Population Structure, Genetic Diversity, and Genome-Wide Association Analysis of Eastern African Rice Landraces for Climate Resilience Breeding: The case of complete submergence tolerance

Population Structure, Genetic Diversity, and Genome-Wide Association Analysis of Eastern African Rice Landraces for Climate Resilience Breeding: The case of complete submergence tolerance | 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 Population Structure, Genetic Diversity, and Genome-Wide Association Analysis of Eastern African Rice Landraces for Climate Resilience Breeding: The case of complete submergence tolerance Victoria Bulegeya, Waseem Hussain, Yong Zhou, Newton Kilasi, Rosemary Murori, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8944214/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Rice landraces remain a crucial reservoir of novel alleles for stress-tolerance and climate-resilience breeding in Africa. East Africa harbors a rich diversity of Oryza sativa germplasm that is under investigated for their potential to improve stress tolerance. This study characterized a panel of 260 Oryza sativa landraces collected from East Africa using whole genome sequencing and phenotypic evaluation under complete submergence stress. Following a genotype-by-sequencing approach, the study identified 168,091 high-quality SNP markers revealing a panel with high genetic diversity, moderate heterozygosity of 0.062, and complex population structure. Population structure analysis of the panel displayed three major subpopulations with extensive admixture, indicating long-term gene flow and local adaptation of the landraces. Genome-wide linkage disequilibrium decay at r 2 = 0.2 is approximately 35kb, revealing moderate mapping resolution. To explore the breeding relevance of the panel, the study carried out a genome-wide association analysis (GWAS) for complete submergence tolerance following a linear mixed model (LMM). GWAS revealed 11 loci associated with submergence tolerance across chromosomes 2, 4, 6, 8, 9, 10, and 11. Candidate gene mining within a 10kb linkage disequilibrium (LD) window found genes involved in cytokinin and auxin signaling, transcription regulation under oxygen-deprived environment, stress signal transduction, and cellular maintenance activities. The study findings present the panel as a genetic resource for breeding strategies in the region and as a novel resource for improving abiotic stress, including submergence tolerance. The findings provide a foundation for functional validation and realistic targets for marker-assisted introgression to improve productivity in rice-producing regions of Africa. Rice landraces genetic diversity genome-wide association study (GWAS) complete submergence climate resilience breeding East Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Rice is among the most important cereals worldwide, providing food to over 3.5 billion people in the world, particularly in Asia, Africa, and Latin America. Rice provides about 20% of calorie intake worldwide, with a significant importance in developing countries as a primary source of energy (Mohidem et al., 2022 ). It is a crop of economic significance to major smallholder farmers in Africa and Asia, providing household income and employment (Mauki et al., 2023 ). Globally, rice is of great economic significance, generating up to $ 20 billion in export value. Despite the increasing economic value, rainfed lowland rice production remains vulnerable to climatic variability, such as drought, salinity, and flooding. Rice is among the major crops vulnerable to climate change in East Africa (Adhikari et al., 2015 ). In Tanzania, approximately 70% of the nation's rice is cultivated in rainfed lowland ecosystems, which include river basins, catchment areas, and extensive grassland plains. The primary rice-producing regions are Mbeya, Morogoro, Mwanza, Shinyanga, Tabora, and Rukwa, named the big six (Mtembeji & Singh, 2021 ). In addition, rice is also grown in coastal regions such as Tanga, Pwani, Lindi, Mtwara, and the islands of Zanzibar (Sekiya et al., 2020 ). These regions are vulnerable to climate change, experiencing extreme weather events and irregular rainfall patterns, resulting in frequent floods, which severely impact rice production in the areas (Michael, Sanga, et al., 2023 ). Rice farmers in major rice-producing zones have been affected by floods, causing up to 100% yield losses due to inadequate drainage infrastructure and the use of flood-susceptible varieties. (Michael, Mwakyusa, et al., 2023 ). Rice landraces have proven to be a valuable source of genetic diversity, harboring novel alleles for local adaptation and for improving biotic and abiotic stress tolerance (Marone et al., 2021 ). The diversity of landraces provides structural variation suitable for genomic scanning to unlock their unique molecular diversity (Corrado & Rao, 2017 ). This will enhance their use in improving modern cultivars and their adaptation to diverse environments (Dwivedi et al., 2016 ). Modern breeding has resulted to genetic bottleneck in genetic diversity, creating a need for recovery of lost alleles from landraces and wild relatives (Van De Wouw et al., 2010 ). Rice is a semi-aquatic plant known for its ability to grow in shallow water, especially in lowland rice varieties. Despite this, rice does not thrive in prolonged complete submergence, with most varieties able to endure for no more than three days in water (S. Singh et al., 2014 ). Complete submergence of plants for up to two weeks may cause 100% yield loss for susceptible cultivars due to poor underwater gas exchange, affecting the physiological growth of rice plants by disrupting key metabolic processes (Panda & Sarkar, 2013 ). Submergence leads to oxygen deficiency, forcing plants to switch to anaerobic respiration, which is less efficient and produces harmful by-products that damage cells (Kumar et al., 2021 ). It also inhibits photosynthesis due to reduced light penetration and CO2 availability, resulting in decreased energy production and stunted growth (A. Singh et al., 2017 ). To cope with submergence stress, rice plants have developed a set of genetic and physiological strategies governing tolerance mechanisms. The SUBMERGENCE 1 (SUB1) gene, mapped on chromosome 9, has been a major determinant of submergence tolerance. The gene plays a pivotal role by modulating ethylene and gibberellin levels, thus conserving energy reserves during submergence under the quiescence strategy (Xu & Mackill’>, 1996 ). The genetic pool of submergence tolerance in rice is very narrow. The SUB1 gene was discovered from a traditional landrace FR13A from Odisha, India long time ago (A. Singh et al., 2017 ; S. Singh et al., 2014 ). The tolerance QTL protects the plant from complete submergence for up to 14 days, allowing plants to conserve energy and regenerate after desubmergence. There is a need to explore more genetic sources of tolerance that can extend the survival period by 14 days and provide additional protection beyond SUB1 (Septiningsih et al., 2012 ). Also, the degree of tolerance expression changes with environmental conditions and the growth stage of the plant, hence additional genetic sources of tolerance are crucial to enhance stable, durable tolerance (Haque et al., 2023 ; Panda et al., 2021 ). In recent years, advances in bioinformatics and the availability of high-density molecular markers have improved the application of genomic tools in dissecting complex quantitative traits (Tibbs Cortes et al., 2021 ). The utilization of approaches such as whole-genome sequencing has been useful in characterizing new genetic resources of stress tolerance. For complete submergence tolerance, genome-wide association studies (GWAS) have been beneficial in identifying new sources of tolerance that can supplement SUB1, the known major gene. When combined with transcriptomic analysis and genomic selection, GWAS can enhance the development of durable protection against complete submergence, surpassing the capabilities of SUB1. GWAS has been used to discover novel potential candidate genes in a diverse population in India, discovering 9 candidate SNPs for complete submergence tolerance (Phukon et al., 2024 ). Tanzania possesses a vast collection of Oryza sativa landraces, cultivated by farmers since introduced by Asian traders over 1000 years ago through Indian Ocean trade routes (Busungu, 2023 ). These landraces have been traditionally maintained and cultivated by farmers in different rice ecologies of the country, ranging from upland, rainfed lowland, and irrigated ecologies agroecology enhancing their adaptation to diverse environmental conditions. Farmers have been maintaining the landraces due to their agronomic, grain, and culinary attributes that are also attached to their cultural values. Despite their potential, the landraces have been underutilized in modern breeding programs (Bulegeya et al., 2025 ). The landraces offer a broad genetic resource of untapped allelic diversity for tolerance to different stresses, including flooding, drought, and salinity. There is a need for a comprehensive genetic and phenotypic characterization of the landraces to uncover sources of adapted QTL for tolerance to existing stresses, enhancing breeding for rice varieties suited for Tanzania’s diverse production ecologies. This study intends to quantify the genome-wide diversity of rice landraces from Tanzania and other East African countries, characterize their population structure and relatedness, and the linkage disequilibrium pattern for association mapping. Also, the study demonstrates the potential of the materials for climate resilience breeding through genome-wide association mapping for complete submergence tolerance. 2. Materials and methods Plant materials and phenotyping A diverse population of 260 individuals was collected from flood-affected regions of Tanzania and other East African countries, including Kenya, Rwanda, Burundi, Mozambique, and Malawi (Fig. 1 ). The panel includes seeds collected from the Tanzania Agriculture Research Institute (TARI), the Tanzania National Gene Bank (TNGB), and the International Rice Research Institute (IRRI) – Tanzania office. Checks were also collected from the IRRI Headquarters office in Manila, Philippines. Phenotyping for complete submergence was conducted according to the IRRI protocol for phenotyping abiotic stress tolerance in rice (IRRI, 2021). The experiment was conducted for two seasons in April 2024 and April 2025 at the Submergence Pond located at the Crop Museum fields, Sokoine University of Agriculture (SUA), Morogoro, Tanzania. The experiments were planted in an alpha lattice design with two replications; all experiments included both tolerant checks, such as FR13A, Swarna Sub1, Ciherang Sub1, Cirerang SUB1-AG1-AG2, and IR 64, which was regarded as a susceptible check. Genotypes were planted in plots of two rows of 1m with a spacing of 20cm x 20cm, with one seedling per hill. Susceptible check IR 64 was planted around the experimental block to confirm the stress intensity. 21 days old seeding were subjected to complete submergence with the water level of 1.5m. Plants were submerged for a period of 14 days, and data were collected on 7, 14, and 21 days after desubmergence. Floodwater was removed when IR 64 had severe rotting symptoms to ensure uniform submergence stress imposition. The Survival Percentage (%) was regarded as the tolerance trait, calculated as the percentage of the genotype that survived at every data collection week. The survival traits were the percentage survival after 7 days (PSUV07), percentage survival after 14 days (PSUV14), and percentage survival after 21 days PSUV21). The 14-day survival percentage (PSUV14) was used for GWAS analysis since the traits have a high correlation (Anumalla et al., 2025 ). Phenotypic evaluation of rice landraces Linear mixed model analysis was performed on the phenotypic data. Genotype effect was modeled as fixed, and the best linear unbiased estimates (BLUEs) were calculated for downstream GWAS analysis (Eq. 1). y i ⱼₖ = µ + G i + Rⱼ + B(R)ⱼₖ + ε i ⱼₖ …………………………………..Eq. 1 Where: y i ⱼₖ = observed survival percentage of genotype i in block k of replicate j, µ = overall mean, G i = fixed effect of genotype i, Rⱼ = random effect of replicate j, B(R)ⱼₖ = random effect of block k nested within replicate j, ε i ⱼₖ = residual error. The generated BLUEs were used for all other statistical analyses, such as clustering analysis, heat-map, and principal component analyses (PCA). All statistical analysis of the phenotypic data of the panel was performed using R software version 4.5.2. DNA isolation and sequencing DNA samples were collected from young healthy leaf tissues using leaf discs for each genotype using a leaf puncher. 2–3 leaf discs were collected per sample and placed in 2 mL microcentrifuge tubes. They were immediately stored in a -80°C freezer and then lyophilized. DNA extraction was performed following a modified cetyltrimethylammonium bromide (CTAB) protocol for plant tissues(Allen et al., 2006 ). DNA quality was evaluated using agarose gel electrophoresis, and DNA concentration was measured using spectrophotometric methods. High-quality DNA samples were submitted for library preparation. Sequencing libraries were prepared using the NEXTFLEX® Rapid DNA-Seq Kit 2.0 (PerkinElmer Inc., USA). Sequencing was done on an Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA) using paired-end sequencing methodology. Standard quality control of sequencing reads was done before downstream bioinformatic analysis. SNP calling Variant calling was performed using the Automated Rice Variant Calling workflow on HPC, following Genome Analysis Toolkit (GATK) best practices. The high-performance computing genome variant calling workflow (HPC-GVCW) has four major phases: mapping, variant calling, call set refinement and consolidation, and variant merging. Phase one involves mapping genomic reads to a reference genome, phase two covers variant calling for each sample to generate gVCF files, phase three deals with combining gVCF files to develop a single file for all samples, and phase four encompasses filtering SNPs and INDELs and merging to produce a chromosome-based variant table (Zhou et al., 2024 ). Before downstream analysis, genotypic data were subjected to quality control filtering to remove loci with more than 20% missing data and a Minor Allele Frequency (MAF) below 0.01. The remaining high-quality SNP markers were used for population structure analysis and genome-wide association studies. Population structure The population structure analysis of the landrace was conducted using Principal Component Analysis (PCA), K-means clustering, hierarchical clustering, and a Neighbor-joining tree joint analysis. The SNP markers were used to calculate the genetic distance matrix, and PCA was performed to visualize genetic variation across the first two principal components. The K-means clustering was applied at K = 3, grouping landraces into 3 distinct clusters. The PCA and cluster analysis were done using the R software package, specifically factoextra and ggplot2 ( Kassambara & Mundt, 2016 ). Hierarchical clustering analysis was carried out based on a pairwise genetic distance matrix calculated among genotypes based on the Euclidean distance metric, which quantifies allelic difference across loci and enables a robust measure of genetic relationship across accessions. Clustering was done following a hierarchical clustering approach to group accessions based on minimum genetic distance. The resulting dendrogram was constructed using the dendextend package in R, where branch height represents genetic distance among clusters. Genetic relatedness of the rice landrace was also determined using a neighbor-joining (NJ) approach using the SNP data of 259 landraces using the packages ape, phangorn, and ggtree . Genetic distances among landraces were calculated by using identity by state (IBS) based matrix to estimate allelic similarity across the genome. The neighbor-joining tree was constructed from a genetic distance matrix using the neighbor-joining algorithm, which clusters genotypes by minimizing total branch length. The results were visualized in an unrooted layout to accommodate most landraces and the identification of the genetic group. Genome-wide association study GWAS analysis was done using the 168,091 SNPs generated from high-throughput whole-genome sequencing data of the panel. Before analysis, the SNPs were filtered before analysis to remove monomorphic markers and SNPs with more than 10% missing data. The minor allele frequency (MAF) of 0.01 was used to retain informative low-frequency variants, excluding unstable variants, and provide efficient power for LD decay and GWAS analysis. Genotypes' mean percentage survival at different time points were adjusted to experimental effects to generate best linear unbiased estimates (BLUEs), which were used for the GWAS analysis. Principal component analysis (PCA) was used to assess the population structure of the SNP data, and the first principal component explaining the majority of the variation was used as a covariate in the GWAS model as a fixed effect. A kinship matrix was used to estimate the genetic relatedness of individuals in the panel and modeled as a random effect in the GWAS model. Genome-wide association analysis was done using the Genome Association and Prediction Integrated Tool ( GAPIT ) package in R, following a linear mixed model accounting for population structure and kinship of the population (Lipka et al., 2012 ). SNP markers and population structure covariates were treated as fixed effects, while kinship effects and experimental design effects of the alpha lattice design were modeled as random effects (Eq. 2). y = 1µ + Xβ + sα + Zg + ε ……………………………. Eq. 2 where: y is the vector of phenotypic observation (BLUEs); X is the design matrix for fixed effects; β is the vector of fixed effects (Principal Components); s is a vector of the number of alleles of each genotype at a particular SNP; α is the effect of a SNP Z is the design matrix for random effects; g is a vector of random effects; ε is the vector of residual errors Manhattan plots were plotted using the ggplot2 package in R, displaying the − log₁₀ (p-values) of associations between SNPs and submergence survival traits across the 12 rice chromosomes. The calculated Bonferroni-corrected significance threshold value was indicated in a horizontal line. Quantile–quantile (Q-Q) plots were also evaluated to confirm the effective control of population structure and kinship. Linkage Disequilibrium (LD) Analysis Genome-wide linkage disequilibrium (LD) was estimated using a squared correlation coefficient (r²) between polymorphic SNP pairs within chromosomes, which is appropriate for diverse landrace panels. LD analysis was performed in R using the LDheatmap package, which utilized high-quality SNPs after filtering for minor allele frequency (MAF) and missing data. LD of 200 SNP samples was plotted by using r² against physical distance (kb) to estimate the LD decay of the population. Identification of Candidate Gene Candidate genes were predicted based on the genome-wide linkage disequilibrium (LD) decay estimated from the population. With the rapid LD decay reaching threshold levels of 0.1 below 50kb, a ± 5kb window of the significant SNP was adopted. Genes flanking the significant SNPs in a 10kb window were retrieved and considered putative candidate genes. The Ensembl genome browser (IRGSP-1.0) was used for identifying the putative genes, and the Rice Genome Annotation Project (MSU) and Rice Annotation Project Database (RAP-DB) were used determine functions of the identified genes. 3. Results Phenotypic performance and heritability of rice landraces to complete submergence Rice landraces exhibited significant variation in coping with complete submergence stress. The tolerance traits displayed a continuous distribution, implying a quantitative trait tolerance mechanism. The BLUEs values showed a moderate median with several high values as outliers, indicating the presence of highly tolerant genotypes outperforming the population (Figure 2). The majority of landraces displayed moderate to low tolerance levels. This suggests a dominant genotypic effect on the observed phenotypic variations. Principal component analysis explained 99.3% of the phenotypic variation, with 97% being explained by the first principal component (Figure 3). Highly tolerant genotypes were separated in a different cluster from the susceptible genotypes. In comparison with tolerant checks such as FR13A and CIHERANG SUB1 AG1 AG2, some landraces such as NAWA TULE NA BWANA, LIWALE_2, and KYELA1_5735 displayed better performance in response to complete submergence stress (Figure 4). The continuous traits distribution and high heritability suggest a strong genetic control of submergence tolerance traits, confirming the reliability of the panel for genetic mapping. High correlation of survival traits early and late post desubmergence indicates the shared genetic control of tolerance governing early survival and recovery. The presence of the best-performing landraces as checks signifies the potential of landraces for broadening genetic sources of tolerance to complete submergence in flood-affected regions. SNP distribution and diversity After quality control,168,091 SNPs were retained, including 117,641 transitions and 50,450 transversions, with a Ts/Tv ratio of 2.33. The ratio designates high SNP calling quality and aligns with expectations for the rice genome datasets. The 168,091 SNPs were distributed across 12 chromosomes of rice, with the largest being chromosome 1, which has a size of 43.27 Mb, containing 19,144 SNPs. In contrast, chromosome 9 had the smallest size, at 22 Mb. 94 Mb with 9633 SNPs. SNP density varied across the chromosomes, ranging from 358.51 SNPs per Mb on chromosome 3 to 547. 58 on chromosome 8, indicating sufficient marker coverage for a genome-wide association study and elaborate heterogeneous polymorphism for rice genotypes (Table 2). Mean heterozygosity and polymorphic information content (PIC) across chromosomes are 0.06 and 0.06, respectively, suggesting uniform distribution of diversity throughout the genome, indicating moderate genetic diversity within the panel. Table 2: SNP markers distribution and diversity across chromosomes Chr SNP count Chromosome length (bp) Chromosome size (Mb) SNP density (Mb) Mean heterozygosity (He) PIC 1 19144 43269550 43.26955 442.4358469 0.063115885 0.063115885 2 15798 35898770 35.89877 440.0707879 0.06108671 0.06108671 3 13053 36409137 36.409137 358.5089095 0.061187382 0.061187382 4 16957 35499430 35.49943 477.6696415 0.060075965 0.060075965 5 16057 29956842 29.956842 536.0044293 0.054291232 0.054291232 6 15365 31210857 31.210857 492.296639 0.060785529 0.060785529 7 11710 29669264 29.669264 394.6845463 0.0593555 0.0593555 8 15572 28438000 28.438 547.5771855 0.057650645 0.057650645 9 9633 22939666 22.939666 419.9276485 0.059797325 0.059797325 10 10701 23191915 23.191915 461.4107977 0.06309659 0.06309659 11 11528 29002613 29.002613 397.4814269 0.059912272 0.059912272 12 12573 27529894 27.529894 456.7035383 0.058516299 0.058516299 Population structure and diversity The PCA of the genotypic displayed the existing genetic variation among rice landraces, with the first two principal components explaining 5.1% and 4.2% of the variation. The cumulative variance explained by the first 20 principal components explains 21.16% of the total variation, which is common in diverse landrace panels (Figure 5). The PCA explains three major, distinct clusters following K-means clustering (K=3). The first cluster (blue) is positioned on the lower quadrant, the second cluster (green) is positioned on the central right, and the third cluster (red) is positioned on the upper left quadrant, indicating genetic divergence, within-cluster similarity, and between-cluster differentiation (Figure 6). The presence of three clusters indicates the presence of at least three genetically distinct pools guiding breeding and improvement strategies. The landraces displayed mixed ancestry sources, suggesting a genetic continuum rather than subpopulations (Figure 7). The majority of the genotypes have major genetic contribution from the first principal component; several landraces have balanced contribution from all three. A few have major contributions from the second and third principal components, indicating a unique genetic background due to local adaptations or geographical positions. The absence of pure genotypes suggests a historical gene flow and shared background among landraces. The ancestry plot shows the admixture of rice landraces, which is common in traditional landraces, caused by natural and human-made selection due to seed exchange and local adaptation. The genetic continuum signifies the importance of landrace conservation for breeding purposes. Unique PC contributions, such as PC3, are crucial for trait discovery and allelic diversity. The rooted Neighbor-Joining phylogenetic tree of the landraces has clustered the panel in 3 Clusters, suggesting the presence of three major genetic groups within the panel (Figure 8a). Cluster 2 is the largest, comprising the majority of the landrace above 82.2% with short internal branch length suggesting genetic similarity among them. Clusters 2 and 3 are distinct, positioned on a separate longer branch, suggesting genetic divergence; the longer branch to these groups indicates the genetic differentiation from Cluster 1. The clustering is also supported by the hierarchical clustering dendrogram, which groups accessions based on their similarity and genetic relatedness (Figure 8b). The presence of subgroups justifies the need for employing Mixed Linear Model (MLM) in GWAS, incorporating kinship and principal components to account for population structure, ensuring vigorous GWAS results. Genome-wide Association Study Genome-wide association mapping for complete submergence tolerance was done on the percentage survival trait 14 days post-submergence. No SNPs surpassed the Bonferroni-corrected genome-wide significance threshold at α = 0.05, with a P-value of 2.97 x 10 -7 and a -log 10 threshold of 6.53 ( Figure 9a ). However, several loci showed suggestive associations, indicating a polygenic architecture underlying submergence tolerance. Nevertheless, multiple SNPs exceed the FDR significance threshold, indicating the presence of significant SNP associations with submergence survival traits ( Figure 9b ). The significant associations were distributed across the chromosomes, signifying the polygenic nature of the submergence tolerance trait. Several peaks are consistently observed at 14 days post-submergence, indicating a stable genetic effect on submergence survival. The most significant, consistent association signal was detected on chromosomes 9 and 6. Other significant associations were identified on chromosomes 2, 4, 8,10, and 11, with several peaks exceeding the false discovery rate (FDR)-corrected threshold. The quantile-quantile (Q-Q) plot of the GWAS analysis for the percentage survival trait at 14 days after submergence compares the observed and expected p-values. The analysis follows the null hypothesis that there is no association between a marker and a trait of interest. As observed in Figure 10 , the majority of SNPs followed the expected line, implying the statistical test efficiency in controlling kinship and population structure with the model. This suggests the minimal chance of having false positive associations and resulting in true genetic associations. The genome-wide association study revealed about 11 significant SNPs for all survival traits, and more than 28 were identified for individual traits. Significant associations were observed on chromosomes 2, 4, 6, 8, 9, and 11 with the presence of multiple genetic sources of tolerance to the trait (Table 3) . The highly significant SNP was observed at chromosome 9 at a P-value of 3.33 × 10 -7 with a MAF of 0.08, indicating a rare allele within the population with a large effect. Other SNPs had a MAF ranging from 0.03 to 0.20, indicating the biological relevance of the significant associations. Two SNPs on chromosome 8 (Chr08_27421228_C_T and Chr08_27421246_C_T) are located in proximity, indicating a shared LD block and suggesting the strength of the region in tolerance delivery. The Hochberg and Benjamini (H&B) test ranged from 0.06 to 0.42, indicating the range in confidence, suggesting the association as suggestive, requiring more confirmation. Generally, the results propose a polygenic nature of submergence tolerance traits with several loci contributing to major and minor tolerance effects. The identified significant SNPs pave the way to the exploration of candidate gene investigation within the identified LD blocks that confer functional contribution to tolerance. Table 3: Significant SNP markers identified for complete submergence tolerance for all survival traits. SNP Chr Position P.value MAF Effect H&B.P.Value 1 Chr09_6772795_C_T 9 6772795 3.33E-07 0.085271 -8.639000634 0.055997937 2 Chr06_23006175_A_T 6 23006175 1.48E-06 0.203488 -5.31434606 0.082829794 3 Chr08_27421228_C_T 8 27421228 2.86E-06 0.189922 -5.331635678 0.120220408 4 Chr11_25850685_A_T 11 25850685 8.00E-07 0.112403 -7.741668141 0.067267777 5 Chr06_26455167_G_A 6 26455167 2.06E-06 0.034884 -11.4591 0.115479 6 Chr11_16978066_G_A 11 16978066 5.92E-06 0.069767 -7.96111 0.165669 7 Chr08_27421246_C_T 8 27421246 6.90E-06 0.186047 -5.276051239 0.231892542 8 Chr04_26829543_G_A 4 26829543 1.49E-05 0.147287 -5.529790846 0.308267371 9 Chr10_5104720_T_C 10 5104720 2.60E-05 0.073643 -7.344946061 0.401066974 10 Chr02_6084641_G_A 2 6084641 5.88E-05 0.114341 -5.864247975 0.42773408 11 Chr02_588105_C_T 2 588105 5.87E-06 0.110465 -6.84363 0.165669 Linkage disequilibrium analysis The LD analysis reveals high r 2 values at short distances, with many SNPs showing strong LD, suggesting tight marker linkage and few or no recombination events within the region. There is a strong decline in LD decay with an increase in physical distance, with a sharp drop in r 2 within a few kilobases. This shows the fast breakdown of recombination with increased physical distance. A minimum LD is observed with increasing distance, suggesting independent segregation across loci, which reaches the background threshold below r² = 0.1 at approximately 35 kb ( Figure 11 ). The rapid LD decay with distance indicates high mapping resolution suitable for GWAS since significant SNPs will likely be close or within variants, and candidate genes should be searched within a narrow LD window. The finding is typical of diverse landrace panels with high historic recombination and small haplotype blocks. Candidate gene identification Candidate genes were identified based on the genome-wide linkage disequilibrium (LD) pattern of the population, which portrayed a decline in LD decay at r 2 = 0.1 within 30-40kb. Therefore, the average LD decay of 5kb was adopted as an estimate of genomic resolution for candidate gene mining around the significant SNP marker. Genes located within +/- 5k flanking the significant SNP markers were considered putative candidate genes, making the window size 10kb, avoiding the likelihood of capturing unrelated genes. Candidate genes within the flanking region were retrieved using the Oryza sativa ssp. japonica reference genome (IRGSP-1.0) . Functional annotation was done by referring to Ensembl Plants and the MSU Rice Genome Annotated Project databases. Within the specified LD intervals, a total of 11 putative loci were identified across multiple chromosomes ( Table 4 ). The identified Loci includes; LOC_Os09g12050, LOC_Os09g12060, LOC_Os06g38750, LOC_Os06g38760, LOC_Os08g43290, LOC_Os11g42900, LOC_Os06g43910, LOC_Os06g43920, LOC_Os06g43930, LOC_Os11g29290, LOC_Os08g43390, LOC_Os04g45370, LOC_Os10g09360, LOC_Os02g11780 and LOC_Os02g02070. The loci were identified from chromosomes 2,4, 6,8,9,10,11, signifying the quantitative nature of genetic control of the submergence tolerance traits. Functional annotation of putative genes identified the loci for coding proteins useful for diverse purposes, including protein expression, retrotransposons, and regulation and transcription factors. The identified loci provide a valuable insight for the functional characterization of genomic regions identified by GWAS. Table 4: Candidate gene annotation of significant SNPs for submergence survival traits Significant SNP MSU_ID Gene stable ID Description Chr Gene start (bp) Gene end (bp) 1 Chr09_6772795_C_T LOC_Os09g12050 Os09g0292300 Myb/SANT-like domain-containing protein. (Os09t0292300-00) 9 6803261 6806757 LOC_Os09g12060 Os09g0292400 Conserved hypothetical protein. (Os09t0292400-00) 9 6810220 6810609 2 Chr06_23006175_A_T LOC_Os06g38750 Os06g0587100 Conserved hypothetical protein. (Os06t0587100-00) 6 23000435 23001408 LOC_Os06g38760 Os06g0587200 LRR-receptor-like kinase (LRR-RLK) family protein (Os06t0587200-01) 6 23005337 23008846 3 Chr08_27421228_C_T _ Os08g0547350 Hypothetical protein. (Os08t0547350-00) 8 27420967 27422836 LOC_Os08g43290 Os08g0546300 Similar to LTP-like protein. (Os08t0546300-01) 8 27364165 27364816 4 Chr11_25850685_A_T LOC_Os11g42900 Os11g0649000 DNA replication helicase 2_14, Drought and salt stress response (Os11t0649000-01) 11 25840593 25846990 5 Chr06_26455167_G_A _ Os06g0647150 Hypothetical protein. (Os06t0647150-00) 6 26450752 26454632 LOC_Os06g43910 Os06g0647200 Similar to Response regulator. (Os06t0647200-01) 6 26450761 26455305 LOC_Os06g43920 Os06g0647200 B-type response regulator, Cytokinin signaling (Os06t0647200-02) 6 26455046 26455961 LOC_Os06g43930 Os06g0647400 Similar to Lysosomal Pro-X carboxypeptidase. (Os06t0647400-02) 6 26456312 26461805 6 Chr11_16978066_G_A _ Os11g0482901 Hypothetical gene. (Os11t0482901-01) 11 16985402 16987473 LOC_Os11g29290 Os11g0483000 ytochrome P450, Oxidase, JA-mediated chilling tolerance (Os11t0483000-01) 11 16985426 16987260 7 Chr08_27421246_C_T _ Os08g0547350 Hypothetical protein. (Os08t0547350-00) 8 27420967 27422836 LOC_Os08g43390 Os08g0547300 Cytochrome P450 protein, CYP78A family protein, Disease resistance, Regulation of growth rate and seed size (Os08t0547300-01) 8 27420633 27422835 8 Chr04_26829543_G_A LOC_Os04g45370 Os04g0537100 Similar to Auxin-induced protein X15. (Os04t0537100-01) 4 26831005 26831867 Os04g0537450 Non-protein coding transcript. (Os04t0537450-00) 4 26834695 26835104 9 Chr10_5104720_T_C LOC_Os10g09360 Os10g0173800 SAM dependent carboxyl methyltransferase domain containing protein. (Os10t0173800-00) 10 5055323 5055736 10 Chr02_6084641_G_A LOC_Os02g11780 Os02g0208600 Transcription elongation factor S-II, central region domain containing protein. (Os02t0208600-01) 2 6083116 6088700 11 Chr02_588105_C_T LOC_Os02g02070 Os02g0110900 Hypothetical conserved gene. (Os02t0110900-00) 2 596645 609187 4. Discussion The study provided a comprehensive analysis of the genetic diversity and structure of East African rice landraces using genotype by sequencing approach. Significant genetic variation was observed within a panel of 260 landraces, confirming the genetic richness and allelic diversity within traditional landraces. The presence of 168,091 high-quality SNPs provides a genome-wide coverage with consistent expectation across the Oryza sativa landraces. The uniform distribution of SNPs across 12 rice chromosomes indicates adequate genome representation and robust information for diversity analysis. The uniform diversity reflected in PIC and moderate heterozygosity validates a well-distributed marker set for genome-wide association studies with minimal gaps in genomic resolution for identifying stress tolerance loci (Roy et al., 2024 ). Population structure analysis, including the Principal Component Analysis (PCA) and the phylogenetic tree of the genotypic data reveal 3 major subpopulations, but the admixture analysis reveals a mixed population typical of rice landraces due to seed exchange and continuous gene flow. The observed genetic stratification validates the use of Mixed Linear Model (MLM) in GWAS analysis to account for kinship (K) and population structure (Q) as covariates to minimize false positive associations (Alamin et al., 2022 ; Anilkumar et al., 2023 ). This study will facilitate the discovery of valuable alleles for climate resilience stress, including complete submergence tolerance highlighting the role of landraces in trait discovery for resilience breeding (Anumalla et al., 2025 ). A sufficient linkage disequilibrium (LD) decay of approximately 35 kb provides sufficient resolution for association mapping for stress tolerance. Such decay narrows the Quantitative trait loci (QTL) region, increasing the precision for candidate gene localization. The utilization of diverse panels enhances the mapping power, capturing novel and rare alleles, including locally adapted alleles absent in elite genotypes. The fast LD decay in the panel of smaller haplotype blocks upsurges the fine mapping efficiency and increases the likelihood of finding true associations (Abhijith et al., 2022 ; Zhou & Zhou, 2025 ). Moreover, due to long-term adaptation to a heterogeneous environment, landraces are a valuable genetic resource for dissecting environment-related traits, such as climate-resilient traits and traits related to genotype by environmental interactions (Lamichhane et al., 2025 ; Nayak et al., 2022 ). The study employed the panel to map significant genomic association with complete submergence tolerance through GWAS (Phukon et al., 2024 ). The phenotypic analysis of the panel’s percentage survival traits shows moderate mean performance of the population, suggesting most genotypes succumb to complete submergence stress. Nevertheless, a few showed high tolerance to submergence above the population mean, indicating the presence of highly tolerant genotypes within the population (Barik et al., 2020 ; Hapa & Al, 2023 ; Maity et al., 2024 ). There is a high broad-sense heritability, suggesting the genetic factor as a determinant of most observed variations (Barik et al., 2020 ). Principal component analysis (PCA) reveals that the first principal component explains 97% of the total phenotypic variance; hence, it can be easily used to predict genetic variation. The genetic mechanisms of rice tolerance to complete submergence have largely been studied in Asia, leading to the discovery of the SUB1 gene from FR13A and an indica landrace from Odisha, India (Bailey-Serres et al., 2010 ; Xu et al., 2006 ; Xu & Mackill’>, 1996 ). The discovery has led to the development of Sub1 varieties, which were adopted and used by farmers in several Asian countries, resulting in yield improvements in flood-affected regions of Asia (Perata & Voesenek, 2007 ; Septiningsih et al., 2009 ; S. Singh et al., 2009 ). The developmental gap in research and variety development for flooding tolerance has left African rice landraces underexplored for their potential in providing sources of tolerance to complete submergence and other types of flooding (Bulegeya et al., 2025 ). This study provides the first insight into the genetic architecture of tolerance to complete submergence in Oryza sativa rice landraces from East Africa, using whole-genome sequencing. This work lays a foundation for the exploration of East African rice landraces as a pivotal genetic resource of tolerance to biotic and abiotic stresses. The GWAS analysis was able to identify 11 loci associated with complete submergence survival in the rice panel. The utilization of genome-wide association analysis and linkage disequilibrium was able identify putative candidate genes for complete submergence tolerance in rice. The approach has been useful to identify and annotate genes for several agronomic traits and abiotic stresses in rice (Jiang et al., 2025 ; Nayyeripasand et al., 2021 ; Tang et al., 2025 ; Yi et al., 2023 ). Although the identified loci do not include SUB1, the loci were distributed across chromosomes 2, 4, 6, 8, 9, 10, and 11. Significant SNPs on chromosome 9 annotate to two genes, LOC_Os09g12050 and LOC_Os09g12060, coding for Myb/SANT-like domain-containing protein and conserved protein, respectively. The Myb/SANT-like domain-containing protein has been reported to be responsible for stress response and regulation (Li et al., 2019 ). On chromosome 6, the identified candidate locus LOC_Os06g38760 is responsible for signal transduction, response to stress, and hormone regulation (Soltabayeva et al., 2022 ; Zhiqi et al., 2025 ). Another identified locus on chromosome 6, LOC_Os06g43920, facilitates B-type response regulator and Cytokinin signaling. The B-type response regulators are critical in plant growth and stress response. Cytokinin negative response is crucial in the quiescence strategy, suppressing shoot elongation during submergence. In tolerant plants, cytokinin levels are downregulated under complete submergence, allowing carbohydrate conservation until desubmergence(Hornai et al., 2024 ; Shi et al., 2020 ). On chromosome 11, the significant marker is linked to the LOC_Os11g42900 locus carrying the DNA replication helicase 2_14 gene involved in DNA replication and repair. The gene was reported to be involved in regulating abiotic stresses, including drought and salinity tolerance in rice(Mohapatra et al., 2023 ; Saleem et al., 2022 ). The identified loci on chromosome 8 have retrieved LOC_Os08g43290, which translates to lipid transfer-like (LTP-like) proteins responsible for enhancing immune response against pathogens and abiotic stresses such as salinity (Jülke & Ludwig-Müller, 2016 ; McLaughlin & Tumer, 2025 ; Patkar & Chattoo, 2006 ). The locus LOC_Os02g11780 was identified by a SNP marker on chromosome 2 translate to Transcription elongation factor S-II, central region domain containing protein. The proteins are critical for successful gene transcription under submergence stress by facilitating hypoxia response genes and continuous expression of submergence adaptive genes (Cermakova et al., 2023 ). Another candidate gene for functional regulation is locus LOC_Os04g45370 on chromosome 4, which is responsible for Auxin-induced protein X15. The Auxin-induced protein X15 is critical for cell wall elongation and plasticity, suppressing shoot elongation, and conserving carbohydrate during submergence. The gene was observed to enable shoot elongation under drought (Jiang et al., 2025 ; Mansoor et al., 2024 ). LOC_Os10g09360 on chromosome 10 produces SAM-dependent carboxyl methyltransferase domain-containing proteins, which are involved in suppression of growth hormone activities and activation of stress response genes. The functional range of identified genes span from a spectrum of pathways from hormonal regulations and gene control for quiescence strategy and energy conservation. The loci LOC_Os06g43920, a B-type response regulator in cytokinin signaling is key. Cytokinin is vital in suppressing shoot elongation and carbohydrate reserve in tolerant plants, aiding the quiescence strategy (Huynh et al., 2005 ; Zwack & Rashotte, 2015 ). Likewise, the LOC_Os04g45370, the Auxin-induced protein X15 facilitate auxin metabolism and growth hormone regulation during complete submergence(Wu & Yang, 2020 ; Zwack & Rashotte, 2015 ). In addition to hormonal regulation, the candidate gene also pinpoints the role of transcription regulation and stress signaling. The transcription elongation factor S-II (LOC_Os02g11780) facilitates the expression of hypoxia-sensitive genes under submergence conditions (Loreti & Perata, 2023 ). Also, the stress response regulators such as Myb/SANT-like domain protein (LOC_Os09g12050) and the signal transduction component (LOC_Os06g38760) were annotated, signifying a range of tolerance pathways for abiotic stresses (Li et al., 2019 ; Rongjun Chen, 2012 ). This is accompanied by genes involved in cellular maintenance, such as LOC_Os11g42900 (DNA replication helicase) and lipid transfer-like protein (LOC_Os11g42900) to facilitate tolerance to complete submergence (Rongjun Chen, 2012 ; Tuteja et al., 2013 ). Collectively, the findings emphasize a harmonized system of hormonal, transcriptional, and cellular pathways involved in submergence tolerance, highlighting targets for functional validation and tolerance improvement. This study presented a comprehensive genomic characterization of the East African rice landraces, documenting the population structure, allelic diversity, and the genetic architecture of the population. Genome-wide SNP analysis revealed moderate nucleotide diversity and polymorphism, suggesting the presence of variations worth exploring. Observed LD patterns provide suitable resolution patterns for association mapping of genomic regions associated with stress tolerance traits such as complete submergence. The study provides a foundation of utilization of the panel for further investigation of tolerance to other stresses in the region and identifies landraces with favorable alleles that can serve as donors for pre-breeding programs targeting climate resilience in the region. 5. Conclusion Conclusively, the East African rice landraces constitute a diverse resource with suitable linkage disequilibrium for association mapping. The panel has proven to have adequate allelic richness and population structure suitable for detecting common and rare alleles for stress tolerance traits. The presence of genetic variations among subpopulations suggests the presence of genetic recombination and adaptation to the local environment, which is key in the buildup of alleles for climate resilience. This provides the platform for allele mining and genomic selection for marker-assisted introgression into the elite genetic pool in East Africa. The identified loci in for complete submergence are regarded as putative, subject to functional validation demanding advanced study beyond statistical marker trait association. Expression profiling of the candidate gene under complete submergence and after desubmergence to quantify their transcriptional performance and dynamics in the tolerant and susceptible genotypes is necessary. The analysis will identify the causative genes and determine whether they are fundamental genes or stress-induced genes. Also, allelic dissection of the loci will provide key information regarding tolerance sources and breeding priorities. All in all, the findings provide a baseline and genomic foundation for improvement targets and marker-assisted introgression to improve tolerance in flood-prone ecologies of Eastern Africa, particularly Tanzania. Abbreviations GWAS Genome-wide Association Study SUB1 SUBMERGENCE1 GENE QTL Quantitative Trait Loci MLM Mixed Linear Model BLUEs Best Linear Unbiased Estimate GATK Genome Analysis Toolkit PCA Principal Component Analysis SNP Single Nucleotide Polymorphism MAF Minor Allele Frequency GAPIT Genome Association and Prediction Integrated Tool LD Linkage Disequilibrium Declarations Acknowledgement Not applicable Authors contribution VB: Conceptualization, experimentation, data collection, data analysis, and manuscript writing. WH: Conceptualization, experimentation, data curation, data analysis, manuscript writing, review, editing, and supervision. YZ: Conceptualization, experimentation, data curation, data analysis, manuscript writing, review, editing, and supervision. 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Frontiers in Genetics , 13 . https://doi.org/10.3389/fgene.2022.1039548 Sekiya, N., Oizumi, N., Kessy, T. T., Fimbo, K. M. J., Tomitaka, M., Katsura, K., & Araki, H. (2020). Importance of market-oriented research for rice production in Tanzania. A review. In Agronomy for Sustainable Development (Vol. 40, Number 1). Springer. https://doi.org/10.1007/s13593-020-0611-1 Septiningsih, E. M., Pamplona, A. M., Sanchez, D. L., Neeraja, C. N., Vergara, G. V., Heuer, S., Ismail, A. M., & Mackill, D. J. (2009). Development of submergence-tolerant rice cultivars: The Sub1 locus and beyond. Annals of Botany , 103 (2), 151–160. https://doi.org/10.1093/aob/mcn206 Septiningsih, E. M., Sanchez, D. L., Singh, N., Sendon, P. M. D., Pamplona, A. M., Heuer, S., & Mackill, D. J. (2012). Identifying novel QTLs for submergence tolerance in rice cultivars IR72 and Madabaru. Theoretical and Applied Genetics , 124 (5), 867–874. https://doi.org/10.1007/s00122-011-1751-0 Shi, F., Wang, M., & An, Y. (2020). Overexpression of a B-type cytokinin response regulator (OsORR2) reduces plant height in rice. Plant Signaling and Behavior , 15 (8). https://doi.org/10.1080/15592324.2020.1780405 Singh, A., Septiningsih, E. M., Balyan, H. S., Singh, N. K., & Rai, V. (2017). Genetics, physiological mechanisms and breeding of flood-tolerant rice (Oryza sativa L.). In Plant and Cell Physiology (Vol. 58, Number 2, pp. 185–197). Oxford University Press. https://doi.org/10.1093/pcp/pcw206 Singh, S., Mackill, D. J., & Ismail, A. M. (2009). Responses of SUB1 rice introgression lines to submergence in the field: Yield and grain quality. Field Crops Research , 113 (1), 12–23. https://doi.org/10.1016/j.fcr.2009.04.003 Singh, S., Mackill, D. J., & Ismail, A. M. (2014). Physiological basis of tolerance to complete submergence in rice involves genetic factors in addition to the SUB1 gene. AoB PLANTS , 6 . https://doi.org/10.1093/aobpla/plu060 Soltabayeva, A., Dauletova, N., Serik, S., Sandybek, M., Omondi, J. O., Kurmanbayeva, A., & Srivastava, S. (2022). Receptor-like Kinases (LRR-RLKs) in Response of Plants to Biotic and Abiotic Stresses. In Plants (Vol. 11, Number 19). MDPI. https://doi.org/10.3390/plants11192660 Tang, C., Bai, D., Wang, X., Dou, G., Lv, J., Bao, Y., Wang, N., Yu, L., Zhou, Y., Zhang, J., Meng, D., Zhu, J., & Shi, Y. (2025). Identification of Candidate Genes for Hypoxia Tolerance in Rice by Genome-Wide Association Analysis and Transcriptome Sequencing. Rice , 18 (1). https://doi.org/10.1186/s12284-025-00765-9 Tibbs Cortes, L., Zhang, Z., & Yu, J. (2021). Status and prospects of genome-wide association studies in plants. In Plant Genome (Vol. 14, Number 1). John Wiley and Sons Inc. https://doi.org/10.1002/tpg2.20077 Tuteja, N., Sahoo, R. K., Garg, B., & Tuteja, R. (2013). OsSUV3 dual helicase functions in salinity stress tolerance by maintaining photosynthesis and antioxidant machinery in rice (Oryza sativa L. cv. IR64). Plant Journal , 76 (1), 115–127. https://doi.org/10.1111/tpj.12277 Van De Wouw, M., Kik, C., Van Hintum, T., Van Treuren, R., & Visser, B. (2010). Genetic erosion in crops: Concept, research results and challenges. Plant Genetic Resources: Characterisation and Utilisation , 8 (1), 1–15. https://doi.org/10.1017/S1479262109990062 Wu, Y. S., & Yang, C. Y. (2020). Comprehensive transcriptomic analysis of auxin responses in submerged rice coleoptile growth. International Journal of Molecular Sciences , 21 (4). https://doi.org/10.3390/ijms21041292 Xu, K., & Mackill’>, D. J. (1996). A major locus for submergence tolerance mapped on rice chromosome 9. In Molecular Breeding (Vol. 2). Kluwer Academic Publishers. Xu, K., Xu, X., Fukao, T., Canlas, P., Maghirang-Rodriguez, R., Heuer, S., Ismail, A. M., Bailey-Serres, J., Ronald, P. C., & Mackill, D. J. (2006). Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice. Nature , 442 (7103), 705–708. https://doi.org/10.1038/nature04920 Yi, Y., Hassan, M. A., Cheng, X., Li, Y., Liu, H., Fang, W., Zhu, Q., & Wang, S. (2023). QTL mapping and analysis for drought tolerance in rice by genome-wide association study. Frontiers in Plant Science , 14 . https://doi.org/10.3389/fpls.2023.1223782 Zhiqi, H., Tingyi, W., Dongdong, C., Lan, S., Guangheng, Z., Qian, Q., & Li, Z. (2025). Leucine-Rich Repeat Protein Family Regulates Stress Tolerance and Development in Plants. In Rice Science (Vol. 32, Number 1, pp. 32–43). Elsevier B.V. https://doi.org/10.1016/j.rsci.2024.12.003 Zhou, Y., Kathiresan, N., Yu, Z., Rivera, L. F., Yang, Y., Thimma, M., Manickam, K., Chebotarov, D., Mauleon, R., Chougule, K., Wei, S., Gao, T., Green, C. D., Zuccolo, A., Xie, W., Ware, D., Zhang, J., McNally, K. L., & Wing, R. A. (2024). A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset. BMC Biology , 22 (1). https://doi.org/10.1186/s12915-024-01820-5 Zhou, Y., & Zhou, Z. (2025). Unlocking rice’s genetic potential: big data-driven insights from population genomics. Genomics Communications , 2 (1), 0–0. https://doi.org/10.48130/gcomm-0025-0012 Zwack, P. J., & Rashotte, A. M. (2015). Interactions between cytokinin signalling and abiotic stress responses. Journal of Experimental Botany , 66 (16), 4863–4871. https://doi.org/10.1093/jxb/erv172 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviews received at journal 11 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 23 Feb, 2026 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. 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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-8944214","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600444189,"identity":"d4fe3d3c-5b5d-4046-83ee-b6895e41b89e","order_by":0,"name":"Victoria Bulegeya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZiBkbGCQgfAqQCLMDURp4YHwzoBEGAloYUDWwtgGJvFrMTjO/NiYd8dhHn7+xcckfs6rjeZvB2r5UbENt5bDbMbJvGcO80jOeJYm2bvteO6Mw4wNjD1nbuPUItnMw3yYt+0wj8GNM2YSvNuO5TYAtTAzthGhxf7G+W+Sf+ccy51PSAs/Mw9zMtgW/h42ad6GmtwNhLWwGRvOPZPOI3GDzdha5tiB3I1ALQfx+YWN//Bjibc7rOX4+w8/vPmmpi533vnDBx/8qMCtBQSYwJEikcAiwcBwGCxyAK96IGD8AXbiAeYPDAx1hBSPglEwCkbBCAQAv1hY2LqYaN8AAAAASUVORK5CYII=","orcid":"","institution":"Tanzania Agriculture Research Institute (TARI)","correspondingAuthor":true,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Bulegeya","suffix":""},{"id":600444191,"identity":"ab2dbcf9-fbb6-45ca-a818-12bded8760f3","order_by":1,"name":"Waseem Hussain","email":"","orcid":"","institution":"International Rice Research Institute (IRRI)","correspondingAuthor":false,"prefix":"","firstName":"Waseem","middleName":"","lastName":"Hussain","suffix":""},{"id":600444193,"identity":"85b7e14d-a98a-44c3-a944-489330415ebf","order_by":2,"name":"Yong Zhou","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zhou","suffix":""},{"id":600444195,"identity":"815d6576-c7f1-449c-8412-825c65ad6197","order_by":3,"name":"Newton Kilasi","email":"","orcid":"","institution":"Sokoine University of Agriculture (SUA)","correspondingAuthor":false,"prefix":"","firstName":"Newton","middleName":"","lastName":"Kilasi","suffix":""},{"id":600444196,"identity":"089d62f5-5203-4f93-b765-696767d3f267","order_by":4,"name":"Rosemary Murori","email":"","orcid":"","institution":"International Rice Research Institute (IRRI)","correspondingAuthor":false,"prefix":"","firstName":"Rosemary","middleName":"","lastName":"Murori","suffix":""},{"id":600444199,"identity":"df3eeda7-e89e-490c-8a8f-b99e515dbfa1","order_by":5,"name":"Atugonza Bilaro","email":"","orcid":"","institution":"Head Office, Tanzania Agriculture Research Institute (TARI)","correspondingAuthor":false,"prefix":"","firstName":"Atugonza","middleName":"","lastName":"Bilaro","suffix":""},{"id":600444200,"identity":"4d705050-6b1b-44c1-8769-6e0b61cf40d2","order_by":6,"name":"Abdelbagi Ismail","email":"","orcid":"","institution":"International Rice Research Institute (IRRI)","correspondingAuthor":false,"prefix":"","firstName":"Abdelbagi","middleName":"","lastName":"Ismail","suffix":""},{"id":600444201,"identity":"35a2d74f-8cde-42a8-b3b8-a21ddc4e2d17","order_by":7,"name":"Susan Nchimba-Msolla","email":"","orcid":"","institution":"Sokoine University of Agriculture (SUA)","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Nchimba-Msolla","suffix":""}],"badges":[],"createdAt":"2026-02-23 07:38:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8944214/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8944214/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104403916,"identity":"8b693015-5a87-4eac-a103-829ee7d66405","added_by":"auto","created_at":"2026-03-11 12:19:22","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":311694,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of collected landraces across East and Southern Africa.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/329bc2d0d07deda1f49a6e3c.jpeg"},{"id":104181256,"identity":"0714f0aa-b89f-478a-a0e9-b7ed1b8fabb5","added_by":"auto","created_at":"2026-03-08 17:26:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31776,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot showing frequency distribution of survival trait in percentage survival collected 7 days, 14 days and 21 days post desubmergence\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/496a07d7e87e9a09699c4dae.png"},{"id":104181247,"identity":"2e8062f0-52aa-4965-9687-068362a74573","added_by":"auto","created_at":"2026-03-08 17:26:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":269723,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) biplot of survival trait\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/e5ebfd58254ae9eb5d93bc46.jpeg"},{"id":104181248,"identity":"41581d91-764c-4b5f-b1da-efd024ee6e19","added_by":"auto","created_at":"2026-03-08 17:26:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":316373,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 best-performing genotypes for under complete submergence stress compared to the tolerant check FR13A and CIHERANG SUB1-AG1-AG2.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/49ae61da0a05324aa5df6665.jpeg"},{"id":104403588,"identity":"bc45d06e-4c5a-46d6-96e1-5e96ffa6d265","added_by":"auto","created_at":"2026-03-11 12:18:38","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":227365,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of principal components (PCs) derived from genome-wide SNP data of 259 rice landraces. The vertical bar represents the proportion of variance explained by each PC, and the red line represents the proportion of variance explained by the principal components\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/bc29e7f92f76201ac7adb941.jpeg"},{"id":104403465,"identity":"72ac347d-a57f-4e5c-9469-08bfe9e86352","added_by":"auto","created_at":"2026-03-11 12:18:24","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":334592,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of rice landraces based on the SNP data. The percentage of variance explained by each principal component is explained by the X-axis (PC1) and the Y-axis (PC2)\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/f3b405cc3eff7c8dadf05989.jpeg"},{"id":104181254,"identity":"e9c3b9e3-45e9-4528-a5bd-38aa20d77eca","added_by":"auto","created_at":"2026-03-08 17:26:57","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":676648,"visible":true,"origin":"","legend":"\u003cp\u003ePCA-based ancestry plots of rice landraces based on the first three principal components, vertical bars representing individual landraces, and colors representing principal component segments showing genotypes come from more than one ancestry\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/d7f0ff9d1fb7617db558475a.jpeg"},{"id":104404455,"identity":"57943e9b-a717-4d77-8f1a-1dd74a235293","added_by":"auto","created_at":"2026-03-11 12:20:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":299197,"visible":true,"origin":"","legend":"\u003cp\u003ea: Phylogenetic tree of rice landraces presented in a Rooted Neighbor-Joining phylogenetic tree constructed from whole genome sequence SNP data. Branch length represents genetic distance, and tips represent the landrace names\u003c/p\u003e\n\u003cp\u003eb: Hierarchical clustering of East African rice landraces based on their genome-wide SNP data, grouped based on their genetic similarity.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/0e00d2300dcda10b412194bd.png"},{"id":104403749,"identity":"ee730cdc-9a27-47f6-b4d4-0f4c61b94128","added_by":"auto","created_at":"2026-03-11 12:18:58","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":524026,"visible":true,"origin":"","legend":"\u003cp\u003ea: Genome-wide association study of survival percentage on 14 days post-submergence, calculated based on the Bonferroni threshold of 5%. Markers above the threshold line are significantly associated with the survival traits.\u003c/p\u003e\n\u003cp\u003eb: Genome-wide association study of survival percentage on 14 days post-submergence, calculated based on the false discovery rate (FDR) of 5%. Markers above the threshold line are significantly associated with the submergence survival traits.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/8037cf6c7ce00f3c25a78eda.png"},{"id":104404147,"identity":"32bfde1f-8ba8-4f2f-b7aa-573e52639b0b","added_by":"auto","created_at":"2026-03-11 12:19:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":87326,"visible":true,"origin":"","legend":"\u003cp\u003eThe Quantile–quantile (Q–Q) plots of genome-wide association study (GWAS) results for survival plot using a mixed linear model (MLM).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/98b19ecbf44dfdbb0ad06b09.png"},{"id":104181251,"identity":"7bfb16b4-c7a5-4659-b119-e694e4622dee","added_by":"auto","created_at":"2026-03-08 17:26:57","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":103593,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of linkage disequilibrium (LD) decay in the study population sample of 200 SNPs\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/dfca1c055b539427c35f8482.png"},{"id":104408891,"identity":"29f2b7af-81d7-4cc1-ac05-df961d468095","added_by":"auto","created_at":"2026-03-11 12:43:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4217729,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8944214/v1/600037cf-547a-4ec8-a8b9-e4db9f639d95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population Structure, Genetic Diversity, and Genome-Wide Association Analysis of Eastern African Rice Landraces for Climate Resilience Breeding: The case of complete submergence tolerance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRice is among the most important cereals worldwide, providing food to over 3.5\u0026nbsp;billion people in the world, particularly in Asia, Africa, and Latin America. Rice provides about 20% of calorie intake worldwide, with a significant importance in developing countries as a primary source of energy (Mohidem et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is a crop of economic significance to major smallholder farmers in Africa and Asia, providing household income and employment (Mauki et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Globally, rice is of great economic significance, generating up to \u003cspan\u003e$\u003c/span\u003e20\u0026nbsp;billion in export value. Despite the increasing economic value, rainfed lowland rice production remains vulnerable to climatic variability, such as drought, salinity, and flooding.\u003c/p\u003e \u003cp\u003eRice is among the major crops vulnerable to climate change in East Africa (Adhikari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In Tanzania, approximately 70% of the nation's rice is cultivated in rainfed lowland ecosystems, which include river basins, catchment areas, and extensive grassland plains. The primary rice-producing regions are Mbeya, Morogoro, Mwanza, Shinyanga, Tabora, and Rukwa, named the big six (Mtembeji \u0026amp; Singh, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, rice is also grown in coastal regions such as Tanga, Pwani, Lindi, Mtwara, and the islands of Zanzibar (Sekiya et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These regions are vulnerable to climate change, experiencing extreme weather events and irregular rainfall patterns, resulting in frequent floods, which severely impact rice production in the areas (Michael, Sanga, et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rice farmers in major rice-producing zones have been affected by floods, causing up to 100% yield losses due to inadequate drainage infrastructure and the use of flood-susceptible varieties. (Michael, Mwakyusa, et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRice landraces have proven to be a valuable source of genetic diversity, harboring novel alleles for local adaptation and for improving biotic and abiotic stress tolerance (Marone et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The diversity of landraces provides structural variation suitable for genomic scanning to unlock their unique molecular diversity (Corrado \u0026amp; Rao, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This will enhance their use in improving modern cultivars and their adaptation to diverse environments (Dwivedi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Modern breeding has resulted to genetic bottleneck in genetic diversity, creating a need for recovery of lost alleles from landraces and wild relatives (Van De Wouw et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRice is a semi-aquatic plant known for its ability to grow in shallow water, especially in lowland rice varieties. Despite this, rice does not thrive in prolonged complete submergence, with most varieties able to endure for no more than three days in water (S. Singh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Complete submergence of plants for up to two weeks may cause 100% yield loss for susceptible cultivars due to poor underwater gas exchange, affecting the physiological growth of rice plants by disrupting key metabolic processes (Panda \u0026amp; Sarkar, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Submergence leads to oxygen deficiency, forcing plants to switch to anaerobic respiration, which is less efficient and produces harmful by-products that damage cells (Kumar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It also inhibits photosynthesis due to reduced light penetration and CO2 availability, resulting in decreased energy production and stunted growth (A. Singh et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo cope with submergence stress, rice plants have developed a set of genetic and physiological strategies governing tolerance mechanisms. The SUBMERGENCE 1 (SUB1) gene, mapped on chromosome 9, has been a major determinant of submergence tolerance. The gene plays a pivotal role by modulating ethylene and gibberellin levels, thus conserving energy reserves during submergence under the quiescence strategy (Xu \u0026amp; Mackill\u0026rsquo;\u0026gt;, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The genetic pool of submergence tolerance in rice is very narrow. The SUB1 gene was discovered from a traditional landrace FR13A from Odisha, India long time ago (A. Singh et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; S. Singh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The tolerance QTL protects the plant from complete submergence for up to 14 days, allowing plants to conserve energy and regenerate after desubmergence. There is a need to explore more genetic sources of tolerance that can extend the survival period by 14 days and provide additional protection beyond SUB1 (Septiningsih et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Also, the degree of tolerance expression changes with environmental conditions and the growth stage of the plant, hence additional genetic sources of tolerance are crucial to enhance stable, durable tolerance (Haque et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Panda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, advances in bioinformatics and the availability of high-density molecular markers have improved the application of genomic tools in dissecting complex quantitative traits (Tibbs Cortes et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The utilization of approaches such as whole-genome sequencing has been useful in characterizing new genetic resources of stress tolerance. For complete submergence tolerance, genome-wide association studies (GWAS) have been beneficial in identifying new sources of tolerance that can supplement SUB1, the known major gene. When combined with transcriptomic analysis and genomic selection, GWAS can enhance the development of durable protection against complete submergence, surpassing the capabilities of SUB1. GWAS has been used to discover novel potential candidate genes in a diverse population in India, discovering 9 candidate SNPs for complete submergence tolerance (Phukon et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTanzania possesses a vast collection of \u003cem\u003eOryza sativa\u003c/em\u003e landraces, cultivated by farmers since introduced by Asian traders over 1000 years ago through Indian Ocean trade routes (Busungu, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These landraces have been traditionally maintained and cultivated by farmers in different rice ecologies of the country, ranging from upland, rainfed lowland, and irrigated ecologies agroecology enhancing their adaptation to diverse environmental conditions. Farmers have been maintaining the landraces due to their agronomic, grain, and culinary attributes that are also attached to their cultural values. Despite their potential, the landraces have been underutilized in modern breeding programs (Bulegeya et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The landraces offer a broad genetic resource of untapped allelic diversity for tolerance to different stresses, including flooding, drought, and salinity.\u003c/p\u003e \u003cp\u003eThere is a need for a comprehensive genetic and phenotypic characterization of the landraces to uncover sources of adapted QTL for tolerance to existing stresses, enhancing breeding for rice varieties suited for Tanzania\u0026rsquo;s diverse production ecologies. This study intends to quantify the genome-wide diversity of rice landraces from Tanzania and other East African countries, characterize their population structure and relatedness, and the linkage disequilibrium pattern for association mapping. Also, the study demonstrates the potential of the materials for climate resilience breeding through genome-wide association mapping for complete submergence tolerance.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e \u003cb\u003ePlant materials and phenotyping\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA diverse population of 260 individuals was collected from flood-affected regions of Tanzania and other East African countries, including Kenya, Rwanda, Burundi, Mozambique, and Malawi (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The panel includes seeds collected from the Tanzania Agriculture Research Institute (TARI), the Tanzania National Gene Bank (TNGB), and the International Rice Research Institute (IRRI) \u0026ndash; Tanzania office. Checks were also collected from the IRRI Headquarters office in Manila, Philippines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePhenotyping for complete submergence was conducted according to the IRRI protocol for phenotyping abiotic stress tolerance in rice (IRRI, 2021). The experiment was conducted for two seasons in April 2024 and April 2025 at the Submergence Pond located at the Crop Museum fields, Sokoine University of Agriculture (SUA), Morogoro, Tanzania. The experiments were planted in an alpha lattice design with two replications; all experiments included both tolerant checks, such as FR13A, Swarna Sub1, Ciherang Sub1, Cirerang SUB1-AG1-AG2, and IR 64, which was regarded as a susceptible check. Genotypes were planted in plots of two rows of 1m with a spacing of 20cm x 20cm, with one seedling per hill. Susceptible check IR 64 was planted around the experimental block to confirm the stress intensity.\u003c/p\u003e \u003cp\u003e21 days old seeding were subjected to complete submergence with the water level of 1.5m. Plants were submerged for a period of 14 days, and data were collected on 7, 14, and 21 days after desubmergence. Floodwater was removed when IR 64 had severe rotting symptoms to ensure uniform submergence stress imposition. The Survival Percentage (%) was regarded as the tolerance trait, calculated as the percentage of the genotype that survived at every data collection week. The survival traits were the percentage survival after 7 days (PSUV07), percentage survival after 14 days (PSUV14), and percentage survival after 21 days PSUV21). The 14-day survival percentage (PSUV14) was used for GWAS analysis since the traits have a high correlation (Anumalla et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhenotypic evaluation of rice landraces\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLinear mixed model analysis was performed on the phenotypic data. Genotype effect was modeled as fixed, and the best linear unbiased estimates (BLUEs) were calculated for downstream GWAS analysis (Eq.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003ey\u003csub\u003ei\u003c/sub\u003eⱼₖ = \u0026micro;\u0026thinsp;+\u0026thinsp;G\u003csub\u003ei\u003c/sub\u003e + Rⱼ + B(R)ⱼₖ + ε\u003csub\u003ei\u003c/sub\u003eⱼₖ \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eWhere: y\u003csub\u003ei\u003c/sub\u003eⱼₖ = observed survival percentage of genotype i in block k of replicate j, \u0026micro;\u0026thinsp;=\u0026thinsp;overall mean, G\u003csub\u003ei\u003c/sub\u003e = fixed effect of genotype i, Rⱼ = random effect of replicate j, B(R)ⱼₖ = random effect of block k nested within replicate j, ε\u003csub\u003ei\u003c/sub\u003eⱼₖ = residual error.\u003c/p\u003e \u003cp\u003eThe generated BLUEs were used for all other statistical analyses, such as clustering analysis, heat-map, and principal component analyses (PCA). All statistical analysis of the phenotypic data of the panel was performed using R software version 4.5.2.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDNA isolation and sequencing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDNA samples were collected from young healthy leaf tissues using leaf discs for each genotype using a leaf puncher. 2\u0026ndash;3 leaf discs were collected per sample and placed in 2 mL microcentrifuge tubes. They were immediately stored in a -80\u0026deg;C freezer and then lyophilized. DNA extraction was performed following a modified cetyltrimethylammonium bromide (CTAB) protocol for plant tissues(Allen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). DNA quality was evaluated using agarose gel electrophoresis, and DNA concentration was measured using spectrophotometric methods. High-quality DNA samples were submitted for library preparation. Sequencing libraries were prepared using the NEXTFLEX\u0026reg; Rapid DNA-Seq Kit 2.0 (PerkinElmer Inc., USA). Sequencing was done on an Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA) using paired-end sequencing methodology. Standard quality control of sequencing reads was done before downstream bioinformatic analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSNP calling\u003c/b\u003e \u003c/p\u003e \u003cp\u003eVariant calling was performed using the Automated Rice Variant Calling workflow on HPC, following Genome Analysis Toolkit (GATK) best practices. The high-performance computing genome variant calling workflow (HPC-GVCW) has four major phases: mapping, variant calling, call set refinement and consolidation, and variant merging. Phase one involves mapping genomic reads to a reference genome, phase two covers variant calling for each sample to generate gVCF files, phase three deals with combining gVCF files to develop a single file for all samples, and phase four encompasses filtering SNPs and INDELs and merging to produce a chromosome-based variant table (Zhou et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Before downstream analysis, genotypic data were subjected to quality control filtering to remove loci with more than 20% missing data and a Minor Allele Frequency (MAF) below 0.01. The remaining high-quality SNP markers were used for population structure analysis and genome-wide association studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePopulation structure\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe population structure analysis of the landrace was conducted using Principal Component Analysis (PCA), K-means clustering, hierarchical clustering, and a Neighbor-joining tree joint analysis. The SNP markers were used to calculate the genetic distance matrix, and PCA was performed to visualize genetic variation across the first two principal components. The K-means clustering was applied at K\u0026thinsp;=\u0026thinsp;3, grouping landraces into 3 distinct clusters. The PCA and cluster analysis were done using the R software package, specifically \u003cem\u003efactoextra\u003c/em\u003e and \u003cem\u003eggplot2 (\u003c/em\u003eKassambara \u0026amp; Mundt, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eHierarchical clustering analysis was carried out based on a pairwise genetic distance matrix calculated among genotypes based on the Euclidean distance metric, which quantifies allelic difference across loci and enables a robust measure of genetic relationship across accessions. Clustering was done following a hierarchical clustering approach to group accessions based on minimum genetic distance. The resulting dendrogram was constructed using the \u003cem\u003edendextend\u003c/em\u003e package in R, where branch height represents genetic distance among clusters.\u003c/p\u003e \u003cp\u003eGenetic relatedness of the rice landrace was also determined using a neighbor-joining (NJ) approach using the SNP data of 259 landraces using the packages \u003cem\u003eape, phangorn, and ggtree\u003c/em\u003e. Genetic distances among landraces were calculated by using identity by state (IBS) based matrix to estimate allelic similarity across the genome. The neighbor-joining tree was constructed from a genetic distance matrix using the neighbor-joining algorithm, which clusters genotypes by minimizing total branch length. The results were visualized in an unrooted layout to accommodate most landraces and the identification of the genetic group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenome-wide association study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGWAS analysis was done using the 168,091 SNPs generated from high-throughput whole-genome sequencing data of the panel. Before analysis, the SNPs were filtered before analysis to remove monomorphic markers and SNPs with more than 10% missing data. The minor allele frequency (MAF) of 0.01 was used to retain informative low-frequency variants, excluding unstable variants, and provide efficient power for LD decay and GWAS analysis. Genotypes' mean percentage survival at different time points were adjusted to experimental effects to generate best linear unbiased estimates (BLUEs), which were used for the GWAS analysis. Principal component analysis (PCA) was used to assess the population structure of the SNP data, and the first principal component explaining the majority of the variation was used as a covariate in the GWAS model as a fixed effect. A kinship matrix was used to estimate the genetic relatedness of individuals in the panel and modeled as a random effect in the GWAS model.\u003c/p\u003e \u003cp\u003eGenome-wide association analysis was done using the Genome Association and Prediction Integrated Tool (\u003cem\u003eGAPIT\u003c/em\u003e) package in R, following a linear mixed model accounting for population structure and kinship of the population (Lipka et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). SNP markers and population structure covariates were treated as fixed effects, while kinship effects and experimental design effects of the alpha lattice design were modeled as random effects (Eq.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1\u0026micro;\u0026thinsp;+\u0026thinsp;Xβ\u0026thinsp;+\u0026thinsp;sα\u0026thinsp;+\u0026thinsp;Zg\u0026thinsp;+\u0026thinsp;ε \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003ewhere: y is the vector of phenotypic observation (BLUEs); X is the design matrix for fixed effects; β is the vector of fixed effects (Principal Components); s is a vector of the number of alleles of each genotype at a particular SNP; α is the effect of a SNP\u003c/p\u003e \u003cp\u003eZ is the design matrix for random effects; g is a vector of random effects; ε is the vector of residual errors\u003c/p\u003e \u003cp\u003eManhattan plots were plotted using the \u003cem\u003eggplot2\u003c/em\u003e package in R, displaying the \u0026minus;\u0026thinsp;log₁₀ (p-values) of associations between SNPs and submergence survival traits across the 12 rice chromosomes. The calculated Bonferroni-corrected significance threshold value was indicated in a horizontal line. Quantile\u0026ndash;quantile (Q-Q) plots were also evaluated to confirm the effective control of population structure and kinship.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLinkage Disequilibrium (LD) Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGenome-wide linkage disequilibrium (LD) was estimated using a squared correlation coefficient (r\u0026sup2;) between polymorphic SNP pairs within chromosomes, which is appropriate for diverse landrace panels. LD analysis was performed in R using the \u003cem\u003eLDheatmap\u003c/em\u003e package, which utilized high-quality SNPs after filtering for minor allele frequency (MAF) and missing data. LD of 200 SNP samples was plotted by using r\u0026sup2; against physical distance (kb) to estimate the LD decay of the population.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of Candidate Gene\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCandidate genes were predicted based on the genome-wide linkage disequilibrium (LD) decay estimated from the population. With the rapid LD decay reaching threshold levels of 0.1 below 50kb, a\u0026thinsp;\u0026plusmn;\u0026thinsp;5kb window of the significant SNP was adopted. Genes flanking the significant SNPs in a 10kb window were retrieved and considered putative candidate genes. The Ensembl genome browser (IRGSP-1.0) was used for identifying the putative genes, and the Rice Genome Annotation Project (MSU) and Rice Annotation Project Database (RAP-DB) were used determine functions of the identified genes.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic performance and heritability of rice landraces to complete submergence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRice landraces exhibited significant variation in coping with complete submergence stress. The tolerance traits displayed a continuous distribution, implying a quantitative trait tolerance mechanism. The BLUEs values showed a moderate median with several high values as outliers, indicating the presence of highly tolerant genotypes outperforming the population (Figure 2).\u003c/p\u003e\n\u003cp\u003eThe majority of landraces displayed moderate to low tolerance levels. This suggests a dominant genotypic effect on the observed phenotypic variations. Principal component analysis explained 99.3% of the phenotypic variation, with 97% being explained by the first principal component (Figure 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHighly tolerant genotypes were separated in a different cluster from the susceptible genotypes. In comparison with tolerant checks such as FR13A and CIHERANG SUB1 AG1 AG2, some landraces such as NAWA TULE NA BWANA, LIWALE_2, and KYELA1_5735 displayed better performance in response to complete submergence stress (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe continuous traits distribution and high heritability suggest a strong genetic control of submergence tolerance traits, confirming the reliability of the panel for genetic mapping. High correlation of survival traits early and late post desubmergence indicates the shared genetic control of tolerance governing early survival and recovery. The presence of the best-performing landraces as checks signifies the potential of landraces for broadening genetic sources of tolerance to complete submergence in flood-affected regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP distribution and diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter quality control,168,091 SNPs were retained, including 117,641 transitions and 50,450 transversions, with a Ts/Tv ratio of 2.33. The ratio designates high SNP calling quality and aligns with expectations for the rice genome datasets. The 168,091 SNPs were distributed across 12 chromosomes of rice, with the largest being chromosome 1, which has a size of 43.27 Mb, containing 19,144 SNPs. In contrast, chromosome 9 had the smallest size, at 22 Mb. 94 Mb with 9633 SNPs. SNP density varied across the chromosomes, ranging from 358.51 SNPs per Mb on chromosome 3 to 547. 58 on chromosome 8, indicating sufficient marker coverage for a genome-wide association study and elaborate heterogeneous polymorphism for rice genotypes (Table 2). Mean heterozygosity and polymorphic information content (PIC) across chromosomes are 0.06 and 0.06, respectively, suggesting uniform distribution of diversity throughout the genome, indicating moderate genetic diversity within the panel.\u003c/p\u003e\n\u003cp\u003eTable 2: SNP markers distribution and diversity across chromosomes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome length (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome size (Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP density (Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean heterozygosity (He)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e19144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e43269550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e43.26955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e442.4358469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.063115885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.063115885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e15798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e35898770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e35.89877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e440.0707879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.06108671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.06108671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e13053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e36409137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e36.409137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e358.5089095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.061187382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.061187382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e16957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e35499430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e35.49943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e477.6696415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.060075965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.060075965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e16057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e29956842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e29.956842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e536.0044293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.054291232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.054291232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e15365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e31210857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e31.210857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e492.296639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.060785529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.060785529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e11710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e29669264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e29.669264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e394.6845463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.0593555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.0593555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e15572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e28438000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e28.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e547.5771855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.057650645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.057650645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e9633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e22939666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e22.939666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e419.9276485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.059797325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.059797325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e10701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e23191915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e23.191915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e461.4107977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.06309659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.06309659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e11528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e29002613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e29.002613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e397.4814269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.059912272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.059912272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.35179%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.79479%;\"\u003e\n \u003cp\u003e12573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e27529894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2638%;\"\u003e\n \u003cp\u003e27.529894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e456.7035383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.7296%;\"\u003e\n \u003cp\u003e0.058516299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.058516299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure and diversity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCA of the genotypic displayed the existing genetic variation among rice landraces, with the first two principal components explaining 5.1% and 4.2% of the variation. The cumulative variance explained by the first 20 principal components explains 21.16% of the total variation, which is common in diverse landrace panels (Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PCA explains three major, distinct clusters following K-means clustering (K=3). The first cluster (blue) is positioned on the lower quadrant, the second cluster (green) is positioned on the central right, and the third cluster (red) is positioned on the upper left quadrant, indicating genetic divergence, within-cluster similarity, and between-cluster differentiation (Figure 6). The presence of three clusters indicates the presence of at least three genetically distinct pools guiding breeding and improvement strategies.\u003c/p\u003e\n\u003cp\u003eThe landraces displayed mixed ancestry sources, suggesting a genetic continuum rather than subpopulations (Figure 7). The majority of the genotypes have major genetic contribution from the first principal component; several landraces have balanced contribution from all three. A few have major contributions from the second and third principal components, indicating a unique genetic background due to local adaptations or geographical positions. The absence of pure genotypes suggests a historical gene flow and shared background among landraces. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ancestry plot shows the admixture of rice landraces, which is common in traditional landraces, caused by natural and human-made selection due to seed exchange and local adaptation. The genetic continuum signifies the importance of landrace conservation for breeding purposes. Unique PC contributions, such as PC3, are crucial for trait discovery and allelic diversity. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rooted Neighbor-Joining phylogenetic tree of the landraces has clustered the panel in 3 Clusters, suggesting the presence of three major genetic groups within the panel (Figure 8a). Cluster 2 is the largest, comprising the majority of the landrace above 82.2% with short internal branch length suggesting genetic similarity among them. Clusters 2 and 3 are distinct, positioned on a separate longer branch, suggesting genetic divergence; the longer branch to these groups indicates the genetic differentiation from Cluster 1. The clustering is also supported by the hierarchical clustering dendrogram, which groups accessions based on their similarity and genetic relatedness (Figure 8b). The presence of subgroups justifies the need for employing Mixed Linear Model (MLM) in GWAS, incorporating kinship and principal components to account for population structure, ensuring vigorous GWAS results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide Association Study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenome-wide association mapping for complete submergence tolerance was done on the percentage survival trait 14 days post-submergence. No SNPs surpassed the Bonferroni-corrected genome-wide significance threshold at \u0026alpha; = 0.05, with a P-value of 2.97 x 10\u003csup\u003e-7\u003c/sup\u003e and a \u003cem\u003e-log\u003csub\u003e10\u003c/sub\u003e\u003c/em\u003e threshold of 6.53 (\u003cem\u003eFigure 9a\u003c/em\u003e). However, several loci showed suggestive associations, indicating a polygenic architecture underlying submergence tolerance. Nevertheless, multiple SNPs exceed the FDR significance threshold, indicating the presence of significant SNP associations with submergence survival traits (\u003cem\u003eFigure 9b\u003c/em\u003e). The significant associations were distributed across the chromosomes, signifying the polygenic nature of the submergence tolerance trait. Several peaks are consistently observed at 14 days post-submergence, indicating a stable genetic effect on submergence survival. The most significant, consistent association signal was detected on chromosomes 9 and 6. Other significant associations were identified on chromosomes 2, 4, 8,10, and 11, with several peaks exceeding the false discovery rate (FDR)-corrected threshold.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe quantile-quantile (Q-Q) plot of the GWAS analysis for the percentage survival trait at 14 days after submergence compares the observed and expected p-values. The analysis follows the null hypothesis that there is no association between a marker and a trait of interest. As observed in \u003cem\u003eFigure 10\u003c/em\u003e, the majority of SNPs followed the expected line, implying the statistical test efficiency in controlling kinship and population structure with the model. This suggests the minimal chance of having false positive associations and resulting in true genetic associations.\u003c/p\u003e\n\u003cp\u003eThe genome-wide association study revealed about 11 significant SNPs for all survival traits, and more than 28 were identified for individual traits. Significant associations were observed on chromosomes 2, 4, 6, 8, 9, and 11 with the presence of multiple genetic sources of tolerance to the trait \u003cem\u003e(Table 3)\u003c/em\u003e. The highly significant SNP was observed at chromosome 9 at a P-value of 3.33 \u0026times; 10\u003csup\u003e-7\u003c/sup\u003e with a MAF of 0.08, indicating a rare allele within the population with a large effect. Other SNPs had a MAF ranging from 0.03 to 0.20, indicating the biological relevance of the significant associations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo SNPs on chromosome 8 (Chr08_27421228_C_T and Chr08_27421246_C_T) are located in proximity, indicating a shared LD block and suggesting the strength of the region in tolerance delivery. The Hochberg and Benjamini (H\u0026amp;B) test ranged from 0.06 to 0.42, indicating the range in confidence, suggesting the association as suggestive, requiring more confirmation. Generally, the results propose a polygenic nature of submergence tolerance traits with several loci contributing to major and minor tolerance effects. The identified significant SNPs pave the way to the exploration of candidate gene investigation within the identified LD blocks that confer functional contribution to tolerance.\u003c/p\u003e\n\u003cp\u003eTable 3: Significant SNP markers identified for complete submergence tolerance for all survival traits.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP.value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u0026amp;B.P.Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr09_6772795_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e6772795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e3.33E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.085271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-8.639000634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.055997937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr06_23006175_A_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e23006175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e1.48E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.203488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-5.31434606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.082829794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr08_27421228_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e27421228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e2.86E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.189922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-5.331635678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.120220408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr11_25850685_A_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e25850685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e8.00E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.112403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-7.741668141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.067267777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr06_26455167_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e26455167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e2.06E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.034884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-11.4591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.115479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr11_16978066_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e16978066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e5.92E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.069767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-7.96111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.165669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr08_27421246_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e27421246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e6.90E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.186047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-5.276051239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.231892542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr04_26829543_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e26829543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e1.49E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.147287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-5.529790846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.308267371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr10_5104720_T_C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e5104720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e2.60E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.073643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-7.344946061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.401066974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr02_6084641_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e6084641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e5.88E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.114341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-5.864247975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.42773408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.71545%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.065%;\"\u003e\n \u003cp\u003eChr02_588105_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01626%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7073%;\"\u003e\n \u003cp\u003e588105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e5.87E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2195%;\"\u003e\n \u003cp\u003e0.110465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2602%;\"\u003e\n \u003cp\u003e-6.84363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.7967%;\"\u003e\n \u003cp\u003e0.165669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLinkage disequilibrium analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LD analysis reveals high r\u003csup\u003e2\u003c/sup\u003e values at short distances, with many SNPs showing strong LD, suggesting tight marker linkage and few or no recombination events within the region. There is a strong decline in LD decay with an increase in physical distance, with a sharp drop in r\u003csup\u003e2\u003c/sup\u003e within a few kilobases. This shows the fast breakdown of recombination with increased physical distance. A minimum LD is observed with increasing distance, suggesting independent segregation across loci, which reaches the background threshold below r\u0026sup2; = 0.1 at approximately 35 kb (\u003cem\u003eFigure 11\u003c/em\u003e). The rapid LD decay with distance indicates high mapping resolution suitable for GWAS since significant SNPs will likely be close or within variants, and candidate genes should be searched within a narrow LD window. The finding is typical of diverse landrace panels with high historic recombination and small haplotype blocks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate gene identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCandidate genes were identified based on the genome-wide linkage disequilibrium (LD) pattern of the population, which portrayed a decline in LD decay at r\u003csup\u003e2\u003c/sup\u003e = 0.1 within 30-40kb. Therefore, the average LD decay of 5kb was adopted as an estimate of genomic resolution for candidate gene mining around the significant SNP marker. Genes located within +/- 5k flanking the significant SNP markers were considered putative candidate genes, making the window size 10kb, avoiding the likelihood of capturing unrelated genes. Candidate genes within the flanking region were retrieved using the \u003cem\u003eOryza sativa ssp. japonica reference genome (IRGSP-1.0)\u003c/em\u003e. Functional annotation was done by referring to \u003cem\u003eEnsembl Plants\u0026nbsp;\u003c/em\u003eand the \u003cem\u003eMSU Rice Genome Annotated Project\u003c/em\u003e databases.\u003c/p\u003e\n\u003cp\u003eWithin the specified LD intervals, a total of 11 putative loci were identified across multiple chromosomes (\u003cem\u003eTable 4\u003c/em\u003e). The identified Loci includes; LOC_Os09g12050, LOC_Os09g12060, LOC_Os06g38750, LOC_Os06g38760, LOC_Os08g43290, LOC_Os11g42900, LOC_Os06g43910, LOC_Os06g43920, LOC_Os06g43930, LOC_Os11g29290, LOC_Os08g43390, LOC_Os04g45370, LOC_Os10g09360, LOC_Os02g11780 and LOC_Os02g02070. The loci were identified from chromosomes 2,4, 6,8,9,10,11, signifying the quantitative nature of genetic control of the submergence tolerance traits. Functional annotation of putative genes identified the loci for coding proteins useful for diverse purposes, including protein expression, retrotransposons, and regulation and transcription factors. The identified loci provide a valuable insight for the functional characterization of genomic regions identified by GWAS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Candidate gene annotation of significant SNPs for submergence survival traits\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificant SNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSU_ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene stable ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene start (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene end (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr09_6772795_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os09g12050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs09g0292300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eMyb/SANT-like domain-containing protein. (Os09t0292300-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e6803261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e6806757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os09g12060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs09g0292400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eConserved hypothetical protein. (Os09t0292400-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e6810220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e6810609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr06_23006175_A_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os06g38750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs06g0587100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eConserved hypothetical protein. (Os06t0587100-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e23000435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e23001408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os06g38760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs06g0587200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eLRR-receptor-like kinase (LRR-RLK) family protein (Os06t0587200-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e23005337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e23008846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr08_27421228_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs08g0547350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eHypothetical protein. (Os08t0547350-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27420967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27422836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os08g43290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs08g0546300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eSimilar to LTP-like protein. (Os08t0546300-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27364165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27364816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr11_25850685_A_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os11g42900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs11g0649000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eDNA replication helicase 2_14, Drought and salt stress response (Os11t0649000-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e25840593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e25846990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr06_26455167_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs06g0647150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eHypothetical protein. (Os06t0647150-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26450752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26454632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os06g43910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs06g0647200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eSimilar to Response regulator. (Os06t0647200-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26450761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26455305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os06g43920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs06g0647200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eB-type response regulator, Cytokinin signaling (Os06t0647200-02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e\u0026nbsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e\u0026nbsp;26455046\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e\u0026nbsp;26455961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os06g43930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs06g0647400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eSimilar to Lysosomal Pro-X carboxypeptidase. (Os06t0647400-02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26456312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26461805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr11_16978066_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs11g0482901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eHypothetical gene. (Os11t0482901-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e16985402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e16987473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os11g29290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs11g0483000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eytochrome P450, Oxidase, JA-mediated chilling tolerance (Os11t0483000-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e16985426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e16987260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr08_27421246_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs08g0547350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eHypothetical protein. (Os08t0547350-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27420967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27422836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os08g43390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs08g0547300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eCytochrome P450 protein, CYP78A family protein, Disease resistance, Regulation of growth rate and seed size (Os08t0547300-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27420633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e27422835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr04_26829543_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os04g45370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs04g0537100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eSimilar to Auxin-induced protein X15. (Os04t0537100-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26831005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26831867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs04g0537450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eNon-protein coding transcript. (Os04t0537450-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26834695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e26835104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr10_5104720_T_C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os10g09360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs10g0173800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eSAM dependent carboxyl methyltransferase domain containing protein. (Os10t0173800-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e5055323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e5055736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr02_6084641_G_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os02g11780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs02g0208600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eTranscription elongation factor S-II, central region domain containing protein. (Os02t0208600-01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e6083116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e6088700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.1543%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4748%;\"\u003e\n \u003cp\u003eChr02_588105_C_T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0623%;\"\u003e\n \u003cp\u003eLOC_Os02g02070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.3917%;\"\u003e\n \u003cp\u003eOs02g0110900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2493%;\"\u003e\n \u003cp\u003eHypothetical conserved gene. (Os02t0110900-00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.48961%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e596645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.089%;\"\u003e\n \u003cp\u003e609187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study provided a comprehensive analysis of the genetic diversity and structure of East African rice landraces using genotype by sequencing approach. Significant genetic variation was observed within a panel of 260 landraces, confirming the genetic richness and allelic diversity within traditional landraces. The presence of 168,091 high-quality SNPs provides a genome-wide coverage with consistent expectation across the \u003cem\u003eOryza sativa\u003c/em\u003e landraces. The uniform distribution of SNPs across 12 rice chromosomes indicates adequate genome representation and robust information for diversity analysis. The uniform diversity reflected in PIC and moderate heterozygosity validates a well-distributed marker set for genome-wide association studies with minimal gaps in genomic resolution for identifying stress tolerance loci (Roy et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePopulation structure analysis, including the Principal Component Analysis (PCA) and the phylogenetic tree of the genotypic data reveal 3 major subpopulations, but the admixture analysis reveals a mixed population typical of rice landraces due to seed exchange and continuous gene flow. The observed genetic stratification validates the use of Mixed Linear Model (MLM) in GWAS analysis to account for kinship (K) and population structure (Q) as covariates to minimize false positive associations (Alamin et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anilkumar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study will facilitate the discovery of valuable alleles for climate resilience stress, including complete submergence tolerance highlighting the role of landraces in trait discovery for resilience breeding (Anumalla et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA sufficient linkage disequilibrium (LD) decay of approximately 35 kb provides sufficient resolution for association mapping for stress tolerance. Such decay narrows the Quantitative trait loci (QTL) region, increasing the precision for candidate gene localization. The utilization of diverse panels enhances the mapping power, capturing novel and rare alleles, including locally adapted alleles absent in elite genotypes. The fast LD decay in the panel of smaller haplotype blocks upsurges the fine mapping efficiency and increases the likelihood of finding true associations (Abhijith et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou \u0026amp; Zhou, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, due to long-term adaptation to a heterogeneous environment, landraces are a valuable genetic resource for dissecting environment-related traits, such as climate-resilient traits and traits related to genotype by environmental interactions (Lamichhane et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nayak et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study employed the panel to map significant genomic association with complete submergence tolerance through GWAS (Phukon et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe phenotypic analysis of the panel\u0026rsquo;s percentage survival traits shows moderate mean performance of the population, suggesting most genotypes succumb to complete submergence stress. Nevertheless, a few showed high tolerance to submergence above the population mean, indicating the presence of highly tolerant genotypes within the population (Barik et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hapa \u0026amp; Al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Maity et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There is a high broad-sense heritability, suggesting the genetic factor as a determinant of most observed variations (Barik et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Principal component analysis (PCA) reveals that the first principal component explains 97% of the total phenotypic variance; hence, it can be easily used to predict genetic variation.\u003c/p\u003e \u003cp\u003eThe genetic mechanisms of rice tolerance to complete submergence have largely been studied in Asia, leading to the discovery of the SUB1 gene from FR13A and an indica landrace from Odisha, India (Bailey-Serres et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Xu \u0026amp; Mackill\u0026rsquo;\u0026gt;, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The discovery has led to the development of Sub1 varieties, which were adopted and used by farmers in several Asian countries, resulting in yield improvements in flood-affected regions of Asia (Perata \u0026amp; Voesenek, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Septiningsih et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; S. Singh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The developmental gap in research and variety development for flooding tolerance has left African rice landraces underexplored for their potential in providing sources of tolerance to complete submergence and other types of flooding (Bulegeya et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This study provides the first insight into the genetic architecture of tolerance to complete submergence in \u003cem\u003eOryza sativa\u003c/em\u003e rice landraces from East Africa, using whole-genome sequencing. This work lays a foundation for the exploration of East African rice landraces as a pivotal genetic resource of tolerance to biotic and abiotic stresses.\u003c/p\u003e \u003cp\u003eThe GWAS analysis was able to identify 11 loci associated with complete submergence survival in the rice panel. The utilization of genome-wide association analysis and linkage disequilibrium was able identify putative candidate genes for complete submergence tolerance in rice. The approach has been useful to identify and annotate genes for several agronomic traits and abiotic stresses in rice (Jiang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nayyeripasand et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yi et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although the identified loci do not include SUB1, the loci were distributed across chromosomes 2, 4, 6, 8, 9, 10, and 11.\u003c/p\u003e \u003cp\u003eSignificant SNPs on chromosome 9 annotate to two genes, LOC_Os09g12050 and LOC_Os09g12060, coding for Myb/SANT-like domain-containing protein and conserved protein, respectively. The Myb/SANT-like domain-containing protein has been reported to be responsible for stress response and regulation (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On chromosome 6, the identified candidate locus LOC_Os06g38760 is responsible for signal transduction, response to stress, and hormone regulation (Soltabayeva et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhiqi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Another identified locus on chromosome 6, LOC_Os06g43920, facilitates B-type response regulator and Cytokinin signaling. The B-type response regulators are critical in plant growth and stress response. Cytokinin negative response is crucial in the quiescence strategy, suppressing shoot elongation during submergence. In tolerant plants, cytokinin levels are downregulated under complete submergence, allowing carbohydrate conservation until desubmergence(Hornai et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn chromosome 11, the significant marker is linked to the LOC_Os11g42900 locus carrying the \u003cem\u003eDNA replication helicase 2_14 gene\u003c/em\u003e involved in DNA replication and repair. The gene was reported to be involved in regulating abiotic stresses, including drought and salinity tolerance in rice(Mohapatra et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Saleem et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The identified loci on chromosome 8 have retrieved LOC_Os08g43290, which translates to lipid transfer-like (LTP-like) proteins responsible for enhancing immune response against pathogens and abiotic stresses such as salinity (J\u0026uuml;lke \u0026amp; Ludwig-M\u0026uuml;ller, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; McLaughlin \u0026amp; Tumer, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Patkar \u0026amp; Chattoo, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe locus LOC_Os02g11780 was identified by a SNP marker on chromosome 2 translate to Transcription elongation factor S-II, central region domain containing protein. The proteins are critical for successful gene transcription under submergence stress by facilitating hypoxia response genes and continuous expression of submergence adaptive genes (Cermakova et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another candidate gene for functional regulation is locus LOC_Os04g45370 on chromosome 4, which is responsible for Auxin-induced protein X15. The Auxin-induced protein X15 is critical for cell wall elongation and plasticity, suppressing shoot elongation, and conserving carbohydrate during submergence. The gene was observed to enable shoot elongation under drought (Jiang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mansoor et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). LOC_Os10g09360 on chromosome 10 produces SAM-dependent carboxyl methyltransferase domain-containing proteins, which are involved in suppression of growth hormone activities and activation of stress response genes.\u003c/p\u003e \u003cp\u003eThe functional range of identified genes span from a spectrum of pathways from hormonal regulations and gene control for quiescence strategy and energy conservation. The loci LOC_Os06g43920, a B-type response regulator in cytokinin signaling is key. Cytokinin is vital in suppressing shoot elongation and carbohydrate reserve in tolerant plants, aiding the quiescence strategy (Huynh et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Zwack \u0026amp; Rashotte, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Likewise, the LOC_Os04g45370, the Auxin-induced protein X15 facilitate auxin metabolism and growth hormone regulation during complete submergence(Wu \u0026amp; Yang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zwack \u0026amp; Rashotte, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to hormonal regulation, the candidate gene also pinpoints the role of transcription regulation and stress signaling. The transcription elongation factor S-II (LOC_Os02g11780) facilitates the expression of hypoxia-sensitive genes under submergence conditions (Loreti \u0026amp; Perata, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Also, the stress response regulators such as Myb/SANT-like domain protein (LOC_Os09g12050) and the signal transduction component (LOC_Os06g38760) were annotated, signifying a range of tolerance pathways for abiotic stresses (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rongjun Chen, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This is accompanied by genes involved in cellular maintenance, such as LOC_Os11g42900 (DNA replication helicase) and lipid transfer-like protein (LOC_Os11g42900) to facilitate tolerance to complete submergence (Rongjun Chen, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tuteja et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Collectively, the findings emphasize a harmonized system of hormonal, transcriptional, and cellular pathways involved in submergence tolerance, highlighting targets for functional validation and tolerance improvement.\u003c/p\u003e \u003cp\u003eThis study presented a comprehensive genomic characterization of the East African rice landraces, documenting the population structure, allelic diversity, and the genetic architecture of the population. Genome-wide SNP analysis revealed moderate nucleotide diversity and polymorphism, suggesting the presence of variations worth exploring. Observed LD patterns provide suitable resolution patterns for association mapping of genomic regions associated with stress tolerance traits such as complete submergence. The study provides a foundation of utilization of the panel for further investigation of tolerance to other stresses in the region and identifies landraces with favorable alleles that can serve as donors for pre-breeding programs targeting climate resilience in the region.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eConclusively, the East African rice landraces constitute a diverse resource with suitable linkage disequilibrium for association mapping. The panel has proven to have adequate allelic richness and population structure suitable for detecting common and rare alleles for stress tolerance traits. The presence of genetic variations among subpopulations suggests the presence of genetic recombination and adaptation to the local environment, which is key in the buildup of alleles for climate resilience. This provides the platform for allele mining and genomic selection for marker-assisted introgression into the elite genetic pool in East Africa.\u003c/p\u003e \u003cp\u003eThe identified loci in for complete submergence are regarded as putative, subject to functional validation demanding advanced study beyond statistical marker trait association. Expression profiling of the candidate gene under complete submergence and after desubmergence to quantify their transcriptional performance and dynamics in the tolerant and susceptible genotypes is necessary. The analysis will identify the causative genes and determine whether they are fundamental genes or stress-induced genes. Also, allelic dissection of the loci will provide key information regarding tolerance sources and breeding priorities. All in all, the findings provide a baseline and genomic foundation for improvement targets and marker-assisted introgression to improve tolerance in flood-prone ecologies of Eastern Africa, particularly Tanzania.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGWAS Genome-wide Association Study\u003c/p\u003e\n\u003cp\u003eSUB1 SUBMERGENCE1 GENE\u003c/p\u003e\n\u003cp\u003eQTL Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003eMLM Mixed Linear Model\u003c/p\u003e\n\u003cp\u003eBLUEs Best Linear Unbiased Estimate\u003c/p\u003e\n\u003cp\u003eGATK Genome Analysis Toolkit\u003c/p\u003e\n\u003cp\u003ePCA Principal Component Analysis\u003c/p\u003e\n\u003cp\u003eSNP Single Nucleotide Polymorphism\u003c/p\u003e\n\u003cp\u003eMAF Minor Allele Frequency\u003c/p\u003e\n\u003cp\u003eGAPIT Genome Association and Prediction Integrated Tool\u003c/p\u003e\n\u003cp\u003eLD Linkage Disequilibrium\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVB: Conceptualization, experimentation, data collection, data analysis, and manuscript writing. WH: Conceptualization, experimentation, data curation, data analysis, manuscript writing, review, editing, and supervision. YZ: Conceptualization, experimentation, data curation, data analysis, manuscript writing, review, editing, and supervision. NK: Conceptualization, experimentation, manuscript writing, review, editing, and supervision. RM: Conceptualization, manuscript writing, review, editing, and supervision. AB: Conceptualization, manuscript writing, review, editing, and supervision. AI: Conceptualization, manuscript writing, review, editing, supervision, and funds acquisition. SN: Conceptualization, experimentation, manuscript writing, review, editing, supervision, and funds acquisition. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are included in the document. Raw data are available upon the authors\u0026apos; request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbhijith, K. P., Gopala Krishnan, S., Ravikiran, K. 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G., Shitindi, M. J., Kwaslema, D. R., Herzog, M., Meliyo, J. L., \u0026amp; Massawe, B. H. J. (2023). Floods stress in lowland rice production: experiences of rice farmers in Kilombero and Lower-Rufiji floodplains, Tanzania. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e. https://doi.org/10.3389/fsufs.2023.1206754\u003c/li\u003e\n \u003cli\u003eMichael, P. S., Sanga, H. G., Shitindi, M. J., Herzog, M., Meliyo, J. L., \u0026amp; Massawe, B. H. J. (2023). Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania. \u003cem\u003eFrontiers in Earth Science\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e. https://doi.org/10.3389/feart.2023.1183834\u003c/li\u003e\n \u003cli\u003eMohapatra, M. Das, Poosapati, S., Sahoo, R. K., \u0026amp; Swain, D. M. (2023). Helicase: A genetic tool for providing stress tolerance in plants. In \u003cem\u003ePlant Stress\u003c/em\u003e (Vol. 9). Elsevier B.V. https://doi.org/10.1016/j.stress.2023.100171\u003c/li\u003e\n \u003cli\u003eMohidem, N. A., Hashim, N., Shamsudin, R., \u0026amp; Man, H. C. (2022). Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process and Nutrient Content. In \u003cem\u003eAgriculture (Switzerland)\u003c/em\u003e (Vol. 12, Number 6). MDPI. https://doi.org/10.3390/agriculture12060741\u003c/li\u003e\n \u003cli\u003eMtembeji, A. L., \u0026amp; Singh, D. R. (2021). Dynamics of rice production among the food crops of Tanzania. \u003cem\u003eSouth African Journal of Science\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e(9\u0026ndash;10). https://doi.org/10.17159/SAJS.2021/11020\u003c/li\u003e\n \u003cli\u003eNayak, S., Habib, M. A., Das, K., Islam, S., Hossain, S. M., Karmakar, B., Neto, R. F., Bhosale, S., Bhardwaj, H., Singh, S., Islam, M. R., Singh, V. K., Kohli, A., Singh, U. S., \u0026amp; Hassan, L. (2022). 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Unlocking rice\u0026rsquo;s genetic potential: big data-driven insights from population genomics. \u003cem\u003eGenomics Communications\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 0\u0026ndash;0. https://doi.org/10.48130/gcomm-0025-0012\u003c/li\u003e\n \u003cli\u003eZwack, P. J., \u0026amp; Rashotte, A. M. (2015). Interactions between cytokinin signalling and abiotic stress responses. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(16), 4863\u0026ndash;4871. https://doi.org/10.1093/jxb/erv172\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rice landraces, genetic diversity, genome-wide association study (GWAS), complete submergence, climate resilience breeding, East Africa","lastPublishedDoi":"10.21203/rs.3.rs-8944214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8944214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRice landraces remain a crucial reservoir of novel alleles for stress-tolerance and climate-resilience breeding in Africa. East Africa harbors a rich diversity of \u003cem\u003eOryza sativa\u003c/em\u003e germplasm that is under investigated for their potential to improve stress tolerance. This study characterized a panel of 260 \u003cem\u003eOryza sativa\u003c/em\u003e landraces collected from East Africa using whole genome sequencing and phenotypic evaluation under complete submergence stress. Following a genotype-by-sequencing approach, the study identified 168,091 high-quality SNP markers revealing a panel with high genetic diversity, moderate heterozygosity of 0.062, and complex population structure. Population structure analysis of the panel displayed three major subpopulations with extensive admixture, indicating long-term gene flow and local adaptation of the landraces. Genome-wide linkage disequilibrium decay at r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2 is approximately 35kb, revealing moderate mapping resolution. To explore the breeding relevance of the panel, the study carried out a genome-wide association analysis (GWAS) for complete submergence tolerance following a linear mixed model (LMM). GWAS revealed 11 loci associated with submergence tolerance across chromosomes 2, 4, 6, 8, 9, 10, and 11. Candidate gene mining within a 10kb linkage disequilibrium (LD) window found genes involved in cytokinin and auxin signaling, transcription regulation under oxygen-deprived environment, stress signal transduction, and cellular maintenance activities. The study findings present the panel as a genetic resource for breeding strategies in the region and as a novel resource for improving abiotic stress, including submergence tolerance. The findings provide a foundation for functional validation and realistic targets for marker-assisted introgression to improve productivity in rice-producing regions of Africa.\u003c/p\u003e","manuscriptTitle":"Population Structure, Genetic Diversity, and Genome-Wide Association Analysis of Eastern African Rice Landraces for Climate Resilience Breeding: The case of complete submergence tolerance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:26:46","doi":"10.21203/rs.3.rs-8944214/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T01:07:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T15:54:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322376133294998616847655044780028081815","date":"2026-04-07T14:39:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153594949382398086055732513851480788016","date":"2026-03-17T06:51:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T10:00:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T22:09:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174072764808567775884732208182888669453","date":"2026-03-04T07:57:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320336417957839304739220557610690505204","date":"2026-03-02T20:25:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-02T08:54:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T12:21:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T12:19:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Rice","date":"2026-02-23T07:34:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d98a9971-fa45-4642-afe4-86979e4c3dfd","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T01:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:26:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8944214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8944214","identity":"rs-8944214","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}