf count, and tiller number at 14, 21, and 28 DAS, and seedling vigor index, shoot dry weight, root dry weight, total dry weight, and root length at 28 DAS \u003cb\u003e(Supplemental Table\u0026nbsp;1)\u003c/b\u003e. Non-segregating parents (WTR1, Haoannong, ChengHui448, and Y134) were grown as checks in ESG and ESV experiments. Any positional effect within the germination chamber and greenhouse was minimized by altering the position of the Petri dish and metal trays every second day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSNP extraction and physical map construction\u003c/h2\u003e \u003cp\u003eFrom the previous publication (Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), all 181 lines and their source tunable genotyping-by-sequencing (tGBS\u0026reg;) sequences utilizing 10 Ion Proton runs were downloaded. Concurrently, the rice reference genome, precisely the Osativa_204_v7.0.fa sequence, was obtained from the Phytozome repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome.jgi.doe.gov/pz/portal.html\u003c/span\u003e\u003cspan address=\"https://phytozome.jgi.doe.gov/pz/portal.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e After acquiring raw sequencing reads, a preprocessing step was undertaken utilizing the Lucy software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tigr.org/softlab\u003c/span\u003e\u003cspan address=\"http://www.tigr.org/softlab\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e to trim sequences, effectively discarding bases exhibiting a PHRED quality score inferior to 15. For SNP extraction, the GSNAP algorithm developed by (Ott et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for dealing with the tGBS\u0026reg; dataset was employed to align the trimmed reads against the reference genome, adopting stringent criteria that limited mismatches to two per 36 base pairs and unaligned bases to less than five per 75 base pairs. The criteria for distinguishing homozygous from heterozygous SNPs were rigorously defined; homozygous SNPs necessitated a PHRED quality score of 20 or above and support from a minimum of three reads. Conversely, heterozygous SNPs required support from at least two reads for each allele, with each allele constituting more than 20% of supporting reads, cumulatively surpassing 90%. SNP selection was subjected to a series of filters, including a missing data rate not exceeding 80%, a stipulation for biallelic SNPs, a minimum of two genotypes, a minor allele frequency threshold of 0.1, and a heterozygosity range between 0 and 10%. The SNP dataset, designated as Low Missing Data 50 (LMD50), underwent further refinement to ensure a 50% or less missing data rate. Subsequent analyses facilitated the determination of major and minor alleles for each SNP, predicated on sub-population-specific ratios of reference to alternative alleles, with ratios greater than one signifying the major allele and those less than one denoting the minor allele. Leveraging the precise physical loci of the SNPs, a physical map was constructed to underpin QTLs associated with weed competitiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and QTL mapping\u003c/h2\u003e \u003cp\u003eA one-way analysis of variance (ANOVA) at a one percent significance level was used to analyze the phenotypic data for ESG. In contrast, two-way ANOVA was used for ESV to observe the effects of genotypes and the growing conditions. Pearson\u0026rsquo;s correlation analysis was conducted to correlate the ESG-related traits among each other, as well as to correlate the ESV-related traits. ANOVA and Pearson\u0026rsquo;s correlation were performed in R version 4.3 studio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rstudio.com/\u003c/span\u003e\u003cspan address=\"http://www.rstudio.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and heat maps were generated using the hmsic package. Principal component analysis of correlations was also done to create biplots using JMP\u0026reg; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jmp.com/en_ph/home.html\u003c/span\u003e\u003cspan address=\"https://www.jmp.com/en_ph/home.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The QTL analysis for ESG used the mean values of phenotypic data gathered, while relative phenotypic values were used for the analysis of ESV. The mapping of QTLs was performed by single-marker regression analysis using the single-marker analysis (SMA) function in IciMapping software v4.1 (Meng et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The association of a phenotypic trait and a QTL was declared significant once it had a threshold level (\u0026minus;\u0026thinsp;log p(F)\u0026thinsp;\u0026ge;\u0026thinsp;4.15) based on a permutation test (1000 permutations, P\u0026thinsp;=\u0026thinsp;0.01). To further delimit the confidence interval of each QTL, the 1-LOD drop method from the estimated QTL position was followed (Pang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Murugaiyan et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCandidate Gene Identification\u003c/h2\u003e \u003cp\u003eThe gene models lying within the QTL intervals for the discovered QTLs controlling ESG and ESV-related phenotypes were extracted from the MSU7 Rice Genome Annotation Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rapdb.dna.affrc.go.jp/\u003c/span\u003e\u003cspan address=\"http://rapdb.dna.affrc.go.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Polymorphisms within the candidate genes of the parental lines were obtained from the Rice SNP-Seek Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://snp-seek.irri.org/\u003c/span\u003e\u003cspan address=\"https://snp-seek.irri.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Further genotyping data on the parents was acquired from the previous Tunable Genotyping-By-Sequencing (tGBS\u0026reg;) study, which included 10 ion proton runs and yielded 794,297 polymorphic SNPs (Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Subsequently, we identified gene models within the candidate loci that exhibited non-synonymous polymorphism between the parents and were evaluated as the most likely candidates for weed competitiveness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eESG Performance of Parental Lines and EB-SILs\u003c/h2\u003e \u003cp\u003eTraits involved in ESG are associated with crop competitiveness against weeds \u003cb\u003e(Additional file 1: Sheet 1).\u003c/b\u003e The results showed high phenotypic variation among the lines. Among the 181 EB-SILs and four parents, all ESG-related traits had significant differences \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Seven out of eight traits had a p-value of less than 0.001. The mean ESG performance of the parental lines and top-performing EB-SILs under germination assay are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed among the parental lines and EB-SILs in second-day germination count, seventh-day germination count, total dry weight, seed vigor index, and shoot length. The second-day and seventh-day germination count ranged from 18 to 25 and 23 to 25 in all lines, respectively, while the total dry weight ranged from 0.38 to 0.54 g. The lowest and highest values for shoot length were 6.28 and 8.18 cm, respectively, while the lowest and highest values for root length were 3.83 and 5.28 cm, respectively. On the other hand, significant differences were observed in germination rate and root length. EB-SILs generally exhibited the highest germination rate, along with the parental lines Haoannong, ChengHui448, Y134, and WTR1. Additionally, the EB-SILs recorded the highest increase in root length among all the lines, ranging from 8.33 to 9.19 cm.\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 early seed germination (ESG) traits.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIN.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAX.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMEAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSUM OF SQUARES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMEAN SQUARE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF-VALUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-VALUE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond-day Germination Count\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\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeventh-day Germination Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e356.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00147**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermination Rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e483.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e876.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Dry Weight of Germinated Seeds (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0. .0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Dry Weight of Germinated Seeds (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.597e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Vigor Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSignificant codes: *0.05\u0026thinsp;\u0026ge;\u0026thinsp;P-value\u0026thinsp;\u0026ge;\u0026thinsp;0.01; **0.01\u0026thinsp;\u0026ge;\u0026thinsp;P-value\u0026thinsp;\u0026ge;\u0026thinsp;0.001; ***P-value\u0026thinsp;\u0026le;\u0026thinsp;0.001\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\u003eESV Performance of Parental Lines and EB-SILs\u003c/h2\u003e \u003cp\u003eAnother associated trait with the weed competitive ability of crops is ESV (\u003cb\u003eAdditional file 1: Sheet 2)\u003c/b\u003e. High phenotypic variations were observed in all 12 ESV-related traits \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. There were significant differences among the parental lines and EB-SILs with p-values of less than 0.001 in all traits except root dry weight at 28 DAS with a p-value of 0.005. On the other hand, all traits had significant differences between the weedy and non-weedy treatments. All ESV-related traits had p-values of less than 0.001. A similar high phenotypic variance was also observed in the interaction of lines and conditions. Only root dry weight at 28 DAS resulted in a p-value of 0.008, while the remaining traits have less than 0.001. Weed interference caused reductions in the mean performance of all lines for all ESV-related traits, as shown in the box-plot distribution \u003cb\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/b\u003e. For further identification of the effect of weed pressure on the parental lines and EB-SILs, mean values of phenotypes were plotted in a graph \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. As early as 14 DAS, all parental lines exhibited tolerance to weeds in terms of plant height. There were no significant differences in mean values between the treatments, except for WTR1, which recorded a higher plant height in the weedy condition than in the non-weedy condition. There was also no significant difference between the mean performance of parental lines under non-weedy and weedy conditions at 21 DAS, except for Y134, which had a higher plant height at 21 DAS in the weedy condition. EB-SILs and ChengHui448 had the highest recorded plant height at 21 DAS under weedy conditions. Significant reductions in tiller numbers were recorded in all lines upon subjecting to weedy conditions. However, it was observed that the top-performing EB-SILs had significantly different mean performances in all parameters. The EB-SILs performed the best among all lines across all measured characteristics.\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\u003eDescriptive statistics of early seedling vigor (ESV) traits.\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=\"char\" char=\".\" 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\u003eTRAITS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNON-WEEDY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eWEEDY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eANOVA RESULT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eG*T\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant Height at 14 DAS (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant Height at 21 DAS (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant Height at 28 DAS (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf Count at 14 DAS\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\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf Count at 21 DAS\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\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.87\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\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf Count at 28 DAS\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\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Tiller at 28 das\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeedling Vigor Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e149.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Dry Weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot Dry Weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Dry Weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eSignificant codes: *0.05\u0026thinsp;\u0026ge;\u0026thinsp;P-value\u0026thinsp;\u0026ge;\u0026thinsp;0.01; **0.01\u0026thinsp;\u0026ge;\u0026thinsp;P-value\u0026thinsp;\u0026ge;\u0026thinsp;0.001; ***P-value\u0026thinsp;\u0026le;\u0026thinsp;0.001. Abbreviations: G, genotype; T, treatment; G*T, genotype \u0026times; treatment interaction.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis among measured traits\u003c/h2\u003e \u003cp\u003eA Pearson pairwise comparison was conducted to understand the relationship between ESG related traits \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A\u003cb\u003e).\u003c/b\u003e Second-day germination count showed a strong positive correlation with germination rate (r\u0026thinsp;=\u0026thinsp;0.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and seed vigor index (r\u0026thinsp;=\u0026thinsp;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, germination rate was positively correlated with seed vigor index (r\u0026thinsp;=\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Total and average dry weights exhibited a significant positive association (r\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, root length displayed negative and significant associations with second-day germination count (r = -0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), seventh-day germination count (r = -0.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), germination rate (r = -0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and seed vigor index (r = -0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFurthermore, correlation analysis among all ESV-related traits was conducted. Relative phenotypic values of non-weedy and weedy treatments were used to assess the response of parental lines and EB-SILs to weed pressure. All traits showed significant positive correlations with each other \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B\u003cb\u003e)\u003c/b\u003e. The strongest correlation was observed between the relative seedling vigor index at 28 DAS and relative total dry weight at 28 DAS (r\u0026thinsp;=\u0026thinsp;1.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, a high positive correlation was found between relative shoot dry weight at 28 DAS and relative total dry weight at 28 DAS (r\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as between relative root dry weight at 28 DAS and relative total dry weight at 28 DAS (r\u0026thinsp;=\u0026thinsp;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the weakest positive correlation was noted between relative plant height at 14 DAS, and relative tiller number at 28 DAS (r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and another low positive correlation was found between relative plant height at 21 DAS and relative leaf count at 28 DAS (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component analysis among measured traits\u003c/h2\u003e \u003cp\u003eThe principal component analysis (PCA) was conducted to identify the relationship between ESG and ESV-related traits. The PCA accounted for 63.3% of the overall variance in eight early germination parameters, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A. PC1 explained 46.9% of the variance, whereas PC2 explained 20.5%. The PCA accounted for 63.3% of the overall variance in 12 early vigor variables for ESV traits \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B\u003cb\u003e)\u003c/b\u003e. PC1 explained 50.1% of the variance, whereas PC2 explained 13.2%. The 15 rice lines that exhibited strong performance were identified by analyzing the main component and loading plot matrix of ESG traits \u003cb\u003e(Supplementary Fig.\u0026nbsp;2-A; Supplementary Table\u0026nbsp;2)\u003c/b\u003e. The lines with excellent ESG features were chosen based on the EB-SILs identified within the region of seed vigor index and germination rate. These lines showed a favorable correlation between significant ESG traits, such as second-day germination count, seventh-day germination count, germination rate, and seed vigor index.\u003c/p\u003e \u003cp\u003eAs measured by ESV-related features, fifteen lines with superior weed competitiveness were identified using principal component and loading plot matrix analysis. These lines have the potential to produce significant yields. \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e summarizes the 15 selected best performing EB-SILs depending upon their location in the biplot matrix \u003cb\u003e(Supplementary Fig.\u0026nbsp;2-B)\u003c/b\u003e. EB-SILs located the nearest with the relative tiller number at 28 DAS were considered the 15 lines with favorable ESV-related traits. The five top-performing EB-SILs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, together with their donor and recipient parents, show ESV traits in both weedy and non-weedy environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSNP markers generated by (tGBS\u0026reg;) sequences for QTL mapping\u003c/h2\u003e \u003cp\u003eA total of 943.4M raw tGBS\u0026reg; sequencing reads were acquired by extracting the raw tGBS\u0026reg; sequencing reads. These reads were produced using 10 Ion Proton runs from the initial population of 564 lines (Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Following the removal of low-quality bases, a total of 881.6M reads were obtained, with 87.8% of the base pairs being retained. By analyzing the sequencing results from the 564 lines that matched primarily to the reference genome (\u003cem\u003eOryza sativa ssp. japonica cv. Nipponbare\u003c/em\u003e), we discovered a total of 794,297 polymorphic sites. These sites were found after analyzing 2,679,180 bases that had at least 5 reads in at least 50% of the 564 samples. The 181 samples used in the current study were selected from a subset of 564 samples taken from three populations including the common recipient parent WTR1 and three donor parents Haoannong, ChengHui448, and Y134. Low-Missing Dataset (LMD50) was filtered, resulting in the identification of the total number of LMD50 SNPs found in all three sub-populations by analyzing the distinct alignments of every read from the 181 samples in relation to the publicly available reference genome. Sub-population 1, which included 112 samples (110 Introgression Lines and the two parental lines), was denoted as WTR1 X Haoannong, and 4,669 LMD50 SNPs were detected. Sub-population 2, which was formed by crossing WTR1 and ChengHui448, had a total of 41 samples. Among these samples, 39 introgression lines (ILs) and the two parental lines were detected, and 5,968 LMD50 SNPs were detected. Sub-population 3, also known as WTR1 X Y134, included 28 samples. This included 26 ILs and the two parental lines, and we were able to identify a total of 4,435 LMD50 SNPs. By integrating all common LMD50 SNPs across the groups, 3,791 LMD SNPs were obtained. Subsequently, these SNPs were used to construct the physical map required for QTL analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of QTLs for Weed Competitive Traits\u003c/h2\u003e \u003cp\u003eNineteen SNPs showed a significant marker-trait association with ESG and ESV-related traits. QTLs were defined by assuming that closely linked significant markers are in the QTL region \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Four of these eight major QTLs (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;10) associated with ESG traits were located on chromosome 2, while two QTLs were on chromosome 12, and one each on chromosomes 3 and 6. The first QTL identified on chromosome 2 was for root length, \u003cem\u003eqRL2\u003c/em\u003e, with a phenotypic variance of 15.25%. On chromosome 2, \u003cem\u003eqTDWG2\u003c/em\u003e, responsible for the total dry weight during the germination stage of rice (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.98%), was also detected. Two QTLs for seedling vigor index were also found on chromosome 2, where \u003cem\u003eqSVI2.1\u003c/em\u003e and \u003cem\u003eqSVI2.1\u003c/em\u003e can be explained by their phenotypic variances of 15.97% and 16.26%, respectively. In addition, another major QTL was detected on chromosome 3, responsible for the seedling vigor index (\u003cem\u003eqSVI3\u003c/em\u003e), with a phenotypic variance of 11.08%. Another major QTL identified was located on chromosome 6 (\u003cem\u003eqSVI6\u003c/em\u003e) for early seedling vigor, explained by a phenotypic variance of 12.20%. On the other hand, two major QTLs were on chromosome 12, \u003cem\u003eqGR12\u003c/em\u003e associated with germination rate (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.57%) and \u003cem\u003eqSVI12\u003c/em\u003e associated with seedling vigor index (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.29%). For ESV-related traits, four QTLs were found on chromosome 10 and one QTL each on chromosomes 1, 3, 4, 5, 8, and 9 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). On chromosome 1, \u003cem\u003eqRPH1\u003c/em\u003e was detected to be related to two ESV-related traits, relative plant height at 14 and 28 DAS, which have a phenotypic variance of 11.29% and 9.94%, respectively. A major QTL was found on chromosome 3 with a phenotypic variance of 14.80% that was associated with the relative tiller number of rice (\u003cem\u003eqRTN3\u003c/em\u003e), while \u003cem\u003eqRLC4\u003c/em\u003e identified on chromosome 4 with a phenotypic variance of 11.34% was a QTL for relative leaf count at 28 DAS. Two QTLs linked with relative plant height at 21 DAS were also identified, namely \u003cem\u003eqRPH5\u003c/em\u003e on chromosome 5 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.10%) and \u003cem\u003eqRPH\u003c/em\u003e9 on chromosome 9 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.87%). For relative root length at 28 DAS, the major QTL (\u003cem\u003eqRRL8)\u003c/em\u003e detected was on chromosome 8 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.60%). Moreover, another QTL (\u003cem\u003eqRRL10)\u003c/em\u003e linked with root length at 28 DAS was on chromosome 10 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.34%), along with \u003cem\u003eqRTN10\u003c/em\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.24%), \u003cem\u003eqRLC10.1\u003c/em\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.15%), and \u003cem\u003eqRLC10.2\u003c/em\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.04%), QTLs responsible for relative tiller number at 28 DAS and relative leaf count at 28 DAS, respectively.\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\u003eQuantitative trait loci (QTLs) associated with weed competitiveness at early germination and early vegetative stages in Early-Backcross Selective Introgression Lines (EB-SILs) breeding population by marker trait association analysis (SMA).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQTL\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePosition\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAssociated Marker\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csup\u003e_\u003c/sup\u003eLOG P(F)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(%)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdditive Effect\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEarly Seed Germination\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqGR12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermination Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5720211\u0026ndash;6950257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS12_6548722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRL2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34379631\u0026ndash;34576493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS2_34576493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqTDWG2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Dry Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8474520\u0026ndash;8752801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS2_8699045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqSVI2.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSeed Vigor Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8474495\u0026ndash;8752801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS2_8474495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqSVI2.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30478421\u0026ndash;30791659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS2_30791659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqSVI3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25401607\u0026ndash;25452773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3_25401672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqSVI6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1542513\u0026ndash;1877725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS6_1698496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqSVI12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6950207\u0026ndash;7011126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS12_6950257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly Seedling Vigor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRPH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative Plant Height at 14 DAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42171596\u0026ndash;42617013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS1_42549502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRPH\u003c/em\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRelative Plant Height at 21 DAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28497478\u0026ndash;28567356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS5_28525048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRPH9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14626500\u0026ndash;14826499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS9_14725794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRPH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative Plant Height at 28 DAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42171596\u0026ndash;42617013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS1_42171596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRLC10.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRelative Leaf Count at 28 DAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e416500\u0026ndash;516499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS10_466091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRLC10.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1393500\u0026ndash;1493499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS10_1441265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRLC\u003c/em\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1753300\u0026ndash;1753386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS4_1753338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRTN\u003c/em\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRelative Tiller Number at 28 DAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35948030\u0026ndash;35965394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS3_35948030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRTN10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18051000\u0026ndash;18164000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS10_18105284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRRL8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRelative Root Length at 28 DAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7492455\u0026ndash;7777214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS8_7541070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqRRL10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18060329\u0026ndash;18452140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS10_18228061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003eClosely linked markers are assumed as the same QTL, \u003csup\u003eb\u003c/sup\u003ePhysical position of markers on chromosomes, \u003csup\u003ec\u003c/sup\u003eMarker associated with QTL. \u003csup\u003ed\u003c/sup\u003eF-statistical analysis indicates association between markers and trait,\u003csup\u003ee\u003c/sup\u003eProportion of phenotypic variance explained. \u003csup\u003ef\u003c/sup\u003ePositive/negative values indicate that additive effect that can increase trait values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCandidate Genes Associated with ESG and ESV Traits\u003c/h2\u003e \u003cp\u003eAmong the 18 QTLs identified in this study, there were 480 gene models present within the QTL interval \u003cb\u003e(Additional file 2: Sheet 1\u0026ndash;17)\u003c/b\u003e. Out of these, 27 gene models were found to be directly associated with biotic and abiotic stress-tolerant genes, thus considered the most likely candidate genes \u003cb\u003e(Supplemental Table\u0026nbsp;4)\u003c/b\u003e. These genes were selected for further SNP analysis to find the most promising candidate genes. Fourteen of the most promising candidate gene models were associated with ESG traits, and another 13 were related to ESV traits. In the most likely candidate gene models, 5,286 SNPs were identified between the parents in the Rice SNP-Seek Database. Among those identified SNPs, 24% showed polymorphism between the parents in 27 genes, and most of these polymorphisms (87.5%) were synonymous mutations, with non-synonymous mutations (12.4%) between the parents in 27 genes. Out of the 27 gene models, 19 genes that contain SNPs resulting in non-synonymous mutations were identified as the most promising candidate genes associated with weed competitiveness in rice \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the genetic basis of weed competitiveness in rice through QTL analysis using a mapping population of 181 EB-SILs derived from the cross between the common recipient parent WTR1 and three donor parents Haoannong, ChengHui448, and Y134. Also, by employing high-density tGBS\u0026reg; SNPs, our study identified nineteen QTL regions associated with weed competitiveness and the specific candidate genes within these regions. Weed competitiveness is an untargeted trait that was not considered during the population development of EB-SILs by assuming no correlation exists between weed competitiveness and the selected traits used in population development, yield under different conditions like irrigated, rainfed, drought, salinity, submergence, and low input conditions. Previously, similar approaches were employed in elite breeding lines to identify QTLs and simultaneously enhance the elite lines for various biotic and abiotic stresses (Pang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The EB-SILs mapping population could be employed as a randomly segregating population to map weed-competitiveness. Gaining a comprehensive understanding of the competitive advantage of rice over weeds is crucial for implementing sustainable farming practices and maintaining food security.\u003c/p\u003e \u003cp\u003eDespite weedy pressure, the pronounced phenotypic variation observed among EB-SILs suggests a solid genetic basis underlying these traits. This variability indicates a high likelihood of detecting QTLs governing these traits, underscoring the potential for genetic improvement in rice competitiveness against weeds. Furthermore, the observed phenotypic variations reflect gene segregation upon backcrossing of genetically distant donor and recipient parents, suggesting that both parental lines contribute favorable traits to the elite EB-SILs (Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The EB-SILs exhibited similar mean performances to parental lines across several ESG traits. This parity suggests that desirable ESG-related traits are inherited from both parents and remain expressed in EB-SILs despite genetic recombination. The seventh-day germination count is of particular significance, which is critical for establishing a competitive advantage over weeds by ensuring timely seedling emergence and higher crop density, thus reducing weed biomass accumulation.\u003c/p\u003e \u003cp\u003eRapid and uniform seedling emergence, measured by the seed vigor index, is crucial for rice competitiveness. Shoot and root length are also essential for resource acquisition and crop competitiveness. Seed vigor, indicating rapid and uniform germination, is pivotal for plant establishment (Manangkil et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The second-day germination count and germination rate positively correlated with the seed vigor index, supporting their importance for rice competitiveness against weeds. These associations align with previous findings highlighting the importance of these ESG traits in determining rice competitiveness against weeds. However, inconsistencies arise regarding root length correlations with ESG traits, contrasting with previous reports (Teixeira et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A fundamental principle of crop-weed competition underscores the advantage of early establishment, emphasizing the critical role of emergence time in field competitiveness (Swanton et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Accordingly, second-day germination count, seventh-day germination count, germination rate, and seed vigor index emerge as pivotal ESG traits influencing rice competitiveness against weeds.\u003c/p\u003e \u003cp\u003eThe mean performances of EB-SILs and parental lines in ESV traits were notably diminished under weedy conditions. Significant differences were observed in plant height, leaf count, and tiller number at 28 DAS among all lines. Conversely, no significant differences were noted in root length, shoot dry weight, root dry weight, total dry weight, and seedling vigor at 28 DAS under non-weedy conditions, consistent even in weedy conditions. This reduction in EB-SILs performance in weedy conditions can be attributed to the competitive pressure exerted by \u003cem\u003eE. colona\u003c/em\u003e, leading to interspecific competition and, subsequently, lower rice performance compared to non-weedy conditions. This observation aligns with previous studies reporting decreased values for plant height, tillering ability, and chlorophyll content under weedy conditions (Dimaano et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A decrease in rice tillers was evident with increasing weed density. In contrast, a negative correlation between \u003cem\u003eEchinochloa\u003c/em\u003e spp., weed dry weight, and rice root dry weight suggested a direct impact of weeds on root development (Mahajan and Chauhan \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Narayana Rao \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, a decrease in seedling vigor index correlated with higher weed density, implying the detrimental effects of weed competition on rice seedling vigor. These findings underscore the negative impacts of weeds on rice growth and development, emphasizing the importance of effective weed management strategies and adoption of weed competitive varieties (Mahender et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, no significant differences in plant height between weedy and non-weedy treatments for parental lines suggest genetic stability under both conditions, indicating the resilience of genotypes to weed competition.\u003c/p\u003e \u003cp\u003eAll ESV-related traits exhibited significant and positive correlations with each other in this study. These ESV-related traits were also significantly and positively correlated in the study conducted by Dimaano et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A positive correlation between plant height and dried vegetative crop biomass under non-weedy and weedy conditions was also observed by (Mennan et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, dried vegetative crop biomass was closely related to tiller number, vigor ratings, and canopy ground cover (Saito et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The results observed in the study, as well as in other studies, revealed that the ability of the seedlings to emerge and have vigorous growth, also known as seedling vigor, is governed by different ESV-related traits, where it is the sum of various properties of a plant associated with the rate and uniformity of seedling growth (Manangkil et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Weed tolerance pertains to the ability of the crop to have a high yield despite weeds in the field. In this study, the tiller number at 28 DAS was used to associate with the grain-yielding capacity of rice plants, as the tiller number is positively correlated with grain yield (Dimaano et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). During the vegetative growth stage, tillering number is a trait highly associated with panicle number, which is a critical yield component of rice (Fageria \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Identifying top-performing EB-SILs based on seed vigor index, germination rate, and tiller number at 28 DAS provides insights into selecting lines with desirable ESG and ESV traits. The top-ranking lines exhibit promising correlations among key ESG traits and between ESV-related traits and tiller number at 28 DAS, suggesting their potential for high yield and weed competitiveness.\u003c/p\u003e \u003cp\u003eIn the current weed competitiveness screening, the population was advanced using the backcross breeding approach, retrieving only a small number of genomic introgression fragments from a donor parent. This limited the number of genomic introgression fragments present in the EB-SILs. However, tunable genotyping-by-sequencing (tGBS\u0026reg;) for genotyping yielded a substantial number of polymorphic markers, enabling a clear distinction of genomic introgression fragments (Ott et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These markers have the potential to provide a comprehensive understanding of genetic variation in the population. QTL mapping for weed competitiveness involved using 3,791 LMD SNP markers, resulting in the mapping of 19 significant SNPs linked to QTLs associated with this trait through marker-trait association. To assess the novelty of our findings, the putative QTL regions identified for weed competitiveness traits were compared with previously reported QTLs. This comparison was based on the physical positions of the associated markers in the Nipponbare genome, using information from the International Rice Genome Sequencing Project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rgp.dna.affrc.go.jp/IRGSP/\u003c/span\u003e\u003cspan address=\"http://rgp.dna.affrc.go.jp/IRGSP/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e In ESG-related traits, a total of eight QTLs were identified. Four QTLs were detected on chromosome 2, linked with the root length (\u003cem\u003eqRL\u003c/em\u003e2), total dry weight (\u003cem\u003eqTDWG\u003c/em\u003e2), and seed vigor index (\u003cem\u003eqSVI\u003c/em\u003e2.1 and \u003cem\u003eqSVI\u003c/em\u003e2.1) of the rice plants. Two were identified on chromosome 12, governing germination rate (\u003cem\u003eqGR\u003c/em\u003e12) and seed vigor index (\u003cem\u003eqSVI\u003c/em\u003e12). Two other QTLs related to the seed vigor index were found on chromosome 3 (\u003cem\u003eqSVI\u003c/em\u003e3) and 6 (\u003cem\u003eqSVI\u003c/em\u003e6). The QTLs detected for ESG-related traits in this study differed from those reported QTLs associated with the said traits (Yang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There were 11 QTLs associated with ESV-related traits. On chromosome 1, a QTL (\u003cem\u003eqRPH\u003c/em\u003e1) was detected that was linked with the relative plant height trait at 14 DAS and relative plant height at 28 DAS. Then, four QTLs were located on chromosome 10, which were associated with relative leaf count at 28 DAS (\u003cem\u003eqRLC\u003c/em\u003e10.1 and \u003cem\u003eqRLC\u003c/em\u003e10.2), relative tiller number at 28 DAS (\u003cem\u003eqRTN\u003c/em\u003e10), and relative root length at 28 DAS (qRRL10) were consistent with previous studies (Singh et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, there were also QTLs detected for relative plant height at 21 DAS on chromosomes 5 and 9 (\u003cem\u003eqRPH\u003c/em\u003e5 and \u003cem\u003eqRPH\u003c/em\u003e9), relative leaf count at 28 DAS on chromosome 4 (\u003cem\u003eqRLC\u003c/em\u003e4), relative tiller number at 28 DAS on chromosome 3 (\u003cem\u003eqRTN\u003c/em\u003e3), and relative root length at 28 DAS on chromosome 8 (\u003cem\u003eqRRL\u003c/em\u003e8). The detected QTL was linked with plant height at 14 and 28 DAS (\u003cem\u003eqRPH\u003c/em\u003e1) and co-localized with qPH-14.1, identified for rice plant height at 14 DAS by (Dimaano et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, other QTLs found on chromosome 1 in the study of (Dimaano et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which are associated with plant height at 21 and 28 DAS, were not detected in the current study. Instead, novel QTLs for relative plant height at 21 DAS were determined on chromosomes 5 and 9, \u003cem\u003eqRPH\u003c/em\u003e5 and \u003cem\u003eqRPH\u003c/em\u003e9, respectively. Moreover, the rest of the identified ESV trait-related QTLs were all novel.\u003c/p\u003e \u003cp\u003eTo enhance the precision of our QTL analysis, we utilized a whole-genome sequencing strategy for the parental lines. This approach aimed to reduce the number of candidate genes within the QTL intervals. Since QTLs mapped in bi-parental populations are confined to loci present in the gene pool of the founder parents, we focused on analyzing non-synonymous mutations between the parental sequences within the QTL interval (Pang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Murugaiyan et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This analysis successfully narrowed down the initial pool of 480 candidate genes to a more manageable 18 candidate genes \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Overall, the whole-genome sequence of parents and gene expression analysis proved to be an effective strategy to narrow down the candidate genes in the QTL intervals. Among these, \u003cem\u003eASR\u003c/em\u003e4 (Abscisic acid-stress-ripening-inducible4 protein) encoded by \u003cem\u003eLOC_Os01g73250\u003c/em\u003e is associated with abscisic acid (ABA) response, which can regulate seed dormancy and germination. ABA influences weed competitiveness by affecting seed germination timing, potentially giving rice seedlings an advantage over weeds (Park et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among these genes, \u003cem\u003eLOC_Os09g24560\u003c/em\u003e and \u003cem\u003eLOC_Os02g15340\u003c/em\u003e, both encoding putative no apical meristem proteins and belonging to the NAC family of plant transcription factors, are implicated in meristem regulation, potentially influencing weed competition through growth modulation (Marques et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, \u003cem\u003eLOC_Os09g24800\u003c/em\u003e, a putative MYB family transcription factor, \u003cem\u003eand LOC_Os02g15350\u003c/em\u003e, encoding a dof zinc finger domain-containing protein, underscore the regulatory mechanisms involved in response to weed pressure (Zou and Sun \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The presence of genes like \u003cem\u003eLOC_Os10g33940\u003c/em\u003e, encoding an auxin response factor 18 (ARF22), and \u003cem\u003eLOC_Os10g33960\u003c/em\u003e, expressing a START domain-containing protein (OSHB2), highlights the involvement of hormonal signaling pathways and transcriptional regulation in weed competitiveness (Schrick et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhai et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, genes such as \u003cem\u003eLOC_Os10g34020\u003c/em\u003e and \u003cem\u003eLOC_Os10g34430\u003c/em\u003e, encoding putative glutathione S-transferase and Dicer proteins, respectively, suggest the importance of stress response mechanisms in weed competition (Kumar and Trivedi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Notably, genes like \u003cem\u003eLOC_Os12g10720\u003c/em\u003e and \u003cem\u003eLOC_Os12g10730\u003c/em\u003e, both encoding glutathione S-transferases, and \u003cem\u003eLOC_Os12g12580\u003c/em\u003e, encoding an NADP-dependent oxidoreductase, indicate the role of detoxification processes in enhancing rice competitiveness against weeds (Dasari et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, genes like \u003cem\u003eLOC_Os02g50240\u003c/em\u003e, encoding glutamine synthetase 1;1, \u003cem\u003eLOC_Os02g50330\u003c/em\u003e, encoding an RNA-dependent RNA polymerase, \u003cem\u003eLOC_Os06g04070\u003c/em\u003e, encoding an arginine decarboxylase, and \u003cem\u003eLOC_Os06g04200\u003c/em\u003e, encoding a starch synthase, further contribute to the multifaceted response of rice plants to weed competition, emphasizing the intricate interplay between various molecular pathways in shaping weed competitiveness in rice (Kusano et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The ZF-HD protein encoded by \u003cem\u003eLOC_Os09g24820\u003c/em\u003e is a transcription factor that regulates multiple developmental processes in rice plants. While its specific role in weed competitiveness is not well understood, transcription factors like ZF-HD are often associated with stress responses and growth regulation. It is plausible that ZF-HD may indirectly influence weed competitiveness by modulating the expression of genes involved in stress tolerance or developmental pathways that affect plant vigor. ZF-HD might regulate the expression of genes involved in root architecture or nutrient uptake, traits crucial for outcompeting weeds (Todaka et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). On the other hand, LEA15 (Late embryogenesis abundant protein 15) encoded by \u003cem\u003eLOC_Os02g15250\u003c/em\u003e is a protein known for protecting plants from various stresses, including drought and salinity. Early seedling vigor and germination in rice are closely linked to stress tolerance during the critical early stages of growth. LEA proteins like LEA15 are involved in maintaining cellular hydration and stabilizing proteins and membranes under stress conditions, which can contribute to improved seedling vigor and germination rates. Enhancing stress tolerance during germination and early seedling growth, LEA15 may indirectly enhance competitiveness against weeds by ensuring a robust start for rice plants (Hundertmark and Hincha \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dirk et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Interestingly, ZF-HD and LEA proteins are part of the complex regulatory network governing plant responses to environmental stimuli. While ZF-HD proteins may be more directly involved in transcriptional regulation, LEA proteins act as molecular chaperones, safeguarding cellular components during stress. Despite their distinct roles, both types of proteins ultimately contribute to the overall fitness and competitiveness of rice plants by ensuring proper growth and development, especially during the early stages when plants are most vulnerable to weed competition and environmental stresses (Hundertmark and Hincha \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Todaka et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dirk et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLimited QTL and candidate gene information is available for weed competitiveness in rice, reflecting a research area still in its early stages. While some studies have identified QTL associated with traits related to weed competitiveness, such as early vigor, root architecture, and allelopathy, the number of known QTLs remains relatively small. Additionally, identifying candidate genes underlying these QTLs has been challenging, further limiting our understanding of the genetic basis of weed competitiveness in rice. The QTLs and candidate genes identified through this study will hold significant implications for sustainable agriculture, as enhancing weed competitiveness in rice can reduce herbicide usage, increase yield stability, and promote resource use efficiency, ultimately contributing to DSR production systems' economic and environmental sustainability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur work investigated the genetic factors that contribute to the ability of rice plants to compete with weeds. We used QTL analysis on a mapping population of 181 EB-SILs created by crossing the recipient parent WTR1 with three different donor parents. We used high-density tGBS\u0026reg; SNPs to detect nineteen QTL areas linked to weed competitiveness. Seventeen out of the identified 19 QTLs associated with the ESG- and ESV-related traits were distinct from the previously reported QTLs. Hence, this study detected 17 novel QTLs that could aid in breeding rice with improved weed competitive ability during its early vegetative stage. Our analysis also highlights the negative impacts of weeds on rice growth and development, emphasizing the importance of effective weed management strategies. The identified QTLs and candidate genes provide valuable insights into the genetic mechanisms underlying weed competitiveness in rice, with implications for sustainable agriculture. Enhancing weed competitiveness in rice can reduce herbicide usage, increase yield stability, and promote resource use efficiency, contributing to the economic and environmental sustainability of the rice production system. Overall, these findings would assist rice breeders in developing rice varieties with a competitive advantage against weeds suitable for DSR systems. In addition, the candidate genes associated with ESG and ESV traits could be further studied to understand better the expression of these genes under high weed pressure.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDry-Seeded Rice\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEB-SILs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEarly backcross selective-introgression lines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEarly seed germination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEarly seedling vigor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative trait locus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide Polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to express their gratitude to the individuals at IRRI who took part in the internal review of the manuscript. Their valuable suggestions and significant efforts have contributed significantly to its improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperiment concept and design, JA, NGD, and VM; JA and VM contributed to the development of breeding materials, Weed Seed material collection, KDN, VM, NGD, CLC; Phenotyping, KDN; QTL analysis, KDN, VM, and NGD; Statistical Analysis and Writing, KDN, VM, NGD, EJDA, AP and JA. The final version of the manuscript received approval from all authors. The work reported in this manuscript is part of KDN\u0026rsquo;s undergraduate thesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Bill \u0026amp; Melinda Gates Foundation (BMGF) is responsible for providing a research grant to the Green Super Rice Project under ID OPP1130530.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research article offers extensive data supporting its conclusions, presented in figures, tables, and additional supplementary tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors assert that this research review was carried out without affiliations with commercial or economic entities that could be interpreted as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest have been declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed S, Jahangir Alam M, Hossain A et al (2021) Interactive effect of weeding regimes, rice cultivars, and seeding rates influence the rice-weed competition under dry direct-seeded condition. Sustain 13:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su13010317\u003c/span\u003e\u003cspan address=\"10.3390/su13010317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli J, Anumalla M, Murugaiyan V, Li Z (2021a) Green Super Rice (GSR) Traits: Breeding and Genetics for Multiple Biotic and Abiotic Stress Tolerance in Rice. Rice Improvement. Springer International Publishing, Cham, pp 59\u0026ndash;97\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli J, Anumalla M, Murugaiyan V, Li Z (2021b) Green Super Rice (GSR) Traits: Breeding and Genetics for Multiple Biotic and Abiotic Stress Tolerance in Rice. Rice Improvement. Springer, Cham, pp 59\u0026ndash;97\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli J, Aslam UM, Tariq R et al (2018) Exploiting the genomic diversity of rice (Oryza sativa L.): SNP-typing in 11 early-backcross introgression-breeding populations. Front Plant Sci 9:315127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2018.00849\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.00849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArouna A, Fatognon IA, Saito K, Futakuchi K (2021) Moving toward rice self-sufficiency in sub-Saharan Africa by 2030: Lessons learned from 10 years of the Coalition for African Rice Development. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.wdp.2021.100291\u003c/span\u003e\u003cspan address=\"10.1016/j.wdp.2021.100291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. World Dev Perspect 21:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwan TH, Chauhan BS, Sta. Cruz PC (2014) Growth Plasticity of Junglerice (Echinochloa colona) for Resource Use When Grown with Different Rice (Oryza sativa) Planting Densities and Nitrogen Rates in Dry-Seeded Conditions. Weed Sci 62:571\u0026ndash;587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1614/ws-d-14-00054.1\u003c/span\u003e\u003cspan address=\"10.1614/ws-d-14-00054.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhandari S, Khanal S, Dhakal S (2020) Adoption of Direct Seeded Rice (Dsr) Over Puddled-Transplanted Rice (Tpr) for Resource Conservation and Increasing Wheat Yield. Rev Food Agric 1:59\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26480/rfna.02.2020.59.66\u003c/span\u003e\u003cspan address=\"10.26480/rfna.02.2020.59.66\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBharamappanavara M, Siddaiah AM, Ponnuvel S et al (2020) Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud. Sci Rep 10:22103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-78675-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-78675-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaudhary A, Venkatramanan V, Kumar Mishra A, Sharma S (2023) Agronomic and Environmental Determinants of Direct Seeded Rice in South Asia. Circ Econ Sustain 3:253\u0026ndash;290\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan BS, Awan TH, Abugho SB et al (2015) Effect of crop establishment methods and weed control treatments on weed management, and rice yield. F Crop Res 172:72\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2014.12.011\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2014.12.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnor M, Cuong OQ, Demont M et al (2022) The influence of climate change knowledge on consumer valuation of sustainably produced rice in Vietnam. Sustain Prod Consum 31:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.spc.2022.01.034\u003c/span\u003e\u003cspan address=\"10.1016/j.spc.2022.01.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDasari S, Ganjayi MS, Yellanurkonda P et al (2018) Role of glutathione S-transferases in detoxification of a polycyclic aromatic hydrocarbon, methylcholanthrene. Chem Biol Interact 294:81\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cbi.2018.08.023\u003c/span\u003e\u003cspan address=\"10.1016/j.cbi.2018.08.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimaano NGB, Ali J, Cruz PCS et al (2017) Performance of Newly Developed Weed-Competitive Rice Cultivars under Lowland and Upland Weedy Conditions. Weed Sci 65:798\u0026ndash;817. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/wsc.2017.57\u003c/span\u003e\u003cspan address=\"10.1017/wsc.2017.57\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimaano NGB, Ali J, Mahender A et al (2020) Identification of quantitative trait loci governing early germination and seedling vigor traits related to weed competitive ability in rice. Euphytica 216:1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-020-02694-8\u003c/span\u003e\u003cspan address=\"10.1007/s10681-020-02694-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirk LMA, Abdel CG, Ahmad I et al (2020) Late embryogenesis abundant protein\u0026ndash;client protein interactions. Plants 9:1\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants9070814\u003c/span\u003e\u003cspan address=\"10.3390/plants9070814\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFageria NK (2007) Yield Physiology of Rice. J Plant Nutr 30:843\u0026ndash;879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15226510701374831\u003c/span\u003e\u003cspan address=\"10.1080/15226510701374831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldberg-Cavalleri A, Onkokesung N, Franco-Ortega S, Edwards R (2023) ABC transporters linked to multiple herbicide resistance in blackgrass (Alopecurus myosuroides). Front Plant Sci 14:1082761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FPLS.2023.1082761/BIBTEX\u003c/span\u003e\u003cspan address=\"10.3389/FPLS.2023.1082761/BIBTEX\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe A, Jiang M, Nie L et al (2022) A preliminary study of \u0026lsquo;Tidy Field Technology\u0026rsquo; to assess growth, development and weed control in direct-seeded rice. F Crop Res 277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2021.108408\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2021.108408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHundertmark M, Hincha DK (2008) LEA (Late Embryogenesis Abundant) proteins and their encoding genes in Arabidopsis thaliana. BMC Genomics 9:118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2164-9-118\u003c/span\u003e\u003cspan address=\"10.1186/1471-2164-9-118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Trivedi PK (2018) Glutathione S-transferases: Role in combating abiotic stresses including arsenic detoxification in plants. Front Plant Sci 9:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2018.00751\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.00751\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKusano M, Fukushima A, Tabuchi-Kobayashi M et al (2020) Cytosolic GLUTAMINE SYNTHETASE1;1 Modulates Metabolism and Chloroplast Development in Roots. Plant Physiol 182:1894\u0026ndash;1909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.19.01118\u003c/span\u003e\u003cspan address=\"10.1104/pp.19.01118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Chuan M, Wang H et al (2022) Genetic and molecular factors in determining grain number per panicle of rice. Front. Plant Sci 13:964246\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan G, Chauhan BS (2013) The role of cultivars in managing weeds in dry-seeded rice production systems. Crop Prot 49:52\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.CROPRO.2013.03.008\u003c/span\u003e\u003cspan address=\"10.1016/J.CROPRO.2013.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan G, Kaur G, Chauhan BS (2017) Seeding rate and genotype effects on weeds and yield of dry-seeded rice. Crop Prot 96:68\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cropro.2017.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.cropro.2017.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahender A, Anandan A, Pradhan SK (2015) Early seedling vigour, an imperative trait for direct-seeded rice: an overview on physio-morphological parameters and molecular markers. Planta 241:1027\u0026ndash;1050\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManangkil OE, Vu HTT, Mori N et al (2013) Mapping of quantitative trait loci controlling seedling vigor in rice (Oryza sativa L.) under submergence. Euphytica 192:63\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S10681-012-0857-Z/FIGURES/3\u003c/span\u003e\u003cspan address=\"10.1007/S10681-012-0857-Z/FIGURES/3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarasini S, Joshi T, Amgain L (2018) Direct seeded rice cultivation method: a new technology for climate change and food security. J Agric Environ 17:30\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/aej.v17i0.19857\u003c/span\u003e\u003cspan address=\"10.3126/aej.v17i0.19857\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarques DN, Reis SP dos, de Souza CRB (2017) Plant NAC transcription factors responsive to abiotic stresses. Plant Gene 11:170\u0026ndash;179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plgene.2017.06.003\u003c/span\u003e\u003cspan address=\"10.1016/j.plgene.2017.06.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNally KL, Henry A (2023) Tools for using the International Rice Genebank to breed for climate-resilient varieties. PLoS Biol 21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pbio.3002215\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.3002215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3:269\u0026ndash;283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.CJ.2015.01.001\u003c/span\u003e\u003cspan address=\"10.1016/J.CJ.2015.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMennan H, Ngouajio M, Sahin M et al (2012) Competitiveness of rice (Oryza sativa L.) cultivars against Echinochloa crus-galli (L.) Beauv. in water-seeded production systems. Crop Prot 41:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cropro.2012.04.027\u003c/span\u003e\u003cspan address=\"10.1016/j.cropro.2012.04.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurugaiyan V, Ali J, Mahender A et al (2019) Mapping of genomic regions associated with arsenic toxicity stress in a backcross breeding populations of rice (Oryza sativa L). Rice 12:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/S12284-019-0321-Y/FIGURES/5\u003c/span\u003e\u003cspan address=\"10.1186/S12284-019-0321-Y/FIGURES/5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarayana Rao A (2021) Echinochloa colona and Echinochloa crus-galli. In: Biology and Management of Problematic Crop Weed Species, 1st Edition. Academic Press, pp 197\u0026ndash;239\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazir A, Bhat MA, Bhat TA et al (2023) Impact of crop establishment techniques and weed management practices on Oryza sativa L. growth and yield. Agron J 115:1812\u0026ndash;1826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/agj2.21363\u003c/span\u003e\u003cspan address=\"10.1002/agj2.21363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtt A, Liu S, Schnable JC et al (2017) tGBS\u0026reg; genotyping-by-sequencing enables reliable genotyping of heterozygous loci. Nucleic Acids Res 45:e178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/NAR/GKX853\u003c/span\u003e\u003cspan address=\"10.1093/NAR/GKX853\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePadma S, Vijayakumar S, Venkatanna B et al (2023) Energy and water budget of rice under different establishment methods. Oryza-An Int J Rice 60:578\u0026ndash;587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.35709/ory.2023.60.4.10\u003c/span\u003e\u003cspan address=\"10.35709/ory.2023.60.4.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanda S, Majhi PK, Anandan A et al (2021) Proofing direct-seeded rice with better root plasticity and architecture. Int J Mol Sci 22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePang Y, Chen K, Wang X et al (2017) Simultaneous improvement and genetic dissection of salt tolerance of rice (Oryza sativa L.) by designed QTL pyramiding. Front Plant Sci 8:273909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2017.01275\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.01275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark SI, Kim JJ, Shin SY et al (2020) ASR Enhances Environmental Stress Tolerance and Improves Grain Yield by Modulating Stomatal Closure in Rice. Front Plant Sci 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.01752\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.01752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeramaiyan P, Srivastava AK, Kumar V et al (2023) Crop establishment and diversification strategies for intensification of rice-based cropping systems in rice-fallow areas in Odisha. F Crop Res 302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2023.109078\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2023.109078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito K, Azoma K, Rodenburg J (2010) Plant characteristics associated with weed competitiveness of rice under upland and lowland conditions in West Africa. F Crop Res 116:308\u0026ndash;317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2010.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2010.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito K, Futakuchi K (2014) Improving estimation of weed suppressive ability of upland rice varieties using substitute weeds. F Crop Res 162:1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2014.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2014.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamal P, Babu SC, Mondal B, Mishra SN (2022) The global rice agriculture towards 2050: An inter-continental perspective. Outlook Agric 51:164\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/00307270221088338\u003c/span\u003e\u003cspan address=\"10.1177/00307270221088338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrick K, Bruno M, Khosla A et al (2014) Shared functions of plant and mammalian StAR-related lipid transfer (START) domains in modulating transcription factor activity. BMC Med 12:1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12915-014-0070-8\u003c/span\u003e\u003cspan address=\"10.1186/s12915-014-0070-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchumaker B, Stallworth S, Tucker A et al (2021) Phenotyping of Weedy Rice to Assess Root Characteristics Associated with Allelopathy. Am J Plant Sci 12:1210\u0026ndash;1221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4236/ajps.2021.128084\u003c/span\u003e\u003cspan address=\"10.4236/ajps.2021.128084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchumaker BC, Stallworth S, De Castro E et al (2019) Repeatable stair-step assay to access the allelopathic potential of weedy rice (Oryza sativa ssp.). J Vis Exp 2020:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3791/60764\u003c/span\u003e\u003cspan address=\"10.3791/60764\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShekhawat K, Rathore SS, Chauhan BS (2020) Weed management in dry direct-seeded rice: A review on challenges and opportunities for sustainable rice production. Agronomy 10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha M, Baral B, Dulal PR (2021) a Review on Weed in Direct-Seeded Rice (Dsr). Sustain Food Agric 2:99\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26480/sfna.02.2021.99.104\u003c/span\u003e\u003cspan address=\"10.26480/sfna.02.2021.99.104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh UM, Yadav S, Dixit S et al (2017) QTL hotspots for early vigor and related traits under dry direct-seeded system in rice (Oryza sativa L). Front Plant Sci 8:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2017.00286\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.00286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwanton CJ, Nkoa R, Blackshaw RE (2015) Experimental Methods for Crop\u0026ndash;Weed Competition Studies. Weed Sci 63:2\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1614/ws-d-13-00062.1\u003c/span\u003e\u003cspan address=\"10.1614/ws-d-13-00062.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeixeira SB, Pires SN, \u0026Aacute;vila GE et al (2021) Application of vigor indexes to evaluate the cold tolerance in rice seeds germination conditioned in plant extract. Sci Rep 2021 111 11:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-90487-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-90487-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTodaka D, Nakashima K, Shinozaki K, Yamaguchi-Shinozaki K (2012) Toward understanding transcriptional regulatory networks in abiotic stress responses and tolerance in rice. Rice 5:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1939-8433-5-6\u003c/span\u003e\u003cspan address=\"10.1186/1939-8433-5-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTonnessen BW, Manosalva P, Lang JM et al (2015) Rice phenylalanine ammonia-lyase gene OsPAL4 is associated with broad spectrum disease resistance. Plant Mol Biol 87:273\u0026ndash;286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11103-014-0275-9\u003c/span\u003e\u003cspan address=\"10.1007/s11103-014-0275-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu D, Shen E, Jiang B et al (2022) Genomic insights into the evolution of Echinochloa species as weed and orphan crop. Nat Commun 13:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-28359-9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-28359-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, Li X, Wang X et al (2019) Comparing the grain yields of direct-seeded and transplanted rice: A meta-analysis. Agronomy 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy9110767\u003c/span\u003e\u003cspan address=\"10.3390/agronomy9110767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu S, Fei Y, Wang Y et al (2023) Identification of a Seed Vigor\u0026ndash;Related QTL Cluster Associated with Weed Competitive Ability in Direct\u0026ndash;Seeded Rice (Oryza Sativa L). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12284-023-00664-x\u003c/span\u003e\u003cspan address=\"10.1186/s12284-023-00664-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Rice 16:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Yang G, Yang M et al (2019) Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice. Front Plant Sci 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.01582\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.01582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu S, Ali J, Zhang C et al (2020) Genomic Breeding of Green Super Rice Varieties and Their Deployment in Asia and Africa. Theor Appl Genet 133:1427\u0026ndash;1442\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu S, Ali J, Zhou S et al (2022) From Green Super Rice to green agriculture: Reaping the promise of functional genomics research. Mol Plant 15:9\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhai R, Ye S, Zhu G et al (2020) Identification and integrated analysis of glyphosate stress-responsive microRNAs, lncRNAs, and mRNAs in rice using genome-wide high-throughput sequencing. BMC Genomics 21:238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12864-020-6637-6\u003c/span\u003e\u003cspan address=\"10.1186/s12864-020-6637-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Li M, Rey JD et al (2023) Simultaneous improvement and genetic dissection of drought and submergence tolerances in rice (Oryza sativa L.) by selective introgression. Front Plant Sci 14:1134450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1134450\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1134450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang F, Shi Y, Ali J et al (2021) Breeding by selective introgression: Theory, practices, and lessons learned from rice. Crop J 9:646\u0026ndash;657\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Zhang J, Quan R et al (2013a) EAR motif mutation of rice OsERF3 alters the regulation of ethylene biosynthesis and drought tolerance. Planta 237:1443\u0026ndash;1451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00425-013-1852-x\u003c/span\u003e\u003cspan address=\"10.1007/s00425-013-1852-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Zhang J, Quan R et al (2013b) EAR motif mutation of rice OsERF3 alters the regulation of ethylene biosynthesis and drought tolerance. Planta 237:1443\u0026ndash;1451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00425-013-1852-x\u003c/span\u003e\u003cspan address=\"10.1007/s00425-013-1852-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou X, Sun H (2023) DOF transcription factors: Specific regulators of plant biological processes. Front Plant Sci 14:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1044918\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1044918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Direct-Seeded Rice, Weed competitiveness, Seedling vigor, Single nucleotide polymorphisms, Quantitative trait loci, Candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-4505794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4505794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDirect-seeded rice (DSR) system is anticipated to become the dominant rice cultivation method in the coming years due to its advantages, such as reduced water usage and labor requirements, less greenhouse gas emission, and adaptation to climate change risks. However, weeds are a significant constraint in the DSR system due to the yield losses incurred by weed infestations. Developing rice cultivars that are competitive against weeds through selective breeding has great potential to solve this challenge. Early seed germination (ESG) and seedling vigor (ESV) are important characteristics for the competitive ability of rice against weeds. This study used 181 early-backcross selective introgression-breeding lines (EB-SILs) that were generated by the process of backcrossing Weed Tolerant Rice 1 (WTR 1) with three specific donor parents (Haoannong, Cheng Hui 448, and Y134). Using the tunable genotyping-by-sequencing (tGBS\u0026reg;) method, we obtained 3,971 single nucleotide polymorphisms (SNPs). These SNPs were then utilized to identify and map quantitative trait loci (QTLs) associated with ESG and ESV features using their precise physical positions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigh phenotypic variations among the EB-SILs and parental lines in different ESG- and ESV-related traits were observed. The association between the phenotypic and genotypic data detected 18 QTLs governing weed competitiveness, with eight and ten QTLs associated with the ESG and ESV traits, respectively. All detected QTLs were novel, except \u003cem\u003eqRPH\u003c/em\u003e1, associated with relative plant height at 14 and 21 days after sowing (DAS). Four ESG-related QTLs (\u003cem\u003eqRL\u003c/em\u003e2, \u003cem\u003eqTDWG\u003c/em\u003e2, \u003cem\u003eqSVI2\u003c/em\u003e.1, and \u003cem\u003eqSVI2\u003c/em\u003e.1) were detected on chromosome 2, with two more QTLs governing germination rate (\u003cem\u003eqGR\u003c/em\u003e12) and seed vigor index (\u003cem\u003eqSVI\u003c/em\u003e12) identified on chromosome 12, and additional QTLs for seed vigor index found on chromosome 3 (\u003cem\u003eqSVI\u003c/em\u003e3) and 6 (\u003cem\u003eqSVI\u003c/em\u003e6). For ESV traits, \u003cem\u003eqRPH\u003c/em\u003e1 detected on chromosome 1 was linked with relative plant height at 14 DAS and 28 DAS, while chromosome 10 harbored four QTLs (\u003cem\u003eqRLC10\u003c/em\u003e.1, \u003cem\u003eqRLC10\u003c/em\u003e.2, \u003cem\u003eqRTN\u003c/em\u003e10, and \u003cem\u003eqRRL\u003c/em\u003e10) associated with relative leaf count, relative tiller number, and relative root length at 28 DAS. Additionally, QTLs were detected for relative plant height at 21 DAS on chromosomes 5 and 9 (\u003cem\u003eqRPH\u003c/em\u003e5 and \u003cem\u003eqRPH\u003c/em\u003e9), relative leaf count at 28 DAS on chromosome 4 (\u003cem\u003eqRLC\u003c/em\u003e4), relative tiller number at 28 DAS on chromosome 3 (\u003cem\u003eqRTN\u003c/em\u003e3), and relative root length at 28 DAS on chromosome 8 (\u003cem\u003eqRRL\u003c/em\u003e8). Candidate genes discovered within the identified QTLs were responsible for the plant\u0026rsquo;s response to various abiotic and biotic stresses.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides a more profound comprehension of the genetic foundation of ESG and ESV traits, which are essential characteristics for the weed competitiveness of rice. The novel QTLs and candidate genes found have the potential to aid in marker-assisted and genomic selection approaches for breeding rice varieties with enhanced weed competitiveness. Simultaneously, the potential genes might be further examined to determine their expression patterns when subjected to intense weed pressure. The findings of this research will contribute to the development of rice varieties capable of competing with weeds. These varieties will be crucial in integrated weed management within the DSR system.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Dissection of Novel QTLs and Genes Associated with Weed Competitiveness Traits Using a Population of Early-Backcross Selective Introgression Lines of Rice (Oryza sativa L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 20:01:22","doi":"10.21203/rs.3.rs-4505794/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ffe38c4-8843-41dd-bd90-e91e8e12ef86","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-03T23:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-14 20:01:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4505794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4505794","identity":"rs-4505794","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}