Predictive models and wild progenitor-derived introgression lines dissect the population dynamics of domestication syndrome traits associated with crop mimicry in weedy rice

Predictive models and wild progenitor-derived introgression lines dissect the population dynamics of domestication syndrome traits associated with crop mimicry in weedy rice | 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 Predictive models and wild progenitor-derived introgression lines dissect the population dynamics of domestication syndrome traits associated with crop mimicry in weedy rice Swayamsiddha Aswita Dhal, Anilkumar Chandrappa, Debashree Dalai, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9327164/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 Weedy-rice, causing 15-90% yield loss of rice across continents, is a conspecific weed originating from natural outcrossing between Oryza sativa and its wild-progenitors. Bidirectional gene flow and phenotypic mimicry of cultivated ecotypes in early growth stages enable evasion of manual weeding, and facilitate rapid proliferation in cultivated ecosystems. Their sustainable management requires understanding of population dynamics in a crop-weed continuum which is challenging in field-populations due to genetic admixtures and multidimensional selection pressures. To resolve this, we generated bulked backcross-derived inbred lines (BILs) and chromosome-segment substitution lines (CSSLs) from Oryza rufipogon using syntenic microsatellite and SNP markers.Leaf-sheath pigmentation and short-term seed dormancy traits were fine-mapped and BILs mimicking weedy-rice populations were selected for awns, seed shattering, and dormancy, but differing in leaf-sheath pigmentation and plant height. A near-isogenic line (NIL) with purple leaf sheath was also developed to facilitate application of bidirectional selection pressure. Simulated models of population dynamics under different levels of gene flow (5 and 10%) and selection pressures were developed after baseline-surveys in natural populations. Using an experimental population of the weedy-derivatives and the recurrent parent or its purple-sheathed NIL at 40:60 ratio, population dynamics under different degree and directions of selection were characterized in field trials. While resurgence of weedy-derivatives was 59.20–64.27% in the absence of selection, it could be restricted to 16.27–19.60% by applying unidirectional selection pressure and further to 3.94–4.33% through bidirectional selection. These findings highlight the effectiveness of population-dynamics guided breeding and agronomic strategies to reduce weedy-rice load. Synteny Conspecific weeds Rotational cultivation Weedy rice evolution Tissue pigmentation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Key message Synteny-guided chromosome segment substitution lines were developed from at reduced cost and efforts through an improved methodology and utilized to genetically dissect an evolution-guided strategy for weedy rice management. Introduction Weedy rice is one of the most serious threats to rice production causing 15–90% yield loss across continents (Roma-Burgos et al. 2021 ). Due to close genomic affinity and cross-compatibility with cultivated rice ecotypes, high seed-shattering, rich phenotypic diversity and aggressive competitive ability, it has become a persistent weed in diverse rice ecosystems (Liang et al., 2025 ). The problem has become more serious with adoption of mechanized direct seeding and harvesting, reduced availability of water for crop management, and cultivation of varieties with narrow genetic base (Singh et al. 2013 ; Cong et al., 2025 ). Management options like manual weeding and conventional herbicide application are ineffective due to phenotypic resemblance and close phylogenetic relationship. Although herbicide tolerant (HT) rice can address the problem, the high cross-compatibility of weedy rice with both genetically modified (GM) and non-GM HT-rice cultivars may sometimes lead to escape of the trait to weedy rice due to natural outcrossing. Before the extensive cultivation of semi-dwarf high yielding rice varieties, farmers practicing direct seeding in India managed the weedy rice load below economic threshold levels through rotational cultivation of tall landraces having purple-pigmented and green leaf sheath, thereby selectively eliminating green- and purple leaf-sheathed weedy rice, respectively (Richharia 1964 ; Tewari 2008 ). However, almost all the present-day high yielding rice varieties lack leaf-sheath pigmentation and the practice of such rotational cultivation involving genotypes showing contrasting visual markers is almost forgotten. Although large scale adoption of transplanting effectively mitigated the threat over a long period of time, gradual build-up of weedy rice population has supported their resurgence even in transplanted fields of India, Japan and Taiwan in recent years (Singh et al. 2013 ; Imaizumi 2018 ; Hsu et al. 2025 ). In the Mekong delta of Vietnam, where yield loss up to 46% due to weedy rice is reported under the predominant direct seeding practice, farmers found it difficult to distinguish weedy rice and manage the problem through their conventional practice of using high seed rates (up to 300 kg ha -1 ) (Chauhan et al., 2015 ). Phenotypic resemblance to cultivated varieties, varying degree of seed dormancy, early maturity and shattering habit make weedy rice a formidable and persistent competitor in rice fields. Therefore, understanding their population dynamics in cultivated environments is critical to decipher the adaptive strategies and design effective control measures (Nadir et al. 2017 ). Botanically classified as Oryza sativa f. spontanea , weedy rice is considered to be originated through natural hybridization between cultivated rice ( Oryza sativa ) and its wild progenitors ( Oryza rufipogon and Oryza nivara ) through de-domestication and feralization (Vigueira et al., 2020 ; Cong et al., 2025 ). Among the two species, O. rufipogon is considered as the most common progenitor across geographical regions (Ma et al. 2008 ; Roma-Burgos et al. 2021 ) Such reverse-domestication process can be monitored by change in the key domestication-syndrome characters, such as dormancy, seed shattering, awn, plant height, and photoperiod sensitivity. These traits define the continuum linking wild, cultivated, and weedy forms under different selection regimes and shape their adaptive trajectories (Delouche et al. 2007 ; Huang et al. 2012 ). In contrast with the rice domestication, which was principally guided by positive artificial selection for few major traits, the de-domestication and feralization of weedy rice is principally affected by independent natural selection in different regions and time periods under cultivated or semi-cultivated environments. The effect of natural selection under such complex situations cannot be interpolated from the traditional gene flow models developed by assuming unrestricted natural environment (Migdałek and Żelawski 2022 ). Hence developing an effective model to understand weedy rice dynamics requires information about basal population structure and the associated changes occurring in both short and long term. Gene flow between two species can be monitored by identification of interspecific recombinants containing fragments of genomes from either of the species (Migdałek and Zelawski 2022). Since weedy rice originate from natural hybridization of O. sativa and O. rufipogon , their interspecific derivatives containing fragments of O. rufipogon genome in the background of O. sativa and harbouring alleles governing weedy forms of domestication-syndrome traits can be effectively utilized to understand the population dynamics of weedy rice. Although O. rufipogon –derived populations have been widely exploited for yield enhancement and stress resilience (Xiao et al. 1998 ; Xie et al. 2007 ; Ge et al. 2024 ), their potential to resolve the genetic architecture of crop-mimicry driven through domestication syndrome traits remains underexplored. Chromosome segment substitution lines (CSSLs) and backcross-derived inbred lines (BILs) with crop-mimicking de-domestication traits can therefore serve as a proxy for weedy rice. Syntenic, cross-transferable markers enabling systematic tracking of introgressed segments (Dalai et al. 2026 ) can further support the genetic dissection of those traits in uniform genetic backgrounds. In this study, we developed CSSLs and BILs of O. rufipogon in the genetic background of cultivated rice using a novel bulked backcrossing strategy guided by syntenic markers, and subsequently identified lines mimicking weedy rice. Selected BILs and NILs were further utilized to interpret phenotype-based selection as an evolutionary process influencing allele frequencies and population structure along the crop–weed continuum and to design future breeding strategies for sustainable management of this recalcitrant invasive weed. Materials and methods The study simultaneously involved development of introgression lines from wild progenitor, computational simulations for predicting population dynamics of weedy rice, and further utilization of both the resources to genetically dissect the population dynamics of domestication syndrome traits associated with crop mimicry. A schematic summary of the overall workflow has been presented in Fig. 1 . Development of introgression lines from wild progenitor Plant materials Four O. rufipogon accessions from India (IRGC 81885, AC 100444, AC 100015) and Bangladesh (IRGC 103404), along with 14 diverse O. sativa cultivars were utilized in the study (Supplementary Table 1). A set of CSSLs, SSSLs and BILs developed in an earlier study (Dalai et al. 2026 ) were included for validation purposes. Identification of syntenic markers A core set of 1K cross-amplifiable STMS markers for the O. sativa complex (1K-CoreCaOA) was previously developed as a resource for CSSL development across A-genome species (Dalai et al. 2026 ). From that basic dataset, common markers among O. sativa ssp. japonica , O. sativa ssp. indica , and O. rufipogon were selected. Markers showing intra- or inter-chromosomal translocations in one or more species or subspecies were excluded to select only a syntenic set. Molecular marker profiling Genomic DNA isolation and molecular marker profiling were carried out following standard protocols (Dalai et al. 2026 ). For pooled analysis, equal concentrations of DNA from three plants were combined after quantification. SNP genotyping was outsourced (Intertek India, Hyderabad) using the 1K rice custom amplicon (1k-RiCA) panel (Arbelaez et al. 2019 ). Selection of cross combination for higher seed-setting Approximately 1000 spikelets of the 14 O. sativa cultivars were emasculated for each cross combination, and separately pollinated with the four wild accessions to generate 56 interspecific hybrids. After hybridity confirmation through polymorphic markers, ~ 1000 emasculated spikelets of each recurrent parent were pollinated with bulked pollens of F 1 plants to generate BC 1 F 1 progenies. Progenies from all cross combinations were characterized for the qualitative characters from a DUS descriptor (Shobha Rani et al. 2004 ) and compared with respective recurrent parents. In the crosses showing higher seed set with all the O. rufipogon accessions, BC 1 F 1 plants were further characterized using one polymorphic marker per chromosome. In other crosses, marker analysis was restricted to phenotypically similar plants. Progenies morphologically resembling the recurrent parent, and not displaying heterozygosity for any of the 12 markers, were considered as selfing derived plants. Modified breeding scheme for development of syntenic marker–anchored BILs and CSSLs A population-based backcrossing strategy was adopted for BIL and CSSL development, replacing conventional methods that require extensive pedigree maintenance and genotyping in every generation (Fig. 2 ). The recurrent parent from the selected cross combination, chosen for higher seed set, was repeatedly backcrossed with bulked pollen collected from BC 1 F 1 and BC 2 F 1 progenies flowering on the same day. In BC 3 F 1 , a total of 282 plants were randomly selected, and ninety-four pooled DNA samples (three plants per pool) were genotyped using 120 syntenic polymorphic STMS markers. The three plants from each pool were subsequently genotyped using only the heterozygous markers identified in the respective pools. A subset of plants representing all markers across the 12 chromosomes through multiple overlapping segments was selected. One panicle from each selected BC 3 F 1 plant was sown, and panicle-to-progeny rows were raised in BC 3 F 2 . Eight plants per row were randomly chosen and advanced through the single-panicle descent method up to BC 3 F 6 , and one plant from each line was selected as a BIL. Following SNP genotyping using the 1K-RiCA platform, a genetic map was constructed using IciMapping v4.1 (Meng et al. 2015 ). Markers with > 5% missing data or showing significant segregation distortion were excluded from linkage map construction. Genetic distances were calculated using the Kosambi mapping function (Kosambi 1943 ). Collinearity between the genetic and physical maps was assessed for the SNP markers and further validated through targeted profiling of the BILs with syntenic STMS markers. Overlapping CSSLs anchored by both SNP and STMS markers were identified using the CSSL Finder tool (Lorieux 2012 ) and visualized through RStudio version 4.5.2. Dissection and mapping of key domestication syndrome traits Leaf sheath pigmentation and seed dormancy were dissected using BILs, CSSLs, single segment substitution line (SSSLs), and advanced substitution lines derived from the same cross combination generated in the present and a previous study (Dalai et al. 2026 ). Phenotypic variation was assessed, overlapping introgressed segments were compared, and common genomic intervals were identified. Recombinants within the target regions were further used for substitution mapping (Nguyen et al. 2021 ) to delimit the minimal marker intervals. Mapping of leaf sheath pigmentation All BILs and a previously developed CSSL set (Dalai et al. 2026 ) were phenotyped for purple versus green leaf sheath. Shared introgressed segments among lines with similar phenotypes were identified, and recombinants were used for fine mapping. An SSSL with a pigmented leaf sheath was crossed with the recurrent parent to generate ~ 2000 IC 1 F 2 plants. A random subset was genotyped using two flanking STMS markers, and the genotype of IC 1 F 2 plants for the gene controlling the trait was determined based on the segregation pattern of their IC 1 F 2:3 progenies. Lines segregating for leaf sheath pigmentation were classified as heterozygous, whereas non-segregating lines were classified as homozygous. The allelic data of flanking markers and trait was utilized to calculate genetic distance. A non-segregating, purple-sheathed recombinant with upright tillers was selected as a NIL. Mapping of seed dormancy All BILs, the viviparous recurrent parent, and two non-viviparous checks (Swarna and CR 1014) were grown in an augmented field design to screen for seed dormancy. Germination tests were conducted with three biological replicates. Primary panicles from 20 plants flowering on the same day were tagged, and seeds were harvested 30 days later. Germination was assessed on the day of harvest and at 7, 14, and 21 days thereafter. The remaining seeds were dried, stored for three months, and retested with and without heat treatment (48°C for seven days) to distinguish dormancy from non-viability. Lines showing no germination up to seven days after harvest but exceeding 50% germination within 21 days were validated across seasons. Overlapping introgressed segments were compared to identify associated marker intervals. Selected BILs carrying the target interval were crossed with the recurrent parent, reciprocal homozygous recombinants were identified and evaluated further. Previously developed CSSLs and BILs (Dalai et al. 2026 ) were further evaluated to reconfirm marker–trait associations. An SSSL carrying the target locus was crossed with the recurrent parent; IC 1 F 2 progenies were space-planted and phenotyped for germination. Samples representing extreme and intermediate classes were genotyped, and homozygous IC 1 F 2:3 progenies for the candidate marker were comprehensively characterized for germination percentage over time. Prediction of population dynamics of weedy rice Baseline data collection A total of 36 paddy fields (≥ 4000 m²) with varying levels of weedy rice infestation were sampled (Supplementary Table 2). Additionally, four fields with complete invasion by weedy rice swarms were selected. Observations were recorded at the booting to flowering stage of the crop. From each field, 100 randomly selected weedy rice plants were scored for the presence and length of awns [short (≤ 20 mm), medium (> 20–40 mm), or long (> 40 mm)], shattering habit (present/absent), and leaf sheath pigmentation (purple or green). Leaf sheath pigmentation of the rice varieties grown continuously over the years or under varietal rotation in the respective fields during the preceding three years was also recorded. Computational simulations Temporal changes in purple- and green–leaf sheath phenotypes in weedy rice were simulated using a probability-based, discrete-generation model implemented in R (R Core Team 2021 ). Model parameters were defined based on published reports and field surveys (Supplementary Table 3). Inheritance of the trait was assumed to follow two alternative genetic models: (i) single-gene control with complete dominance (F 2 segregation 3:1, purple: green), and (ii) complementary gene action (9:7), each present in equal proportion in the base population. The mating structure considered the predominantly autogamous nature of rice, assuming 90–95% selfing and 5–10% outcrossing per generation. The base population composition was set at a 70:30 ratio of crop to weedy rice for each season. Initial dimorphism in weedy rice was fixed at 30:70 for purple and green leaf sheaths, respectively. Simulation methods were initially developed based on a single-locus model and later extended to incorporate two-gene interactions. Simulations were conducted with 100 replications. Model validation was performed by comparing the simulated decline in heterozygosity under partial selfing with theoretical expectations and by reproducing Hardy–Weinberg equilibrium under complete outcrossing maintained at constant allele frequencies across generations. This two-pronged strategy confirmed the correctness of the algorithms implemented in the simulation based on mating and segregation. The decline in heterozygosity was evaluated using the equation H t =H 0 (1 − s/2) t , where ‘H t ​’ represents heterozygosity at the t th generation, ‘H 0 ​’ is the heterozygosity in the base population, and ‘s’ denotes the rate of selfing. Simulations were conducted for both pure weedy rice swarms and a crop–weed continuum, assuming that the cultivated variety was uniformly purple or green and that each year, the crop was grown in the same field using the seed harvested from previous season without selection bias. Selection against plants phenotypically contrasting with the crop was modelled under two extreme scenarios: no selection (0%) or complete unidirectional selection (100%). Phenotypic and allelic frequencies were updated in each generation based on genotypic frequencies through iterative computation (100 nos.) under different selection regimes. Mutation, migration, random genetic drift, penetrance, expressivity and any other factors affecting allele frequencies were assumed to act uniformly on both phenotypic classes. Field testing of weedy rice population dynamics Selection of BILs All the BILs were initially characterized for the presence of awns. Lines possessing medium to long awns were further evaluated for seed shattering. BILs exhibiting spontaneous seed shattering along with short-term seed dormancy induced by the mapped gene were shortlisted. The selected lines were then genotyped using sd1(h), considered as a perfect marker for the sd1 gene (Ellis and Spielmeyer 2002 ), and further classified according to leaf sheath pigmentation. Using combinations of sd1 alleles and leaf sheath pigmentation, four BILs were selected to represent contrasting adaptive phenotypic configurations of weedy rice. To constitute the mixed populations, seeds from each of the four selected BILs (10% each) were thoroughly combined with seeds of CR Dhan 307 or its purple leaf sheath NIL (60%) on a seed-count basis. Prior to mixing, all seeds were sun-dried and stored for six months. Simulated field trial The selected experimental plot had no history of rice cultivation during the preceding two seasons. Pre-sowing irrigation was applied before final land preparation. To control non- Oryza weed species, bispyribac sodium and a commercial formulation of florpyrauxifen-benzyl (2.13%) + cyhalofop-butyl (10.64% EC) were applied as early- and late-post-emergence herbicides, respectively, at recommended doses in all plots during both seasons. The entire field was divided into two main plots, one following manual weeding (MP1) and the other without manual weeding (MP2). During season-1 (wet season 2024), each main plot was randomly subdivided into four subplots (SP1–SP4) comprising: (i) CR Dhan 307, (ii) purple leaf sheath NIL, (iii) a seed mixture of 60% CR Dhan 307 and 40% awned BILs, and (iv) a seed mixture of 60% purple leaf sheath NIL and 40% awned BILs. Subplots were bunded on all sides, and sowing was performed at a 20 cm row distance after leaving a 60 cm buffer zone from the bunds. At maturity, plants possessing awns were allowed to shatter seeds naturally. Manual shaking of panicles was also carried out before harvest to ensure uniform dispersal of remaining seeds within a sub-plot. All sides of the respective subplots were guarded with shade nets during shaking. Subsequently, all crop residues, including panicles, were removed from the field. After four weeks, the field was manually prepared using spades. During this gap period, all subplots were covered with shade nets to minimize seed movement by birds, squirrels, or rainfall. During season-2 (dry season 2025), each subplot with residual shattered seeds from the previous season was further divided into two sub-subplots (SSP1 and SSP2), where CR Dhan 307 or the purple leaf sheath NIL were randomly sown. Three rows were skipped between the two sub-subplots in every sub-plot. Within MP1, all seedlings emerging between rows were manually removed during weeding, irrespective of their leaf sheath pigmentation. Additionally, any plant showing a distinct leaf sheath colour from the respective variety within the sub-subplot rows was removed up to 45 days after sowing during both seasons. Proportions of green- and purple-leaf-sheathed plants in every subplot or sub-subplot were recorded at 60 and 75 days after sowing. The number of hills bearing awned panicles was counted in each subplot (season-1) and sub-subplot (season-2) after flowering. For each subplot or sub-subplot, three randomly selected grids of 6 m² were sampled and treated as replications. Statistical analysis and data representation Statistical analyses were carried out using R (R Core Team 2021 ). The split-plot experiment was analyzed by considering replication, main plots, subplots, and sub-subplots according to the design structure. Treatment effects and their interactions were tested using appropriate error terms. Mean comparisons among genotypes and treatment combinations were performed using Tukey’s honestly significant difference (HSD) test at the 5% significance level with the agricolae package. CSSL representation with anchored-markers, boxplots and violin plots were prepared in RStudio (version 4.5.2). Graphical genotyping was performed using GGT 2.0 (Villanueva et al. 2023 ) and MG2C (Chao et al. 2021 ). Results Selection of cross combination for introgression line development Among the 56 cross combinations and their backcross progenies tested in the study, CR Dhan 307 recorded substantially higher F₁ and BC₁F₁ seed set with all four accessions of O. rufipogon . Seed setting was lowest with Heera, followed by CR1014. Variation was also recorded among the different O. rufipogon accessions. Better seed setting was observed for both AC100444 and AC100015 in all the crosses (Supplementary Fig. 1). Identification of syntenic STMS markers A total of 8,975 common cross-amplifiable markers were identified among O. sativa ssp. japonica , O. sativa ssp. indica , and O. rufipogon , of which 265 were non-syntenic (Fig. 3 A, 3 B; Supplementary Datasheet 1). Only one marker, AUT23344, showed intra-chromosomal translocation in O. rufipogon , where it was positioned at 7.27 Mb compared with ~ 24.16 Mb in japonica and 25.71 Mb in indica . All other non-syntenic markers exhibited inter-chromosomal translocations in either indica or O. rufipogon . Chromosome 11 harboured the highest number of non-syntenic markers (76), followed by chromosome 12 (41), with most translocations in these chromosomes observed in indica . The lowest number of translocations was recorded on chromosome 9. In general, non-syntenic markers showed translocations in either indica or O. rufipogon . A rare exception was RM23936, located on chromosome 9 of japonica , chromosome 3 in indica , and chromosome 4 of O. rufipogon . Three closely linked markers (RM12185, RM20789, and RM17271) near the short arm telomeric region of chromosome 7 were found on chromosome 4 of both indica and O. rufipogon at similar physical positions. The syntenic markers were only considered for further studies (Fig. 3 C; Supplementary Datasheet 2). Development of synteny-guided BILs and identification of CSSLs The CR Dhan 307/AC100444 cross combination was selected for BIL and CSSL development. All 398 polymorphic SNP markers detected between the parents could be traced among the BILs. However, due to significant segregation distortion, 73 markers were not considered for linkage map construction (Fig. 3 A). The remaining 325 markers were mapped to 13 linkage groups (Fig. 4 A). Only chromosome 3 was represented by two linkage groups due to a lack of sufficient polymorphic SNP markers to provide comprehensive coverage in one region. The marker order in the genetic map matched the physical positions in Nipponbare reference genome. The 120 polymorphic STMS markers previously verified for synteny were combined with the linear SNP markers to generate a physical map of the 12 chromosomes with better genome coverage (Fig. 4 B). Targeted STMS marker profiling of the BILs for the introgressed segments of O. rufipogon revalidated the linkage map and achieved higher genome coverage. A total of 54 CSSLs comprehensively covering all the polymorphic marker intervals across the 12 chromosomes were identified (Fig. 4 C). While chromosomes 1, 4, and 5 were represented by six CSSLs each, only two CSSLs covered chromosomes 2 and 10. All the CSSLs had at least one overlapping marker with their preceding and succeeding CSSLs without any gap. The CSSLs were named COr-B-CSSL ( C R Dhan 307 and O ryza r ufipogon B ulk C hromosome S egment S ubstitution L ines). Each CSSL was further hyphenated with the chromosome number followed by the CSSL number for that chromosome. COr-B-CSSL-1-1 represents the first CSSL for chromosome 1, whereas COr-B-CSSL-1-6 represents the sixth CSSL of the same chromosome. Overall, the CSSLs covered ~ 358.54 Mb genomic region detected through all the polymorphic syntenic markers identified between parents. All the CSSLs also retained a few additional segments from the O. rufipogon genome (Supplementary Fig. 2). Mapping and validation of locus for leaf sheath pigmentation Among the 54 CSSLs, the only common introgressed region shared by the pigmented lines was located on chromosome 6 (Fig. 5 A, 5 B). Further survey of introgressed segments of the remaining 354 BILs delimited the trait to the interval between Chr06_4757948 and RM253. Two BILs showing recombination within this interval displayed contrasting phenotypes, one purple and the other green. The segregating IC 1 F 2 progenies derived from the cross of SSSL-U2-6 and CR Dhan 307 mapped the leaf sheath pigmentation locus at a distance of 0.42 cM from RM253. Colocalization with reported genes and QTLs identified OsC1 ( LOC_Os06g10350 ) as the most likely candidate gene homologue. Substantial variation in the intensity and onset of pigmentation was recorded among the homozygous progenies for RM253 (Fig. 5 A). In the IC 1 F 2 generation of SSSL-U2-6/CR Dhan 307, 1492 plants showed pigmented leaf sheath and 503 were green, fitting the 3:1 segregation ratio (χ² = 0.048, p > 0.05) expected for a single dominant gene. While 499 green-sheathed progenies were erect like CR Dhan 307, 1478 plants with purple leaf sheath were semi-spreading like SSSL-U2-6. The 14 erect and pigmented plants were selected, and in the IC 1 F 2:3 generation, one line showing robust seedling growth, pigmentation from the early developmental stage, and no segregation for pigmentation or upright tillering was selected as the NIL (equivalent to BC 6 F 3 ). Based on STMS and SNP allele profiles, the NIL showed 98.40% background recovery. During identification of syntenic marker among O. sativa ssp. japonica , O. sativa ssp. indica , and O. rufipogon , ~ 22.39 syntenic STMS markers per Mb were identified (Supplementary Datasheet 2). Using that dataset, 17 additional syntenic STMS markers were identified between Chr06_4757948 and RM253, spanning a ~ 0.6 Mb physical distance, which may be utilized for further fine mapping after study of polymorphism (Fig. 5 C). Characterization and mapping of locus for seed dormancy Among the 408 BILs, 56 lines with viable seeds did not show any germination immediately after harvest. Three lines remained dormant even after 140 days of harvest. The remaining 53 lines did not germinate up to seven days. However, within 21 days, > 50% of the seeds germinated (Fig. 6 A, 6 B). Two closely linked markers, Ghd7-2-02 and MSU7_7_9152479_C-G, were found as common among 49 BILs (Fig. 6 C). Reciprocal recombinants between Chr07_5442983 and Ghd7-2-02 or between Ghd7-2-02 and Chr07_14729119, identified from IC₁BC₁F₂ progenies derived from three selected BILs, further confirmed the association of Ghd7-2-02 and MSU7_7_9152479_C-G with short-term seed dormancy (STSD) (Fig. 6 D). Notably, two STMS markers, RM21263 (~ 7.42 Mb) and RM21323 (~ 8.87 Mb), located within the interval between Chr07_5442983 and Ghd7-2-02/MSU7_7_9152479_C-G, and another polymorphic SNP marker, Chr07_11290725, located at ~ 11.29 Mb, were non-syntenic, and therefore not included in the study (Fig. 3 A). CSSL-L-7-7, SSSL-L-7-7, and 19 other BILs out of 21 with the STSD feature identified from 215 backup BILs developed earlier from the same cross using a different approach (Fig. 2 ) reconfirmed the linkage of Ghd7-2-02 or MSU7_7_9152479_C-G with a key locus for STSD induction. The contrasting set of 94 plants selected from the IC 1 F 2 generation of the SSSL-L-7-7/CR Dhan 307 cross further validated the role of the genomic region associated with Ghd7-2-02 or MSU7_7_9152479_C-G from O. rufipogon in STSD induction in the viviparous recurrent parent. All homozygous plants carrying the Ghd7-2-02 allele of O. rufipogon showed 50% after 21 days. The heterozygous plants for Ghd7-2-02 recorded > 30.0% germination on the day of harvest, whereas the homozygous progenies carrying the Ghd7-2-02 allele of CR Dhan 307 showed 53.33–90.00% germination. The 47 homozygous plants for Ghd7-2-02 were retested in IC 1 F 2:3 , and STSD induction was found to be consistent during validation (Fig. 6 E). Prediction of population dynamics of weedy rice for leaf sheath pigmentation Field surveys detected weedy rice populations under both direct-seeded and transplanted conditions. Except in certain fields of Cuttack (Odisha), where the traditional landrace Kalachampa with purple leaf sheath has been cultivated in recent years, weedy rice populations at other locations were nearly monomorphic for green leaf sheath. At the same location, although weedy rice in cultivated fields predominantly had green leaf sheaths, the invasive swarms in non-cropped areas exhibited both types of leaf sheath pigmentations in significant proportions (Supplementary Table 3). The predictions showed that, in the absence of any selection pressure against a particular leaf sheath pigmentation, weedy rice populations in natural swarms can maintain dimorphism even when only 30% of the initial population possesses purple leaf sheath. After a short-term decline in purple- and increase in green-sheathed plants, the trends are generally reversed. If the trait is governed by a single dominant gene, irrespective of the outcrossing percentage, the proportions of green- and purple-sheathed plants stabilize rapidly (Fig. 7A 1 –A4). When complementary gene action governs the trait, after a steep decline during the first season, the proportion of purple leaf-sheathed plants gradually increases and, at a certain point, both leaf sheath colours reach equal frequencies. Higher outcrossing rates accelerate the process, and the populations gradually stabilize (Fig. 7B1–B4). When single dominant gene and complementary gene action are present in equal magnitudes, both phenotypes reach equal proportions at a higher speed (Fig. 7C 1 –C 4 ). In a crop–weed continuum without selection pressure, the proportion of weedy rice plants with contrasting leaf sheath pigmentation may change constantly at a very slow rate, provided that a single dominant gene and complementary gene action equally control the inheritance of pigmentation in the base population. A certain proportion of heterozygotes for purple leaf sheath continues to survive in every generation under such conditions (Fig. 8A 1 –A 4 ; 4B 1 –B 4 ). Even under 100% selection pressure to remove all weedy rice plants with contrasting leaf sheath pigmentation, purple-sheathed weedy rice can persist at very low frequency for 25–30 crop seasons/generations among green-sheathed plants comprising both weedy rice and the crop variety (Fig. 8C 1 –C 4 ). Under the opposite scenario, green-sheathed weedy rice plants may be eliminated within 7–8 crop seasons/generations (Fig. 8D 1 –D 4 ). The outcrossing rate significantly influences the population dynamics when selection pressure is not applied. However, under unidirectional selection, the role of the outcrossing rate is negligible. Selection of weedy derivatives for field study Among the BILs, substantial variation was recorded for awn morphology (Fig. 9 A). The awns were purple or white during flowering. Notably, when the genotype was awned, all plants with purple leaf sheath also exhibited pigmented awns. The majority of the BILs with medium to large awns showed grain shattering habit. All medium- to large-awned BILs with grain shattering features were further screened for Ghd7-2-02 marker and retested for STSD. We detected 17 such lines among the 623 BILs. Further classification based on sd1 and RM253 identified four types of awned genotypes with STSD and shattering habit, viz., tall plants with purple leaf sheath; semidwarf plants with purple leaf sheath; tall plants with green leaf sheath; and semidwarf plants with green leaf sheath (Fig. 9 B). Considering a 40% basal infestation level, 10% seeds of each of the four BILs were mixed with 60% seeds of CR Dhan 307 or its purple leaf sheath NIL on seed count basis. Population dynamics of weedy rice under different selection regimes During season-1 (wet season 2024), almost all the plants were present within their respective rows in different treatments. Within MP1, manual weeding reduced the population of weedy derivatives in both subplots (MP1SP3 and MP1SP4) where mixed populations were sown (Fig. 10 A and 10 B). However, weedy derivatives with similar leaf sheath colour were present in both subplots. Weedy populations were substantially higher in MP2SP3 and MP2SP4 compared to MP1SP3 and MP1SP4. During season-2 (dry season 2025), Oryza seedlings were also found between the rows in both main plots. Manual weeding removed such plants from MP1. Within MP1SP3, where a mixed population of CR Dhan 307 and its weedy derivatives was grown in season-1, the population of weedy derivatives was substantially reduced in sub-subplot 1 (MP1SP3SSP1), which grew the purple leaf sheathed NIL. However, in MP1SP3SSP2, the population of weedy types was significantly higher. On the contrary, the weedy population in MP1SP4SSP1 was like that in MP1SP3SSP2. The population of awned weedy plants in MP1SP4SSP2 was at par with MP1SP3SSP1. The populations of weedy derivatives in both sub-subplots of MP2SP3 and MP2SP4 were nearly three times higher than those in MP1SP3SSP2 or MP1SP4SSP1 (Fig. 10 A, 10 B, 10 C, and 10 D). Discussion This study identified, validated, and systematically utilized syntenic STMS and SNP markers in a modified breeding scheme to develop overlapping introgression lines from O. rufipogon with comprehensive genome coverage at substantially reduced cost and efforts compared with conventional strategies. Beyond trait discovery and pre-breeding, the introgressed population and marker resources were deployed to dissect crop-weed evolutionary dynamics and to examine the effects of imposing directional selection pressure against adaptive traits of weedy rice. Synteny-guided introgression as an efficient framework for wide hybridization Modern pre-breeding programmes require structured populations and strategic deployment of molecular markers, especially during the utilization of wild species. Syntenic cross-transferable markers facilitate precise tracking of wild genomic segments during backcrossing (Ray et al. 2016 ). However, no marker system is suitable at every stage of a breeding programme for all laboratories. For example, custom amplicon-based SNP arrays are excellent tools; however, they are suitable only when whole-genome information is required. Moreover, such genotyping services are not available in every country or laboratory at all times. On the contrary, PCR-based markers can be routinely used in laboratories with basic facilities but are not economical to use at a very large scale (Dalai et al. 2021 ). The present study systematically combined a modified breeding strategy with both marker systems for CSSL development. Bulk backcrossing without any genotyping until BC 2 F 1 , genotyping of pooled subsets in BC 3 F 1 with a limited set of anchored STMS markers, advancing the population until BC 3 F 5 without genotyping, genetic map construction using custom SNP amplicons in BC 3 F 6 , and selective reuse of STMS markers to fill genomic gaps minimized the genotyping cost, eliminated the need for exhaustive pedigree maintenance, and substantially improved genome coverage compared with conventional strategies (Dalai et al. 2026 ). Moreover, ~ 22.5 syntenic STMS markers per Mb was identified between O. rufipogon and the two subspecies of Asiatic rice, facilitating high-density coverage of delineated genomic intervals after a locus is mapped. The same population and marker framework were directly utilized to identify loci governing two domestication-related traits. These overlapping introgression lines enabled evaluation of domestication-related traits in a largely uniform genetic background. This is particularly valuable for traits placed at the interface of domestication and de-domestication, which often remain masked in the cultivated gene pool of rice (Li et al. 2017 ). Domestication-related traits in the crop-weedy rice continuum Leaf sheath pigmentation, plant height, seed dormancy, awn development, and grain shattering are critical for the persistence of weedy rice in cultivated fields. Mapping the leaf sheath pigmentation locus near RM253 and its colocalization with OsC1 homologue align with earlier reports (Xiong et al. 1999 ; Lorieux et al. 2000 ; Fan et al. 2008 ; Chen et al. 2024 ; Jiang et al. 2024 ). However, it may be noted that the purple leaf sheath phenotype in rice may occur due to other genes, besides OsC1 . We have been able to produce purple leaf sheath NILs through independent introgression of both OsC1 homologue from O. rufipogon and OsDFR ( LOC_Os01g44260 ) gene from a landrace in the same recurrent parent ‘Swarna’ (unpublished data). This also explains why different F 2 -segregation ratios (3:1, 9:7 or even complex higher order) are observed in different populations for the trait (Chin et al. 2016 ). Among all the OsC1 introgressed BILs of CR Dhan 307, whenever present, the awns were always purple. If the awns were absent, only the apiculus regions were pigmented. This also indicate the possible pleiotropic effect of OsC1 or tight linkage among the genes controlling those tissue specific pigmentations. Although the O. rufipogon homologue ensured pigmentation, variation in the stage and intensity of expression suggests quantitative modulation by additional loci. Dominant inheritance and selection of NILs expressing high pigment intensity at the early growth stage further enhanced its value as a visual marker. The tight coupling-phase linkage between the genes for leaf sheath pigmentation and tiller angle could also be circumvented through visual selection in large populations. In other cases, high-density syntenic STMS markers may help to reduce linkage drags, which often create a major hurdle in utilization of wild species in crop improvement. Variable levels of seed dormancy also play a critical role in the persistence of weedy rice populations (Olajumoke et al. 2016 ). Short-term dormancy enables staggered germination in rice–rice cropping systems, whereas deep dormancy sustains populations under rice–fallow or crop rotations (Singh et al. 2013 ; Ajaykumar et al. 2025 ). Use of a semidwarf recurrent parent with viviparous germination, along with selection of grain-shattering BILs with short-term seed dormancy enabled completion of the simulated field experiment within two consecutive seasons. A genic SNP marker Ghd7-2-02 associated with short-term dormancy was colocalized with Ghd7 , which is known for its role to induce dormancy and prevent vivipary by increasing the abscisic acid (ABA)/gibberellin (GA 3 ) ratio during rice seed germination (Hu et al. 2021 ). While Ghd7-0 is a null (Xue et al. 2008 ) and ghd7 is the recessive allele (Hu et al. 2021 ), Ghd7-1 and Ghd7-3 are the two fully functional dominant alleles of the gene (Saito et al. 2019 ). Ghd7-2 is a comparatively weaker allele of Ghd7 (Wang et al. 2021 ). Although the nearest polymorphic flanking markers on both sides were > 4.0 Mb apart, the trait could still be traced directly to the genic SNP marker Ghd7-2-02 through substitution mapping. This would otherwise have been difficult to precisely locate and quantify through conventional interval mapping approaches as the three other polymorphic markers predicted as closely linked to Ghd7-2-02 as per Nipponbare reference genome, were non-syntenic and showed significant segregation distortion in the population. Another study to map QTLs for seed dormancy from a weedy rice accession ‘Ludao’ identified chromosome 7 as the sole genomic hotspot for the trait (Nguyen et al. 2019 ). Evolutionary basis of the traditional weedy rice management practice Short- and long-term projections of weedy rice population dynamics in natural swarms and crop–weed continuums provided an evolution guided explanation for the traditional practice of rotating landraces with contrasting leaf sheath pigmentation. Simulations indicated that complementary gene action and natural outcrossing allow dimorphism for leaf sheath pigmentation to persist and stabilize, even when uniformly pigmented varieties are cultivated over extended periods. Under unidirectional selection, conspecific weeds phenotypically resembling crop varieties are favored, although the number of generations required for complete elimination of contrasting phenotypes differs significantly between green and purple sheath types. When phenotypically uniform varieties are cultivated in long term, such as modern semi-dwarf varieties with green leaf sheaths, selection promotes convergence toward crop mimicry. In contrast, bidirectional selection disrupts this convergence, cyclically discriminate between crop variety and weedy plants, and thereby enables elimination of weedy types at the vegetative stage. These findings have practical implications in determining the frequency of varietal rotation required in the continuum of a crop and its conspecific weeds. Field observations were consistent with these projections. The substantial reduction in resurgence of weedy derivatives in the second season after the introduction of contrasting pigmentation indicates that phenotypic divergence may constrain their persistence across generations. Although complete elimination was expected under rigorous weeding up to 45 days, a few plants escaped selection and reached maturity, possibly due to scattered germination or variable expressivity of pigmentation trait during early vegetative stage in few weedy plants. Continuous monitoring and rouging of leftover weedy plants at early flowering stage, therefore, should be an essential component of any sustainable management strategy. Implications for evolution-guided management under different production systems Direct seeding creates ecological conditions that favor rapid invasion of weedy rice. Continuous monoculture and exclusive reliance on chemical control can progressively select for adaptive responses against such management strategies. Integrating evolutionary principles into varietal development, however, can offer a sustainable alternative. Breeding varieties or near-isogenic lines that enable creation of artificial selection pressure against weedy derivatives mimicking the crop without dependence on specific herbicides can broaden their applicability. Where herbicide tolerant varieties are possible to cultivate, development of near-isogenic lines of herbicide-tolerant varieties with phenotypic contrasts can help in developing stewardship strategies against gene flow. Transplanted rice has historically suppressed early emerging weeds through seedling age advantage and puddling. However, our surveys in some parts of India, along with recent reports from Japan (Imaizumi 2018 ) and Taiwan (Hsu et al. 2025 ) indicate that weedy rice is steadily invading transplanted paddy fields. Introducing contrasting pigmentation through varietal rotation will also support the removal of weedy derivatives shortly after transplanting. Hence, irrespective of crop establishment method, adoption of evolution-guided management of weedy rice represents an example of ecological intensification. By harnessing the genetic diversity of crop wild relatives to alter population dynamics of conspecific weeds, genomic resources and introgression lines can be utilized beyond trait mapping and marker-assisted breeding. Conclusion and future perspectives The study demonstrates how synteny-guided introgression lines derived from a wild progenitor can be used as a platform to integrate genomics with the genetics of crop domestication and apply these insights to crop management and breeding. Strategic deployment of traits to overcome crop mimicry in breeding programmes can create directional selection pressure against a recalcitrant conspecific weed and reduce its fitness. A traditional agronomic practice involving low-yielding landraces was reintroduced as an evolution-guided management strategy suitable for modern direct-seeded systems with semidwarf high-yielding varieties. By integrating wild germplasm, genomic tools, and ecological understanding, the study opens new avenues for sustainable management of conspecific weeds while expanding the scope for incorporating the diversity of crop wild relatives into modern breeding programs. Declarations Compliance with ethical standards The authors of this publication declare that the experiments conducted for the study have followed the extant national and international laws. Conflict of interest It is hereby declared that there is no conflict of interest among the authors of this publication. Funding This study was financially supported by Indian Council of Agricultural Research through the “Consortium Research Project on Molecular Breeding (EAP-211)” and ICAR-CRRI in-house project no. 1.3. Author Contribution S.A.D., D.D., A.H., A.B.K..: Marker profiling, field works, phenotyping, data recording; A.C., K.A.M., P.S., D.B.: Genetic modelling, data analysis, writing– review & editing; VK, KKJ, NPM, CKP, TM: Guidance, resources, data visualization, writing– review & editing; S.R.: Bioinformatics; D.R.P., M.K.K.: Breeding works, field evaluations, data collection; resources; writing– review & editing; M.C.: Conceptualization, designing experiments, breeding works, bioinformatics, supervision, funding acquisition, formal analysis, data curation. data-visualization, validation, writing- original draft. This work is a part of Ph.D. thesis of the first author. All authors read, reviewed and approved the manuscript. Acknowledgement The team is grateful to Post Graduate Department of Botany, Utkal University, India for approving the first author to carry out her research work ICAR-CRRI, Cuttack. We acknowledge the support received from Dr. S.K. Das and Dr. S. Sarkar from ICAR-CRRI, Cuttack for providing resources to synthesize large number of STMS markers. International Rice Research Institute, Philippines provided two wild germplasm accessions used in the study. Administrative support was provided by Director, ICAR-CRRI. Large Language Model (LLM) ChatGPT was used for grammar and language check during initial stage of manuscript preparation. However, authors take all responsibilities for correctness of sentences/statements. Data Availability All the relevant tables, additional data in the form of datasheets or required figures have been provided as electronic supplementary material. Specific additional information, if required, will also be shared on request. References Ajaykumar R, Sivasabari K, Vigneshwaran R, Kumaresan P (2025) Weedy rice (Oryza sativa f. spontanea) complexes on direct seeded rice (DSR) and its management strategy: A review. Agricultural Reviews 46: 62–69. https://doi.org/10.18805/ag.R-2538 Arbelaez JD, Dwiyanti MS, Tandayu E, Lantada K, Jarana A, Ignacio JC, Platten JD, Cobb J, Rutkoski JE, Thomson MJ, Kretzschmar T (2019) 1k-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice. Rice 12(1):55. https://doi.org/10.1186/s12284-019-0311-0 Chao J, Li Z, Sun Y, Aluko OO, Wu X, Wang Q, Liu G (2021) MG2C: a user-friendly online tool for drawing genetic maps. Molecular Horticulture 1(1):16. https://doi.org/10.1186/s43897-021-00020-x Chauhan BS, Namuco OS, Ocampo LAL, Son TTN, Thu TTA, Nam NN, Phuong LN, Bajwa AA (2015) Weedy rice ( Oryza sativa f. spontanea ) problems and management in wet direct-seeded rice ( O. sativa L.) in the Mekong Delta of Vietnam. Crop Protection 78:40–47. https://doi.org/10.1016/j.cropro.2015.08.016 Chen HJ, Sawasdee A, Lin YL, Chiang MY, Chang HY, Li WH, Wang CS (2024) Reverse mutations in pigmentation induced by Sodium Azide in the IR64 rice variety. Current Issues in Molecular Biology 46(12):13328–13346. https://doi.org/10.3390/cimb46120795 Chin HS, Wu YP, Hour AL, Hong CY, Lin YR (2016) Genetic and evolutionary analysis of purple leaf sheath in rice. Rice 9(1):8. https://doi.org/10.1186/s12284-016-0080-y Cong Y, Gui Y, Yong K, Zhu M, Liu K, Liu J, Qiu J (2025) Deciphering rice feralization: insights from genomics of weedy rice. Genomics Communications 2(1): e007. https://doi.org/10.48130/gcomm-0025-0007 Dalai D, Chakraborti M, Mondal TK, Ray S, Kar MK, Chakraborty K, Pani DR, Sarkar S, Bose LK, Behera M, Chattopadhyay K, Deepa, Vijayan J, Dash SK, Pradhan C, Patra BC, Marndi BC (2021) The core set of sequence-tagged microsatellite sites markers between halophytic wild rice Oryza coarctata and Oryza sativa complex. Euphytica 217(4):57. https://doi.org/10.1007/s10681-021-02790-3 Dalai D, Pani DR, Dhal SA, Behera M, Mondal TK, Azaharudheen MTP, Vijayan J, Sarkar D, Ghose P, Roy A, Molla KA, C A, Bose LK, Mohapatra T, Ray S, Kar MK, Chakraborti M (2026) A robust 1K-core marker set for wild germplasm management and targeted pre-breeding of rice: development and applications. Theoretical and Applied Genetics 139(1):23. https://doi.org/10.1007/s00122-025-05133-1 Delouche JC, Burgos NR, Gealy DR, De San Martin GZ, Labrada R, Larinde M, Rosell C (2007) Weedy rices: Origin, biology, ecology, and control. In. FAO Plant Production and Protection Paper No.188. Food and Agriculture Organization (FAO) of the United Nations, Rome, Italy. Ellis MH, Spielmeyer W (2002) “Perfect” Markers for the Semidwarfing Gene sd1 in Rice. International Rice Research Notes. 27(2):1–1. https://doi.org/10.5281/zenodo.6823534 Fan FJ, Fan YY, Du JH, Zhuang JY (2008) Fine mapping of C (Chromogen for anthocyanin) gene in rice. Rice Science 15(1):1–6. https://doi.org/10.1016/s1672-6308(08)60012-8 Ge Y, Chen G, Cheng X, Li C, Tian Y, Chi W, Li J, Dai Z, Wang C, Wang C, Duan E, Liu Y, Sun Z, Li J, Wang B, Xu D, Sun X, Zhang H, Zhang W, Wang C, Wang C, Wan J (2024) The superior allele LEA12 OR in wild rice enhances salt tolerance and yield. Plant Biotechnology Journal 22(11):2971–2984. https://doi.org/10.1111/pbi.14419 Hsu YT, Wang YC, Du PR, Li CP, Wu DH (2025) Morphological diversity and crop mimicry strategies of weedy rice under the transplanting cultivation system. Agronomy 15(4):984. https://doi.org/10.3390/agronomy15040984 Hu Y, Song S, Weng X, You A, Yongzhong X (2021) The heading-date gene Ghd7 inhibits seed germination by modulating the balance between abscisic acid and gibberellins. The Crop Journal 9(2):297–304. https://doi.org/10.1016/j.cj.2020.09.004 Huang X, Kurata N, Wei X, Wang Z-X, Wang A, Zhao Q, Zhao Y, Liu K, Lu H, Li W, Guo Y, Lu Y, Zhou C, Fan D, Weng Q, Zhu C, Huang T, Zhang L, Wang Y, Feng L, Furuumi H, Kubo T, Miyabayashi T, Yuan X, Xu Q, Dong G, Zhan Q, Li C, Fujiyama A, Toyoda A, Lu T, Feng Q, Qian Q, Li J, Han B (2012) A map of rice genome variation reveals the origin of cultivated rice. Nature 490(7421):497–501. https://doi.org/10.1038/nature11532 Imaizumi T (2018) Weedy rice represents an emerging threat to transplanted rice production systems in Japan. Weed Biology and Management 18(2):99–102. https://doi.org/10.1111/wbm.12146 Jiang L, Lyu S, Yu H, Zhang J, Sun B, Liu Q, Mao X, Chen P, Pan D, Chen W, Fan Z, Li C (2024) Transcription factor encoding gene OsC1 regulates leaf sheath color through anthocyanidin metabolism in Oryza rufipogon and Oryza sativa . BMC Plant Biology 24(1):147. https://doi.org/10.1186/s12870-024-04823-0 Kosambi DD (1943) The estimation of map distances from recombination values. Annals of Eugenics 12(1):172–175. https://doi.org/10.1111/j.1469-1809.1943.tb02321.x Li LF, Li YL, Jia Y, Caicedo AL, Olsen KM (2017) Signatures of adaptation in the weedy rice genome. Nature Genetics 49(5):811–814. https://doi.org/10.1038/ng.3825 Liang X, Zhao C, Liu K, Wang W, Huo Z, Song X, Qiang S (2025) Advances in research on the biological characteristics of weedy rice. Plants 14(20):3188. https://doi.org/10.3390/plants14203188 Lorieux M (2012) MapDisto: fast and efficient computation of genetic linkage maps. Molecular Breeding 30(2):1231–1235. https://doi.org/10.1007/s11032-012-9706-y Lorieux M, MN. Ndijiondjip MN and Gesquiere A (2000) A first interspecific Oryza sativa × Oryza glaberrima microsatellite-based genetic linkage map. Theoretical and Applied Genetics 100:593–601. https://doi.org/10.1007/s001229900061 Ma D, Li M, Sun J (2008) Studies on biological diversity and genetic differentiation of Liaoning weedy rice. Journal of Shenyang Agricultural University 39 265–269. DOI: 10.1007/s10499-007-9164-4 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. The Crop Journal 3(3):269–283. https://doi.org/10.1016/j.cj.2015.01.001 Migdałek G, Żelawski M (2022) Measuring population-level plant gene flow with topological data analysis. Ecological Informatics 70:101740. https://doi.org/10.1016/j.ecoinf.2022.101740 Nadir S, Xiong HB, Zhu Q, Zhang XL, Xu HY, Li J, Dongchen W, Henry D, Guo X-Q, Khan S, Suh H-S, Lee DS, Chen L-J (2017) Weedy rice in sustainable rice production. A review. Agronomy for Sustainable Development 37(5):46. https://doi.org/10.1007/s13593-017-0456-4 Nguyen CD, Zheng SH, Sanada-Morimura S, Matsumura M, Yasui H, Fujita D (2021) Substitution mapping and characterization of brown planthopper resistance genes from indica rice variety, ‘PTB33’ ( Oryza sativa L.). Breeding Science 71(5):497–509. https://doi.org/10.1270/jsbbs.21034 Nguyen T, Zhou C, Zhang T, Yu J, Miao R, Huang Y, Zhu X, Song W, Liu X, Mou C, Lan J, Liu S, Tian Y, Zhao Z, Jiang L, Wan J (2019) Identification of QTL for seed dormancy from weedy rice and its application to elite rice cultivar ‘Ninggeng 4’. Molecular Breeding 39:123. https://doi.org/10.1007/s11032-019-1031-2 Olajumoke B, Juraimi AS, Uddin MK, Husni MHA, Alam MA (2016) Competitive ability of cultivated rice against weedy rice biotypes: A review. Chilean Journal of Agricultural Research 76(2):242–251. https://doi.org/10.4067/s0718-58392016000200015 R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org Ray S, Bose LK, Ray J, Ngangkham U, Katara JL, Samantaray S, Behera L, Anumalla M, Singh ON, Chen M, Wing RA, Mohapatra T (2016) Development and validation of cross-transferable and polymorphic DNA markers for detecting alien genome introgression in Oryza sativa from Oryza brachyantha . Molecular Genetics and Genomics 291(4):1783–1794. https://doi.org/10.1007/s00438-016-1214-z Richharia RH. 1964. Wild rice menace and possible methods of its control. Central Rice Research Institute (CRRI) publication, India. Roma-Burgos N, Sudo MPS, Olsen KM, Werle I, Song BK (2021) Weedy rice ( Oryza spp. ): what’s in a name? Weed Science 69:505–513. doi: 10.1017/wsc.2021.22 Saito H, Okumoto Y, Tsukiyama T, Xu C, Teraishi M, Tanisaka T (2019) Allelic differentiation at the E1/GHD7 locus has allowed expansion of rice cultivation area. Plants 8(12):550. https://doi.org/10.3390/plants8120550 Shobha Rani N, Shobha Rao LV, Viraktamath BC, Mishra B (2004) National guidelines for the conduct of tests for distinctiveness, uniformity and stability. Directorate of Rice Research, Hyderabad, Telangana, India: 6–13. Singh K, Kumar V, Saharawat YS, Gathala M, Ladha JK, Chauhan BS (2013) Weedy rice: An emerging threat for direct seeded rice production systems in India. Rice Research: Open Access 1:1000106. http://dx.doi.org/10.4172/jrr.1000106 Tewari AN (2008) Weedy rice and its management. In: Singh Y, Singh VP, Chauhan B, Orr A, Mortimer AM, Johnson DE, Hardy B (Eds). Direct seeding of rice and weed management in the irrigated rice–wheat cropping system of the Indo-Gangetic Plains. International Rice Research Institute, Los Baños, Philippines, pp: 205–212. Van Berloo R (2008) GGT 2.0: Versatile software for visualization and analysis of genetic data. Journal of Heredity 99(2):232–236. https://doi.org/10.1093/jhered/esm109 Varshney JG, Tiwari JP (2008) Studies on weedy rice infestation and assessment of its impact on rice production. Indian Journal of Weed Science 40(3–4):115–123. Vigueira PA, Olsen KM, Wagner CR, Chittick ZB, Vigueira CC (2020) Weedy rice from South Korea arose from two distinct de-domestication events. Frontiers in Agronomy 2:602612 https://doi.org/10.3389/fagro.2020.602612 Villanueva G. Rosa-Martínez E, Vilanova S, Gramazio P, Prohens, J, Plazas M (2023) Chromosome visualization with introgressions: a tutorial using tassel and GGT2 software. EDULEARN23 Proceedings. p. 8419–8422. https://doi.org/10.21125/edulearn.2023.2187 Wang Q, Su Q, Nian J, Zhang J, Guo M, Dong G, Hu J, Wang R, Wei C, Li G, Wang W, Guo H-S, Lin S, Qian W, Xie X, Qian Q, Chen F, Zuo J (2021) The Ghd7 transcription factor represses ARE1 expression to enhance nitrogen utilization and grain yield in rice. Molecular Plant 14(6):1012–1023. https://doi.org/10.1016/j.molp.2021.04.012 Xia HB, Wang W, Xia H, Zhao W, Lu BR (2011) Conspecific crop-weed introgression influences evolution of weedy rice ( Oryza sativa f. spontanea ) across a geographical range. PLoS ONE 6(1):e16189. https://doi.org/10.1371/journal.pone.0016189 Xiao J, Li J, Grandillo S, Ahn SN, Yuan L, Tanksley SD, McCouch SR (1998) Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon . Genetics 150(2):899–909. DOI: 10.1093/genetics/150.2.899 Xie X, Jin F, Song MH, Suh JP, Hwang HG, Kim YG, McCouch SR, Ahn SN (2007) Fine mapping of a yield-enhancing QTL cluster associated with transgressive variation in an Oryza sativa × O. rufipogon cross. Theoretical and Applied Genetics 116(5):613–622. https://doi.org/10.1007/s00122-007-0695-x Xiong LZ, Lin KD, Dai XK, Xu CG and Zhang Q (1999) Identification of genetic factors controlling domestication-related traits of rice using an F 2 population of a cross between Oryza sativa and O. rufipogon . Theoretical and Applied Genetics 98: 243–251. https://doi.org/10.1007/s001220051064 Xue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q (2008) Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nature Genetics 40(6):761–767. https://doi.org/10.1038/ng.143 Additional Declarations No competing interests reported. Supplementary Files SupplementaryDatasheet1.xlsx SupplementaryDatasheet2.xlsx SupplementaryFigure1and2.pdf SupplementaryTables.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-9327164","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628239556,"identity":"ead4efdf-23f5-47f8-8959-a3a851bfa716","order_by":0,"name":"Swayamsiddha Aswita Dhal","email":"","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Swayamsiddha","middleName":"Aswita","lastName":"Dhal","suffix":""},{"id":628239557,"identity":"dd4ac966-7e86-4631-bb89-88244510c7cc","order_by":1,"name":"Anilkumar Chandrappa","email":"","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Anilkumar","middleName":"","lastName":"Chandrappa","suffix":""},{"id":628239558,"identity":"0b2476c3-0c8e-4f0d-a629-3cf10b6bddf4","order_by":2,"name":"Debashree Dalai","email":"","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Debashree","middleName":"","lastName":"Dalai","suffix":""},{"id":628239559,"identity":"7bcb5ca4-2e89-454c-b399-55a6cc399dd0","order_by":3,"name":"Anowar Hossain","email":"","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Anowar","middleName":"","lastName":"Hossain","suffix":""},{"id":628239560,"identity":"a2b2904c-4322-400f-8a27-86bcb560ff09","order_by":4,"name":"Anarghya Brahmankodu Kumar","email":"","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Anarghya","middleName":"Brahmankodu","lastName":"Kumar","suffix":""},{"id":628239561,"identity":"9613a1e4-f9c3-4895-b663-49e905bc6e1a","order_by":5,"name":"Virender Kumar","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Virender","middleName":"","lastName":"Kumar","suffix":""},{"id":628239562,"identity":"8e7def85-e634-4e23-8147-ba41854136d3","order_by":6,"name":"Soham Ray","email":"","orcid":"","institution":"Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Soham","middleName":"","lastName":"Ray","suffix":""},{"id":628239563,"identity":"bc5b1d41-028b-4724-ae4c-4574754bce94","order_by":7,"name":"Kshirod Kumar Jena","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kshirod","middleName":"Kumar","lastName":"Jena","suffix":""},{"id":628239564,"identity":"dc9545ac-2287-4dc5-b591-c5a1ed36853f","order_by":8,"name":"Nimai Prasad Mandal","email":"","orcid":"","institution":"ICAR-CRRI-Central Rainfed Upland Rice Research Station","correspondingAuthor":false,"prefix":"","firstName":"Nimai","middleName":"Prasad","lastName":"Mandal","suffix":""},{"id":628239565,"identity":"ab232988-9646-43f5-b69a-ea7c3d523ebc","order_by":9,"name":"Divya Balakrishnan","email":"","orcid":"","institution":"Indian Institute of Rice Research","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"","lastName":"Balakrishnan","suffix":""},{"id":628239566,"identity":"7f867ccf-d513-4e1f-97bb-550f34841310","order_by":10,"name":"Kutubuddin A Molla","email":"","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kutubuddin","middleName":"A","lastName":"Molla","suffix":""},{"id":628239567,"identity":"56a954fb-5b01-4f17-9c67-d9048a83f214","order_by":11,"name":"Pratik Satya","email":"","orcid":"","institution":"Central Research Institute for Jute and Allied Fibres","correspondingAuthor":false,"prefix":"","firstName":"Pratik","middleName":"","lastName":"Satya","suffix":""},{"id":628239568,"identity":"0696766e-8534-4163-8d43-9b8d52f43608","order_by":12,"name":"Chinmay Kumar Pradhan","email":"","orcid":"","institution":"Utkal University","correspondingAuthor":false,"prefix":"","firstName":"Chinmay","middleName":"Kumar","lastName":"Pradhan","suffix":""},{"id":628239569,"identity":"ff0e44d2-8df1-4257-b803-4829b76e594d","order_by":13,"name":"Trilochan Mohapatra","email":"","orcid":"","institution":"Protection of Plant Varieties and Farmers’ Rights Authority","correspondingAuthor":false,"prefix":"","firstName":"Trilochan","middleName":"","lastName":"Mohapatra","suffix":""},{"id":628239570,"identity":"0957f05a-bb53-4127-b162-a2845c8a485b","order_by":14,"name":"Dipti Ranjan Pani","email":"","orcid":"","institution":"ICAR-National Bureau of Plant Genetic Resources, Base Centre","correspondingAuthor":false,"prefix":"","firstName":"Dipti","middleName":"Ranjan","lastName":"Pani","suffix":""},{"id":628239571,"identity":"a2a98015-891c-4e2e-b9ff-b67052700722","order_by":15,"name":"Meera Kumari Kar","email":"","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Meera","middleName":"Kumari","lastName":"Kar","suffix":""},{"id":628239572,"identity":"901c57b9-350a-41ae-8f1f-7a38a34fe185","order_by":16,"name":"Mridul Chakraborti","email":"data:image/png;base64,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","orcid":"","institution":"Central Rice Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Mridul","middleName":"","lastName":"Chakraborti","suffix":""}],"badges":[],"createdAt":"2026-04-05 15:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9327164/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9327164/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107869518,"identity":"3fa58f34-dd45-4c41-ba0a-c24064019ea6","added_by":"auto","created_at":"2026-04-27 07:37:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":267112,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic summary of the overall workflow followed for dissecting the population dynamics of domestication syndrome traits associated with crop mimicry in weedy rice\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/bca533280598d122ace756c6.png"},{"id":107869355,"identity":"c9be1c79-c78e-4f5e-882b-794337fc187f","added_by":"auto","created_at":"2026-04-27 07:36:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":462143,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram illustrating the modified methodology followed for developing synteny-guided backcross-derived inbred lines (BILs) and chromosome segment substitution lines (CSSLs) from \u003cem\u003eO. rufipogon\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/fd049c86e88e81b43898df16.png"},{"id":107730472,"identity":"0827d1e3-316c-461a-ba03-50cf38ecfe39","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":689357,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification\u003cstrong\u003e \u003c/strong\u003eand categorization\u003cstrong\u003e \u003c/strong\u003eof common cross-amplifiable STMS markers among \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003ejaponica\u003c/em\u003e, \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003eindica\u003c/em\u003e, and \u003cem\u003eO. rufipogon into syntenic and non-syntenic classes. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eNon-syntenic STMS markers identified from whole-genome sequences (blue) and polymorphic SNPs from the 1k-RiCA panel showing segregation distortion (red) in the BC\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e population of CR Dhan 307 × O. rufipogon accession AC100444. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Graphical\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003erepresentation of intra- and inter-chromosomal translocations of the non-syntenic STMS markers among O. sativa\u003c/em\u003e ssp. \u003cem\u003ejaponica\u003c/em\u003e, \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003eindica\u003c/em\u003e, and \u003cem\u003eO. rufipogon. Marker names and predicted physical positions in different subspecies or species have been provided in Supplementary Datasheet 1. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative proportions of syntenic and non-syntenic STMS markers identified across the 12 chromosomes. Marker names and predicted physical positions of syntenic STMS markers have been provided in Supplementary Datasheets 1 and 2.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/42cb4971b8a70c5f40580bc2.png"},{"id":107730474,"identity":"56e32446-5474-4e20-8942-7113ff8ecc31","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":441241,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic map of SNP markers and consensus physical map of syntenic STMS and SNP markers used for the development and characterization of backcross-derived inbred lines (BILs) and identification of chromosome segment substitution lines (CSSLs) from the BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e6\u003c/sub\u003e population of \u003cem\u003eCR Dhan 307 × O. rufipogon accession AC100444. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eGenetic map of \u003c/em\u003e325 SNP markers without segregation distortion. \u003cstrong\u003eB.\u003c/strong\u003e Consensus physical map showing 120 syntenic and polymorphic STMS markers (blue) and 325 SNP markers (red) between \u003cem\u003eCR Dhan 307 and O. rufipogon accession AC100444. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA total of 54 CSSLs were identified from the population, each represented with its left- and right-anchored markers (SNP/STMS).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/b5448d9fd8d47fd15b3c34a3.png"},{"id":107730482,"identity":"3b87af34-51b8-48b5-aa12-884adc26283f","added_by":"auto","created_at":"2026-04-24 12:56:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":429090,"visible":true,"origin":"","legend":"\u003cp\u003eMapping of the locus for leaf sheath pigmentation using introgression lines, including chromosome segment substitution lines (CSSLs), backcross-derived inbred lines (BILs), and single segment substitution lines (SSSLs) developed in the present study and a previous work (Dalai et al. 2026) from backcross progenies of \u003cem\u003eCR Dhan 307 × O. rufipogon accession AC100444. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003ePhenotypic variation among selected introgression lines for leaf sheath pigmentation at the early seedling stage. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Substitution mapping with introgression lines delimited the trait to the genomic interval between Chr06_4757948 and RM253 on chromosome 6. \u003c/em\u003eMultiple earlier studies with both cultivated and wild rice indicated that one of the gene (\u003cem\u003eOsC1\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eassociated with purple leaf sheath of rice is located on chromosome 6 and show a tight linkage with RM253 (Xiong et al. 1999; Lorieux et al. 2000; Fan et al. 2008; Chen et al. 2024). \u003cem\u003eGLS: Green Leaf Sheath; PLS: Purple Leaf Sheath. Genotype numbers (1–13) in the graphical genotyping diagram correspond to the plant numbers shown in 7A.\u003c/em\u003e \u003cstrong\u003eC. \u003c/strong\u003eSeventeen additional syntenic STMS markers identified within the interval between Chr06_4757948 and RM253 from syntenic marker dataset (Supplementary Datasheet 2) as further fine mapping resource.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/74dee00758343054f83850c8.png"},{"id":107730476,"identity":"7f4a2d8a-220e-4bb6-9c44-1eabb88852d0","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":353003,"visible":true,"origin":"","legend":"\u003cp\u003eMapping of the locus for seed dormancy using introgression lines, including chromosome segment substitution lines (CSSLs), backcross-derived inbred lines (BILs), and single segment substitution lines (SSSLs) identified from backcross progenies of \u003cem\u003eCR Dhan 307 × O. rufipogon accession AC100444 \u003c/em\u003edeveloped in the present study and a previous work (Dalai et al. 2026). \u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Representative photograph showing germination percentage of BIL Q12-9 (ii), compared with CR Dhan 307 (i) and O. rufipogon accession AC100444 (iii). These representative seeds were tested for germination seven days after harvest, and the photograph was captured 14 days thereafter. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Contrasting germination patterns between CR Dhan 307 and BILs showing short-term seed dormancy (STSD): a–f, seeds tested seven days after harvest (37 days after flowering) and photographed 14 days thereafter; a\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e–f\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, seeds tested 14 days after harvest (44 days after flowering) and photographed seven days thereafter. While a \u0026amp; a\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e represent CR Dhan 307, b–f and b\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e–f\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e represent selected BILs showing STSD. \u003c/em\u003e\u003cstrong\u003eC. \u003c/strong\u003eSubstitution mapping with BILs and CSSLs predicted a locus regulating STSD, located either between Chr07_5442983 and Ghd7-2-02 or between Ghd7-2-02 and Chr07_14729119.\u003cstrong\u003e D.\u003c/strong\u003e Reciprocal recombinants between Chr07_5442983 and Ghd7-2-02 or between Ghd7-2-02 and Chr07_14729119 (6a/6b; 8a/8b; 9a/9b), identified from IC\u003csub\u003e1\u003c/sub\u003eBC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e progenies of three selected BILs (shown as 6, 8, and 9 in Fig. 8C), confirmed the association of Ghd7-2-02 and MSU7_7_9152479_C-G with STSD. \u003cstrong\u003eE. \u003c/strong\u003eVariation in germination percentage among 47 IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2:3 \u003c/sub\u003eprogenies homozygous for the Ghd7-2-02 allele of \u003cem\u003eO. rufipogon \u003c/em\u003eidentified from IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e progenies of SSSL-L-7-7/CR Dhan 307 at different durations after flowering (30, 37, 44, 51, and 140 days after flowering). UT: Untreated; HT: Heat treated to break dormancy.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/3442b78310fa48335bbdf7f8.png"},{"id":107868975,"identity":"a4ccb45a-e4df-49a1-ab1f-b24a3aaea97e","added_by":"auto","created_at":"2026-04-27 07:35:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":97745,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted short- and long-term changes in weedy rice population dynamics for leaf sheath pigmentation (purple vs. green) in weedy rice swarms. Trait inheritance was modeled under single-gene dominance and/or complementary gene action, assuming 90–95% selfing and 5–10% outcrossing per generation. Initial weedy rice populations were set at 30:70 for purple and green leaf sheaths. \u003cstrong\u003eA: \u003c/strong\u003eOnly\u003cstrong\u003e s\u003c/strong\u003eingle dominant gene controlling the trait in the base population\u003cstrong\u003e; A\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, A\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e: \u003c/strong\u003e5% outcrossing;\u003cstrong\u003e A\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, A\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e: \u003c/strong\u003e10% outcrossing. \u003cstrong\u003eB: \u003c/strong\u003eOnly\u003cstrong\u003e c\u003c/strong\u003eomplementary gene action controlling the trait in the base population; \u003cstrong\u003eB\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, B\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 5% outcrossing; \u003cstrong\u003eB\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, B\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 10% outcrossing. \u003cstrong\u003eC: \u003c/strong\u003eEqual contribution of single dominant gene and complementary gene action in the base population; \u003cstrong\u003eC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, C\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 5% outcrossing; \u003cstrong\u003eC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, C\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 10% outcrossing.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/e4f0161ac4fe42b7e338a606.png"},{"id":107730478,"identity":"5f00bbf8-3935-4d92-83dd-479b98ef9a21","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":121346,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of temporal changes (short- and long-term) in weedy rice population dynamics for leaf sheath pigmentation (purple vs. green) in a crop–weed continuum under differential selection pressure. Trait inheritance was assumed to be controlled by both single gene with complete dominance and complementary gene action, present in equal proportion in the base population of weedy rice. The mating structure assumed 90–95% selfing and 5–10% outcrossing per generation. The base population was set at a 70:30 ratio of crop to weedy rice for each season, with initial polymorphism in weedy rice fixed at 30:70 for purple and green leaf sheaths, respectively. \u003cstrong\u003eA:\u003c/strong\u003e No selection pressure applied and the continuously grown cultivar has green leaf sheath. \u003cstrong\u003eA\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, A\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 5% outcrossing; \u003cstrong\u003eA\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, A\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e: \u003c/strong\u003e10% outcrossing. \u003cstrong\u003eB:\u003c/strong\u003e No selection pressure applied and the continuously grown cultivar has purple leaf sheath. \u003cstrong\u003eB\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, B\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 5% outcrossing; \u003cstrong\u003eB\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, B\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 10% outcrossing. \u003cstrong\u003eC:\u003c/strong\u003e Absolute selection pressure applied against purple leaf-sheathed plants and the continuously grown cultivar has green leaf sheath. \u003cstrong\u003eC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, C\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 5% outcrossing; \u003cstrong\u003eC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, C\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 10% outcrossing. \u003cstrong\u003eD:\u003c/strong\u003e Absolute selection pressure applied against green leaf-sheathed plants and the continuously grown cultivar has purple leaf sheath. \u003cstrong\u003eD\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, D\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 5% outcrossing; \u003cstrong\u003eD\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, D\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e:\u003c/strong\u003e 10% outcrossing. All the models were validated with 100- replications and reiterations.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/4597b48dd9f07fe4089d91e8.png"},{"id":107730480,"identity":"2c4f6822-f9a2-4ccf-bb2b-7509a18a4019","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1246251,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative variation for awn morphology and leaf sheath pigmentation at the seedling stage among introgression lines of \u003cem\u003eCR Dhan 307 × O. rufipogon accession AC100444. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Variation in awn morphology among BILs (1–12) compared with the recurrent parent CR Dhan 307 (C307).\u003c/em\u003e \u003cstrong\u003eB. \u003c/strong\u003eVariation in leaf sheath pigmentation among the BILs and NIL selected for the field simulation study: i. near isogenic line (NIL) derived from IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e progenies of SSSL-U2-6/CR Dhan 307; ii. CR Dhan 307; iii. BIL with purple leaf sheath and \u003cem\u003esd1 \u003c/em\u003eallele; iv. BIL with green leaf sheath and \u003cem\u003esd1 \u003c/em\u003eallele; v. BIL with purple leaf sheath and \u003cem\u003eSD1 \u003c/em\u003eallele, vi. BIL with green leaf sheath and \u003cem\u003eSD1 \u003c/em\u003eallele; vii. \u003cem\u003eO. rufipogon accession AC100444. PLS: Purple leaf sheath; GLS: Green leaf sheath; sd1: semi-dwarfing allele of sd1 gene. SD1: wild type (tall phenotype) allele of sd1 gene. The seedlings shown here were nursery raised during January 2026 at ICAR-CRRI, Cuttack. The seedlings with purple basal pigmentation also showed healthier growth without any cold injury symptoms when compared to those with green leaf sheath.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/ff4f36651259059b43a8cc3d.png"},{"id":107869163,"identity":"759f917b-5d96-4aa2-8ff8-f9cf4b164d3d","added_by":"auto","created_at":"2026-04-27 07:36:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":183236,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated field experiment for dissection of population dynamics of weedy derivatives under different selection regimes. \u003cstrong\u003eA.\u003c/strong\u003eExperimental design for season-1. The field was divided into two main plots: one with manual weeding (MP1) and the other without manual weeding (MP2). Each main plot was randomly subdivided into four subplots (SP1–SP4) comprising: (i) CR Dhan 307, (ii) purple leaf sheath NIL, (iii) a seed mixture of 60% CR Dhan 307 and 40% awned BILs, and (iv) a seed mixture of 60% purple leaf sheath NIL and 40% awned BILs. Any variant of the main cultivar for leaf sheath pigmentation in respective subplots within MP1 were removed within 45 days. The experiment was laid out in a split-plot design. \u003cstrong\u003eB.\u003c/strong\u003e Relative frequencies of weedy derivatives observed after flowering in the four subplots (SP) of the two main plots (MP). \u003cstrong\u003eC.\u003c/strong\u003e Experimental design for season 2. Each subplot with residual shattered seeds from the previous season in both main plots was further divided into two sub-subplots (SSP1 and SSP2), where CR Dhan 307 or the purple leaf sheath NIL were randomly sown. Any variant for leaf sheath pigmentation of the main cultivar in the respective sub-subplots of all subplots within MP1 were removed within 45 days. The experiment was laid out in a split-plot design. \u003cstrong\u003eD.\u003c/strong\u003e Relative frequencies of weedy derivatives observed after flowering in the two sub-subplots (SSP) of all the four subplots (SP) under the two main plots (MP). Alphabetical letters on the histograms indicate Tukey’s HSD test-based comparisons. Different letters (a, b) indicate statistically significant differences at 5% level. In 10B, comparisons were made among subplots (SP) within a main plot (MP); and in 10D, comparisons were made between the two sub-subplots (SSP) within a subplot (SP).\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/a543ff646b58ec6460caa0dd.png"},{"id":109203736,"identity":"964f06cd-d9e6-45e8-82e0-e81186f8a21b","added_by":"auto","created_at":"2026-05-13 14:44:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4497032,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/bbde648f-43cf-4800-80c6-de44cfcb3a0b.pdf"},{"id":107730468,"identity":"392dac5a-ccbd-4195-8cba-cc4e463cd8f1","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":566662,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDatasheet1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/590530df1935acb1d5b6a5c8.xlsx"},{"id":107730469,"identity":"4f7c5168-5010-4f76-94fd-1dc59c7d516f","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1189187,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDatasheet2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/0f2ed299da6aba040ab2bca1.xlsx"},{"id":107730471,"identity":"94a6c5f4-9b36-49c5-aa48-8bd1a98cdc77","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":523525,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1and2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/10db90b67460f10491757479.pdf"},{"id":107730473,"identity":"f8ea9825-65fb-43a3-9ca8-a0561077216d","added_by":"auto","created_at":"2026-04-24 12:56:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22712,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9327164/v1/64b6e2dfef4c8ed43eb24540.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive models and wild progenitor-derived introgression lines dissect the population dynamics of domestication syndrome traits associated with crop mimicry in weedy rice","fulltext":[{"header":"Key message","content":"\u003cp\u003eSynteny-guided chromosome segment substitution lines were developed from at reduced cost and efforts through an improved methodology and utilized to genetically dissect an evolution-guided strategy for weedy rice management.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWeedy rice is one of the most serious threats to rice production causing 15\u0026ndash;90% yield loss across continents (Roma-Burgos et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to close genomic affinity and cross-compatibility with cultivated rice ecotypes, high seed-shattering, rich phenotypic diversity and aggressive competitive ability, it has become a persistent weed in diverse rice ecosystems (Liang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The problem has become more serious with adoption of mechanized direct seeding and harvesting, reduced availability of water for crop management, and cultivation of varieties with narrow genetic base (Singh et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cong et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Management options like manual weeding and conventional herbicide application are ineffective due to phenotypic resemblance and close phylogenetic relationship. Although herbicide tolerant (HT) rice can address the problem, the high cross-compatibility of weedy rice with both genetically modified (GM) and non-GM HT-rice cultivars may sometimes lead to escape of the trait to weedy rice due to natural outcrossing. Before the extensive cultivation of semi-dwarf high yielding rice varieties, farmers practicing direct seeding in India managed the weedy rice load below economic threshold levels through rotational cultivation of tall landraces having purple-pigmented and green leaf sheath, thereby selectively eliminating green- and purple leaf-sheathed weedy rice, respectively (Richharia \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1964\u003c/span\u003e; Tewari \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, almost all the present-day high yielding rice varieties lack leaf-sheath pigmentation and the practice of such rotational cultivation involving genotypes showing contrasting visual markers is almost forgotten. Although large scale adoption of transplanting effectively mitigated the threat over a long period of time, gradual build-up of weedy rice population has supported their resurgence even in transplanted fields of India, Japan and Taiwan in recent years (Singh et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Imaizumi \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hsu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the Mekong delta of Vietnam, where yield loss up to 46% due to weedy rice is reported under the predominant direct seeding practice, farmers found it difficult to distinguish weedy rice and manage the problem through their conventional practice of using high seed rates (up to 300 kg ha\u003csup\u003e-1\u003c/sup\u003e) (Chauhan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Phenotypic resemblance to cultivated varieties, varying degree of seed dormancy, early maturity and shattering habit make weedy rice a formidable and persistent competitor in rice fields. Therefore, understanding their population dynamics in cultivated environments is critical to decipher the adaptive strategies and design effective control measures (Nadir et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBotanically classified as \u003cem\u003eOryza sativa\u003c/em\u003e f. \u003cem\u003espontanea\u003c/em\u003e, weedy rice is considered to be originated through natural hybridization between cultivated rice (\u003cem\u003eOryza sativa\u003c/em\u003e) and its wild progenitors (\u003cem\u003eOryza rufipogon\u003c/em\u003e and \u003cem\u003eOryza nivara\u003c/em\u003e) through de-domestication and feralization (Vigueira et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cong et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among the two species, \u003cem\u003eO. rufipogon\u003c/em\u003e is considered as the most common progenitor across geographical regions (Ma et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Roma-Burgos et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Such reverse-domestication process can be monitored by change in the key domestication-syndrome characters, such as dormancy, seed shattering, awn, plant height, and photoperiod sensitivity. These traits define the continuum linking wild, cultivated, and weedy forms under different selection regimes and shape their adaptive trajectories (Delouche et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast with the rice domestication, which was principally guided by positive artificial selection for few major traits, the de-domestication and feralization of weedy rice is principally affected by independent natural selection in different regions and time periods under cultivated or semi-cultivated environments. The effect of natural selection under such complex situations cannot be interpolated from the traditional gene flow models developed by assuming unrestricted natural environment (Migdałek and Żelawski \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence developing an effective model to understand weedy rice dynamics requires information about basal population structure and the associated changes occurring in both short and long term.\u003c/p\u003e \u003cp\u003eGene flow between two species can be monitored by identification of interspecific recombinants containing fragments of genomes from either of the species (Migdałek and Zelawski 2022). Since weedy rice originate from natural hybridization of \u003cem\u003eO. sativa\u003c/em\u003e and \u003cem\u003eO. rufipogon\u003c/em\u003e, their interspecific derivatives containing fragments of \u003cem\u003eO. rufipogon\u003c/em\u003e genome in the background of \u003cem\u003eO. sativa\u003c/em\u003e and harbouring alleles governing weedy forms of domestication-syndrome traits can be effectively utilized to understand the population dynamics of weedy rice. Although \u003cem\u003eO. rufipogon\u003c/em\u003e\u0026ndash;derived populations have been widely exploited for yield enhancement and stress resilience (Xiao et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ge et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), their potential to resolve the genetic architecture of crop-mimicry driven through domestication syndrome traits remains underexplored. Chromosome segment substitution lines (CSSLs) and backcross-derived inbred lines (BILs) with crop-mimicking de-domestication traits can therefore serve as a proxy for weedy rice. Syntenic, cross-transferable markers enabling systematic tracking of introgressed segments (Dalai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) can further support the genetic dissection of those traits in uniform genetic backgrounds. In this study, we developed CSSLs and BILs of \u003cem\u003eO. rufipogon\u003c/em\u003e in the genetic background of cultivated rice using a novel bulked backcrossing strategy guided by syntenic markers, and subsequently identified lines mimicking weedy rice. Selected BILs and NILs were further utilized to interpret phenotype-based selection as an evolutionary process influencing allele frequencies and population structure along the crop\u0026ndash;weed continuum and to design future breeding strategies for sustainable management of this recalcitrant invasive weed.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe study simultaneously involved development of introgression lines from wild progenitor, computational simulations for predicting population dynamics of weedy rice, and further utilization of both the resources to genetically dissect the population dynamics of domestication syndrome traits associated with crop mimicry. A schematic summary of the overall workflow has been presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of introgression lines from wild progenitor\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eFour \u003cem\u003eO. rufipogon\u003c/em\u003e accessions from India (IRGC 81885, AC 100444, AC 100015) and Bangladesh (IRGC 103404), along with 14 diverse \u003cem\u003eO. sativa\u003c/em\u003e cultivars were utilized in the study (Supplementary Table\u0026nbsp;1). A set of CSSLs, SSSLs and BILs developed in an earlier study (Dalai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) were included for validation purposes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of syntenic markers\u003c/h3\u003e\n\u003cp\u003eA core set of 1K cross-amplifiable STMS markers for the \u003cem\u003eO. sativa\u003c/em\u003e complex (1K-CoreCaOA) was previously developed as a resource for CSSL development across A-genome species (Dalai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). From that basic dataset, common markers among \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003ejaponica\u003c/em\u003e, \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003eindica\u003c/em\u003e, and \u003cem\u003eO. rufipogon\u003c/em\u003e were selected. Markers showing intra- or inter-chromosomal translocations in one or more species or subspecies were excluded to select only a syntenic set.\u003c/p\u003e\n\u003ch3\u003eMolecular marker profiling\u003c/h3\u003e\n\u003cp\u003eGenomic DNA isolation and molecular marker profiling were carried out following standard protocols (Dalai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). For pooled analysis, equal concentrations of DNA from three plants were combined after quantification. SNP genotyping was outsourced (Intertek India, Hyderabad) using the 1K rice custom amplicon (1k-RiCA) panel (Arbelaez et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSelection of cross combination for higher seed-setting\u003c/h3\u003e\n\u003cp\u003eApproximately 1000 spikelets of the 14 \u003cem\u003eO. sativa\u003c/em\u003e cultivars were emasculated for each cross combination, and separately pollinated with the four wild accessions to generate 56 interspecific hybrids. After hybridity confirmation through polymorphic markers, ~\u0026thinsp;1000 emasculated spikelets of each recurrent parent were pollinated with bulked pollens of F\u003csub\u003e1\u003c/sub\u003e plants to generate BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e progenies. Progenies from all cross combinations were characterized for the qualitative characters from a DUS descriptor (Shobha Rani et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and compared with respective recurrent parents. In the crosses showing higher seed set with all the \u003cem\u003eO. rufipogon\u003c/em\u003e accessions, BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plants were further characterized using one polymorphic marker per chromosome. In other crosses, marker analysis was restricted to phenotypically similar plants. Progenies morphologically resembling the recurrent parent, and not displaying heterozygosity for any of the 12 markers, were considered as selfing derived plants.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModified breeding scheme for development of syntenic marker\u0026ndash;anchored BILs and CSSLs\u003c/h2\u003e \u003cp\u003eA population-based backcrossing strategy was adopted for BIL and CSSL development, replacing conventional methods that require extensive pedigree maintenance and genotyping in every generation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The recurrent parent from the selected cross combination, chosen for higher seed set, was repeatedly backcrossed with bulked pollen collected from BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e and BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e progenies flowering on the same day.\u003c/p\u003e \u003cp\u003eIn BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e, a total of 282 plants were randomly selected, and ninety-four pooled DNA samples (three plants per pool) were genotyped using 120 syntenic polymorphic STMS markers. The three plants from each pool were subsequently genotyped using only the heterozygous markers identified in the respective pools. A subset of plants representing all markers across the 12 chromosomes through multiple overlapping segments was selected. One panicle from each selected BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plant was sown, and panicle-to-progeny rows were raised in BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e. Eight plants per row were randomly chosen and advanced through the single-panicle descent method up to BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e6\u003c/sub\u003e, and one plant from each line was selected as a BIL.\u003c/p\u003e \u003cp\u003eFollowing SNP genotyping using the 1K-RiCA platform, a genetic map was constructed using IciMapping v4.1 (Meng et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Markers with \u0026gt;\u0026thinsp;5% missing data or showing significant segregation distortion were excluded from linkage map construction. Genetic distances were calculated using the Kosambi mapping function (Kosambi \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1943\u003c/span\u003e). Collinearity between the genetic and physical maps was assessed for the SNP markers and further validated through targeted profiling of the BILs with syntenic STMS markers. Overlapping CSSLs anchored by both SNP and STMS markers were identified using the CSSL Finder tool (Lorieux \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and visualized through RStudio version 4.5.2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDissection and mapping of key domestication syndrome traits\u003c/h3\u003e\n\u003cp\u003eLeaf sheath pigmentation and seed dormancy were dissected using BILs, CSSLs, single segment substitution line (SSSLs), and advanced substitution lines derived from the same cross combination generated in the present and a previous study (Dalai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Phenotypic variation was assessed, overlapping introgressed segments were compared, and common genomic intervals were identified. Recombinants within the target regions were further used for substitution mapping (Nguyen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to delimit the minimal marker intervals.\u003c/p\u003e\n\u003ch3\u003eMapping of leaf sheath pigmentation\u003c/h3\u003e\n\u003cp\u003eAll BILs and a previously developed CSSL set (Dalai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) were phenotyped for purple versus green leaf sheath. Shared introgressed segments among lines with similar phenotypes were identified, and recombinants were used for fine mapping. An SSSL with a pigmented leaf sheath was crossed with the recurrent parent to generate\u0026thinsp;~\u0026thinsp;2000 IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants. A random subset was genotyped using two flanking STMS markers, and the genotype of IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants for the gene controlling the trait was determined based on the segregation pattern of their IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2:3\u003c/sub\u003e progenies. Lines segregating for leaf sheath pigmentation were classified as heterozygous, whereas non-segregating lines were classified as homozygous. The allelic data of flanking markers and trait was utilized to calculate genetic distance. A non-segregating, purple-sheathed recombinant with upright tillers was selected as a NIL.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMapping of seed dormancy\u003c/h2\u003e \u003cp\u003eAll BILs, the viviparous recurrent parent, and two non-viviparous checks (Swarna and CR 1014) were grown in an augmented field design to screen for seed dormancy. Germination tests were conducted with three biological replicates. Primary panicles from 20 plants flowering on the same day were tagged, and seeds were harvested 30 days later. Germination was assessed on the day of harvest and at 7, 14, and 21 days thereafter. The remaining seeds were dried, stored for three months, and retested with and without heat treatment (48\u0026deg;C for seven days) to distinguish dormancy from non-viability.\u003c/p\u003e \u003cp\u003eLines showing no germination up to seven days after harvest but exceeding 50% germination within 21 days were validated across seasons. Overlapping introgressed segments were compared to identify associated marker intervals. Selected BILs carrying the target interval were crossed with the recurrent parent, reciprocal homozygous recombinants were identified and evaluated further. Previously developed CSSLs and BILs (Dalai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) were further evaluated to reconfirm marker\u0026ndash;trait associations. An SSSL carrying the target locus was crossed with the recurrent parent; IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e progenies were space-planted and phenotyped for germination. Samples representing extreme and intermediate classes were genotyped, and homozygous IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2:3\u003c/sub\u003e progenies for the candidate marker were comprehensively characterized for germination percentage over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of population dynamics of weedy rice\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eBaseline data collection\u003c/h2\u003e \u003cp\u003eA total of 36 paddy fields (\u0026ge;\u0026thinsp;4000 m\u0026sup2;) with varying levels of weedy rice infestation were sampled (Supplementary Table\u0026nbsp;2). Additionally, four fields with complete invasion by weedy rice swarms were selected. Observations were recorded at the booting to flowering stage of the crop. From each field, 100 randomly selected weedy rice plants were scored for the presence and length of awns [short (\u0026le;\u0026thinsp;20 mm), medium (\u0026gt;\u0026thinsp;20\u0026ndash;40 mm), or long (\u0026gt;\u0026thinsp;40 mm)], shattering habit (present/absent), and leaf sheath pigmentation (purple or green). Leaf sheath pigmentation of the rice varieties grown continuously over the years or under varietal rotation in the respective fields during the preceding three years was also recorded.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComputational simulations\u003c/h2\u003e \u003cp\u003eTemporal changes in purple- and green\u0026ndash;leaf sheath phenotypes in weedy rice were simulated using a probability-based, discrete-generation model implemented in R (R Core Team \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Model parameters were defined based on published reports and field surveys (Supplementary Table\u0026nbsp;3). Inheritance of the trait was assumed to follow two alternative genetic models: (i) single-gene control with complete dominance (F\u003csub\u003e2\u003c/sub\u003e segregation 3:1, purple: green), and (ii) complementary gene action (9:7), each present in equal proportion in the base population. The mating structure considered the predominantly autogamous nature of rice, assuming 90\u0026ndash;95% selfing and 5\u0026ndash;10% outcrossing per generation. The base population composition was set at a 70:30 ratio of crop to weedy rice for each season. Initial dimorphism in weedy rice was fixed at 30:70 for purple and green leaf sheaths, respectively.\u003c/p\u003e \u003cp\u003eSimulation methods were initially developed based on a single-locus model and later extended to incorporate two-gene interactions. Simulations were conducted with 100 replications. Model validation was performed by comparing the simulated decline in heterozygosity under partial selfing with theoretical expectations and by reproducing Hardy\u0026ndash;Weinberg equilibrium under complete outcrossing maintained at constant allele frequencies across generations. This two-pronged strategy confirmed the correctness of the algorithms implemented in the simulation based on mating and segregation. The decline in heterozygosity was evaluated using the equation H\u003csub\u003et\u003c/sub\u003e=H\u003csub\u003e0\u003c/sub\u003e(1\u0026thinsp;\u0026minus;\u0026thinsp;s/2)\u003csup\u003et\u003c/sup\u003e, where \u0026lsquo;H\u003csub\u003et\u003c/sub\u003e​\u0026rsquo; represents heterozygosity at the t\u003csup\u003eth\u003c/sup\u003e generation, \u0026lsquo;H\u003csub\u003e0\u003c/sub\u003e​\u0026rsquo; is the heterozygosity in the base population, and \u0026lsquo;s\u0026rsquo; denotes the rate of selfing.\u003c/p\u003e \u003cp\u003eSimulations were conducted for both pure weedy rice swarms and a crop\u0026ndash;weed continuum, assuming that the cultivated variety was uniformly purple or green and that each year, the crop was grown in the same field using the seed harvested from previous season without selection bias. Selection against plants phenotypically contrasting with the crop was modelled under two extreme scenarios: no selection (0%) or complete unidirectional selection (100%). Phenotypic and allelic frequencies were updated in each generation based on genotypic frequencies through iterative computation (100 nos.) under different selection regimes. Mutation, migration, random genetic drift, penetrance, expressivity and any other factors affecting allele frequencies were assumed to act uniformly on both phenotypic classes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eField testing of weedy rice population dynamics\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eSelection of BILs\u003c/h2\u003e \u003cp\u003eAll the BILs were initially characterized for the presence of awns. Lines possessing medium to long awns were further evaluated for seed shattering. BILs exhibiting spontaneous seed shattering along with short-term seed dormancy induced by the mapped gene were shortlisted. The selected lines were then genotyped using sd1(h), considered as a perfect marker for the \u003cem\u003esd1\u003c/em\u003e gene (Ellis and Spielmeyer \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and further classified according to leaf sheath pigmentation.\u003c/p\u003e \u003cp\u003eUsing combinations of \u003cem\u003esd1\u003c/em\u003e alleles and leaf sheath pigmentation, four BILs were selected to represent contrasting adaptive phenotypic configurations of weedy rice. To constitute the mixed populations, seeds from each of the four selected BILs (10% each) were thoroughly combined with seeds of CR Dhan 307 or its purple leaf sheath NIL (60%) on a seed-count basis. Prior to mixing, all seeds were sun-dried and stored for six months.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSimulated field trial\u003c/h2\u003e \u003cp\u003eThe selected experimental plot had no history of rice cultivation during the preceding two seasons. Pre-sowing irrigation was applied before final land preparation. To control non-\u003cem\u003eOryza\u003c/em\u003e weed species, bispyribac sodium and a commercial formulation of florpyrauxifen-benzyl (2.13%) + cyhalofop-butyl (10.64% EC) were applied as early- and late-post-emergence herbicides, respectively, at recommended doses in all plots during both seasons.\u003c/p\u003e \u003cp\u003eThe entire field was divided into two main plots, one following manual weeding (MP1) and the other without manual weeding (MP2). During season-1 (wet season 2024), each main plot was randomly subdivided into four subplots (SP1\u0026ndash;SP4) comprising: (i) CR Dhan 307, (ii) purple leaf sheath NIL, (iii) a seed mixture of 60% CR Dhan 307 and 40% awned BILs, and (iv) a seed mixture of 60% purple leaf sheath NIL and 40% awned BILs. Subplots were bunded on all sides, and sowing was performed at a 20 cm row distance after leaving a 60 cm buffer zone from the bunds. At maturity, plants possessing awns were allowed to shatter seeds naturally. Manual shaking of panicles was also carried out before harvest to ensure uniform dispersal of remaining seeds within a sub-plot. All sides of the respective subplots were guarded with shade nets during shaking. Subsequently, all crop residues, including panicles, were removed from the field. After four weeks, the field was manually prepared using spades. During this gap period, all subplots were covered with shade nets to minimize seed movement by birds, squirrels, or rainfall.\u003c/p\u003e \u003cp\u003eDuring season-2 (dry season 2025), each subplot with residual shattered seeds from the previous season was further divided into two sub-subplots (SSP1 and SSP2), where CR Dhan 307 or the purple leaf sheath NIL were randomly sown. Three rows were skipped between the two sub-subplots in every sub-plot. Within MP1, all seedlings emerging between rows were manually removed during weeding, irrespective of their leaf sheath pigmentation. Additionally, any plant showing a distinct leaf sheath colour from the respective variety within the sub-subplot rows was removed up to 45 days after sowing during both seasons. Proportions of green- and purple-leaf-sheathed plants in every subplot or sub-subplot were recorded at 60 and 75 days after sowing. The number of hills bearing awned panicles was counted in each subplot (season-1) and sub-subplot (season-2) after flowering. For each subplot or sub-subplot, three randomly selected grids of 6 m\u0026sup2; were sampled and treated as replications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and data representation\u003c/h2\u003e \u003cp\u003eStatistical analyses were carried out using R (R Core Team \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The split-plot experiment was analyzed by considering replication, main plots, subplots, and sub-subplots according to the design structure. Treatment effects and their interactions were tested using appropriate error terms. Mean comparisons among genotypes and treatment combinations were performed using Tukey\u0026rsquo;s honestly significant difference (HSD) test at the 5% significance level with the agricolae package. CSSL representation with anchored-markers, boxplots and violin plots were prepared in RStudio (version 4.5.2). Graphical genotyping was performed using GGT 2.0 (Villanueva et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and MG2C (Chao et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSelection of cross combination for introgression line development\u003c/h2\u003e \u003cp\u003eAmong the 56 cross combinations and their backcross progenies tested in the study, CR Dhan 307 recorded substantially higher F₁ and BC₁F₁ seed set with all four accessions of \u003cem\u003eO. rufipogon\u003c/em\u003e. Seed setting was lowest with Heera, followed by CR1014. Variation was also recorded among the different \u003cem\u003eO. rufipogon\u003c/em\u003e accessions. Better seed setting was observed for both AC100444 and AC100015 in all the crosses (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of syntenic STMS markers\u003c/h2\u003e \u003cp\u003eA total of 8,975 common cross-amplifiable markers were identified among \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003ejaponica\u003c/em\u003e, \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003eindica\u003c/em\u003e, and \u003cem\u003eO. rufipogon\u003c/em\u003e, of which 265 were non-syntenic (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Supplementary Datasheet 1). Only one marker, AUT23344, showed intra-chromosomal translocation in \u003cem\u003eO. rufipogon\u003c/em\u003e, where it was positioned at 7.27 Mb compared with ~\u0026thinsp;24.16 Mb in \u003cem\u003ejaponica\u003c/em\u003e and 25.71 Mb in \u003cem\u003eindica\u003c/em\u003e. All other non-syntenic markers exhibited inter-chromosomal translocations in either \u003cem\u003eindica\u003c/em\u003e or \u003cem\u003eO. rufipogon\u003c/em\u003e. Chromosome 11 harboured the highest number of non-syntenic markers (76), followed by chromosome 12 (41), with most translocations in these chromosomes observed in \u003cem\u003eindica\u003c/em\u003e. The lowest number of translocations was recorded on chromosome 9. In general, non-syntenic markers showed translocations in either \u003cem\u003eindica\u003c/em\u003e or \u003cem\u003eO. rufipogon\u003c/em\u003e. A rare exception was RM23936, located on chromosome 9 of \u003cem\u003ejaponica\u003c/em\u003e, chromosome 3 in \u003cem\u003eindica\u003c/em\u003e, and chromosome 4 of \u003cem\u003eO. rufipogon\u003c/em\u003e. Three closely linked markers (RM12185, RM20789, and RM17271) near the short arm telomeric region of chromosome 7 were found on chromosome 4 of both \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003eO. rufipogon\u003c/em\u003e at similar physical positions. The syntenic markers were only considered for further studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC; Supplementary Datasheet 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of synteny-guided BILs and identification of CSSLs\u003c/h2\u003e \u003cp\u003eThe CR Dhan 307/AC100444 cross combination was selected for BIL and CSSL development. All 398 polymorphic SNP markers detected between the parents could be traced among the BILs. However, due to significant segregation distortion, 73 markers were not considered for linkage map construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The remaining 325 markers were mapped to 13 linkage groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Only chromosome 3 was represented by two linkage groups due to a lack of sufficient polymorphic SNP markers to provide comprehensive coverage in one region. The marker order in the genetic map matched the physical positions in Nipponbare reference genome. The 120 polymorphic STMS markers previously verified for synteny were combined with the linear SNP markers to generate a physical map of the 12 chromosomes with better genome coverage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Targeted STMS marker profiling of the BILs for the introgressed segments of \u003cem\u003eO. rufipogon\u003c/em\u003e revalidated the linkage map and achieved higher genome coverage. A total of 54 CSSLs comprehensively covering all the polymorphic marker intervals across the 12 chromosomes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). While chromosomes 1, 4, and 5 were represented by six CSSLs each, only two CSSLs covered chromosomes 2 and 10. All the CSSLs had at least one overlapping marker with their preceding and succeeding CSSLs without any gap. The CSSLs were named COr-B-CSSL (\u003cb\u003eC\u003c/b\u003eR Dhan 307 and \u003cb\u003eO\u003c/b\u003e\u003cem\u003eryza\u003c/em\u003e \u003cb\u003er\u003c/b\u003e\u003cem\u003eufipogon\u003c/em\u003e \u003cb\u003eB\u003c/b\u003eulk \u003cb\u003eC\u003c/b\u003ehromosome \u003cb\u003eS\u003c/b\u003eegment \u003cb\u003eS\u003c/b\u003eubstitution \u003cb\u003eL\u003c/b\u003eines). Each CSSL was further hyphenated with the chromosome number followed by the CSSL number for that chromosome. COr-B-CSSL-1-1 represents the first CSSL for chromosome 1, whereas COr-B-CSSL-1-6 represents the sixth CSSL of the same chromosome. Overall, the CSSLs covered\u0026thinsp;~\u0026thinsp;358.54 Mb genomic region detected through all the polymorphic syntenic markers identified between parents. All the CSSLs also retained a few additional segments from the \u003cem\u003eO. rufipogon\u003c/em\u003e genome (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMapping and validation of locus for leaf sheath pigmentation\u003c/h2\u003e \u003cp\u003eAmong the 54 CSSLs, the only common introgressed region shared by the pigmented lines was located on chromosome 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Further survey of introgressed segments of the remaining 354 BILs delimited the trait to the interval between Chr06_4757948 and RM253. Two BILs showing recombination within this interval displayed contrasting phenotypes, one purple and the other green. The segregating IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e progenies derived from the cross of SSSL-U2-6 and CR Dhan 307 mapped the leaf sheath pigmentation locus at a distance of 0.42 cM from RM253. Colocalization with reported genes and QTLs identified \u003cem\u003eOsC1\u003c/em\u003e (\u003cem\u003eLOC_Os06g10350\u003c/em\u003e) as the most likely candidate gene homologue. Substantial variation in the intensity and onset of pigmentation was recorded among the homozygous progenies for RM253 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eIn the IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e generation of SSSL-U2-6/CR Dhan 307, 1492 plants showed pigmented leaf sheath and 503 were green, fitting the 3:1 segregation ratio (χ\u0026sup2; = 0.048, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) expected for a single dominant gene. While 499 green-sheathed progenies were erect like CR Dhan 307, 1478 plants with purple leaf sheath were semi-spreading like SSSL-U2-6. The 14 erect and pigmented plants were selected, and in the IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2:3\u003c/sub\u003e generation, one line showing robust seedling growth, pigmentation from the early developmental stage, and no segregation for pigmentation or upright tillering was selected as the NIL (equivalent to BC\u003csub\u003e6\u003c/sub\u003eF\u003csub\u003e3\u003c/sub\u003e). Based on STMS and SNP allele profiles, the NIL showed 98.40% background recovery.\u003c/p\u003e \u003cp\u003eDuring identification of syntenic marker among \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003ejaponica\u003c/em\u003e, \u003cem\u003eO. sativa\u003c/em\u003e ssp. \u003cem\u003eindica\u003c/em\u003e, and \u003cem\u003eO. rufipogon\u003c/em\u003e, ~\u0026thinsp;22.39 syntenic STMS markers per Mb were identified (Supplementary Datasheet 2). Using that dataset, 17 additional syntenic STMS markers were identified between Chr06_4757948 and RM253, spanning a\u0026thinsp;~\u0026thinsp;0.6 Mb physical distance, which may be utilized for further fine mapping after study of polymorphism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization and mapping of locus for seed dormancy\u003c/h2\u003e \u003cp\u003eAmong the 408 BILs, 56 lines with viable seeds did not show any germination immediately after harvest. Three lines remained dormant even after 140 days of harvest. The remaining 53 lines did not germinate up to seven days. However, within 21 days, \u0026gt;\u0026thinsp;50% of the seeds germinated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Two closely linked markers, Ghd7-2-02 and MSU7_7_9152479_C-G, were found as common among 49 BILs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Reciprocal recombinants between Chr07_5442983 and Ghd7-2-02 or between Ghd7-2-02 and Chr07_14729119, identified from IC₁BC₁F₂ progenies derived from three selected BILs, further confirmed the association of Ghd7-2-02 and MSU7_7_9152479_C-G with short-term seed dormancy (STSD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Notably, two STMS markers, RM21263 (~\u0026thinsp;7.42 Mb) and RM21323 (~\u0026thinsp;8.87 Mb), located within the interval between Chr07_5442983 and Ghd7-2-02/MSU7_7_9152479_C-G, and another polymorphic SNP marker, Chr07_11290725, located at ~\u0026thinsp;11.29 Mb, were non-syntenic, and therefore not included in the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eCSSL-L-7-7, SSSL-L-7-7, and 19 other BILs out of 21 with the STSD feature identified from 215 backup BILs developed earlier from the same cross using a different approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reconfirmed the linkage of Ghd7-2-02 or MSU7_7_9152479_C-G with a key locus for STSD induction. The contrasting set of 94 plants selected from the IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e generation of the SSSL-L-7-7/CR Dhan 307 cross further validated the role of the genomic region associated with Ghd7-2-02 or MSU7_7_9152479_C-G from \u003cem\u003eO. rufipogon\u003c/em\u003e in STSD induction in the viviparous recurrent parent. All homozygous plants carrying the Ghd7-2-02 allele of \u003cem\u003eO. rufipogon\u003c/em\u003e showed\u0026thinsp;\u0026lt;\u0026thinsp;10% germination on the day of harvest and reached\u0026thinsp;\u0026gt;\u0026thinsp;50% after 21 days. The heterozygous plants for Ghd7-2-02 recorded\u0026thinsp;\u0026gt;\u0026thinsp;30.0% germination on the day of harvest, whereas the homozygous progenies carrying the Ghd7-2-02 allele of CR Dhan 307 showed 53.33\u0026ndash;90.00% germination. The 47 homozygous plants for Ghd7-2-02 were retested in IC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2:3\u003c/sub\u003e, and STSD induction was found to be consistent during validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePrediction of population dynamics of weedy rice for leaf sheath pigmentation\u003c/h2\u003e \u003cp\u003eField surveys detected weedy rice populations under both direct-seeded and transplanted conditions. Except in certain fields of Cuttack (Odisha), where the traditional landrace Kalachampa with purple leaf sheath has been cultivated in recent years, weedy rice populations at other locations were nearly monomorphic for green leaf sheath. At the same location, although weedy rice in cultivated fields predominantly had green leaf sheaths, the invasive swarms in non-cropped areas exhibited both types of leaf sheath pigmentations in significant proportions (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eThe predictions showed that, in the absence of any selection pressure against a particular leaf sheath pigmentation, weedy rice populations in natural swarms can maintain dimorphism even when only 30% of the initial population possesses purple leaf sheath. After a short-term decline in purple- and increase in green-sheathed plants, the trends are generally reversed. If the trait is governed by a single dominant gene, irrespective of the outcrossing percentage, the proportions of green- and purple-sheathed plants stabilize rapidly (Fig.\u0026nbsp;7A\u003csub\u003e1\u003c/sub\u003e\u0026ndash;A4). When complementary gene action governs the trait, after a steep decline during the first season, the proportion of purple leaf-sheathed plants gradually increases and, at a certain point, both leaf sheath colours reach equal frequencies. Higher outcrossing rates accelerate the process, and the populations gradually stabilize (Fig.\u0026nbsp;7B1\u0026ndash;B4). When single dominant gene and complementary gene action are present in equal magnitudes, both phenotypes reach equal proportions at a higher speed (Fig.\u0026nbsp;7C\u003csub\u003e1\u003c/sub\u003e\u0026ndash;C\u003csub\u003e4\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a crop\u0026ndash;weed continuum without selection pressure, the proportion of weedy rice plants with contrasting leaf sheath pigmentation may change constantly at a very slow rate, provided that a single dominant gene and complementary gene action equally control the inheritance of pigmentation in the base population. A certain proportion of heterozygotes for purple leaf sheath continues to survive in every generation under such conditions (Fig.\u0026nbsp;8A\u003csub\u003e1\u003c/sub\u003e\u0026ndash;A\u003csub\u003e4\u003c/sub\u003e; 4B\u003csub\u003e1\u003c/sub\u003e\u0026ndash;B\u003csub\u003e4\u003c/sub\u003e). Even under 100% selection pressure to remove all weedy rice plants with contrasting leaf sheath pigmentation, purple-sheathed weedy rice can persist at very low frequency for 25\u0026ndash;30 crop seasons/generations among green-sheathed plants comprising both weedy rice and the crop variety (Fig.\u0026nbsp;8C\u003csub\u003e1\u003c/sub\u003e\u0026ndash;C\u003csub\u003e4\u003c/sub\u003e). Under the opposite scenario, green-sheathed weedy rice plants may be eliminated within 7\u0026ndash;8 crop seasons/generations (Fig.\u0026nbsp;8D\u003csub\u003e1\u003c/sub\u003e\u0026ndash;D\u003csub\u003e4\u003c/sub\u003e). The outcrossing rate significantly influences the population dynamics when selection pressure is not applied. However, under unidirectional selection, the role of the outcrossing rate is negligible.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eSelection of weedy derivatives for field study\u003c/h2\u003e \u003cp\u003eAmong the BILs, substantial variation was recorded for awn morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The awns were purple or white during flowering. Notably, when the genotype was awned, all plants with purple leaf sheath also exhibited pigmented awns. The majority of the BILs with medium to large awns showed grain shattering habit. All medium- to large-awned BILs with grain shattering features were further screened for Ghd7-2-02 marker and retested for STSD. We detected 17 such lines among the 623 BILs. Further classification based on \u003cem\u003esd1\u003c/em\u003e and RM253 identified four types of awned genotypes with STSD and shattering habit, viz., tall plants with purple leaf sheath; semidwarf plants with purple leaf sheath; tall plants with green leaf sheath; and semidwarf plants with green leaf sheath (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Considering a 40% basal infestation level, 10% seeds of each of the four BILs were mixed with 60% seeds of CR Dhan 307 or its purple leaf sheath NIL on seed count basis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003ePopulation dynamics of weedy rice under different selection regimes\u003c/h2\u003e \u003cp\u003eDuring season-1 (wet season 2024), almost all the plants were present within their respective rows in different treatments. Within MP1, manual weeding reduced the population of weedy derivatives in both subplots (MP1SP3 and MP1SP4) where mixed populations were sown (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). However, weedy derivatives with similar leaf sheath colour were present in both subplots. Weedy populations were substantially higher in MP2SP3 and MP2SP4 compared to MP1SP3 and MP1SP4.\u003c/p\u003e \u003cp\u003eDuring season-2 (dry season 2025), \u003cem\u003eOryza\u003c/em\u003e seedlings were also found between the rows in both main plots. Manual weeding removed such plants from MP1. Within MP1SP3, where a mixed population of CR Dhan 307 and its weedy derivatives was grown in season-1, the population of weedy derivatives was substantially reduced in sub-subplot 1 (MP1SP3SSP1), which grew the purple leaf sheathed NIL. However, in MP1SP3SSP2, the population of weedy types was significantly higher. On the contrary, the weedy population in MP1SP4SSP1 was like that in MP1SP3SSP2. The population of awned weedy plants in MP1SP4SSP2 was at par with MP1SP3SSP1. The populations of weedy derivatives in both sub-subplots of MP2SP3 and MP2SP4 were nearly three times higher than those in MP1SP3SSP2 or MP1SP4SSP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC, and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified, validated, and systematically utilized syntenic STMS and SNP markers in a modified breeding scheme to develop overlapping introgression lines from \u003cem\u003eO. rufipogon\u003c/em\u003e with comprehensive genome coverage at substantially reduced cost and efforts compared with conventional strategies. Beyond trait discovery and pre-breeding, the introgressed population and marker resources were deployed to dissect crop-weed evolutionary dynamics and to examine the effects of imposing directional selection pressure against adaptive traits of weedy rice.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eSynteny-guided introgression as an efficient framework for wide hybridization\u003c/h2\u003e \u003cp\u003eModern pre-breeding programmes require structured populations and strategic deployment of molecular markers, especially during the utilization of wild species. Syntenic cross-transferable markers facilitate precise tracking of wild genomic segments during backcrossing (Ray et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, no marker system is suitable at every stage of a breeding programme for all laboratories. For example, custom amplicon-based SNP arrays are excellent tools; however, they are suitable only when whole-genome information is required. Moreover, such genotyping services are not available in every country or laboratory at all times. On the contrary, PCR-based markers can be routinely used in laboratories with basic facilities but are not economical to use at a very large scale (Dalai et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study systematically combined a modified breeding strategy with both marker systems for CSSL development. Bulk backcrossing without any genotyping until BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e, genotyping of pooled subsets in BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e with a limited set of anchored STMS markers, advancing the population until BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e5\u003c/sub\u003e without genotyping, genetic map construction using custom SNP amplicons in BC\u003csub\u003e3\u003c/sub\u003eF\u003csub\u003e6\u003c/sub\u003e, and selective reuse of STMS markers to fill genomic gaps minimized the genotyping cost, eliminated the need for exhaustive pedigree maintenance, and substantially improved genome coverage compared with conventional strategies (Dalai et al. \u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, ~ 22.5 syntenic STMS markers per Mb was identified between \u003cem\u003eO. rufipogon\u003c/em\u003e and the two subspecies of Asiatic rice, facilitating high-density coverage of delineated genomic intervals after a locus is mapped. The same population and marker framework were directly utilized to identify loci governing two domestication-related traits. These overlapping introgression lines enabled evaluation of domestication-related traits in a largely uniform genetic background. This is particularly valuable for traits placed at the interface of domestication and de-domestication, which often remain masked in the cultivated gene pool of rice (Li et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDomestication-related traits in the crop-weedy rice continuum\u003c/h3\u003e\n\u003cp\u003eLeaf sheath pigmentation, plant height, seed dormancy, awn development, and grain shattering are critical for the persistence of weedy rice in cultivated fields. Mapping the leaf sheath pigmentation locus near RM253 and its colocalization with \u003cem\u003eOsC1\u003c/em\u003e homologue align with earlier reports (Xiong et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e; Lorieux et al. \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Fan et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chen et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jiang et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, it may be noted that the purple leaf sheath phenotype in rice may occur due to other genes, besides \u003cem\u003eOsC1\u003c/em\u003e. We have been able to produce purple leaf sheath NILs through independent introgression of both \u003cem\u003eOsC1\u003c/em\u003e homologue from \u003cem\u003eO. rufipogon\u003c/em\u003e and \u003cem\u003eOsDFR\u003c/em\u003e (\u003cem\u003eLOC_Os01g44260\u003c/em\u003e) gene from a landrace in the same recurrent parent ‘Swarna’ (unpublished data). This also explains why different F\u003csub\u003e2\u003c/sub\u003e-segregation ratios (3:1, 9:7 or even complex higher order) are observed in different populations for the trait (Chin et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Among all the \u003cem\u003eOsC1\u003c/em\u003e introgressed BILs of CR Dhan 307, whenever present, the awns were always purple. If the awns were absent, only the apiculus regions were pigmented. This also indicate the possible pleiotropic effect of \u003cem\u003eOsC1\u003c/em\u003e or tight linkage among the genes controlling those tissue specific pigmentations. Although the \u003cem\u003eO. rufipogon\u003c/em\u003e homologue ensured pigmentation, variation in the stage and intensity of expression suggests quantitative modulation by additional loci. Dominant inheritance and selection of NILs expressing high pigment intensity at the early growth stage further enhanced its value as a visual marker. The tight coupling-phase linkage between the genes for leaf sheath pigmentation and tiller angle could also be circumvented through visual selection in large populations. In other cases, high-density syntenic STMS markers may help to reduce linkage drags, which often create a major hurdle in utilization of wild species in crop improvement.\u003c/p\u003e \u003cp\u003eVariable levels of seed dormancy also play a critical role in the persistence of weedy rice populations (Olajumoke et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Short-term dormancy enables staggered germination in rice–rice cropping systems, whereas deep dormancy sustains populations under rice–fallow or crop rotations (Singh et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ajaykumar et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Use of a semidwarf recurrent parent with viviparous germination, along with selection of grain-shattering BILs with short-term seed dormancy enabled completion of the simulated field experiment within two consecutive seasons. A genic SNP marker Ghd7-2-02 associated with short-term dormancy was colocalized with \u003cem\u003eGhd7\u003c/em\u003e, which is known for its role to induce dormancy and prevent vivipary by increasing the abscisic acid (ABA)/gibberellin (GA\u003csub\u003e3\u003c/sub\u003e) ratio during rice seed germination (Hu et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). While \u003cem\u003eGhd7-0\u003c/em\u003e is a null (Xue et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) and \u003cem\u003eghd7\u003c/em\u003e is the recessive allele (Hu et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), \u003cem\u003eGhd7-1\u003c/em\u003e and \u003cem\u003eGhd7-3\u003c/em\u003e are the two fully functional dominant alleles of the gene (Saito et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eGhd7-2\u003c/em\u003e is a comparatively weaker allele of \u003cem\u003eGhd7\u003c/em\u003e (Wang et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although the nearest polymorphic flanking markers on both sides were \u0026gt; 4.0 Mb apart, the trait could still be traced directly to the genic SNP marker Ghd7-2-02 through substitution mapping. This would otherwise have been difficult to precisely locate and quantify through conventional interval mapping approaches as the three other polymorphic markers predicted as closely linked to Ghd7-2-02 as per Nipponbare reference genome, were non-syntenic and showed significant segregation distortion in the population. Another study to map QTLs for seed dormancy from a weedy rice accession ‘Ludao’ identified chromosome 7 as the sole genomic hotspot for the trait (Nguyen et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eEvolutionary basis of the traditional weedy rice management practice\u003c/h2\u003e \u003cp\u003eShort- and long-term projections of weedy rice population dynamics in natural swarms and crop–weed continuums provided an evolution guided explanation for the traditional practice of rotating landraces with contrasting leaf sheath pigmentation. Simulations indicated that complementary gene action and natural outcrossing allow dimorphism for leaf sheath pigmentation to persist and stabilize, even when uniformly pigmented varieties are cultivated over extended periods. Under unidirectional selection, conspecific weeds phenotypically resembling crop varieties are favored, although the number of generations required for complete elimination of contrasting phenotypes differs significantly between green and purple sheath types. When phenotypically uniform varieties are cultivated in long term, such as modern semi-dwarf varieties with green leaf sheaths, selection promotes convergence toward crop mimicry. In contrast, bidirectional selection disrupts this convergence, cyclically discriminate between crop variety and weedy plants, and thereby enables elimination of weedy types at the vegetative stage. These findings have practical implications in determining the frequency of varietal rotation required in the continuum of a crop and its conspecific weeds.\u003c/p\u003e \u003cp\u003eField observations were consistent with these projections. The substantial reduction in resurgence of weedy derivatives in the second season after the introduction of contrasting pigmentation indicates that phenotypic divergence may constrain their persistence across generations. Although complete elimination was expected under rigorous weeding up to 45 days, a few plants escaped selection and reached maturity, possibly due to scattered germination or variable expressivity of pigmentation trait during early vegetative stage in few weedy plants. Continuous monitoring and rouging of leftover weedy plants at early flowering stage, therefore, should be an essential component of any sustainable management strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eImplications for evolution-guided management under different production systems\u003c/h2\u003e \u003cp\u003eDirect seeding creates ecological conditions that favor rapid invasion of weedy rice. Continuous monoculture and exclusive reliance on chemical control can progressively select for adaptive responses against such management strategies. Integrating evolutionary principles into varietal development, however, can offer a sustainable alternative. Breeding varieties or near-isogenic lines that enable creation of artificial selection pressure against weedy derivatives mimicking the crop without dependence on specific herbicides can broaden their applicability. Where herbicide tolerant varieties are possible to cultivate, development of near-isogenic lines of herbicide-tolerant varieties with phenotypic contrasts can help in developing stewardship strategies against gene flow.\u003c/p\u003e \u003cp\u003eTransplanted rice has historically suppressed early emerging weeds through seedling age advantage and puddling. However, our surveys in some parts of India, along with recent reports from Japan (Imaizumi \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Taiwan (Hsu et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) indicate that weedy rice is steadily invading transplanted paddy fields. Introducing contrasting pigmentation through varietal rotation will also support the removal of weedy derivatives shortly after transplanting. Hence, irrespective of crop establishment method, adoption of evolution-guided management of weedy rice represents an example of ecological intensification. By harnessing the genetic diversity of crop wild relatives to alter population dynamics of conspecific weeds, genomic resources and introgression lines can be utilized beyond trait mapping and marker-assisted breeding.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion and future perspectives","content":"\u003cp\u003eThe study demonstrates how synteny-guided introgression lines derived from a wild progenitor can be used as a platform to integrate genomics with the genetics of crop domestication and apply these insights to crop management and breeding. Strategic deployment of traits to overcome crop mimicry in breeding programmes can create directional selection pressure against a recalcitrant conspecific weed and reduce its fitness. A traditional agronomic practice involving low-yielding landraces was reintroduced as an evolution-guided management strategy suitable for modern direct-seeded systems with semidwarf high-yielding varieties. By integrating wild germplasm, genomic tools, and ecological understanding, the study opens new avenues for sustainable management of conspecific weeds while expanding the scope for incorporating the diversity of crop wild relatives into modern breeding programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompliance with ethical standards\u003c/h2\u003e \u003cp\u003eThe authors of this publication declare that the experiments conducted for the study have followed the extant national and international laws.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eIt is hereby declared that there is no conflict of interest among the authors of this publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was financially supported by Indian Council of Agricultural Research through the \u0026ldquo;Consortium Research Project on Molecular Breeding (EAP-211)\u0026rdquo; and ICAR-CRRI in-house project no. 1.3.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.A.D., D.D., A.H., A.B.K..: Marker profiling, field works, phenotyping, data recording; A.C., K.A.M., P.S., D.B.: Genetic modelling, data analysis, writing\u0026ndash; review \u0026amp; editing; VK, KKJ, NPM, CKP, TM: Guidance, resources, data visualization, writing\u0026ndash; review \u0026amp; editing; S.R.: Bioinformatics; D.R.P., M.K.K.: Breeding works, field evaluations, data collection; resources; writing\u0026ndash; review \u0026amp; editing; M.C.: Conceptualization, designing experiments, breeding works, bioinformatics, supervision, funding acquisition, formal analysis, data curation. data-visualization, validation, writing- original draft. This work is a part of Ph.D. thesis of the first author. All authors read, reviewed and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe team is grateful to Post Graduate Department of Botany, Utkal University, India for approving the first author to carry out her research work ICAR-CRRI, Cuttack. We acknowledge the support received from Dr. S.K. Das and Dr. S. Sarkar from ICAR-CRRI, Cuttack for providing resources to synthesize large number of STMS markers. International Rice Research Institute, Philippines provided two wild germplasm accessions used in the study. Administrative support was provided by Director, ICAR-CRRI. Large Language Model (LLM) ChatGPT was used for grammar and language check during initial stage of manuscript preparation. However, authors take all responsibilities for correctness of sentences/statements.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the relevant tables, additional data in the form of datasheets or required figures have been provided as electronic supplementary material. Specific additional information, if required, will also be shared on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjaykumar R, Sivasabari K, Vigneshwaran R, Kumaresan P (2025) Weedy rice (Oryza sativa f. spontanea) complexes on direct seeded rice (DSR) and its management strategy: A review. Agricultural Reviews 46: 62\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18805/ag.R-2538\u003c/span\u003e\u003cspan address=\"10.18805/ag.R-2538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArbelaez JD, Dwiyanti MS, Tandayu E, Lantada K, Jarana A, Ignacio JC, Platten JD, Cobb J, Rutkoski JE, Thomson MJ, Kretzschmar T (2019) 1k-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice. Rice 12(1):55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12284-019-0311-0\u003c/span\u003e\u003cspan address=\"10.1186/s12284-019-0311-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao J, Li Z, Sun Y, Aluko OO, Wu X, Wang Q, Liu G (2021) MG2C: a user-friendly online tool for drawing genetic maps. Molecular Horticulture 1(1):16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s43897-021-00020-x\u003c/span\u003e\u003cspan address=\"10.1186/s43897-021-00020-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan BS, Namuco OS, Ocampo LAL, Son TTN, Thu TTA, Nam NN, Phuong LN, Bajwa AA (2015) Weedy rice (\u003cem\u003eOryza sativa\u003c/em\u003e f. \u003cem\u003espontanea\u003c/em\u003e) problems and management in wet direct-seeded rice (\u003cem\u003eO. sativa\u003c/em\u003e L.) in the Mekong Delta of Vietnam. Crop Protection 78:40\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cropro.2015.08.016\u003c/span\u003e\u003cspan address=\"10.1016/j.cropro.2015.08.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen HJ, Sawasdee A, Lin YL, Chiang MY, Chang HY, Li WH, Wang CS (2024) Reverse mutations in pigmentation induced by Sodium Azide in the IR64 rice variety. Current Issues in Molecular Biology 46(12):13328\u0026ndash;13346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cimb46120795\u003c/span\u003e\u003cspan address=\"10.3390/cimb46120795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChin HS, Wu YP, Hour AL, Hong CY, Lin YR (2016) Genetic and evolutionary analysis of purple leaf sheath in rice. Rice 9(1):8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12284-016-0080-y\u003c/span\u003e\u003cspan address=\"10.1186/s12284-016-0080-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCong Y, Gui Y, Yong K, Zhu M, Liu K, Liu J, Qiu J (2025) Deciphering rice feralization: insights from genomics of weedy rice. Genomics Communications 2(1): e007. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48130/gcomm-0025-0007\u003c/span\u003e\u003cspan address=\"10.48130/gcomm-0025-0007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalai D, Chakraborti M, Mondal TK, Ray S, Kar MK, Chakraborty K, Pani DR, Sarkar S, Bose LK, Behera M, Chattopadhyay K, Deepa, Vijayan J, Dash SK, Pradhan C, Patra BC, Marndi BC (2021) The core set of sequence-tagged microsatellite sites markers between halophytic wild rice \u003cem\u003eOryza coarctata\u003c/em\u003e and \u003cem\u003eOryza sativa\u003c/em\u003e complex. Euphytica 217(4):57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-021-02790-3\u003c/span\u003e\u003cspan address=\"10.1007/s10681-021-02790-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalai D, Pani DR, Dhal SA, Behera M, Mondal TK, Azaharudheen MTP, Vijayan J, Sarkar D, Ghose P, Roy A, Molla KA, C A, Bose LK, Mohapatra T, Ray S, Kar MK, Chakraborti M (2026) A robust 1K-core marker set for wild germplasm management and targeted pre-breeding of rice: development and applications. Theoretical and Applied Genetics 139(1):23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-025-05133-1\u003c/span\u003e\u003cspan address=\"10.1007/s00122-025-05133-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelouche JC, Burgos NR, Gealy DR, De San Martin GZ, Labrada R, Larinde M, Rosell C (2007) Weedy rices: Origin, biology, ecology, and control. In. FAO Plant Production and Protection Paper No.188. Food and Agriculture Organization (FAO) of the United Nations, Rome, Italy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis MH, Spielmeyer W (2002) \u0026ldquo;Perfect\u0026rdquo; Markers for the Semidwarfing Gene \u003cem\u003esd1\u003c/em\u003e in Rice. International Rice Research Notes. 27(2):1\u0026ndash;1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.6823534\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.6823534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan FJ, Fan YY, Du JH, Zhuang JY (2008) Fine mapping of C (Chromogen for anthocyanin) gene in rice. Rice Science 15(1):1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1672-6308(08)60012-8\u003c/span\u003e\u003cspan address=\"10.1016/s1672-6308(08)60012-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGe Y, Chen G, Cheng X, Li C, Tian Y, Chi W, Li J, Dai Z, Wang C, Wang C, Duan E, Liu Y, Sun Z, Li J, Wang B, Xu D, Sun X, Zhang H, Zhang W, Wang C, Wang C, Wan J (2024) The superior allele \u003cem\u003eLEA12\u003c/em\u003e\u003csup\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/sup\u003e in wild rice enhances salt tolerance and yield. Plant Biotechnology Journal 22(11):2971\u0026ndash;2984. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pbi.14419\u003c/span\u003e\u003cspan address=\"10.1111/pbi.14419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu YT, Wang YC, Du PR, Li CP, Wu DH (2025) Morphological diversity and crop mimicry strategies of weedy rice under the transplanting cultivation system. Agronomy 15(4):984. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy15040984\u003c/span\u003e\u003cspan address=\"10.3390/agronomy15040984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Song S, Weng X, You A, Yongzhong X (2021) The heading-date gene \u003cem\u003eGhd7\u003c/em\u003e inhibits seed germination by modulating the balance between abscisic acid and gibberellins. The Crop Journal 9(2):297\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cj.2020.09.004\u003c/span\u003e\u003cspan address=\"10.1016/j.cj.2020.09.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Kurata N, Wei X, Wang Z-X, Wang A, Zhao Q, Zhao Y, Liu K, Lu H, Li W, Guo Y, Lu Y, Zhou C, Fan D, Weng Q, Zhu C, Huang T, Zhang L, Wang Y, Feng L, Furuumi H, Kubo T, Miyabayashi T, Yuan X, Xu Q, Dong G, Zhan Q, Li C, Fujiyama A, Toyoda A, Lu T, Feng Q, Qian Q, Li J, Han B (2012) A map of rice genome variation reveals the origin of cultivated rice. Nature 490(7421):497\u0026ndash;501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature11532\u003c/span\u003e\u003cspan address=\"10.1038/nature11532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImaizumi T (2018) Weedy rice represents an emerging threat to transplanted rice production systems in Japan. Weed Biology and Management 18(2):99\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/wbm.12146\u003c/span\u003e\u003cspan address=\"10.1111/wbm.12146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang L, Lyu S, Yu H, Zhang J, Sun B, Liu Q, Mao X, Chen P, Pan D, Chen W, Fan Z, Li C (2024) Transcription factor encoding gene \u003cem\u003eOsC1\u003c/em\u003e regulates leaf sheath color through anthocyanidin metabolism in \u003cem\u003eOryza rufipogon\u003c/em\u003e and \u003cem\u003eOryza sativa\u003c/em\u003e. BMC Plant Biology 24(1):147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-024-04823-0\u003c/span\u003e\u003cspan address=\"10.1186/s12870-024-04823-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosambi DD (1943) The estimation of map distances from recombination values. Annals of Eugenics 12(1):172\u0026ndash;175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-1809.1943.tb02321.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-1809.1943.tb02321.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi LF, Li YL, Jia Y, Caicedo AL, Olsen KM (2017) Signatures of adaptation in the weedy rice genome. Nature Genetics 49(5):811\u0026ndash;814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.3825\u003c/span\u003e\u003cspan address=\"10.1038/ng.3825\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang X, Zhao C, Liu K, Wang W, Huo Z, Song X, Qiang S (2025) Advances in research on the biological characteristics of weedy rice. Plants 14(20):3188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants14203188\u003c/span\u003e\u003cspan address=\"10.3390/plants14203188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorieux M (2012) MapDisto: fast and efficient computation of genetic linkage maps. Molecular Breeding 30(2):1231\u0026ndash;1235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-012-9706-y\u003c/span\u003e\u003cspan address=\"10.1007/s11032-012-9706-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorieux M, MN. Ndijiondjip MN and Gesquiere A (2000) A first interspecific \u003cem\u003eOryza sativa\u003c/em\u003e \u0026times; \u003cem\u003eOryza glaberrima\u003c/em\u003e microsatellite-based genetic linkage map. Theoretical and Applied Genetics 100:593\u0026ndash;601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001229900061\u003c/span\u003e\u003cspan address=\"10.1007/s001229900061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa D, Li M, Sun J (2008) Studies on biological diversity and genetic differentiation of Liaoning weedy rice. Journal of Shenyang Agricultural University 39 265\u0026ndash;269. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10499-007-9164-4\u003c/span\u003e\u003cspan address=\"10.1007/s10499-007-9164-4\" 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. The Crop Journal 3(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\u003eMigdałek G, Żelawski M (2022) Measuring population-level plant gene flow with topological data analysis. Ecological Informatics 70:101740. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoinf.2022.101740\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoinf.2022.101740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNadir S, Xiong HB, Zhu Q, Zhang XL, Xu HY, Li J, Dongchen W, Henry D, Guo X-Q, Khan S, Suh H-S, Lee DS, Chen L-J (2017) Weedy rice in sustainable rice production. A review. Agronomy for Sustainable Development 37(5):46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13593-017-0456-4\u003c/span\u003e\u003cspan address=\"10.1007/s13593-017-0456-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen CD, Zheng SH, Sanada-Morimura S, Matsumura M, Yasui H, Fujita D (2021) Substitution mapping and characterization of brown planthopper resistance genes from indica rice variety, \u0026lsquo;PTB33\u0026rsquo; (\u003cem\u003eOryza sativa\u003c/em\u003e L.). Breeding Science 71(5):497\u0026ndash;509. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1270/jsbbs.21034\u003c/span\u003e\u003cspan address=\"10.1270/jsbbs.21034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen T, Zhou C, Zhang T, Yu J, Miao R, Huang Y, Zhu X, Song W, Liu X, Mou C, Lan J, Liu S, Tian Y, Zhao Z, Jiang L, Wan J (2019) Identification of QTL for seed dormancy from weedy rice and its application to elite rice cultivar \u0026lsquo;Ninggeng 4\u0026rsquo;. Molecular Breeding 39:123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-019-1031-2\u003c/span\u003e\u003cspan address=\"10.1007/s11032-019-1031-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlajumoke B, Juraimi AS, Uddin MK, Husni MHA, Alam MA (2016) Competitive ability of cultivated rice against weedy rice biotypes: A review. Chilean Journal of Agricultural Research 76(2):242\u0026ndash;251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4067/s0718-58392016000200015\u003c/span\u003e\u003cspan address=\"10.4067/s0718-58392016000200015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay S, Bose LK, Ray J, Ngangkham U, Katara JL, Samantaray S, Behera L, Anumalla M, Singh ON, Chen M, Wing RA, Mohapatra T (2016) Development and validation of cross-transferable and polymorphic DNA markers for detecting alien genome introgression in \u003cem\u003eOryza sativa\u003c/em\u003e from \u003cem\u003eOryza brachyantha\u003c/em\u003e. Molecular Genetics and Genomics 291(4):1783\u0026ndash;1794. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00438-016-1214-z\u003c/span\u003e\u003cspan address=\"10.1007/s00438-016-1214-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichharia RH. 1964. Wild rice menace and possible methods of its control. Central Rice Research Institute (CRRI) publication, India.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoma-Burgos N, Sudo MPS, Olsen KM, Werle I, Song BK (2021) Weedy rice (\u003cem\u003eOryza spp.\u003c/em\u003e): what\u0026rsquo;s in a name? Weed Science 69:505\u0026ndash;513. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/wsc.2021.22\u003c/span\u003e\u003cspan address=\"10.1017/wsc.2021.22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito H, Okumoto Y, Tsukiyama T, Xu C, Teraishi M, Tanisaka T (2019) Allelic differentiation at the \u003cem\u003eE1/GHD7\u003c/em\u003e locus has allowed expansion of rice cultivation area. Plants 8(12):550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants8120550\u003c/span\u003e\u003cspan address=\"10.3390/plants8120550\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShobha Rani N, Shobha Rao LV, Viraktamath BC, Mishra B (2004) National guidelines for the conduct of tests for distinctiveness, uniformity and stability. Directorate of Rice Research, Hyderabad, Telangana, India: 6\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh K, Kumar V, Saharawat YS, Gathala M, Ladha JK, Chauhan BS (2013) Weedy rice: An emerging threat for direct seeded rice production systems in India. Rice Research: Open Access 1:1000106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.4172/jrr.1000106\u003c/span\u003e\u003cspan address=\"10.4172/jrr.1000106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTewari AN (2008) Weedy rice and its management. In: Singh Y, Singh VP, Chauhan B, Orr A, Mortimer AM, Johnson DE, Hardy B (Eds). Direct seeding of rice and weed management in the irrigated rice\u0026ndash;wheat cropping system of the Indo-Gangetic Plains. International Rice Research Institute, Los Ba\u0026ntilde;os, Philippines, pp: 205\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Berloo R (2008) GGT 2.0: Versatile software for visualization and analysis of genetic data. Journal of Heredity 99(2):232\u0026ndash;236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jhered/esm109\u003c/span\u003e\u003cspan address=\"10.1093/jhered/esm109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarshney JG, Tiwari JP (2008) Studies on weedy rice infestation and assessment of its impact on rice production. Indian Journal of Weed Science 40(3\u0026ndash;4):115\u0026ndash;123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigueira PA, Olsen KM, Wagner CR, Chittick ZB, Vigueira CC (2020) Weedy rice from South Korea arose from two distinct de-domestication events. Frontiers in Agronomy 2:602612 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fagro.2020.602612\u003c/span\u003e\u003cspan address=\"10.3389/fagro.2020.602612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillanueva G. Rosa-Mart\u0026iacute;nez E, Vilanova S, Gramazio P, Prohens, J, Plazas M (2023) Chromosome visualization with introgressions: a tutorial using tassel and GGT2 software. EDULEARN23 Proceedings. p. 8419\u0026ndash;8422. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21125/edulearn.2023.2187\u003c/span\u003e\u003cspan address=\"10.21125/edulearn.2023.2187\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Su Q, Nian J, Zhang J, Guo M, Dong G, Hu J, Wang R, Wei C, Li G, Wang W, Guo H-S, Lin S, Qian W, Xie X, Qian Q, Chen F, Zuo J (2021) The \u003cem\u003eGhd7\u003c/em\u003e transcription factor represses \u003cem\u003eARE1\u003c/em\u003e expression to enhance nitrogen utilization and grain yield in rice. Molecular Plant 14(6):1012\u0026ndash;1023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molp.2021.04.012\u003c/span\u003e\u003cspan address=\"10.1016/j.molp.2021.04.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia HB, Wang W, Xia H, Zhao W, Lu BR (2011) Conspecific crop-weed introgression influences evolution of weedy rice (\u003cem\u003eOryza sativa\u003c/em\u003e f. \u003cem\u003espontanea\u003c/em\u003e) across a geographical range. PLoS ONE 6(1):e16189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0016189\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0016189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao J, Li J, Grandillo S, Ahn SN, Yuan L, Tanksley SD, McCouch SR (1998) Identification of trait-improving quantitative trait loci alleles from a wild rice relative, \u003cem\u003eOryza rufipogon\u003c/em\u003e. Genetics 150(2):899\u0026ndash;909. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/genetics/150.2.899\u003c/span\u003e\u003cspan address=\"10.1093/genetics/150.2.899\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie X, Jin F, Song MH, Suh JP, Hwang HG, Kim YG, McCouch SR, Ahn SN (2007) Fine mapping of a yield-enhancing QTL cluster associated with transgressive variation in an \u003cem\u003eOryza sativa \u0026times; O. rufipogon\u003c/em\u003e cross. Theoretical and Applied Genetics 116(5):613\u0026ndash;622. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-007-0695-x\u003c/span\u003e\u003cspan address=\"10.1007/s00122-007-0695-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong LZ, Lin KD, Dai XK, Xu CG and Zhang Q (1999) Identification of genetic factors controlling domestication-related traits of rice using an F\u003csub\u003e2\u003c/sub\u003e population of a cross between \u003cem\u003eOryza sativa\u003c/em\u003e and \u003cem\u003eO. rufipogon\u003c/em\u003e. Theoretical and Applied Genetics 98: 243\u0026ndash;251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001220051064\u003c/span\u003e\u003cspan address=\"10.1007/s001220051064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q (2008) Natural variation in \u003cem\u003eGhd7\u003c/em\u003e is an important regulator of heading date and yield potential in rice. Nature Genetics 40(6):761\u0026ndash;767. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.143\u003c/span\u003e\u003cspan address=\"10.1038/ng.143\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Synteny, Conspecific weeds, Rotational cultivation, Weedy rice evolution, Tissue pigmentation","lastPublishedDoi":"10.21203/rs.3.rs-9327164/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9327164/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWeedy-rice, causing 15-90% yield loss of rice across continents, is a conspecific weed originating from natural outcrossing between \u003cem\u003eOryza sativa\u003c/em\u003e and its\u003cem\u003e \u003c/em\u003ewild-progenitors. Bidirectional gene flow and phenotypic mimicry of cultivated ecotypes in early growth stages enable evasion of manual weeding, and facilitate rapid proliferation in cultivated ecosystems. Their sustainable management requires understanding of population dynamics in a crop-weed continuum which is challenging in field-populations due to genetic admixtures and multidimensional selection pressures. To resolve this, we generated bulked backcross-derived inbred lines (BILs) and chromosome-segment substitution lines (CSSLs) from \u003cem\u003eOryza rufipogon\u003c/em\u003e using syntenic microsatellite and SNP markers.Leaf-sheath pigmentation and short-term seed dormancy traits were fine-mapped and BILs mimicking weedy-rice populations were selected for awns, seed shattering, and dormancy, but differing in leaf-sheath pigmentation and plant height. A near-isogenic line (NIL) with purple leaf sheath was also developed to facilitate application of bidirectional selection pressure. Simulated models of population dynamics under different levels of gene flow (5 and 10%) and selection pressures were developed after baseline-surveys in natural populations. Using an experimental population of the weedy-derivatives and the recurrent parent or its purple-sheathed NIL at 40:60 ratio, population dynamics under different degree and directions of selection were characterized in field trials. While resurgence of weedy-derivatives was 59.20–64.27% in the absence of selection, it could be restricted to 16.27–19.60% by applying unidirectional selection pressure and further to 3.94–4.33% through bidirectional selection. These findings highlight the effectiveness of population-dynamics guided breeding and agronomic strategies to reduce weedy-rice load.\u003c/p\u003e","manuscriptTitle":"Predictive models and wild progenitor-derived introgression lines dissect the population dynamics of domestication syndrome traits associated with crop mimicry in weedy rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 12:56:48","doi":"10.21203/rs.3.rs-9327164/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":"bc830ddc-6d91-443a-8d6f-4c6ce92097b2","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-01T11:56:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T00:29:04+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T12:09:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 12:56:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9327164","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9327164","identity":"rs-9327164","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}