Harnessing genetic diversity in Sudanese sorghum wild relatives for stay-green drought tolerance via microsatellite SSR marker assessment

Harnessing genetic diversity in Sudanese sorghum wild relatives for stay-green drought tolerance via microsatellite SSR marker assessment | 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 Harnessing genetic diversity in Sudanese sorghum wild relatives for stay-green drought tolerance via microsatellite SSR marker assessment Alaa Ahmed, Aisha Abdalhady Ahmed Abdalla, Mohammed Elsafy, Alaa Ezzeldin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5014252/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 10 You are reading this latest preprint version Abstract Sudan is the birthplace of sorghum, and vast genetic diversity exists among its wild relatives. To assess the genetic potential of Sudan wild sorghum accessions, we used 41 stay-green-specific microsatellite markers to analyze the genetic variability and population structure of 256 accessions. Overall, 17 SSR markers were polymorphic, with 55 alleles on average 3.3 per locus. The polymorphic information content (PIC) ranged from 0.49 to 0.57, with an overall mean of 0.53, indicating the potential of these markers for capturing the genetic construction of wild sorghum. Linkage disequilibrium analysis identified the two most informative markers, Xcup05 and Xtxp212 . Accordingly, the Nei gene diversity of the populations varied from 0.032 to 0.127, with an overall mean of 0.083. Molecular variance analysis (AMOVA) demonstrated that 99% and 1% of the genetic variations were within and among populations (Fst = 0.066; P 0.001), respectively. However, gene flow (Nm) values varied from 0.058 in populations 1 and 2 to 1.018 in populations 2 and 3. Neighbor-joining trees identified from 21 Sudanese wild sorghum accessions clustered closely to the universally drought-tolerant landrace B35. Structural analysis generated the highest Delta K value (58.2) at K = 2, revealing two distinct subpopulations. While this work provides valuable information about the potential of sorghum wild relatives from Sudan as sources for stay-green drought tolerance, further research should be directed toward identifying the exact mechanisms and genes underlying this stay-green trait using advanced molecular omics techniques. In conclusion, this study highlights the potential role of Sudanese sorghum accessions as reservoirs of ready-to-use stay-green genes for the design of climate-resilient sorghum cultivars in drought-prone areas of Sudan and beyond. Biodiversity Climate-resilient crop Molecular marker Sorghum bicolor Stay-green trait Figures Figure 1 Figure 2 Figure 3 Introduction Climate change and environmental stress are major drivers of biodiversity loss and food insecurity at the global, regional, and local scales. Global warming is one of the most pressing drought-driven factors threatening sustainable food production worldwide. Recent reports from the Intergovernmental Panel on Climate Change (IPCC) emphasize the need for urgent action to mitigate climate change and adapt sustainable crop production for food security (IPCC 2023 ). Sorghum ( Sorghum bicolor (L.) Moenches) is a climate-resilient and stable food crop for more than 500 million people in developing countries, particularly in dry and semi-dry areas where drought is a significant obstacle (Abreha et al. 2022 ). Hadebe et al. ( 2017 ) reported that Sub-Saharan Africa faces a water deficit and limited food access. With approximately 43% of the land classified as arid or semi-arid, small-scale rainfed agriculture is the primary source of livelihood. The increasing demand for food puts pressure on sorghum farming, which must often cope with limited water supplies. Although sorghum is known for its adaptability and ability to thrive under low-input conditions, it remains vulnerable to drought during the critical anthesis and grain-filling stages of growth (Thomas and Ougham 2014 ; Borrell et al. 2014a , a ; Blümmel et al. 2015 ). Water stress during the vegetative and reproductive stages significantly decreased yield by more than 36% and 55%, respectively (Assefa et al. 2010 ). Efforts have focused on assessing the effects of water stress on sorghum cropping and performance in water-deficient environments. This information is crucial for developing drought- and stress-tolerant sorghum varieties. In response to drought stress, sorghum plants employ various survival mechanisms, such as shortening their life cycle, enhancing water uptake, reducing transpiration, increasing tissue tolerance to dehydration, and undergoing biochemical changes involving proline and other metabolites. (Hadebe et al. 2017 ; Abreha et al. 2022 ; Liaqat et al. 2024 ). Stay-green is an adaptation mechanism that helps sorghum maintain its green leaf area and functional photosynthesis during water-limited conditions, particularly during the post-flowering stage. Postponed leaf senescence during grain filling is an emergent outcome of dynamics that occur earlier in sorghum growth and is essentially due to an enhanced balance between water supply and demand, as well as the efficiency with which the sorghum plant converts water to biomass and grain yield (Borrell et al. 2014a ; Thomas and Ougham 2014 ). Such functional “stay-green” ( Stg ) individuals retain the green leaf area (GL) for a more extended period following the onset of a “drought spell,” which can be expected to result in more stable grain yield performance across sites and years in their zones of adaptation (Kamal et al. 2019 ). Thomas and Ougham ( 2014 ) described stay-green phenotypes as delaying senescence (type A), reducing the senescence rate (type B), retaining chlorophyll (type C), maintaining greenness through rapid death (type D), and resulting in a naturally greener phenotype. The functionality of stay-green traits relies on sorghum production in areas with limited water availability. Functional stay green refers to the ability of leaves to perform photosynthesis, whereas cosmetic stay green refers to the enhancement of photosynthesis and greenness. However, not all functional enhancements positively affect an organization’s output rate. Therefore, selecting stay-green traits and grain yield in breeding programs is essential because observations of delayed senescence are related to sink demands. Previous studies on sorghum have identified four quantitative trait loci (QTL) related to stay-green characteristics: Stg1 , Stg2 , Stg3 , and Stg4 (Borrell et al. 2014a ; Kamal et al. 2019 ; Ochieng et al. 2021 ). The mapped QTLs explained 54% of the phenotypic variance of sorghum genotypes with the “stay green” character. The QTLs Stg1 and Stg2 were discovered on chromosome 3 via chromosome mapping. Stg3 and Stg4 were also found on the 2 and 5 chromosomes, respectively (Borrell et al. 2014b ; George-Jaeggli et al. 2017 ). These loci are associated with increased grain yield (Jordan et al. 2012 ) and improved fodder quality (Blümmel et al. 2015 ). Most studies used B35/BTx642 as a prominent source of stay green, whereas few used SC56 and E36-1. In contrast, Ochieng et al. ( 2021 ) identified other sources of stay-green traits in wild accessions. These included wild accessions GBK045827, GBK016114, GBK048922, GBK016109, and GBK047293, which fell into a different cluster from B35 and E36-1. These results indicate that wild sorghum accessions represent potential new sources of stay-green drought tolerance that can be used for breeding programs. Sudan is the motherland of sorghum, and wild sorghum has substantial genetic variation. Therefore, it is crucial to identify and characterize new sources of stay-green germplasm from wild and weedy sorghum to incorporate these genetic variations into breeding strategies for improved drought tolerance. To achieve this, we aimed to identify genetic variations among 256 Sudanese wild sorghum accessions using 17 simple sequence repeat (SSR) markers associated with stay-green QTLs based on the B35 landrace. Materials and methods Plant material: We collected 256 wild and weedy Sudanese sorghum species, locally known as Adar, from the border regions between Sudan, Eritrea, and Ethiopia. We assume that sorghum was first domesticated in these areas 8000 years ago because of the substantial genetic diversity of its wild relatives (Abdelhalim et al., 2019). Wild sorghum accession seeds were collected in 2013, and five subsequent cultivation cycles were conducted using a single-seed descent breeding method to ensure homogeneity and purity. The landrace sorghum B35, a universal donor for the stay-green trait, is a BC1 derivative of the Ethiopian Durra line IS12555 (Subudhi et al. 2000 ) and was included as a positive control. The Sudanese drought-sensitive sorghum cultivar Tabat was used as a negative control. DNA extraction Five seeds from each genotype were randomly selected, planted in plastic trays, and placed in a designated section of a lab house at the Biotechnology and Biosafety Research Center, Agricultural Research Corporation (ARC), Sudan. The soil mixture comprised a 1:1 ratio of Shambat cotton clay to sand. Tissue samples were harvested from the leaves of three plants of each genotype in the second week after anthesis. The collected leaves were placed in Ziplock plastic bags and stored on silica gel at room temperature until DNA extraction. Total DNA was extracted from silica gel-dried leaves using modified cetyl-trimethyl ammonium bromide (CTAB) (Jinlu et al. 2013 ) in an option solution containing M Tris-HCl (pH 8.0), 5 M NaCl, 0.5 M EDTA, 2% 2β-ME, and 2% CTAB. Genomic DNA quality was determined by mixing 3 µL of this sample with 7 µL of 1% agarose gel and applying it at 100 V for 40 min. The concentration of each genomic DNA sample was increased to 100 ng/µL using double-distilled sterilized water. This step was performed to prepare for PCR amplification of DNA. The samples were then frozen at -20°C. Genotyping Forty-one stay-green-specific SSR primers were used for PCR amplification of genomic DNA from Sudanese wild sorghum accessions (Tao et al. 2000; Vadez et al. 2013; R & G 2015). PCR optimization and testing of SSR primers were performed using two contrasting controls: B35, a stay-green donor, and Tabat, a Sudanese drought-released cultivar. Of the 41 SSR markers linked to stay-green trait QTLs, 17 were polymorphic and produced a clear amplicon (Bhattramakki et al. 2000 ). Table 1 lists the primer names, chromosome locations, and allele sizes of these samples. A 20 µL PCR master mix, 4 µL of Solis BioDyne 5 × Blend Master Mix Buffer, 0.5 microliters of the genomic DNA sample, 0.4-0 microliters of forward primer, 0.4 microliters of reverse primer, 14.7 µl of ddH2O. Amplification was performed in a Biometra thermal cycler under the following conditions: The first step involved denaturing the samples at 94°C for 4 min. In the first step, the samples were denatured at 94°C for 30 s, and the annealing temperature was then varied from 50°C to 60°C according to the primer leaflet for 30 s. The third step included 35 extension cycles at 72°C for 1 min. The fourth step involved a final extension step at 72°C for 7 min, following which the samples were stored at 4°C until gel electrophoresis. On a 1% agarose gel, 10 µL of the PCR products were loaded with 20 mL of TBE/100 mL, 10 µL of Red Safe (nucleic acid staining solution 20.0000 ×), and 80 mL of deionized water. To assess the size of the PCR bands, a 100-bp DNA ladder was used. To isolate the PCR products, gel electrophoresis was performed on Bio-Rad at 100 V and 400 mA for 90 min in tris-based ethoxybenzyl (TBE) buffer (54mmol/l tris, 27. 5 g of boric acid, and 20 mmol/l EDTA). acid( 20 ml EDTA/1000). Table 1 Primer names, chromosomes with identified stay-green QTLs, primer sequences, annealing temperatures, and allele sizes of controls detected at 17 microsatellite loci in 256 Sudanese wild sorghum accessions. Markers names Chromosomes with stay-green QTLs Sequences Annealing temperature Allele sizes in checks bp Forward Reverse Tabat B35 Xtxp 088 SBI-01 ATATGGAAGGAAGAAGCCGG AACACAACATGCACGCATG 57.4 135 121 Xtxp 014 SBI-05 GTAATAGTCATGACCGAGG TAA TAG ACG AGT GAA AGC CC 53 165 156 Xcup 24 SBI-01 AAACTGGATGCCACACCAAG AGCTATACCAACACGGGCAG 58.8 210 191 Xsb AGB 03 SBI-02 GTGTGTGTAGCTTCTTGGG ACGTAGGAGTAGTTTCTAGGATT 55.8 205 200 Xtxp 286 SBI-02 AGCAGCAGCAGCAACAG GCGTGGTCTTTGTGGTTC 57 210 215 Xtxp 43 SBI-01 AGTCACAGCACACTGCTTGTC AATTTACCTGGCGCTCTGC 57.3 190 175 Xcup 05 SBI-04 GGAAGGTTTGCAAGAACAGG CCAGCCCAACAAGTGCTATC 57 210 200 Xtxp 41 SBI-04 TCT GGC CAT GAC TTA TCA C AAA TGG CGT AGA CTC CCT TG 56.2 280 300 Sb AGA 01 SBI_03 CGAACCATGATAAATGACTG ATCCGTTTCACAAAAAAAGT 50 100 110 Xtxp274 SBI-06 GAA ATT ACA ATG CTA CCC CTA AAA GT ACT CTA CTC CTT CCG TCC ACA T 57.2 350 380 Xtxp 114 SBI_03 CGTCTTCTACCGCGTCCT CATAATCCCACTCAACAATCC 57.8 260 251 Xtxp 212 SBI-04 TTTCCCCTCTTTCTTGTGTC CTCGGCGTCGTCGTA 52 150 180 Xtxp 445 SBI-02 GCCAGTTGAATCCGCTACAT GAATTGCAATACATAAGCACACC 57.2 250 238 Xtxp6 SBI-06 ATCGGATCCGTCAGATC TCTAGGGAGGTTGCCAC 53 110 140 xtxp034 SBI_03 TGGTTCGTATCCTTCTCTACAG CATATACCTCCTCGTCGCTC 60.2 380 360 xtxp031 SBI_03 TGCGAGGCTGCCCTACTAG TGGACGTACCTATTGGTGC 59.5 205 200 xtxp019 SBI-02 CTTTCAATCGGTTCCAGAC CTTCCACCTCCGTACTC 56.2 295 300 Data analysis The sizes of all PCR-amplified microsatellite regions were estimated using a Syngene ultraviolet documentation system with a 100-bp standard ladder. We measured genetic diversity parameters, such as the number of alleles (Ne), major allele frequencies (MAF), gene diversity (h), and polymorphism information content (PIC), using Power Marker version 3.25 software (Liu and Muse 2005 ). However, the observed heterozygosity (Ho), expected heterozygosity (He), and Shannon’s Information Index (I) (Sherwin et al. 2006 ), and the Hardy–Weinberg equilibrium (HWE) was measured using GenAlex version 6.5 (Peakall and Smouse 2012 ). Additionally, we conducted a molecular variance analysis (AMOVA) (Meirmans 2006 ) after grouping accessions based on cluster evaluation. The GenAlex software was used to measure pairwise population genetic distances, and gene flows. We used the neighbor-joining method and Jaccard’s genetic similarity coefficients based on the genetic distance matrix to construct a dendrogram. Using R statistical software, we measured the kinship matrix of Jaccardn distances for the 256 Sudanese wild sorghum accession genotypes based on SSR markers. R statistical software was used to calculate the kinship matrix of Jaccard distances [31] for the 256 Sudanese wild sorghum accession genotypes based on SSR markers. The population structure was calculated via Bayesian analysis using the STRUCTURE (version 2.3.4) program to estimate the number of hypothetical subpopulations (K) and the membership probability of each genotype to the identified subpopulations (Pritchard et al. 2000). The model-based Bayesian clustering approach used Markov chain Monte Carlo (MCMC) algorithms to test hypotheses from one to ten subpopulations. The burn-in and number of iterations of the MCMC algorithm were set to 9,999. This process was independently repeated 10 times using the admixture model. The log-likelihood of the observed data Pr (X|K) for each value of K was retrieved from the structure output (Pritchard JK et al. 2007 ). The ad hoc quantity analysis was based on the second-order rate of change of the likelihood probability function presented by Evanno et al. (2005). Delta K produced the highest outcome within the Bayesian clustering approach, with a maximum K-value of 2, indicating that the population was grouped into two subpopulations. Results Characteristics of SSR markers across sorghum accessions The allelic sizes of the SSR markers in sorghum specific to the stay-green trait ranged from 110 bp for Sb AGA 01 to 380 bp for Xtxp274 for landrace B35, which is a universal stay-green donor that can handle drought. On the other hand, for the susceptible check Tabat, the allelic size values ranged from 100 bp for the marker ( Sb AGA 01 ) to 380 bp for the marker ( Xtxp034 ). Out of 41 SSR-specific stay green markers, 17 were polymorphic and generated 55 alleles, with an overall average of 3.3 per locus. Alleles per locus ranged from 3 to 5, with the highest number (5.0) observed for marker Xtxp_114 . The study found that the major significant alleles (MAF) had a frequency range of (0.43) for the Xtxp031 marker to (0.50) for the Xcup24 , Xtxp286 , XsbAGB01 , Xtxp274 , Xtxp212 , and Xtxp445 markers, with a mean frequency of (0.48) per locus (Table 2). Regarding Shannon’s information index, the range was from 3.48 for the markers ( Xcup05 , Xtxp445 , and Xtxp6 ) to 3.5 for the markers ( Xtxp41 , XsAGB01 , Xtxp212 , Xtxp3.5 , and Xtxp031 ), with an overall mean of 3.49. The genetic diversity among the different genetic locations varied from 0.58 ( Xtxp_41 , Xtxp274 , and Xtxp212 ) to 0.64 ( Xtxp_114 and Xtxp031 ), with an average of 0.60 per location. The expected heterozygosity (He) of each locus differed slightly, ranging from 0.297 ( Xtxp274 ) to 0. 491 ( Xcup_24 ), with a mean of 0. 389. Similarly, the heterozygosity levels ranged from 0. 293 ( Xcup_05 ) to 0. 491 ( Xcup_24 ), resulting in an average of 0. 397 per locus. Markers Xsb_AGB_01 and Xtxp_41 exhibited the highest levels of informativeness, as indicated by an information index of 3.503 (Table 2). The PIC values ranged from 0.49 ( Xtxp_088 and Xtxp_41 ) to 0.57 ( Xtxp_114 and Xtxp031 ), with an average of 0.53. All 17 SSR markers showed highly significant (p < 0.0001) deviations from the Hardy–Weinberg equilibrium (HWE). According to the results presented in Fig. 1 , markers Xtxp212 and Xcup05 were most useful. Genetic relationships between and within populations Population 1 had the highest number of individuals (117), followed by Population 2 (88), and Population 3 had the lowest number of individuals (52) (Table 3 ). The number of alleles in all populations was 2.00 (data not shown). Population 3 had the highest effective allele frequency (1.69) and Shannon information index (0.59). In contrast, Population 1 had the lowest number of effective alleles (1.61) and the lowest Shannon information index (0.54). These results indicate private or localized common alleles in a single population. Population 2 had the highest Nei genetic diversity (0.127), and population 3 had the lowest (0.032). The percentage of polymorphic loci per population was 100% (Table 3 ). Genetic differentiation, distance, and gene flow Pairwise genetic differentiation between Sudanese wild sorghum populations for the stay-green trait ranged from 0.002 to 0.010 (Table 4 ). Populations 3 and 1 exhibited the highest degrees of population differentiation (Fst = 0.010), followed by populations 2 and 1 (Fst = 0.007). Populations 3 and 2 show the lowest population differentiation levels (Fst = 0.002). The gene flow (Nm) between each population and the other populations ranged from 0.058 to 1.018 (Table 4 ). Table 4 presents the highest gene flow (1.018) between populations 2 and 3, followed by populations 1 and 2 (0.994), and the lowest gene flow (0.58) between populations 1 and 2. AMOVA for the stay-green trait revealed that variability among and within populations accounted for 1% and 99% of the total genetic variation, respectively (Table 5 ). The overall fixation index value used to measure population differentiation was moderate (Fst = 0.066). Table 2. Genetic diversity index summary statistics of 17 SSR loci across 256 Sudanese wild Sorghum accessions. Marker MAF Na I GD He Ho PIC PHWE Xtxp_088 0.49 3 3.5 0.58 0.3 0.3 0.49 0.000*** Xtxp_014 0.48 3 3.49 0.62 0.44 0.44 0.54 0.000*** Xcup_24 0.5 4 3.49 0.62 0.49 0.49 0.55 0.000*** Xsb_AGB_03 0.49 3 3.49 0.62 0.45 0.45 0.54 0.000*** Xtxp_286 0.5 3 3.49 0.6 0.39 0.39 0.52 0.000*** Xtxp_043 0.46 3 3.49 0.61 0.41 0.38 0.53 0.000*** Xcup_05 0.45 3 3.48 0.6 0.35 0.29 0.51 0.000*** Xtxp_41 0.49 3 3.5 0.58 0.39 0.32 0.49 0.000*** Xsb_AGB_01 0.5 3 3.5 0.61 0.48 0.42 0.53 0.000*** Xtxp274 0.5 3 3.49 0.58 0.3 0.33 0.5 0.000*** Xtxp_114 0.46 5 3.49 0.64 0.37 0.46 0.57 0.000*** Xtxp_212 0.5 3 3.5 0.58 0.38 0.33 0.5 0.000*** Xtxp_445 0.5 3 3.48 0.6 0.33 0.41 0.52 0.000*** Xtxp6 0.48 3 3.48 0.62 0.46 0.45 0.54 0.000*** xtxp034 0.49 4 3.5 0.61 0.33 0.39 0.53 0.000*** xtxp031 0.43 3 3.5 0.64 0.37 0.46 0.57 0.000*** xtxp019 0.45 3 3.48 0.63 0.39 0.43 0.55 0.000*** Mean 0.48 3.2 3.49 0.6 0.39 0.4 0.53 Min 0.43 3 3.48 0.58 0.3 0.29 0.49 Max 0.50 5 3.5 0.64 0.49 0.49 0.57 Table 3 Allelic patterns and diversity indices of Sudanese wild sorghum germplasm populations compared with 17 stay-green-specific SSR loci Population Size Ne NPA NLCA I h PPL Population1 117 1.61 0.00 0.00 0.54 0.090 100% Population2 88 1.65 0.00 0.00 0.57 0.127 100% Population3 52 1.69 0.00 0.00 0.59 0.032 100% Mean 85.7 1.651 0.00 0.00 0.57 0.083 Na = Observed number of alleles; Ne = Number of effective alleles; NPA = Number of Private Alleles (i.e., the number of alleles unique to a single population); NLCA = Number of Locally Common Alleles ( < = 25% and < = 50%), (frequency 5%) found in 25% or fewer populations; I = Shannon’s information statistic; h = Nei’s genetic diversity; and PPL = the Percentage of Polymorphic Loci; Table 4 Pairwise Nei genetic distances measured using Fst (A) and gene flow (Nm) values (B) among three Sudanese wild sorghum populations. A Population 1 Population 2 Population 3 Population 1 0.000 Population 2 0.007 0.000 Population 3 0.010 0.002 0.000 Probability P(rand > = data) based on 999 permutations. B Population 1 Population 2 Population 3 Population 1 0.000 0.944 0.920 Population 2 0.058 0.000 1.018 Population 3 0.083 0.000 0.000 Table 5 Analysis of molecular variance (AMOVA) showing the partitioning of genetic variation within and among populations using 17 SSR markers Source df SS MS Est. Var. % P value Among Pops 2 32.3 16.15 0.06 1% 0.001 Among Indiv 253 1825.1 7.21 0.00 0% 0.985 Within Indiv 256 1922.0 7.51 7.51 99% 0.910 Total 511 3779.4 7.56 100% Cluster analysis The 256 samples of Sudanese wild sorghum used for neighbor-joining cluster analysis were divided into two distinct genetic groups (Fig. 2 a). Cluster I comprised 21 accessions, including the widely recognized drought-tolerant landrace B35. On the other hand, we further divided Cluster II into smaller clusters C1 and C2. The sub-cluster C1 consisted of 57 accessions, whereas the sub-cluster C2 consisted of 177 accessions, one of which was the drought-sensitive cultivar Tabat. This distinction underscores the genetic differences between drought-tolerant and drought-vulnerable accessions. According to the results of the neighbor-joining cluster analysis (Fig. 2 b), the kinship map confirmed that the accessions were split into two separate groups. Consistency between methodologies reinforces the dependability of established genetic classifications. Principal component analysis (PCA) revealed genetic similarities between sorghum accessions (Fig. 2 c). The first two main component axes explain 21.05% of total genetic variation. The first and second axes contributed 73% and 9.32%, respectively (Fig. 2 c). PCA confirmed the identified genetic clusters, emphasizing gene diversity in Sudanese wild sorghum populations. Table 6 Mean log-likelihood, standard deviations for log-likelihood, log-likelihood differences, and delta K for structural analysis K Reps Mean LnP (K) Stdev LnP (K) Ln'(K) |Ln''(K)| Delta K 1 10 -5077.2 0.1 NA NA NA 2 10 -4811.0 1.6 266.2 93.7 58.2 3 10 -4638.5 2.6 172.5 10.3 3.9 4 10 -4476.3 18.2 162.2 51.3 2.8 5 10 -4365.4 12.8 110.9 22.4 1.8 6 10 -4232.1 5.8 133.3 54.4 9.3 7 10 -4153.2 35.1 78.9 77.7 2.2 8 10 -4152.0 42.2 1.2 69.9 17 9 10 -4220.7 222.8 -68.7 133.7 0.6 10 10 -4155.7 102.9 65.0 NA NA The population structure evaluation revealed that the mean log-likelihood values varied from the lowest of -5077.2 for K = 1 to the highest for K = 8 (-4152.0). However, increasing the value of K increased the mean likelihood until K = 8. Subsequently, a slight reduction in the mean log-likelihood from K = 9 was observed (Table 6 ). Higher K values exhibited a higher standard deviation of log-likelihood, suggesting higher instability in the clustering solution. We observed the highest log-likelihood difference (266.2) between K = 1 and K = 2 and the lowest difference (-68.7) between K = 8 and 9. Eventually, the highest Delta K value (58.2) was recorded at K = 2, revealing that two distinct subpopulations probably existed among the 256 Sudanese wild sorghum accessions tested based on the 17 stay-green-specific SSR markers. Clumpak’s results (bar plot) detected a genetic admixture based on this value; hence, there was clear genetic-based structuring of accessions (Fig. 3 a, b, and c). Discussion This study assessed genetic variation in Sudanese wild sorghum using microsatellite SSR markers specific to the stay-green trait. The informativeness of the markers was based on reactions to the drought-tolerant stay-green trait of landrace B35 and the drought-susceptible Sudanese sorghum cultivar Tabat (Table 1 ). This study used 41 SSR markers, 17 of which were polymorphic. These 17 markers yielded 55 alleles with an average of 3.3 per marker (Table 2), revealing their usefulness in detecting genotypic variations among Sudanese wild sorghum accessions for drought tolerance, especially stay-green traits. The equal distribution of alleles across Sudanese wild sorghum accessions indicates the likelihood of successfully incorporating this untapped germplasm into climate-resilient sorghum cultivars, thereby minimizing food insecurity in drought-prone regions. These findings are consistent with those of Ochieng et al. ( 2021 ), who reported the importance of wild sorghum as a potential stay-green source for drought-tolerant breeding efforts. These authors also underscored the potential value of gene bank collections of germplasm from sorghum wild relatives as novel sources for crop improvement and productivity enhancement, especially in places like Sudan, where sorghum is believed to have originated and was domesticated 8000 years ago. There is substantial potential for Sudanese wild sorghum germplasm, which has not yet been identified. Our results identified two SSR markers, Xtxp212 and Xcup05 because their high informativeness makes them suitable candidates for marker-assisted selection backcrossing breeding programs (Fig. 1 ). However, large-scale field trials and phenotyping-based research are required to confirm the efficacy of these markers under different stress conditions. Harnessing these markers to breeding programs may provide far-reaching outreach for developing climate-resilient sorghum cultivars that exploit the potential of wild sorghum in Sudan and beyond. The results indicate that the genetic distances of the three sorghum populations differed entirely (Table 3 ). For instance, population 1 had the highest number of individuals among the three populations (117) but ranked lowest in the effective number of alleles per population or Shannon’s information index. Surprisingly, population 3, which contained the smallest number (58) of wild sorghum individuals, had the highest proportion and adequate number of alleles, along with Shannon’s index values, indicating that genetic variation was higher. This is important for breeding programs that improve drought resilience. This study emphasized the significance of gene bank collections in safeguarding the forgotten treasure trove of Sudanese wild sorghum germplasm and ensuring its long-term conservation. According to Sagnard et al. ( 2011 ), collecting and preserving these wild species should be conducted case-by-case, considering social changes and agricultural practices. This study explored gene flow among different populations of Sudanese wild sorghum accessions to assess their genetic diversity and potential for conservation and breeding. Our results showed that gene flow, measured as Nm (a unit indicating the number of migrants per generation), ranged from 0.58 to 1.018. We observed the highest gene flow between Populations 2 and 3. Conversely, the lowest gene flow was observed between populations 1 and 2 (Table 4 ). These findings are consistent with those of Sagnard et al. ( 2011 ), who encountered potential gene flows between guinea margaritiferum and wild weedy sorghum. Therefore, further investigation is needed to pinpoint the extent and direction of gene flow, the contribution of domesticated sorghum to gene flow, and the rates of introgression compared with wild-type sorghum. Sagnard et al. ( 2011 ) indicated the potential use of maternally inherited molecular markers (chloroplastic or mitochondrial) to estimate the relative contributions of gene flow evolutionary processes. Similarly, Mutegi et al. ( 2010 ) stated that gene flow between domesticated sorghum and their wild relatives may be governed by overlaps in their natural habitats and ecological distribution. Accordingly, there is a need for the integration of many disciplines, including socialists, GIS scientists, plant breeders, botanists, and ecologists, to attain a comprehensive understanding of gene flow processes and factors governing the distribution of genetic diversity in Sudanese wild, weedy, and cultivated pools. The neighbor-joining tree also helped clarify the genetic relationship between the accessions by grouping B35, a drought-resistant Sudanese cultivar, Tabat. Grouping Sudanese wild sorghum accessions with B35, a universally drought-tolerant landrace, is essential. This implies that there are genetic factors that are common to these accessions that are responsible for drought tolerance. As such, they can be used to breed drought-tolerant sorghum varieties. However, further investigation is required to identify stay-green attributes and genes involved in drought resistance. Comparative transcriptomics, metabolomics, and proteomics should be performed for genetic regulation networks, along with the stay-green phenotype (Altaf et al., 2023 ; Liaqat et al.. 2024 ). Two distinct subpopulations of wild Sudanese sorghum germplasm were identified. This implies that genetic makeup can be used to develop breeding strategies. These subpopulations can parent stocks with high genetic variation and traits such as drought tolerance. We can help breeders grow sorghum cultivars that best suit the climatic conditions in Sudan and, at the same time, contribute to sustainable agriculture by integrating these observations into breeding programs. This study underscores the need to integrate genetic structure into breeding strategies to maximize the genetic diversity of wild sorghum for the next generation. Conclusions This work showed that Sudanese wild sorghum accessions could be used to source stay-green genes for the design of climate-smart sorghum cultivars. The genetic diversity, useful markers, and subpopulations defined in this study will be useful for future work and breeding. This study is crucial in light of the current climate change challenges, especially in arid and semi-arid regions such as Sudan; wild sorghum accessions should be conserved and used in food production systems. Additional work should be directed at the functional characterization of stay-green alleles and their use in breeding high-yielding drought-tolerant sorghum in Sudan and other similar environments. Declarations Author Contribution Alaa Ahmed: Molecular marker analysis, data analysis, writing, and review; Aisha Abdalhady Ahmed Abdalla: Molecular marker analysis, data analysis, writing, and review; Mohammed Elsafy: Conceptualization, investigation, writing, review, editing, and validation; Alaa Ezzeldin: Molecular marker analysis, data analysis, writing, and review; Mahbubjon Rahmatov: Investigation, writing, review, editing, validation, and funding acquisition; Tilal Abdelhalim: Conceptualization, investigation, writing, review, editing, validation, and funding acquisition. Acknowledgement We acknowledge financial support from the Swedish Research Council (Vetenskapsrådet) and the Agricultural Research Corporation Sudan for funding and supporting this research. References Abreha KB, Enyew M, Carlsson AS, et al. (2022) Sorghum in dryland: morphological, physiological, and molecular responses of sorghum under drought stress. Planta 255:20. https://doi.org/10.1007/s00425-021-03799-7 Altaf MT, Liaqat W, Baloch FS, et al (2023) Omics Approaches for Sorghum: Paving the Way to a Resilient and Sustainable Bioenergy Future. In: Aasim M, Baloch FS, Nadeem MA, et al. (eds) Biotechnology and Omics Approaches for Bioenergy Crops. Springer Nature, Singapore, pp 99–121 Assefa Y, Staggenborg SA, Prasad VPV (2010) Grain Sorghum Water Requirement and Responses to Drought Stress: A Review. Crop Management 9:1–11. https://doi.org/10.1094/CM-2010-1109-01-RV Bhattramakki D, Dong J, Chhabra AK, Hart GE (2000) An integrated SSR and RFLP linkage map of Sorghum bicolor (L.) Moench. 43: Blümmel M, Deshpande S, Kholova J, Vadez V (2015) Introgression of staygreen QLT’s for concomitant improvement of food and fodder traits in sorghum bicolor. Field Crops Research 180:228–237. https://doi.org/10.1016/j.fcr.2015.06.005 Borrell AK, Mullet JE, George-Jaeggli B, et al (2014a) Drought adaptation of stay-green sorghum is associated with canopy development, leaf anatomy, root growth, and water uptake. Journal of Experimental Botany 65:6251–6263. https://doi.org/10.1093/jxb/eru232 Borrell AK, van Oosterom EJ, Mullet JE, et al (2014b) Stay-green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns. New Phytologist 203:817–830. https://doi.org/10.1111/nph.12869 Dwyer LM, Tollenaar M, Houwing L (1991) A nondestructive method to monitor leaf greenness in corn. Can J Plant Sci 71:505–509. https://doi.org/10.4141/cjps91-070 George-Jaeggli B, Mortlock MY, Borrell AK (2017) Bigger is not always better: Reducing leaf area helps stay-green sorghum use soil water more slowly. Environmental and Experimental Botany 138:119–129. https://doi.org/10.1016/j.envexpbot.2017.03.002 Hadebe ST, Modi AT, Mabhaudhi T (2017) Drought Tolerance and Water Use of Cereal Crops: A Focus on Sorghum as a Food Security Crop in Sub-Saharan Africa. J Agronomy Crop Science 203:177–191. https://doi.org/10.1111/jac.12191 IPCC, 2023: Sections. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35–115, doi: 10.59327/IPCC/AR6-9789291691647 Jinlu L, Shuo W, Jing Y, et al (2013) A Modified CTAB Protocol for Plant DNA Extraction: A Modified CTAB Protocol for Plant DNA Extraction. CHINESE BULLETIN OF BOTANY 48:72–78. https://doi.org/10.3724/SP.J.1259.2013.00072 Jordan DR, Hunt CH, Cruickshank AW, et al (2012) The Relationship Between the Stay-Green Trait and Grain Yield in Elite Sorghum Hybrids Grown in a Range of Environments. Crop Science 52:1153–1161. https://doi.org/10.2135/cropsci2011.06.0326 Kamal NM, Gorafi YSA, Abdelrahman M, et al (2019) Stay-Green Trait: A Prospective Approach for Yield Potential, and Drought and Heat Stress Adaptation in Globally Important Cereals. IJMS 20:5837. https://doi.org/10.3390/ijms20235837 Liaqat W, Altaf MT, Barutçular C, et al (2024) Drought stress in sorghum: physiological tools, breeding technology, Omics approaches and Genomic-assisted breeding -A review. J Soil Sci Plant Nutr 24:1665–1691. https://doi.org/10.1007/s42729-024-01702-3 Liu K, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129. https://doi.org/10.1093/bioinformatics/bti282 Meirmans PG (2006) Using the AMOVA framework to estimate a standarized genetic differentiation measure. Evolution 60:2399–2402. https://doi.org/10.1111/j.0014-3820.2006.tb01874.x Ochieng G, Ngugi K, Wamalwa LN, et al (2021) Novel sources of drought tolerance from landraces and wild sorghum relatives. Crop Science 61:104–118. https://doi.org/10.1002/csc2.20300 Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539. https://doi.org/10.1093/bioinformatics/bts4 Mutegi E, Sagnard F, Muraya M, et al (2010) Ecogeographical distribution of wild, weedy and cultivated Sorghum bicolor (L.) Moench in Kenya: implications for conservation and crop-to-wild gene flow. Genet Resour Crop Evol 57:243–253. https://doi.org/10.1007/s10722-009-9466-7 Pritchard JK, Xiaoquan W, Falushb D (2007) Structure Software for Population Genetics Inference: V2.2. https://web.stanford.edu/group/pritchardlab/software/structure2_2.html . Accessed 9 Aug 2024 Sagnard F, Deu M, Dembélé D, et al (2011) Genetic diversity, structure, gene flow and evolutionary relationships within the Sorghum bicolor wild–weedy–crop complex in a western African region. Theor Appl Genet 123:1231–1246. https://doi.org/10.1007/s00122-011-1662-0 Sherwin WB, Jabot F, Rush R, Rossetto M (2006) Measurement of biological information with applications from genes to landscapes. Molecular Ecology 15:2857–2869. https://doi.org/10.1111/j.1365-294X.2006.02992.x Subudhi PK, Rosenow DT, Nguyen HT (2000) Quantitative trait loci for the stay green trait in sorghum (Sorghum bicolor L. Moench): consistency across genetic backgrounds and environments. Theor Appl Genet 101:733–741. https://doi.org/10.1007/s001220051538 Thomas H, Ougham H (2014) The stay-green trait. Journal of Experimental Botany 65:3889–3900. https://doi.org/10.1093/jxb/eru037 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Revision requested 18 Oct, 2024 Reviews received at journal 18 Oct, 2024 Reviews received at journal 17 Oct, 2024 Reviewers agreed at journal 13 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers invited by journal 02 Sep, 2024 Editor assigned by journal 02 Sep, 2024 Submission checks completed at journal 02 Sep, 2024 First submitted to journal 01 Sep, 2024 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-5014252","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350784375,"identity":"1053df03-0f5c-430f-b91b-073d461a1447","order_by":0,"name":"Alaa Ahmed","email":"","orcid":"","institution":"Agricultural Research Corporation (ARC)","correspondingAuthor":false,"prefix":"","firstName":"Alaa","middleName":"","lastName":"Ahmed","suffix":""},{"id":350784376,"identity":"1749dc21-8cdb-4b11-923c-6d3d39f62a90","order_by":1,"name":"Aisha Abdalhady Ahmed Abdalla","email":"","orcid":"","institution":"Agricultural Research Corporation (ARC)","correspondingAuthor":false,"prefix":"","firstName":"Aisha","middleName":"Abdalhady Ahmed","lastName":"Abdalla","suffix":""},{"id":350784377,"identity":"691e8960-bee8-4392-b831-00967f68a803","order_by":2,"name":"Mohammed Elsafy","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Elsafy","suffix":""},{"id":350784378,"identity":"951111ce-acbc-4ee2-9af6-f20e73dcf6f2","order_by":3,"name":"Alaa Ezzeldin","email":"","orcid":"","institution":"Agricultural Research Corporation (ARC)","correspondingAuthor":false,"prefix":"","firstName":"Alaa","middleName":"","lastName":"Ezzeldin","suffix":""},{"id":350784379,"identity":"0df085b6-7c5c-4ce6-9f21-09c0ab1347a9","order_by":4,"name":"Mahbubjon Rahmatov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYFACHjYGxgY2MJMZIsJ8gOEBKVokGBjYEhgSCGthQNbCY4BXi2772WOPeXfwyZkz8D78XJhjV8cvkfP5Q2Ibgz0/Di1mZ/LSjXnPsBlbNrAbS8/cliwhOSN3mwRQS+LMBhxaDuSYSfO2sSVuOMDGIM27jVnC4MzZbQxALQkGB3BoOf8GroX5N++2eqCWM4/BDrPHpeUGwhY2oC2HJQyO9zCAHMa4AZdfbrwxN5wL9IvBYTY2a95txyVntreZSSSck0icgdNhOWYP3u44JmdwvI35Nu+2an5+ZubHHz6U2djz4/A+FByDRz0MSOBVDwQ1hBSMglEwCkbBSAYA+oFUxlHJ4psAAAAASUVORK5CYII=","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mahbubjon","middleName":"","lastName":"Rahmatov","suffix":""},{"id":350784380,"identity":"22180787-44c4-40f1-a937-bdddd1822c82","order_by":5,"name":"Tilal Abdelhalim","email":"","orcid":"","institution":"Agricultural Research Corporation (ARC)","correspondingAuthor":false,"prefix":"","firstName":"Tilal","middleName":"","lastName":"Abdelhalim","suffix":""}],"badges":[],"createdAt":"2024-09-01 19:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5014252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5014252/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-024-02236-4","type":"published","date":"2024-11-06T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65810779,"identity":"44290a0b-bbe9-4f7c-8dbc-1fde6c0fd1e6","added_by":"auto","created_at":"2024-10-03 04:21:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":474109,"visible":true,"origin":"","legend":"\u003cp\u003eLinkage disequilibrium analysis shows the most informative markers across 17 SSR markers.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5014252/v1/783eb0ffe037bef8825e6e08.png"},{"id":65810782,"identity":"8dfa523d-1e22-450e-997c-47a3778838e5","added_by":"auto","created_at":"2024-10-03 04:21:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":893919,"visible":true,"origin":"","legend":"\u003cp\u003ea Neighbor-joining cluster analysis showing the clusters of 256 accessions of Sudanese wild sorghum based on stay green trait SSR markers.\u003c/p\u003e\n\u003cp\u003eb Kinship map of wild sorghum accessions based on stay green trait-specific SSR markers.\u003c/p\u003e\n\u003cp\u003e(c): Principal Component Analysis (PCA) of 256 Sudanese wild sorghum accessions based on the 17-SSR-specific stay green trait. Samples coded with the same color belong to the same population.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5014252/v1/2ceb88a0365dd6ece32330e8.png"},{"id":65811534,"identity":"f1fa42fc-c824-46da-838c-bfe75bd2130e","added_by":"auto","created_at":"2024-10-03 04:29:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100423,"visible":true,"origin":"","legend":"\u003cp\u003ea Mean log-likelihood values of the population structure of Sudanese wild sorghum accessions.\u003c/p\u003e\n\u003cp\u003eb Estimated top delta K values obtained by the method proposed by Evanno et al. (2005) estimated the Bayesian population structure for \u003cem\u003eK \u003c/em\u003e= 2.\u003c/p\u003e\n\u003cp\u003ec Different colors (blue and orange) represent genetic groups or subpopulations designated by the Structure Harvester. (b) Population structure of sorghum accessions representing SWS populations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5014252/v1/64fc5734dd320667442e85a2.png"},{"id":68749796,"identity":"4d82e850-7b78-44df-bff0-40b0648c9972","added_by":"auto","created_at":"2024-11-11 16:05:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2193849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5014252/v1/c3e6112b-7efd-4387-976d-904617196660.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Harnessing genetic diversity in Sudanese sorghum wild relatives for stay-green drought tolerance via microsatellite SSR marker assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change and environmental stress are major drivers of biodiversity loss and food insecurity at the global, regional, and local scales. Global warming is one of the most pressing drought-driven factors threatening sustainable food production worldwide. Recent reports from the Intergovernmental Panel on Climate Change (IPCC) emphasize the need for urgent action to mitigate climate change and adapt sustainable crop production for food security (IPCC \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e (L.) Moenches) is a climate-resilient and stable food crop for more than 500\u0026nbsp;million people in developing countries, particularly in dry and semi-dry areas where drought is a significant obstacle (Abreha et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hadebe et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that Sub-Saharan Africa faces a water deficit and limited food access. With approximately 43% of the land classified as arid or semi-arid, small-scale rainfed agriculture is the primary source of livelihood. The increasing demand for food puts pressure on sorghum farming, which must often cope with limited water supplies. Although sorghum is known for its adaptability and ability to thrive under low-input conditions, it remains vulnerable to drought during the critical anthesis and grain-filling stages of growth (Thomas and Ougham \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Borrell et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003ea\u003c/span\u003e; Bl\u0026uuml;mmel et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Water stress during the vegetative and reproductive stages significantly decreased yield by more than 36% and 55%, respectively (Assefa et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEfforts have focused on assessing the effects of water stress on sorghum cropping and performance in water-deficient environments. This information is crucial for developing drought- and stress-tolerant sorghum varieties. In response to drought stress, sorghum plants employ various survival mechanisms, such as shortening their life cycle, enhancing water uptake, reducing transpiration, increasing tissue tolerance to dehydration, and undergoing biochemical changes involving proline and other metabolites. (Hadebe et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Abreha et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liaqat et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStay-green is an adaptation mechanism that helps sorghum maintain its green leaf area and functional photosynthesis during water-limited conditions, particularly during the post-flowering stage. Postponed leaf senescence during grain filling is an emergent outcome of dynamics that occur earlier in sorghum growth and is essentially due to an enhanced balance between water supply and demand, as well as the efficiency with which the sorghum plant converts water to biomass and grain yield (Borrell et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e; Thomas and Ougham \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such functional \u0026ldquo;stay-green\u0026rdquo; (\u003cem\u003eStg\u003c/em\u003e) individuals retain the green leaf area (GL) for a more extended period following the onset of a \u0026ldquo;drought spell,\u0026rdquo; which can be expected to result in more stable grain yield performance across sites and years in their zones of adaptation (Kamal et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thomas and Ougham (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) described stay-green phenotypes as delaying senescence (type A), reducing the senescence rate (type B), retaining chlorophyll (type C), maintaining greenness through rapid death (type D), and resulting in a naturally greener phenotype. The functionality of stay-green traits relies on sorghum production in areas with limited water availability. Functional stay green refers to the ability of leaves to perform photosynthesis, whereas cosmetic stay green refers to the enhancement of photosynthesis and greenness. However, not all functional enhancements positively affect an organization\u0026rsquo;s output rate. Therefore, selecting stay-green traits and grain yield in breeding programs is essential because observations of delayed senescence are related to sink demands.\u003c/p\u003e \u003cp\u003ePrevious studies on sorghum have identified four quantitative trait loci (QTL) related to stay-green characteristics: \u003cem\u003eStg1\u003c/em\u003e, \u003cem\u003eStg2\u003c/em\u003e, \u003cem\u003eStg3\u003c/em\u003e, and \u003cem\u003eStg4\u003c/em\u003e (Borrell et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e; Kamal et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ochieng et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The mapped QTLs explained 54% of the phenotypic variance of sorghum genotypes with the \u0026ldquo;stay green\u0026rdquo; character. The QTLs \u003cem\u003eStg1\u003c/em\u003e and \u003cem\u003eStg2\u003c/em\u003e were discovered on chromosome 3 via chromosome mapping. \u003cem\u003eStg3\u003c/em\u003e and \u003cem\u003eStg4\u003c/em\u003e were also found on the 2 and 5 chromosomes, respectively (Borrell et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e; George-Jaeggli et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These loci are associated with increased grain yield (Jordan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and improved fodder quality (Bl\u0026uuml;mmel et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Most studies used B35/BTx642 as a prominent source of stay green, whereas few used SC56 and E36-1. In contrast, Ochieng et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified other sources of stay-green traits in wild accessions. These included wild accessions GBK045827, GBK016114, GBK048922, GBK016109, and GBK047293, which fell into a different cluster from B35 and E36-1. These results indicate that wild sorghum accessions represent potential new sources of stay-green drought tolerance that can be used for breeding programs.\u003c/p\u003e \u003cp\u003eSudan is the motherland of sorghum, and wild sorghum has substantial genetic variation. Therefore, it is crucial to identify and characterize new sources of stay-green germplasm from wild and weedy sorghum to incorporate these genetic variations into breeding strategies for improved drought tolerance. To achieve this, we aimed to identify genetic variations among 256 Sudanese wild sorghum accessions using 17 simple sequence repeat (SSR) markers associated with stay-green QTLs based on the B35 landrace.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material:\u003c/h2\u003e \u003cp\u003eWe collected 256 wild and weedy Sudanese sorghum species, locally known as Adar, from the border regions between Sudan, Eritrea, and Ethiopia. We assume that sorghum was first domesticated in these areas 8000 years ago because of the substantial genetic diversity of its wild relatives (Abdelhalim et al., 2019). Wild sorghum accession seeds were collected in 2013, and five subsequent cultivation cycles were conducted using a single-seed descent breeding method to ensure homogeneity and purity. The landrace sorghum B35, a universal donor for the stay-green trait, is a BC1 derivative of the Ethiopian Durra line IS12555 (Subudhi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and was included as a positive control. The Sudanese drought-sensitive sorghum cultivar Tabat was used as a negative control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003eFive seeds from each genotype were randomly selected, planted in plastic trays, and placed in a designated section of a lab house at the Biotechnology and Biosafety Research Center, Agricultural Research Corporation (ARC), Sudan. The soil mixture comprised a 1:1 ratio of Shambat cotton clay to sand. Tissue samples were harvested from the leaves of three plants of each genotype in the second week after anthesis. The collected leaves were placed in Ziplock plastic bags and stored on silica gel at room temperature until DNA extraction. Total DNA was extracted from silica gel-dried leaves using modified cetyl-trimethyl ammonium bromide (CTAB) (Jinlu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in an option solution containing M Tris-HCl (pH 8.0), 5 M NaCl, 0.5 M EDTA, 2% 2β-ME, and 2% CTAB. Genomic DNA quality was determined by mixing 3 \u0026micro;L of this sample with 7 \u0026micro;L of 1% agarose gel and applying it at 100 V for 40 min. The concentration of each genomic DNA sample was increased to 100 ng/\u0026micro;L using double-distilled sterilized water. This step was performed to prepare for PCR amplification of DNA. The samples were then frozen at -20\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eForty-one stay-green-specific SSR primers were used for PCR amplification of genomic DNA from Sudanese wild sorghum accessions (Tao et al. 2000; Vadez et al. 2013; R \u0026amp; G 2015). PCR optimization and testing of SSR primers were performed using two contrasting controls: B35, a stay-green donor, and Tabat, a Sudanese drought-released cultivar. Of the 41 SSR markers linked to stay-green trait QTLs, 17 were polymorphic and produced a clear amplicon (Bhattramakki et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the primer names, chromosome locations, and allele sizes of these samples. A 20 \u0026micro;L PCR master mix, 4 \u0026micro;L of Solis BioDyne 5 \u0026times; Blend Master Mix Buffer, 0.5 microliters of the genomic DNA sample, 0.4-0 microliters of forward primer, 0.4 microliters of reverse primer, 14.7 \u0026micro;l of ddH2O. Amplification was performed in a Biometra thermal cycler under the following conditions: The first step involved denaturing the samples at 94\u0026deg;C for 4 min. In the first step, the samples were denatured at 94\u0026deg;C for 30 s, and the annealing temperature was then varied from 50\u0026deg;C to 60\u0026deg;C according to the primer leaflet for 30 s. The third step included 35 extension cycles at 72\u0026deg;C for 1 min. The fourth step involved a final extension step at 72\u0026deg;C for 7 min, following which the samples were stored at 4\u0026deg;C until gel electrophoresis. On a 1% agarose gel, 10 \u0026micro;L of the PCR products were loaded with 20 mL of TBE/100 mL, 10 \u0026micro;L of Red Safe (nucleic acid staining solution 20.0000 \u0026times;), and 80 mL of deionized water. To assess the size of the PCR bands, a 100-bp DNA ladder was used. To isolate the PCR products, gel electrophoresis was performed on Bio-Rad at 100 V and 400 mA for 90 min in tris-based ethoxybenzyl (TBE) buffer (54mmol/l tris, 27. 5 g of boric acid, and 20 mmol/l EDTA). acid( 20 ml EDTA/1000).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer names, chromosomes with identified stay-green QTLs, primer sequences, annealing temperatures, and allele sizes of controls detected at 17 microsatellite loci in 256 Sudanese wild sorghum accessions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarkers names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChromosomes with stay-green QTLs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnnealing temperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAllele sizes in checks bp\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTabat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB35\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATATGGAAGGAAGAAGCCGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAACACAACATGCACGCATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTAATAGTCATGACCGAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTAA TAG ACG AGT GAA AGC CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXcup 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAACTGGATGCCACACCAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAGCTATACCAACACGGGCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXsb AGB 03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTGTGTGTAGCTTCTTGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACGTAGGAGTAGTTTCTAGGATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGCAGCAGCAGCAACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCGTGGTCTTTGTGGTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGTCACAGCACACTGCTTGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAATTTACCTGGCGCTCTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXcup 05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAAGGTTTGCAAGAACAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCAGCCCAACAAGTGCTATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCT GGC CAT GAC TTA TCA C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAA TGG CGT AGA CTC CCT TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSb AGA 01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGAACCATGATAAATGACTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATCCGTTTCACAAAAAAAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAA ATT ACA ATG CTA CCC CTA AAA GT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACT CTA CTC CTT CCG TCC ACA T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGTCTTCTACCGCGTCCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCATAATCCCACTCAACAATCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTTCCCCTCTTTCTTGTGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCTCGGCGTCGTCGTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp 445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCCAGTTGAATCCGCTACAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGAATTGCAATACATAAGCACACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXtxp6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATCGGATCCGTCAGATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCTAGGGAGGTTGCCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003extxp034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGTTCGTATCCTTCTCTACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCATATACCTCCTCGTCGCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003extxp031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGCGAGGCTGCCCTACTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTGGACGTACCTATTGGTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003extxp019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBI-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTTTCAATCGGTTCCAGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCTTCCACCTCCGTACTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe sizes of all PCR-amplified microsatellite regions were estimated using a Syngene ultraviolet documentation system with a 100-bp standard ladder. We measured genetic diversity parameters, such as the number of alleles (Ne), major allele frequencies (MAF), gene diversity (h), and polymorphism information content (PIC), using Power Marker version 3.25 software (Liu and Muse \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, the observed heterozygosity (Ho), expected heterozygosity (He), and Shannon\u0026rsquo;s Information Index (I) (Sherwin et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and the Hardy\u0026ndash;Weinberg equilibrium (HWE) was measured using GenAlex version 6.5 (Peakall and Smouse \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, we conducted a molecular variance analysis (AMOVA) (Meirmans \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) after grouping accessions based on cluster evaluation. The GenAlex software was used to measure pairwise population genetic distances, and gene flows. We used the neighbor-joining method and Jaccard\u0026rsquo;s genetic similarity coefficients based on the genetic distance matrix to construct a dendrogram. Using R statistical software, we measured the kinship matrix of Jaccardn distances for the 256 Sudanese wild sorghum accession genotypes based on SSR markers. R statistical software was used to calculate the kinship matrix of Jaccard distances [31] for the 256 Sudanese wild sorghum accession genotypes based on SSR markers.\u003c/p\u003e \u003cp\u003eThe population structure was calculated via Bayesian analysis using the STRUCTURE (version 2.3.4) program to estimate the number of hypothetical subpopulations (K) and the membership probability of each genotype to the identified subpopulations (Pritchard et al. 2000). The model-based Bayesian clustering approach used Markov chain Monte Carlo (MCMC) algorithms to test hypotheses from one to ten subpopulations. The burn-in and number of iterations of the MCMC algorithm were set to 9,999. This process was independently repeated 10 times using the admixture model. The log-likelihood of the observed data Pr (X|K) for each value of K was retrieved from the structure output (Pritchard JK et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The ad hoc quantity analysis was based on the second-order rate of change of the likelihood probability function presented by Evanno et al. (2005). Delta K produced the highest outcome within the Bayesian clustering approach, with a maximum K-value of 2, indicating that the population was grouped into two subpopulations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of SSR markers across sorghum accessions\u003c/h2\u003e \u003cp\u003eThe allelic sizes of the SSR markers in sorghum specific to the stay-green trait ranged from 110 bp for \u003cem\u003eSb AGA 01\u003c/em\u003e to 380 bp for \u003cem\u003eXtxp274\u003c/em\u003e for landrace B35, which is a universal stay-green donor that can handle drought. On the other hand, for the susceptible check Tabat, the allelic size values ranged from 100 bp for the marker (\u003cem\u003eSb AGA 01\u003c/em\u003e) to 380 bp for the marker (\u003cem\u003eXtxp034\u003c/em\u003e). Out of 41 SSR-specific stay green markers, 17 were polymorphic and generated 55 alleles, with an overall average of 3.3 per locus. Alleles per locus ranged from 3 to 5, with the highest number (5.0) observed for marker \u003cem\u003eXtxp_114\u003c/em\u003e. The study found that the major significant alleles (MAF) had a frequency range of (0.43) for the \u003cem\u003eXtxp031\u003c/em\u003e marker to (0.50) for the \u003cem\u003eXcup24\u003c/em\u003e, \u003cem\u003eXtxp286\u003c/em\u003e, \u003cem\u003eXsbAGB01\u003c/em\u003e, \u003cem\u003eXtxp274\u003c/em\u003e, \u003cem\u003eXtxp212\u003c/em\u003e, and \u003cem\u003eXtxp445\u003c/em\u003e markers, with a mean frequency of (0.48) per locus (Table\u0026nbsp;2). Regarding Shannon\u0026rsquo;s information index, the range was from 3.48 for the markers (\u003cem\u003eXcup05\u003c/em\u003e, \u003cem\u003eXtxp445\u003c/em\u003e, and \u003cem\u003eXtxp6\u003c/em\u003e) to 3.5 for the markers (\u003cem\u003eXtxp41\u003c/em\u003e, \u003cem\u003eXsAGB01\u003c/em\u003e, \u003cem\u003eXtxp212\u003c/em\u003e, \u003cem\u003eXtxp3.5\u003c/em\u003e, and \u003cem\u003eXtxp031\u003c/em\u003e), with an overall mean of 3.49. The genetic diversity among the different genetic locations varied from 0.58 (\u003cem\u003eXtxp_41\u003c/em\u003e, \u003cem\u003eXtxp274\u003c/em\u003e, and \u003cem\u003eXtxp212\u003c/em\u003e) to 0.64 (\u003cem\u003eXtxp_114\u003c/em\u003e and \u003cem\u003eXtxp031\u003c/em\u003e), with an average of 0.60 per location. The expected heterozygosity (He) of each locus differed slightly, ranging from 0.297 (\u003cem\u003eXtxp274\u003c/em\u003e) to 0. 491 (\u003cem\u003eXcup_24\u003c/em\u003e), with a mean of 0. 389. Similarly, the heterozygosity levels ranged from 0. 293 (\u003cem\u003eXcup_05\u003c/em\u003e) to 0. 491 (\u003cem\u003eXcup_24\u003c/em\u003e), resulting in an average of 0. 397 per locus. Markers \u003cem\u003eXsb_AGB_01\u003c/em\u003e and \u003cem\u003eXtxp_41\u003c/em\u003e exhibited the highest levels of informativeness, as indicated by an information index of 3.503 (Table\u0026nbsp;2). The PIC values ranged from 0.49 (\u003cem\u003eXtxp_088\u003c/em\u003e and \u003cem\u003eXtxp_41\u003c/em\u003e) to 0.57 (\u003cem\u003eXtxp_114\u003c/em\u003e and \u003cem\u003eXtxp031\u003c/em\u003e), with an average of 0.53. All 17 SSR markers showed highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) deviations from the Hardy\u0026ndash;Weinberg equilibrium (HWE). According to the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, markers \u003cem\u003eXtxp212\u003c/em\u003e and \u003cem\u003eXcup05\u003c/em\u003e were most useful.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenetic relationships between and within populations\u003c/h2\u003e \u003cp\u003ePopulation 1 had the highest number of individuals (117), followed by Population 2 (88), and Population 3 had the lowest number of individuals (52) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The number of alleles in all populations was 2.00 (data not shown). Population 3 had the highest effective allele frequency (1.69) and Shannon information index (0.59). In contrast, Population 1 had the lowest number of effective alleles (1.61) and the lowest Shannon information index (0.54). These results indicate private or localized common alleles in a single population. Population 2 had the highest Nei genetic diversity (0.127), and population 3 had the lowest (0.032). The percentage of polymorphic loci per population was 100% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGenetic differentiation, distance, and gene flow\u003c/h2\u003e \u003cp\u003ePairwise genetic differentiation between Sudanese wild sorghum populations for the stay-green trait ranged from 0.002 to 0.010 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Populations 3 and 1 exhibited the highest degrees of population differentiation (Fst\u0026thinsp;=\u0026thinsp;0.010), followed by populations 2 and 1 (Fst\u0026thinsp;=\u0026thinsp;0.007). Populations 3 and 2 show the lowest population differentiation levels (Fst\u0026thinsp;=\u0026thinsp;0.002). The gene flow (Nm) between each population and the other populations ranged from 0.058 to 1.018 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the highest gene flow (1.018) between populations 2 and 3, followed by populations 1 and 2 (0.994), and the lowest gene flow (0.58) between populations 1 and 2. AMOVA for the stay-green trait revealed that variability among and within populations accounted for 1% and 99% of the total genetic variation, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The overall fixation index value used to measure population differentiation was moderate (Fst\u0026thinsp;=\u0026thinsp;0.066).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;2. Genetic diversity index summary statistics of 17 SSR loci across 256 Sudanese wild Sorghum accessions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePHWE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_088\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXcup_24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXsb_AGB_03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_286\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXcup_05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXsb_AGB_01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp274\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_114\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_212\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp_445\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eXtxp6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003extxp034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003extxp031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003extxp019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAllelic patterns and diversity indices of Sudanese wild sorghum germplasm populations compared with 17 stay-green-specific SSR loci\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eNa\u0026nbsp;=\u0026nbsp;Observed number of alleles; Ne\u0026nbsp;=\u0026nbsp;Number of effective alleles; NPA\u0026nbsp;=\u0026nbsp;Number of Private Alleles (i.e., the number of alleles unique to a single population); NLCA\u0026nbsp;=\u0026nbsp;Number of Locally Common Alleles (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;25% and \u0026lt;\u0026thinsp;=\u0026thinsp;50%), (frequency 5%) found in 25% or fewer populations; I\u0026nbsp;=\u0026nbsp;Shannon\u0026rsquo;s information statistic; h\u0026nbsp;=\u0026nbsp;Nei\u0026rsquo;s genetic diversity; and PPL\u0026nbsp;=\u0026nbsp;the Percentage of Polymorphic Loci;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise Nei genetic distances measured using Fst (A) and gene flow (Nm) values (B) among three Sudanese wild sorghum populations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eProbability P(rand\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;data) based on 999 permutations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular variance (AMOVA) showing the partitioning of genetic variation within and among populations using 17 SSR markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEst. Var.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmong Pops\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmong Indiv\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1825.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithin Indiv\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1922.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3779.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCluster analysis\u003c/h2\u003e \u003cp\u003eThe 256 samples of Sudanese wild sorghum used for neighbor-joining cluster analysis were divided into two distinct genetic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Cluster I comprised 21 accessions, including the widely recognized drought-tolerant landrace B35. On the other hand, we further divided Cluster II into smaller clusters C1 and C2. The sub-cluster C1 consisted of 57 accessions, whereas the sub-cluster C2 consisted of 177 accessions, one of which was the drought-sensitive cultivar Tabat. This distinction underscores the genetic differences between drought-tolerant and drought-vulnerable accessions. According to the results of the neighbor-joining cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), the kinship map confirmed that the accessions were split into two separate groups. Consistency between methodologies reinforces the dependability of established genetic classifications. Principal component analysis (PCA) revealed genetic similarities between sorghum accessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The first two main component axes explain 21.05% of total genetic variation. The first and second axes contributed 73% and 9.32%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). PCA confirmed the identified genetic clusters, emphasizing gene diversity in Sudanese wild sorghum populations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean log-likelihood, standard deviations for log-likelihood, log-likelihood differences, and delta K for structural analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean LnP (K)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStdev LnP (K)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLn'(K)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e|Ln''(K)|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDelta K\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5077.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4811.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4638.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4476.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4365.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4232.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4153.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4152.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4220.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4155.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe population structure evaluation revealed that the mean log-likelihood values varied from the lowest of -5077.2 for K\u0026thinsp;=\u0026thinsp;1 to the highest for K\u0026thinsp;=\u0026thinsp;8 (-4152.0). However, increasing the value of K increased the mean likelihood until K\u0026thinsp;=\u0026thinsp;8. Subsequently, a slight reduction in the mean log-likelihood from K\u0026thinsp;=\u0026thinsp;9 was observed (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Higher K values exhibited a higher standard deviation of log-likelihood, suggesting higher instability in the clustering solution. We observed the highest log-likelihood difference (266.2) between K\u0026thinsp;=\u0026thinsp;1 and K\u0026thinsp;=\u0026thinsp;2 and the lowest difference (-68.7) between K\u0026thinsp;=\u0026thinsp;8 and 9. Eventually, the highest Delta K value (58.2) was recorded at K\u0026thinsp;=\u0026thinsp;2, revealing that two distinct subpopulations probably existed among the 256 Sudanese wild sorghum accessions tested based on the 17 stay-green-specific SSR markers. Clumpak\u0026rsquo;s results (bar plot) detected a genetic admixture based on this value; hence, there was clear genetic-based structuring of accessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, and c).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed genetic variation in Sudanese wild sorghum using microsatellite SSR markers specific to the stay-green trait. The informativeness of the markers was based on reactions to the drought-tolerant stay-green trait of landrace B35 and the drought-susceptible Sudanese sorghum cultivar Tabat (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study used 41 SSR markers, 17 of which were polymorphic. These 17 markers yielded 55 alleles with an average of 3.3 per marker (Table\u0026nbsp;2), revealing their usefulness in detecting genotypic variations among Sudanese wild sorghum accessions for drought tolerance, especially stay-green traits. The equal distribution of alleles across Sudanese wild sorghum accessions indicates the likelihood of successfully incorporating this untapped germplasm into climate-resilient sorghum cultivars, thereby minimizing food insecurity in drought-prone regions. These findings are consistent with those of Ochieng et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who reported the importance of wild sorghum as a potential stay-green source for drought-tolerant breeding efforts. These authors also underscored the potential value of gene bank collections of germplasm from sorghum wild relatives as novel sources for crop improvement and productivity enhancement, especially in places like Sudan, where sorghum is believed to have originated and was domesticated 8000 years ago. There is substantial potential for Sudanese wild sorghum germplasm, which has not yet been identified.\u003c/p\u003e \u003cp\u003eOur results identified two SSR markers, \u003cem\u003eXtxp212\u003c/em\u003e and \u003cem\u003eXcup05\u003c/em\u003e because their high informativeness makes them suitable candidates for marker-assisted selection backcrossing breeding programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, large-scale field trials and phenotyping-based research are required to confirm the efficacy of these markers under different stress conditions. Harnessing these markers to breeding programs may provide far-reaching outreach for developing climate-resilient sorghum cultivars that exploit the potential of wild sorghum in Sudan and beyond.\u003c/p\u003e \u003cp\u003eThe results indicate that the genetic distances of the three sorghum populations differed entirely (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For instance, population 1 had the highest number of individuals among the three populations (117) but ranked lowest in the effective number of alleles per population or Shannon\u0026rsquo;s information index. Surprisingly, population 3, which contained the smallest number (58) of wild sorghum individuals, had the highest proportion and adequate number of alleles, along with Shannon\u0026rsquo;s index values, indicating that genetic variation was higher. This is important for breeding programs that improve drought resilience. This study emphasized the significance of gene bank collections in safeguarding the forgotten treasure trove of Sudanese wild sorghum germplasm and ensuring its long-term conservation. According to Sagnard et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), collecting and preserving these wild species should be conducted case-by-case, considering social changes and agricultural practices.\u003c/p\u003e \u003cp\u003eThis study explored gene flow among different populations of Sudanese wild sorghum accessions to assess their genetic diversity and potential for conservation and breeding. Our results showed that gene flow, measured as Nm (a unit indicating the number of migrants per generation), ranged from 0.58 to 1.018. We observed the highest gene flow between Populations 2 and 3. Conversely, the lowest gene flow was observed between populations 1 and 2 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings are consistent with those of Sagnard et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who encountered potential gene flows between guinea margaritiferum and wild weedy sorghum. Therefore, further investigation is needed to pinpoint the extent and direction of gene flow, the contribution of domesticated sorghum to gene flow, and the rates of introgression compared with wild-type sorghum. Sagnard et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) indicated the potential use of maternally inherited molecular markers (chloroplastic or mitochondrial) to estimate the relative contributions of gene flow evolutionary processes. Similarly, Mutegi et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) stated that gene flow between domesticated sorghum and their wild relatives may be governed by overlaps in their natural habitats and ecological distribution. Accordingly, there is a need for the integration of many disciplines, including socialists, GIS scientists, plant breeders, botanists, and ecologists, to attain a comprehensive understanding of gene flow processes and factors governing the distribution of genetic diversity in Sudanese wild, weedy, and cultivated pools.\u003c/p\u003e \u003cp\u003eThe neighbor-joining tree also helped clarify the genetic relationship between the accessions by grouping B35, a drought-resistant Sudanese cultivar, Tabat. Grouping Sudanese wild sorghum accessions with B35, a universally drought-tolerant landrace, is essential. This implies that there are genetic factors that are common to these accessions that are responsible for drought tolerance. As such, they can be used to breed drought-tolerant sorghum varieties. However, further investigation is required to identify stay-green attributes and genes involved in drought resistance. Comparative transcriptomics, metabolomics, and proteomics should be performed for genetic regulation networks, along with the stay-green phenotype (Altaf et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liaqat et al.. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo distinct subpopulations of wild Sudanese sorghum germplasm were identified. This implies that genetic makeup can be used to develop breeding strategies. These subpopulations can parent stocks with high genetic variation and traits such as drought tolerance. We can help breeders grow sorghum cultivars that best suit the climatic conditions in Sudan and, at the same time, contribute to sustainable agriculture by integrating these observations into breeding programs. This study underscores the need to integrate genetic structure into breeding strategies to maximize the genetic diversity of wild sorghum for the next generation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis work showed that Sudanese wild sorghum accessions could be used to source stay-green genes for the design of climate-smart sorghum cultivars. The genetic diversity, useful markers, and subpopulations defined in this study will be useful for future work and breeding. This study is crucial in light of the current climate change challenges, especially in arid and semi-arid regions such as Sudan; wild sorghum accessions should be conserved and used in food production systems. Additional work should be directed at the functional characterization of stay-green alleles and their use in breeding high-yielding drought-tolerant sorghum in Sudan and other similar environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAlaa Ahmed: Molecular marker analysis, data analysis, writing, and review; Aisha Abdalhady Ahmed Abdalla: Molecular marker analysis, data analysis, writing, and review; Mohammed Elsafy: Conceptualization, investigation, writing, review, editing, and validation; Alaa Ezzeldin: Molecular marker analysis, data analysis, writing, and review; Mahbubjon Rahmatov: Investigation, writing, review, editing, validation, and funding acquisition; Tilal Abdelhalim: Conceptualization, investigation, writing, review, editing, validation, and funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eWe acknowledge financial support from the Swedish Research Council (Vetenskapsr\u0026aring;det) and the Agricultural Research Corporation Sudan for funding and supporting this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbreha KB, Enyew M, Carlsson AS, et al. 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Journal of Experimental Botany 65:3889\u0026ndash;3900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/eru037\u003c/span\u003e\u003cspan address=\"10.1093/jxb/eru037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biodiversity, Climate-resilient crop, Molecular marker, Sorghum bicolor, Stay-green trait","lastPublishedDoi":"10.21203/rs.3.rs-5014252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5014252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSudan is the birthplace of sorghum, and vast genetic diversity exists among its wild relatives. To assess the genetic potential of Sudan wild sorghum accessions, we used 41 stay-green-specific microsatellite markers to analyze the genetic variability and population structure of 256 accessions. Overall, 17 SSR markers were polymorphic, with 55 alleles on average 3.3 per locus. The polymorphic information content (PIC) ranged from 0.49 to 0.57, with an overall mean of 0.53, indicating the potential of these markers for capturing the genetic construction of wild sorghum. Linkage disequilibrium analysis identified the two most informative markers, \u003cem\u003eXcup05\u003c/em\u003e and \u003cem\u003eXtxp212\u003c/em\u003e. Accordingly, the Nei gene diversity of the populations varied from 0.032 to 0.127, with an overall mean of 0.083. Molecular variance analysis (AMOVA) demonstrated that 99% and 1% of the genetic variations were within and among populations (Fst\u0026thinsp;=\u0026thinsp;0.066; P 0.001), respectively. However, gene flow (Nm) values varied from 0.058 in populations 1 and 2 to 1.018 in populations 2 and 3. Neighbor-joining trees identified from 21 Sudanese wild sorghum accessions clustered closely to the universally drought-tolerant landrace B35. Structural analysis generated the highest Delta K value (58.2) at K\u0026thinsp;=\u0026thinsp;2, revealing two distinct subpopulations. While this work provides valuable information about the potential of sorghum wild relatives from Sudan as sources for stay-green drought tolerance, further research should be directed toward identifying the exact mechanisms and genes underlying this stay-green trait using advanced molecular omics techniques. In conclusion, this study highlights the potential role of Sudanese sorghum accessions as reservoirs of ready-to-use stay-green genes for the design of climate-resilient sorghum cultivars in drought-prone areas of Sudan and beyond.\u003c/p\u003e","manuscriptTitle":"Harnessing genetic diversity in Sudanese sorghum wild relatives for stay-green drought tolerance via microsatellite SSR marker assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-03 04:21:28","doi":"10.21203/rs.3.rs-5014252/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-18T10:34:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-18T09:27:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-17T18:54:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28559205729674101377030952502098041069","date":"2024-10-13T18:01:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13305573344676893745530719149997808150","date":"2024-10-10T05:21:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4947358878592592098370611556671953439","date":"2024-10-08T13:58:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-02T08:44:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-02T07:27:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-02T07:27:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2024-09-01T19:48:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1a734306-9f15-4841-8647-6191b1f2c9d3","owner":[],"postedDate":"October 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T15:59:07+00:00","versionOfRecord":{"articleIdentity":"rs-5014252","link":"https://doi.org/10.1007/s10722-024-02236-4","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2024-11-06 15:57:04","publishedOnDateReadable":"November 6th, 2024"},"versionCreatedAt":"2024-10-03 04:21:28","video":"","vorDoi":"10.1007/s10722-024-02236-4","vorDoiUrl":"https://doi.org/10.1007/s10722-024-02236-4","workflowStages":[]},"version":"v1","identity":"rs-5014252","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5014252","identity":"rs-5014252","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}