Simple Sequence Repeats (SSR) marker-based analysis on the genetic variation and population structure of local and exotic sorghum germplasm collection conserved ex-situ in Sri Lanka

Simple Sequence Repeats (SSR) marker-based analysis on the genetic variation and population structure of local and exotic sorghum germplasm collection conserved ex-situ in Sri Lanka | 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 Simple Sequence Repeats (SSR) marker-based analysis on the genetic variation and population structure of local and exotic sorghum germplasm collection conserved ex-situ in Sri Lanka D. V. S. Kaluthanthri, S. A.C.N. Perera, P. N. Dasanayaka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4332824/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 8 You are reading this latest preprint version Abstract Sorghum ( Sorghum bicolor (L.) Moench) is one of the most important cereal crops occupying the fifth position based on the cultivated extent among the cereal crops in the world. Characterization of genetic resources is a pre-requisite for utilization of conserved genetic resources in breeding programmes and cultivation. The present study was carried out to reveal the genetic variation and population structure of local and exotic sorghum germplasm collection conserved in ex-situ seed gene bank at the Plant Genetic Resources Centre, Sri Lanka. Total genomic DNA was extracted from 60 germplasm accessions using CTAB miniprep DNA extraction protocol. A two-step PCR amplification was performed at 16 Simple Sequence Repeat (SSR) loci. Four differentially labeled PCR products were multiplexed and size-fractioned using capillary electrophoresis. Data analyses were performed using GeneMapper 4.0, OSIRIS, PowerMarker 3.25, Structure 2.2 and STRUCTURE HARVESTER. The 16 SSR loci recorded polymorphism and the dendrogram revealed four distinct clusters. The optimum number of subpopulations was three in addition to two admixture subpopulations. The revealed population structure did not depict the geographical origin of the germplasm accessions. The present study confirmed that the majority of local sorghum germplasm accessions tested were genetically distinct. Varying degrees of outcrossing selfing in subsequent generations may have led to the creation of novel sorghum genotypes at global level. Ex-situ conservation Genetic variation Molecular characterization Population structure Sorghum bicolor Figures Figure 1 Figure 2 Figure 3 Introduction Sorghum bicolor is an annual, monoecious cereal belonging to family Poaceae . It is a predominantly self-pollinated diploid (2n = 20), displaying varying degrees of natural cross-pollination. (Doggett, 1970 ). Eastern Africa, centering Ethiopia is considered to be the origin of sorghum with several domestication events leading to current cultivars. Sorghum is widely cultivated globally for food and feed and being a nutritious staple for the rural poor in certain tropical areas, it is reported to be the fifth most important cereal crop in the world (Statista, 2023 ). The C 4 photosynthetic pathway and the potential for drought and heat tolerance make sorghum an ideal crop for marginal lands and arid areas in the tropics playing a crucial role as a resilient crop in facing the challenges of climate change. Diverse genetic forms such as wild and related species, breeding stocks, mutant lines etc. are vital sources of desirable alleles, providing material for crop breeding. However, genetically improved novel cultivars used in modern intensive agriculture are genetically highly uniform and consist of a narrow genetic base which make them vulnerable to natural catastrophes, diseases and pests (Liu et al., 2003 ). Understanding the distribution of genetic diversity among gene pools is crucial in breeding for desirable traits, efficient management of germplasm collections and trans-boundary germplasm exchanges. Accordingly, for ever-changing breeding goals, different genes/alleles need to be reserved in cultivated and cultivable crop species and such genetic resources should be extensively characterized for effective utilization. Unravelling genetic diversity at molecular level facilitates allelic level differentiation and evaluation of genetic stocks. Simple sequence repeats (SSR) are quite popular for the study of genetic diversity of crops due to numerous advantages that they offer (Ghebru et al., 2002 ). Population structure analysis has been employed to categorize sorghum accessions into distinct subpopulations or groups based on genetic relatedness in various studies (Mamo et al., 2023 : Yahaya et al., 2023 ). This helps in understanding the underlying genetic structure within the species. Principal Component Analysis (PCA) and model-based clustering algorithms such as STRUCTURE are commonly used to infer population structure. Diverse domestication events, exchange of seeds via formal and informal channels for extensive worldwide cultivation and the considerable levels of cross-pollination followed by fixing of alleles via self-pollination has led to wide variability among sorghum landraces world over (Mutegi et al., 2010 ). In Sri Lanka, the ex-situ gene bank of sorghum is rich with a considerable number of local accessions with different vernacular names and accessions of exotic origin. Yet, the effective use of material from this collection is hindered due to lack of information on their genetic diversity. Accordingly, the aim of the current study was to identify the genetic diversity and to elucidate the population structure of ex-situ conserved local and exotic sorghum germplasm collection using SSR markers and to make recommendations for conservation and breeding. Materials and methods Germplasm materials and experimental design The study was based on 60 sorghum germplasm accessions including both local (Table 1) and exotic (Table 2) germplasm that are conserved at the seed gene bank of Plant Genetic Resources Center (PGRC), Gannoruwa, Sri Lanka. Seeds of germplasm accessions were sown in small plastic pots filled with soil and the plants were grown in a plant house under controlled environment. DNA extraction and Capillary electrophoresis For each of the germplasm accessions, the total genomic DNA was extracted using leaf materials harvested from twenty-day-old plants. Fresh, immature leaves were surface sterilized by washing with sterilized distilled water and 70% ethanol, air dried and cut into small pieces. An approximate weight of 5 g was obtained and put into a 1.5 mL microcentrifuge tube and stored at -80 0C for 24 hours. The frozen pieces of immature sorghum leaves were crushed and total genomic DNA was extracted from ground leaf material using CTAB miniprep DNA extraction protocol (Taylor and Powell, 1982 ) followed by quantification and dilution to a final concentration of 25 ng µL − 1 . Extracted DNA was amplified at 16 SSR loci (Table 3) via a two-step PCR protocol using a modified M13 tagged (5’TGTAAAACGACGGCCAGT3’) forward primer and a pig tailed (5’GTTTCTT3’) reverse primer with M13 oligo labeled with one of four fluorescent dyes; 6-HEX, FAM, TAMN or PET in a T100™ Thermal cycler (Bio-Rad laboratories Inc.). Four differentially labeled PCR products were multiplexed and size-fractioned via capillary electrophoresis on an ABI3500 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). Data analysis Electrophoretic data derived from capillary electrophoresis were imported to GeneMapper software version 4.0 for allele characterization based on molecular weights of SSR products. OSIRIS (Open Source, Independent Review and Interpretation System) software version 2.13 was applied for further validation of size peak patterns, using the ABI-LIZ-600-80-TO-400 internal lane standard for allele calling. The peaks from polymorphic SSR markers on capillary electrophoresis were scored as present (1) or absent (0) or missing data (-9) for each germplasm accession by manual inspection. Data were tabulated in a matrix using Excel version 2010 followed by analyzing using PowerMarker V3.25 software (Liu and Muse, 2005 ). Determination of total number of alleles and polymorphism information were estimated from the genotyping data, considering that each SSR primer pair represents a unique locus. Nei’s ( 1972 ) standard genetic distances between the germplasm accessions were obtained. Cluster analysis was performed based on the unweighted pair group mean algorithm (UPGMA) to draw the dendrogram. Genotypic data were analyzed using Structure 2.2 (Pritchard et al., 2000 ), to determine structured groups or subpopulations (K clusters) in the collection. Analysis was performed using settings for admixture ancestry model and correlated allele frequencies of the programme. The K values were set at 1 to 10, with a burn in value of 10,000 and 30,000 of Markov Chain Monte Carlo replications (MCMC reps) after burning followed by 3 iterations. Optimal K clusters were identified using the online based software STRUCTURE HARVESTER (Earl and VonHoldt, 2012 ). Results The studied sorghum population recorded polymorphic loci at all 16 SSR marker loci. Frequency of major SSR alleles at different SSR loci recorded a mean of 0.257 ranging from 0.17 to 0.33. A total of 170 alleles were recorded across the 16 loci with number of genotypes ranging from 4 to 29 per locus with a mean of 16.8 (Table 4). The mean number of alleles per locus was recorded as 10.6. The results provide ample evidence for high allelic richness in this specific germplasm collection in Sri Lanka. Availability, which is an indication of the amplification efficiency of the marker was more than 50% at all marker loci with an average of 80.63%. Three markers (Xtxp32, Xtxp266 and Xtxp265) showed a high amplification efficiency exceeding 90% of availability. Gene diversity, which is a measure of genetic diversity, varied within the range of 0.694 to 0.887 recording a mean of 0.821. More than one allele was amplified in some accessions at certain loci indicating the residual heterogeneity within these accessions. The observed heterozygosity at each locus over all accessions ranged from zero at marker locus Xtxp295 to 0.672 at Xtxp265 with an average of 0.399 per locus. All the primer pairs used in the study were highly informative with PIC values ranging from 0.632 to 0.877 at marker loci Xtxp295 and Xtxp265 respectively, recording an average of 0.797. Frequency based standard genetic distances and dendrogram The expected value of the standard genetic distance developed by Nei in 1972, is proportional to evolutionary time based on both effects of mutation and genetic drift. The genetic distance of 1.0 was observed between a large numbers of sorghum accessions indicating the high genetic divergence between them. The lowest genetic distance of 0.0 was observed between the accessions UNKNOWN 8917 – ETHIOPIA 6014 and UNKNOWN 8915 – UNKNOWN 8943 indicating probable genetic duplication. The dendrogram of the 60 accessions (Fig. 1 ) constructed based on Nei’s ( 1972 ) standard genetic distance matrix using unweighted pair group mean algorithm (UPGMA) revealed four distinct clusters, I-IV. Cluster I was the most homogenous including 15 local sorghum accessions. The remaining clusters comprised of local, origin unknown and exotic germplasm accessions. Two of local and thirteen of exotic accessions were identified in cluster II whereas four local accessions and eleven accessions with unknown origin grouped in cluster III. The remaining fifteen-germplasm accessions including local, exotic (European) and two accessions of unknown origin clustered in cluster IV. Summary of Nei’s standard genetic distances with respect to each cluster is given in Table 5. The lowest means and standard deviations (SD) of the genetic distances were observed in cluster I whereas they were the highest in cluster II. Considerably low mean standard genetic distance and SD values of cluster I revealed the homogenous nature and comparatively low genetic divergence of the 15 local germplasm accessions that grouped in cluster I. On the other hand, cluster II consisted of genetically more divergent germplasm accessions by recording considerably high mean standard genetic distance and SD values. Meanwhile, the difference of mean standard genetic distance was the highest between clusters I and II. Determination of population structure of sorghum accessions The admixture ancestry model-based approach with correlated allele frequencies was performed using STRUCTURE software to examine the relatedness among 60 sorghum germplasm accessions, using genotypic data at 16 SSR loci. The K values were set at 1 to 10, with a burn in value of 10,000 and 30,000 of Markov Chain Monte Carlo replications (MCMC reps) after burning followed by three iterations. Optimal K clusters were identified following the method described by Evanno et al. ( 2005 ) based on the improvements in the estimated natural log of likelihood [Ln P(D)] in three independent runs (Casa et al., 2008 ) through the software STRUCTURE HARVESTER (Earl and VonHoldt, 2012 ). The estimated LnP(D) values and the highest value of the ad hoc quantity showed that the optimum number of subpopulation K which best explained the population structure was 3. In order to better understand the geographical differentiation based on the defined geographical regions, K = 4 which had the second highest ad hoc quantity ∆k was also considered. Graphical representations of K = 3 and K = 4 are presented in Figs. 2 and 3 respectively. Germplasm accessions with probabilities greater than 0.95 and less than 0.95 were considered as pure lines and admixtures respectively. Discussion The mean number of alleles per locus recorded in this study was greater than the values recorded by Ali et al. ( 2008 ) and Schloss et al. ( 2002 ) on 72 and 25 sorghum accessions which recorded 3.22 and 3.4 mean number of alleles respectively. Comparatively low mean number of alleles per locus were also reported by Agrama and Tuinstra ( 2003 ), Menz et al. ( 2004 ), Egbadzor ( 2007 ) and Pei et al. ( 2010 ). However, Burow et al. ( 2012 ) reported a considerably high number of alleles per locus in their study on Chinese sorghum landraces. Sorghum genetic diversity values were reported to be 0.6 across 29 SSR loci (Schloss et al. 2002 ) and ranged from 0.44 to 0.79 at five SSR loci in Somalian landraces (Manzelli et al. 2005 ). Gene diversity values for sorghum were recorded as 0.29 (Dje et al., 2000 ) and 0.32 (Barnaud et al., 2007 ) in Morocco and 0.65, 0.61, 0.66 and 0.66 respectively in Eritrea (Ghebru et al., 2002 ), Niger (Deu et al., 2008 ), Kenya (Ngugi and Onyango, 2012 ) and Ethiopia (Adugna, 2014 ) respectively. All the above studies evaluated the genetic diversity of sorghum in Africa which is the center of origin for sorghum and despite being in South Asia, the Sri Lankan collection is noted to be harboring a higher diversity. The observed heterozygosity was comparatively higher to what were observed by Dje et al. ( 2000 ), Barnaud et al. ( 2007 ) and Deu et al. ( 2008 ) recording 0.134, 0.11 and 0.042 respectively in their studies. In contrast to the mean values for heterozygosity and gene diversity recorded in this study, Ng’uni et al. ( 2011 ) and Motlhaodi et al. ( 2014 ) reported considerably lower values in their studies. However, the observed heterozygosity in the current study was not significantly high and it indicated that the majority of genotypes carried homozygous loci which led to a high level of genetic stability. High levels of allelic variability but low levels of heterozygosity at SSR loci were reported on Eritrean sorghum landraces (Ghebru et al., ( 2002 ). The predominantly inbreeding nature of sorghum may be the cause for the low level of heterozygosity observed, yet with the gene pool as a whole maintaining a high level of allelic variation. Besides, the high gene diversity (expected heterozygosity) values compared to the observed heterozygosity values across the 16 loci indicated low cross-pollination rates among the accessions as expected. In congruence with this study, most of the genetic diversity studies in sorghum using SSRs (e.g., Ghebru et al., 2002 ; Barro-Kondombo et al., 2010 ; Deu et al., 2010 ; Ngugi and Onyango, 2012 ) reported high gene diversity (expected heterozygosity) values compared to the observed heterozygosity. The discriminatory power of SSR markers can be assessed with polymorphic information content (PIC) as calculated by the number of alleles detected and their relative frequencies (Smith et al., 2000 ). In the present study, the tested markers are highly recommended for allelic studies in sorghum. Burrow et al. (2012), Sapey et al. ( 2012 ) and Madhusudhana et al. ( 2012 ) reported less average values for both number of alleles per locus and PIC compared to the results of the current study. A total of 23 unique alleles which may serve as diagnostic tools for particular regions of the genome of respective genotypes, were observed in certain accessions at eleven SSR loci; Xtxp266, Xtxp258, Xtxp141, Xtxp210, Xtxp230, Xtxp265, Xtxp21, Xtxp12, Xtxp297, Xtxp46 and Xtxp302. The lowest genetic distance of 0.0 was observed between two pairs of germplasm accessions indicating probable genetic duplication. Such duplicates might be an indication of germplasm exchange within and between regions. It is important to note that, clusters I and IV depicted the influence of geographic origin on genetic diversity by solely comprising of local, Italian, and French accessions respectively. Exchange of seeds may be the reason for the genetic proximity and clustering of such accessions together. Seed exchange has been reported in the baseline survey conducted in eastern Kenya on sorghum seed systems, which revealed a tradition among farmers in selection and exchange of seeds (Muui et al., 2013 ). Furthermore, Ochieng et al. ( 2011 ) studied sorghum production systems in Kenya and reported the important role of farmers in seed selection, exchange and movement. Research conducted by, Ayana et al. ( 2000 ), Ghebru et al. ( 2002 ), Nkongolo and Nsapato ( 2003 ) and Deu et al. ( 2008 ), revealed similarities in genotypes as a result of region proximity in Africa for cultivated sorghum and its wild relatives. Mutegi et al. ( 2010 ) reported the clustering of sorghum in Kenya into a distinct and unique genetic group. Similarly, the current study revealed the inclusion of a majority of Sri Lankan sorghum accessions in a unique cluster. This is attributed to the fact that Sri Lanka is an island, which is relatively geographically remote from other sorghum growing regions. The majority of genetic material from local sorghum accessions tested in this study appear to be genetically distinct. Clustering of local and exotic accessions together in cluster IV of dendrogram revealed the existing genetic similarity irrespective of the geographical origin. The existence of accessions with wider genetic distances (e.g., MONARAGALA 9002 and MONARAGALA 2971 in clusters III and IV respectively) within the same locality indicates the introduction of seeds of unique landraces from distant localities. Furthermore, some of local accessions collected from Kurunegala district in Sri Lanka with vernacular name Edal Eringu , grouped into different two clusters I and III. Generally, farmers save seeds from previous harvest and in cases of insufficiency they tend to obtain seeds from neighboring farmers or markets. As a consequence, there would be a lot of variation derived from generations of selection even within the varieties having the same vernacular name. This also indicates the high stepwise mutation rates of SSR (Dje et al., 1999 ). Despite being a predominantly inbreeding species, outcrossing rates as high as 0.10% − 0.15% (Doggett, 1988 ) and spontaneous hybridization are reported in sorghum. This might possibly explain the molecular level differences that exist between other studied sorghum germplasm accessions as well. Under K = 3 in population structure analysis, SP1comprised of 28 germplasm accessions including six local, 21 of unknown origin and one exotic (ETHIOPIA 6014) accession. Purelines of 15 local accessions assigned into SP2 while purelines of five local and eight exotic accessions of European origin (Italian and French) assigned into SP3 along with two accessions of unknown origin. The results thus provide further evidence for the genetic similarity of certain accessions irrespective of geographical origin spanning across three continents. The formation of SP2 solely with local accessions indicates their genetic uniqueness which makes them even more exclusive. The admixtures of all three subpopulations were observed in two germplasm accessions with unknown origins (UNKNOWN 8918 and UNKNOWN 8942). This observation could be due to the heterogonous nature of the two accessions, sorghum being a cross-pollinating crop. In parallel to the present study, Nemera et al. ( 2022 ) revealed two sub-populations irrespective to the geographical origin of 80 Ethiopian sorghum accessions studied at 15 SSR loci. Similarly, population structure of 100 genotypes of Ethiopian sorghum profiled at 15 SSR loci, revealed three sub-populations with no clear geographical origin-based structure Mamo et al. ( 2023 ). Furthermore, a total of evenly distributed 108,107 high-quality single-nucleotide polymorphism (SNPs) markers differentiated six sub-populations within 304 Ethiopian sorghum accessions (Wondimu et al. ( 2021 ), Enyew et al. ( 2022 ) identified two sub-populations within 24 Ethiopian sorghum accessions using 5,000 SNPs. Interestingly, Chakrabarty et al. ( 2022 ) recently inferred five sub-populations within 3333 Ugandan sorghum accessions using 12,742 SNP markers. However, Menamo et al. ( 2021 ) reported that agro-ecology is more important than the administrative region in defining the genetic variation in sorghum based on redundancy analysis. Results revealed the inclusion of certain morphologically different local accessions with different vernacular names such as ‘ Waguru ’ (MONARAGALA 106), ‘ Edal Iringu ’ (MATALE 285) and ‘ Karal Iringu ’ (HAMABANTOTA 971) in the same subpopulation (SP2). Similarly, SP3 contained morphologically different exotic germplasm accessions collected from Italy (‘Roce’ [ITALY 6006]; ‘ Vespa ’ [ITALY 6008]; ‘Wonder Dwarf’ [ITALY 6013]) and from France (‘ Arval ’ [FRANCE 6004]; ‘ Argence ’ [FRANCE 6005]). The occurrence of morphologically distinct accessions having different vernacular names in the same sub-population could be due to a multitude of reasons; narrow genetic base despite the morphological differences, farmers’ directional selection for different morphological traits and non-detectable nature of observed morphological differences with neutral genetic markers being the most probable reasons. This observation is consistent with the observations made by Sagnard et al. ( 2011 ) and Labeyrie et al. ( 2014 ). At K = 4 of population structure analysis, SP1 and SP3 remained unchanged from that of K = 3 confirming their genetic sub-populations. At K = 4, SP2 contained 15 accessions including only two accessions (RATNAPURA 8638 and KEGALLA 8867) with local origin while the rest had exotic and unknown origins. SP4 included purelines of two local germplasm accessions (MONARAGALA 9002 and KEGALLA 9012) and nine accessions of unknown origin. Population structure of common self-pollinated field crops such as wheat (Vanzetti et al., 2013 ) and barley (Naeem et al., 2011 ) have been shown to be strongly influenced by geographic adaptation. Previous studies have reported sorghum population structure based on botanical races and geography (Deu et al., 2010 ; Maina et al., 2018 ). It was not possible to relate the observed genetic structure with racial category because the germplasm accessions used in the current study have not been characterized for racial groups. Of the different sorghum growing agroecological regions in Sri Lanka, the country’s ‘wet zone’, the western face of the mountain range, and the southwest windy slopes receive an average of 2500 mm of rainfall each year whereas the ‘dry zone’ receives a rainfall between 1200 to 1900 mm, mainly through the northeast monsoon (Alahacoon and Edirisinghe, 2021 ). This spatial analysis separated SP1 which consisted of purelines of local germplasm accessions inhabiting contrasting environments, at least in terms of rainfall. In contrast, two local germplasm accessions (MATALE 310 and KURUNEGALA 380) and two accessions of unknown origin (UNKNOWN 8953 and UNKNOWN 8956) were observed with admixtures of all four subpopulations suggesting that the population structure may also be affected by other factors such as human activities including seed exchange and food preferences. Deu et al. ( 2010 ) reported similar observation on spatio-temporal dynamics of genetic diversity in Sorghum bicolor in Niger. Sorghum is a globally widely cultivated crop which has been distributed through transboundary seed exchanges across continents and within different areas within countries. In addition, the crop is cultivated in diverse agro-climatological areas and the cultivars may have genetic adaptations specific to diverse localities. Sorghum displays varying degrees of outcrossing leading to the creation of novel genotypes and getting them genetically fixed upon selfing in subsequent generations. Accordingly, the wide scale cultivation across continents coupled with the creation of novel genotypes have led to a huge genetic variation of the global sorghum germplasm. Sorghum being an ideal crop candidate in marginal areas especially in facing the adverse effects of global climate change the vast diversity unveiled in the current study as well as in other parts of the world should be appropriately utilized in breeding programs. Conclusions The current study attempted to elucidate the genetic diversity and population structure of a collection of 60, Sri Lankan ex-situ conserved sorghum germplasm accessions comprising of Sri Lankan, South Asian, African and European collections. The collection was observed to be highly polymorphic at 16 SSR loci that were tested giving evidence for high allelic richness and the availability of three to four genetic subpopulations within the collection. The revealed genetic diversity and population structure did not exactly depict the geographical origin of the germplasm accessions. Furthermore, the majority of local sorghum germplasm accessions tested in this study were genetically distinct. The study gave evidence for the importance of systematic studies of sorghum genetic resources for better decision making for sustainable cultivation, further collection and conservation and in utilization of conserved material in genetic improvement of the sorghum crop as a candidate for marginal areas and as a climate resilient crop. The findings will be of high importance with regard to decision making on sustainability of cultivation, further collection and conservation of genetic resources and genetic improvement of sorghum at a global level. The findings of the study reiterate the importance of systematic evaluation of the genetic diversity and genetic structures of populations for better decision making in selection for cultivation and more importantly in genetic improvement programmes. Declarations Acknowledgments: Authors gratefully acknowledge Plant Genetic Resources Center, Gannoruwa, Sri Lanka for providing seeds and the Institute of Biochemistry, Molecular Biology and Biotechnology (IBMBB), Sri Lanka for providing the laboratory facility. Author contribution D. V. S. Kaluthanthri contributed to the Conceptualization, Data curation, Formal analysis, Software, Writing – original draft. S. A. C. N. Perera contributed to the Conceptualization, Methodology, Supervision, Writing – review and editing. P. N. Dasanayaka contributed to the Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – review and editing. Financial support This research was supported by University of Sri Jayewardenepura, Sri Lanka. (Grant No. ASP/01/RE/SCI/2017/07, ASP/01/RE/SCI/2021/08). University of Sri Jayewardenepura, Sri Lanka had no role in the design, analysis or writing of this article. Competing interests : The authors declare there are no competing interests. Ethical standards : Not applicable References Adugna, A. (2014). Analysis of in-situ diversity and population structure in Ethiopian cultivated Sorghum bicolor (L.) landraces using phenotypic traits and SSR markers. Springerplus 3 , 212. Agrama, H. A. and Tuinstra, M. R . (2003). Phylogenetic diversity and relationships among sorghum accessions using SSRs and RAPDs. African Journal of Biotechnology , 2 (10), 334-340. Alahacoon, N. and Edirisinghe, M . (2021). 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Baseline survey on factors affecting sorghum production and use in eastern Kenya. African Journal of Food, Agriculture, Nutrition and Development , 13 (1), 7339 -7342. Naeem, M., Khan, M.M.A., Moinuddin, Idrees, M. and Aftab, T. (2011). Triacontanol-mediated regulation of growth and other physiological attributes, active constituents and yield of Mentha arvensis L. Plant Growth Regulation , 65 (1), 195-206. Nei, M. (1972). Genetic Distance between Populations. American Naturalist , 106, 283-292. Nemera, B., Kebede, M., Enyew, M. and Tileye Feyissa (2022). Genetic diversity and population structure of sorghum [ Sorghum bicolor (L.) Moench] in Ethiopia as revealed by microsatellite markers. Acta Agriculturae Scandinavica, Section B — Soil and Plant Science . 72 (1), 873–884. Ng’uni, D., Geleta, M. and Bryngelsson, T. (2011). Genetic diversity in sorghum ( Sorghum bicolor (L.) Moench) accessions of Zambia as revealed by simple sequence repeats (SSR). Hereditas , 148 (2), 52-62. Ngugi, K. and Onyango, C. M. (2012). Analysis of the molecular diversity of Kenyan Sorghum Germplasm using microsatellites. Journal of Crop Science and Biotechnology, 15 (3),189-194. Nkongolo, K. K. and Nsapato, L. (2003). Genetic diversity in Sorghum bicolor (L.) Moench accessions from different ecogeographical regions in Malawi assessed with RAPDs. Genetic Resource Crop Evolution , 50 , 149-156. Ochieng, L. A., Mathenge, P. W. and Muasya, R. M. (2011). A survey of on farm seed production practices of sorghum ( Sorghum bicolor L. Moench) in Bomet district of Kenya, African Journal of Food, Agriculture, Nutrition and Development , 11 (5), 5232-5253. Pei, Z., Gao, J., Chen, Q., Wei, J., Li, Z., Luo, F., Shi, L., Ding, B. and Sun, S. (2010). Genetic diversity of elite sweet sorghum genotypes assessed by SSR markers. Biologia planetarium, 54 (4), 653-658. Pritchard, J. K., Stephens, M. and Donnelly, P. (2000). Inference of Population Structure Using Multi locus Genotype Data . Genetics , 155 (2), 945-959. Sagnard, F., Deu, M., Dembele, D., Leblois, R., Toure, L., Diakite, M., Calatayud, C., Vaksmann, M., Bouchet, S., Malle, Y., Togola, S. and Traore, P. C. (2011). Genetic diversity, structure, gene flow and evolutionary relationships within the Sorghum bicolor wild-weedy-crop complex in a western African region. Theoretical Applied Genetics, 123 (7), 1231-1246. Sapey, E., Offei, S. K., Danquah, E. Y and Asante, I. K (2012). Assessment of genetic diversity among 60 sorghum accessions in Ghana using microsatellites. Elixir Bio Technology , 44 , 7171-7177. Schloss, S. J., Mitchell, S. E., White, G. M., Kukatla, R., Bowers, J. E., Paterson, A. H. and Kresovich, S. (2002). Characterization of RFLP probe sequences for gene discovery and SSR development in Sorghum bicolor L. Moench. Theoretical and Applied Genetics, 105 , 912-920. Smith, J. S. C., Kresovich, S., Hopkins, M. S., Mitchell, S. E., Dean, R. E., Woodman, W. L., Lee, M. and Porter, K. (2000). Genetic diversity among elite sorghum inbred lines assessed with simple sequence repeats. Crop Science Journal, 40 , 226-232. Statista (2023). Worldwide production of grain in 2022/23, by type (in million metric tons)*| Statista . https://www.statista.com/statistics/263977/world-grain-production-by-type/ Taylor, B. and Powell, A. (1982). Isolation of Plant DNA and RNA. Focus , 4 , 4-6 Vanzetti, L.S., Yerkovich, N., Chialvo, E., Lombardo, L., Vaschetto, L. and Helguer, M. (2013). Genetic structure of Argentinean hexaploid wheat germplasm. Genetics and Molecular Biology , 36 (3), 391-399. Wondimu, Z., Dong, H., Paterson, A. H., Worku, W., and Bantte, K. (2021). Genetic diversity, population structure and selection signature in Ethiopian Sorghum ( Sorghum bicolor L.[Moench]) germplasm. Genes, Genomes, Genetics . 11 (6), 87-97. Yahaya ,M.A., Shimelis, H., Nebie, B., Ojiewo, C.O., Rathore, A. and Das, R. 2023. Genetic Diversity and Population Structure of African Sorghum ( Sorghum bicolor L. Moench) Accessions Assessed through Single Nucleotide Polymorphisms Markers. Genes . 14 (7):1480. https://doi.org/10.3390/genes14071480 Tables Table 1: Details of studied sorghum germplasm accessions of local origin Accession number Accession Name Origin Gene bank 000004 IDAL IRINGU ANURADHAPURA PGRC 000093 EDAL ERINGU MONARAGALA PGRC 000101 EDAL ERINGU MONARAGALA PGRC 000104 POTH IRINGU MONARAGALA PGRC 000106 WAGURU MONARAGALA PGRC 000110 KARAL IRINGU MONARAGALA PGRC 000111 RATHU THIRINGU MONARAGALA PGRC 000112 SUDU THIRINGU MONARAGALA PGRC 000117 WAGURU MONARAGALA PGRC 000130 EDAL ERINGU BADULLA PGRC 000207 IDAL IRINGU MONARAGALA PGRC 000253 EDAL ERINGU HAMBANTOTA PGRC 000285 EDAL ERINGU MATALE PGRC 000310 SORGHUM MATALE PGRC 000318 KARAL IRINGU MATALE PGRC 000380 EDAL ERINGU KURUNEGALA PGRC 000403 EDAL ERINGU KURUNEGALA PGRC 000421 EDAL ERINGU KURUNEGALA PGRC 000430 KARAL IRINGU KURUNEGALA PGRC 000736 RATA KURAKKAN MATALE PGRC 000744 KARAL IRINGU MATALE PGRC 000844 KARALLIYA GALLE PGRC 000845 SORGHUM GALLE PGRC 000971 KARAL IRINGU HAMBANTOTA RSRS/AK 001532 RICE SORGHUM ANURADHAPURA RARS/MI 001546 EDAL ERINGU NUWARA ELIYA PGRC 001701 SORGHUM SRI LANKA ICRISAT 001824 EDAL ERINGU KANDY PGRC 002971 IDAL IRINGU MONARAGALA PGRC 003015 BATH IRINGU HAMBANTOTA PGRC 007969 SHAN JATHAKA KANDY NYES 008638 UNKNOWN RATNAPURA PGRC 008867 IDAL IRINGU KEGALLA PGRC 009002 RED SORGHUM MONARAGALA PGRC 009012 Not Given KEGALLA PGRC Table 2: Details of sorghum germplasm accessions of known and unknown exotic origins Acc. No. Accession Name Origin Gene bank 001700 RICE SORGHUM UNKNOWN ICRISAT 005363 UNKNOWN RARS/AR 005364 UNKNOWN RARS/AR 005365 UNKNOWN RARS/AR 006004 ARVAL FRANCE SEIC 006005 ARGENCE FRANCE SEIC 006006 ROCE ITALY SEIC 006007 SOAVE ITALY SEIC 006008 VESPA ITALY SEIC 006010 MN 150 ITALY SEIC 006012 SOAVE ITALY SEIC 006013 WONDER DWARF ITALY SEIC 006014 GAMBELLA 107 ETHIOPIA SEIC 008911 IS 1159 UNKNOWN RARS/MI 008912 IS 2311 UNKNOWN RARS/MI 008913 IS 2341 UNKNOWN RARS/MI 008914 IS 2432 UNKNOWN RARS/MI 008915 IS 3525 UNKNOWN RARS/MI 008916 IS 4010 UNKNOWN RARS/MI 008917 IS 8344 UNKNOWN RARS/MI 008918 IS 9371 UNKNOWN RARS/MI 008940 OOO234 UNKNOWN RARS/MI 008941 PJH 53 UNKNOWN RARS/MI 008942 PJH 62 UNKNOWN RARS/MI 008943 93064 UNKNOWN RARS/MI 008944 93050 UNKNOWN RARS/MI 008945 94002 UNKNOWN RARS/MI 008946 94004 UNKNOWN RARS/MI 008947 PSLL 8320 UNKNOWN RARS/MI 008948 OOO745 UNKNOWN RARS/MI 008949 IS 2941 UNKNOWN RARS/MI 008952 ICSV - 112 UNKNOWN RARS/MI 008953 ICSV - 745 UNKNOWN RARS/MI 008954 ICSV 239 UNKNOWN RARS/MI 008955 ICSV 93050 UNKNOWN RARS/MI 008956 ICSV 758 UNKNOWN RARS/MI 008957 ICSV 93028 UNKNOWN RARS/MI 008958 ICSV 93046 UNKNOWN RARS/MI 008959 ICSV 96007 UNKNOWN RARS/MI 008960 ICSV 95076 UNKNOWN RARS/MI 008961 A-2267-2 UNKNOWN RARS/MI Table 3: Details of SSR primers used to determine the genetic diversity and population structure of sorghum. Primer Sequence of forward primer (5'to 3') Sequence of reverse primer (5' to 3') Annealing Temp. ( 0 C) Xtxp302 TAGGTTCTGGACCACTTTTCTTTTTGTGTT GAATCAACTATGTGCTTGCATTGTGCT 55 Xtxp32 AGAAATTCACCATGCTGCAG ACCTCACAGGCCATGTCG 60 Xtxp46 GGGCAATCTTGATGGCGACAT AGGTGTGGCTCGGGGAGAAC 55 Xtxp297 GACCCATATGTGGTTTAGTCGCAAAG GCACAATCTTCGCCTAAATCAACAAT 55 Xtxp266 GTTGTCTAGTATAGCAAGGTGGG ATAATAGTAGATGCGTGTCAAAAGAA 55 Xtxp12 AGATCTGGCGGCAACG AGTCACCCATCGATCATC 55 Xtxp21 GAGCTGCCATAGATTTGGTCG ACCTCGTCCCACCTTTGTTG 60 Xtxp136 GCGAATAGCATCTTACAACA ACTGATCATTGGCAGGAC 55 Xtxp265 GTCTACAGGCGTGCAAATAAAA TTACCATGCTACCCCTAAAAGTGG 55 Xtxp17 CGGACCAACGACGATTATC ACTCGTCTCACTGCAATACTG 55 Xtxp295 AAATCATGCATCCATGTTCGTCTTC CTCCCGCTACAAGAGTACATTCATAGCTTA 55 Xtxp210 CGCTTTTCTGAAAATATTAAGGAC GATGAGCGATGGAGGAGAG 55 Xtxp321 TAACCCAAGCCTGAGCATAAGA CCCATTCACACATGAGACGAG 55 Xtxp258 CACCAAGTGTCGCGAACTGAA GCTTAGTGTGAGCGCTGACCAG 55 Xtxp230 GCTACCGCTGCTGCTCT AGGGGGCATCCAAGAAAT 55 Xtxp141 TGTATGGCCTAGCTTATCT CAACAAGCCAACCTAAA 55 Table 4: Summary statistics of SSR markers used to study sorghum germplasm accessions. Marker Major Allele Frequency No. of Genotypes No. of Alleles Availability Gene Diversity Heterozygosity PIC Xtxp32 0.219 15.000 7.000 0.950 0.819 0.298 0.793 Xtxp46 0.266 12.000 10.000 0.783 0.802 0.511 0.774 Xtxp302 0.233 17.000 11.000 0.750 0.845 0.511 0.826 Xtxp297 0.260 20.000 14.000 0.833 0.861 0.580 0.848 Xtxp266 0.315 22.000 13.000 0.900 0.835 0.426 0.819 Xtxp21 0.265 17.000 12.000 0.817 0.824 0.490 0.802 Xtxp12 0.287 17.000 12.000 0.783 0.816 0.383 0.793 Xtxp136 0.300 11.000 7.000 0.750 0.783 0.400 0.749 Xtxp17 0.207 19.000 9.000 0.683 0.853 0.342 0.836 Xtxp265 0.173 29.000 17.000 0.917 0.887 0.673 0.877 Xtxp295 0.333 4.000 4.000 0.700 0.694 0.000 0.632 Xtxp321 0.245 18.000 11.000 0.817 0.830 0.347 0.809 Xtxp210 0.271 14.000 10.000 0.800 0.811 0.250 0.786 Xtxp258 0.225 19.000 13.000 0.817 0.838 0.469 0.818 Xtxp230 0.322 13.000 10.000 0.750 0.777 0.222 0.744 Xtxp141 0.186 22.000 10.000 0.850 0.864 0.490 0.849 Mean 0.257 16.800 10.600 0.806 0.821 0.400 0.797 Abbreviation: PIC – polymorphic information content Table 5: Summary of Nei’s standard genetic distances with respect to clusters resulted in cluster analysis of sorghum accessions (SD=Standard deviation). Standard Genetic distance Cluster I Cluster II Cluster III Cluster IV Degrees of freedom 14 14 14 14 Mean 0.3 0.6 0.5 0.4 Standard Deviation 0.08 0.27 0.19 0.09 Standard Error 0.02 0.07 0.05 0.02 Minimum 0.073 0.000 0.184 0.125 Maximum 0.506 1.000 1.000 0.568 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Accepted 13 Aug, 2024 Reviewers agreed at journal 08 May, 2024 Reviews received at journal 08 May, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers invited by journal 27 Apr, 2024 Submission checks completed at journal 27 Apr, 2024 Editor assigned by journal 27 Apr, 2024 First submitted to journal 27 Apr, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4332824","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297913557,"identity":"cf316d6e-e665-4c8a-86cb-39344ca4b96e","order_by":0,"name":"D. V. S. Kaluthanthri","email":"","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":false,"prefix":"","firstName":"D.","middleName":"V. S.","lastName":"Kaluthanthri","suffix":""},{"id":297913559,"identity":"66326c68-6ab0-49c0-ae57-b775935fd607","order_by":1,"name":"S. A.C.N. Perera","email":"","orcid":"","institution":"University of Peradeniya","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"A.C.N.","lastName":"Perera","suffix":""},{"id":297913561,"identity":"7593f1f1-abb8-4ec0-9150-21b13a409622","order_by":2,"name":"P. N. Dasanayaka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACZuYGIHmAh4EhgYHhAwMbRPTBAXxaGIFaEiBaGGfAtCTg08IA0QJSxsDMAxPEp8WcnbHxc+GPOzL87MmPP9v84ctjYD/8gCHhDG4tls2MzdIzEp7xSPY8M5PObWMrZuBJM2BIuIFbi8FhxgZpnoTDPAY3EsyYcxvYEhsYcoAO+4BXS/NvkBb7G+mfP1v8AWrhf0NQSxvEFokcA2kGNqAWCZAt+B3WZs2TdphH4sybMsleoF/YJJ4ZHMDnfYPzhw/f5rE5bM/fnr75w48/x/L4+ZMfPvhwDLcWdHAsARSZB4jXwMBQk0CK6lEwCkbBKBgZAAAH9VO5ysIYggAAAABJRU5ErkJggg==","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":true,"prefix":"","firstName":"P.","middleName":"N.","lastName":"Dasanayaka","suffix":""}],"badges":[],"createdAt":"2024-04-27 07:25:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4332824/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4332824/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-024-02128-7","type":"published","date":"2024-08-22T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55787398,"identity":"ebbabc21-6bfc-4a2e-8e84-0e1f9c3bbebd","added_by":"auto","created_at":"2024-05-03 08:00:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189860,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree based on the Nei’s standard genetic distances of 60 sorghum germplasm accessions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4332824/v1/1c2772467f1f4b9a8ecd1057.png"},{"id":55787400,"identity":"c554f73d-c40f-4b10-9f41-979dcd32da50","added_by":"auto","created_at":"2024-05-03 08:00:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157658,"visible":true,"origin":"","legend":"\u003cp\u003eThree subpopulations of 60 sorghum germplasm accessions assessed by STRUCTURE.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4332824/v1/6a8f6cb607b099efc4f9555b.png"},{"id":55787794,"identity":"6cf97774-8a8c-4fe4-a8e7-62f48157f52c","added_by":"auto","created_at":"2024-05-03 08:08:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174167,"visible":true,"origin":"","legend":"\u003cp\u003eFour subpopulations of 60 sorghum germplasm accessions assessed by STRUCTURE.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4332824/v1/66adfcc64f11073cb81454be.png"},{"id":63300499,"identity":"606b66ca-0424-4ed4-8a92-9435a7c87e1e","added_by":"auto","created_at":"2024-08-26 16:14:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1913336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4332824/v1/e1cc6a4c-6ea5-4c99-9d3c-040dc6f8859b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simple Sequence Repeats (SSR) marker-based analysis on the genetic variation and population structure of local and exotic sorghum germplasm collection conserved ex-situ in Sri Lanka","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eSorghum bicolor\u003c/em\u003e is an annual, monoecious cereal belonging to family \u003cem\u003ePoaceae\u003c/em\u003e. It is a predominantly self-pollinated diploid (2n\u0026thinsp;=\u0026thinsp;20), displaying varying degrees of natural cross-pollination. (Doggett, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). Eastern Africa, centering Ethiopia is considered to be the origin of sorghum with several domestication events leading to current cultivars. Sorghum is widely cultivated globally for food and feed and being a nutritious staple for the rural poor in certain tropical areas, it is reported to be the fifth most important cereal crop in the world (Statista, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The C\u003csub\u003e4\u003c/sub\u003e photosynthetic pathway and the potential for drought and heat tolerance make sorghum an ideal crop for marginal lands and arid areas in the tropics playing a crucial role as a resilient crop in facing the challenges of climate change.\u003c/p\u003e \u003cp\u003eDiverse genetic forms such as wild and related species, breeding stocks, mutant lines etc. are vital sources of desirable alleles, providing material for crop breeding. However, genetically improved novel cultivars used in modern intensive agriculture are genetically highly uniform and consist of a narrow genetic base which make them vulnerable to natural catastrophes, diseases and pests (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Understanding the distribution of genetic diversity among gene pools is crucial in breeding for desirable traits, efficient management of germplasm collections and trans-boundary germplasm exchanges. Accordingly, for ever-changing breeding goals, different genes/alleles need to be reserved in cultivated and cultivable crop species and such genetic resources should be extensively characterized for effective utilization. Unravelling genetic diversity at molecular level facilitates allelic level differentiation and evaluation of genetic stocks. Simple sequence repeats (SSR) are quite popular for the study of genetic diversity of crops due to numerous advantages that they offer (Ghebru et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePopulation structure analysis has been employed to categorize sorghum accessions into distinct subpopulations or groups based on genetic relatedness in various studies (Mamo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e: Yahaya et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This helps in understanding the underlying genetic structure within the species. Principal Component Analysis (PCA) and model-based clustering algorithms such as STRUCTURE are commonly used to infer population structure.\u003c/p\u003e \u003cp\u003eDiverse domestication events, exchange of seeds via formal and informal channels for extensive worldwide cultivation and the considerable levels of cross-pollination followed by fixing of alleles via self-pollination has led to wide variability among sorghum landraces world over (Mutegi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In Sri Lanka, the \u003cem\u003eex-situ\u003c/em\u003e gene bank of sorghum is rich with a considerable number of local accessions with different vernacular names and accessions of exotic origin. Yet, the effective use of material from this collection is hindered due to lack of information on their genetic diversity. Accordingly, the aim of the current study was to identify the genetic diversity and to elucidate the population structure of \u003cem\u003eex-situ\u003c/em\u003e conserved local and exotic sorghum germplasm collection using SSR markers and to make recommendations for conservation and breeding.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGermplasm materials and experimental design\u003c/h2\u003e \u003cp\u003eThe study was based on 60 sorghum germplasm accessions including both local (Table\u0026nbsp;1) and exotic (Table\u0026nbsp;2) germplasm that are conserved at the seed gene bank of Plant Genetic Resources Center (PGRC), Gannoruwa, Sri Lanka. Seeds of germplasm accessions were sown in small plastic pots filled with soil and the plants were grown in a plant house under controlled environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and Capillary electrophoresis\u003c/h2\u003e \u003cp\u003eFor each of the germplasm accessions, the total genomic DNA was extracted using leaf materials harvested from twenty-day-old plants. Fresh, immature leaves were surface sterilized by washing with sterilized distilled water and 70% ethanol, air dried and cut into small pieces. An approximate weight of 5 g was obtained and put into a 1.5 mL microcentrifuge tube and stored at -80 0C for 24 hours. The frozen pieces of immature sorghum leaves were crushed and total genomic DNA was extracted from ground leaf material using CTAB miniprep DNA extraction protocol (Taylor and Powell, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) followed by quantification and dilution to a final concentration of 25 ng \u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExtracted DNA was amplified at 16 SSR loci (Table\u0026nbsp;3) via a two-step PCR protocol using a modified M13 tagged (5\u0026rsquo;TGTAAAACGACGGCCAGT3\u0026rsquo;) forward primer and a pig tailed (5\u0026rsquo;GTTTCTT3\u0026rsquo;) reverse primer with M13 oligo labeled with one of four fluorescent dyes; 6-HEX, FAM, TAMN or PET in a T100\u0026trade; Thermal cycler (Bio-Rad laboratories Inc.). Four differentially labeled PCR products were multiplexed and size-fractioned via capillary electrophoresis on an ABI3500 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eElectrophoretic data derived from capillary electrophoresis were imported to GeneMapper software version 4.0 for allele characterization based on molecular weights of SSR products. OSIRIS (Open Source, Independent Review and Interpretation System) software version 2.13 was applied for further validation of size peak patterns, using the ABI-LIZ-600-80-TO-400 internal lane standard for allele calling. The peaks from polymorphic SSR markers on capillary electrophoresis were scored as present (1) or absent (0) or missing data (-9) for each germplasm accession by manual inspection. Data were tabulated in a matrix using Excel version 2010 followed by analyzing using PowerMarker V3.25 software (Liu and Muse, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Determination of total number of alleles and polymorphism information were estimated from the genotyping data, considering that each SSR primer pair represents a unique locus. Nei\u0026rsquo;s (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) standard genetic distances between the germplasm accessions were obtained.\u003c/p\u003e \u003cp\u003eCluster analysis was performed based on the unweighted pair group mean algorithm (UPGMA) to draw the dendrogram. Genotypic data were analyzed using Structure 2.2 (Pritchard et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), to determine structured groups or subpopulations (K clusters) in the collection. Analysis was performed using settings for admixture ancestry model and correlated allele frequencies of the programme. The K values were set at 1 to 10, with a burn in value of 10,000 and 30,000 of Markov Chain Monte Carlo replications (MCMC reps) after burning followed by 3 iterations. Optimal K clusters were identified using the online based software STRUCTURE HARVESTER (Earl and VonHoldt, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe studied sorghum population recorded polymorphic loci at all 16 SSR marker loci. Frequency of major SSR alleles at different SSR loci recorded a mean of 0.257 ranging from 0.17 to 0.33. A total of 170 alleles were recorded across the 16 loci with number of genotypes ranging from 4 to 29 per locus with a mean of 16.8 (Table\u0026nbsp;4). The mean number of alleles per locus was recorded as 10.6. The results provide ample evidence for high allelic richness in this specific germplasm collection in Sri Lanka. Availability, which is an indication of the amplification efficiency of the marker was more than 50% at all marker loci with an average of 80.63%. Three markers (Xtxp32, Xtxp266 and Xtxp265) showed a high amplification efficiency exceeding 90% of availability. Gene diversity, which is a measure of genetic diversity, varied within the range of 0.694 to 0.887 recording a mean of 0.821. More than one allele was amplified in some accessions at certain loci indicating the residual heterogeneity within these accessions. The observed heterozygosity at each locus over all accessions ranged from zero at marker locus Xtxp295 to 0.672 at Xtxp265 with an average of 0.399 per locus. All the primer pairs used in the study were highly informative with PIC values ranging from 0.632 to 0.877 at marker loci Xtxp295 and Xtxp265 respectively, recording an average of 0.797.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFrequency based standard genetic distances and dendrogram\u003c/h2\u003e \u003cp\u003eThe expected value of the standard genetic distance developed by Nei in 1972, is proportional to evolutionary time based on both effects of mutation and genetic drift. The genetic distance of 1.0 was observed between a large numbers of sorghum accessions indicating the high genetic divergence between them. The lowest genetic distance of 0.0 was observed between the accessions UNKNOWN 8917 \u0026ndash; ETHIOPIA 6014 and UNKNOWN 8915 \u0026ndash; UNKNOWN 8943 indicating probable genetic duplication.\u003c/p\u003e \u003cp\u003eThe dendrogram of the 60 accessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) constructed based on Nei\u0026rsquo;s (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) standard genetic distance matrix using unweighted pair group mean algorithm (UPGMA) revealed four distinct clusters, I-IV. Cluster I was the most homogenous including 15 local sorghum accessions. The remaining clusters comprised of local, origin unknown and exotic germplasm accessions. Two of local and thirteen of exotic accessions were identified in cluster II whereas four local accessions and eleven accessions with unknown origin grouped in cluster III. The remaining fifteen-germplasm accessions including local, exotic (European) and two accessions of unknown origin clustered in cluster IV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSummary of Nei\u0026rsquo;s standard genetic distances with respect to each cluster is given in Table\u0026nbsp;5. The lowest means and standard deviations (SD) of the genetic distances were observed in cluster I whereas they were the highest in cluster II. Considerably low mean standard genetic distance and SD values of cluster I revealed the homogenous nature and comparatively low genetic divergence of the 15 local germplasm accessions that grouped in cluster I. On the other hand, cluster II consisted of genetically more divergent germplasm accessions by recording considerably high mean standard genetic distance and SD values. Meanwhile, the difference of mean standard genetic distance was the highest between clusters I and II.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of population structure of sorghum accessions\u003c/h2\u003e \u003cp\u003eThe admixture ancestry model-based approach with correlated allele frequencies was performed using STRUCTURE software to examine the relatedness among 60 sorghum germplasm accessions, using genotypic data at 16 SSR loci. The K values were set at 1 to 10, with a burn in value of 10,000 and 30,000 of Markov Chain Monte Carlo replications (MCMC reps) after burning followed by three iterations. Optimal K clusters were identified following the method described by Evanno et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) based on the improvements in the estimated natural log of likelihood [Ln P(D)] in three independent runs (Casa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) through the software STRUCTURE HARVESTER (Earl and VonHoldt, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The estimated LnP(D) values and the highest value of the ad hoc quantity showed that the optimum number of subpopulation K which best explained the population structure was 3. In order to better understand the geographical differentiation based on the defined geographical regions, K\u0026thinsp;=\u0026thinsp;4 which had the second highest ad hoc quantity ∆k was also considered. Graphical representations of K\u0026thinsp;=\u0026thinsp;3 and K\u0026thinsp;=\u0026thinsp;4 are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e respectively. Germplasm accessions with probabilities greater than 0.95 and less than 0.95 were considered as pure lines and admixtures respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe mean number of alleles per locus recorded in this study was greater than the values recorded by Ali et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Schloss et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) on 72 and 25 sorghum accessions which recorded 3.22 and 3.4 mean number of alleles respectively. Comparatively low mean number of alleles per locus were also reported by Agrama and Tuinstra (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Menz et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), Egbadzor (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Pei et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, Burow et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported a considerably high number of alleles per locus in their study on Chinese sorghum landraces.\u003c/p\u003e \u003cp\u003eSorghum genetic diversity values were reported to be 0.6 across 29 SSR loci (Schloss et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and ranged from 0.44 to 0.79 at five SSR loci in Somalian landraces (Manzelli et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Gene diversity values for sorghum were recorded as 0.29 (Dje et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and 0.32 (Barnaud et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) in Morocco and 0.65, 0.61, 0.66 and 0.66 respectively in Eritrea (Ghebru et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Niger (Deu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Kenya (Ngugi and Onyango, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Ethiopia (Adugna, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) respectively. All the above studies evaluated the genetic diversity of sorghum in Africa which is the center of origin for sorghum and despite being in South Asia, the Sri Lankan collection is noted to be harboring a higher diversity.\u003c/p\u003e \u003cp\u003eThe observed heterozygosity was comparatively higher to what were observed by Dje et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Barnaud et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Deu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) recording 0.134, 0.11 and 0.042 respectively in their studies. In contrast to the mean values for heterozygosity and gene diversity recorded in this study, Ng\u0026rsquo;uni et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Motlhaodi et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported considerably lower values in their studies. However, the observed heterozygosity in the current study was not significantly high and it indicated that the majority of genotypes carried homozygous loci which led to a high level of genetic stability. High levels of allelic variability but low levels of heterozygosity at SSR loci were reported on Eritrean sorghum landraces (Ghebru et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The predominantly inbreeding nature of sorghum may be the cause for the low level of heterozygosity observed, yet with the gene pool as a whole maintaining a high level of allelic variation. Besides, the high gene diversity (expected heterozygosity) values compared to the observed heterozygosity values across the 16 loci indicated low cross-pollination rates among the accessions as expected. In congruence with this study, most of the genetic diversity studies in sorghum using SSRs (e.g., Ghebru et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Barro-Kondombo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Deu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ngugi and Onyango, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported high gene diversity (expected heterozygosity) values compared to the observed heterozygosity.\u003c/p\u003e \u003cp\u003eThe discriminatory power of SSR markers can be assessed with polymorphic information content (PIC) as calculated by the number of alleles detected and their relative frequencies (Smith et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In the present study, the tested markers are highly recommended for allelic studies in sorghum. Burrow \u003cem\u003eet al.\u003c/em\u003e (2012), Sapey et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Madhusudhana et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported less average values for both number of alleles per locus and PIC compared to the results of the current study. A total of 23 unique alleles which may serve as diagnostic tools for particular regions of the genome of respective genotypes, were observed in certain accessions at eleven SSR loci; Xtxp266, Xtxp258, Xtxp141, Xtxp210, Xtxp230, Xtxp265, Xtxp21, Xtxp12, Xtxp297, Xtxp46 and Xtxp302.\u003c/p\u003e \u003cp\u003eThe lowest genetic distance of 0.0 was observed between two pairs of germplasm accessions indicating probable genetic duplication. Such duplicates might be an indication of germplasm exchange within and between regions. It is important to note that, clusters I and IV depicted the influence of geographic origin on genetic diversity by solely comprising of local, Italian, and French accessions respectively. Exchange of seeds may be the reason for the genetic proximity and clustering of such accessions together. Seed exchange has been reported in the baseline survey conducted in eastern Kenya on sorghum seed systems, which revealed a tradition among farmers in selection and exchange of seeds (Muui et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, Ochieng et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) studied sorghum production systems in Kenya and reported the important role of farmers in seed selection, exchange and movement. Research conducted by, Ayana et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Ghebru et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Nkongolo and Nsapato (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Deu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), revealed similarities in genotypes as a result of region proximity in Africa for cultivated sorghum and its wild relatives. Mutegi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) reported the clustering of sorghum in Kenya into a distinct and unique genetic group. Similarly, the current study revealed the inclusion of a majority of Sri Lankan sorghum accessions in a unique cluster. This is attributed to the fact that Sri Lanka is an island, which is relatively geographically remote from other sorghum growing regions. The majority of genetic material from local sorghum accessions tested in this study appear to be genetically distinct.\u003c/p\u003e \u003cp\u003eClustering of local and exotic accessions together in cluster IV of dendrogram revealed the existing genetic similarity irrespective of the geographical origin. The existence of accessions with wider genetic distances (e.g., MONARAGALA 9002 and MONARAGALA 2971 in clusters III and IV respectively) within the same locality indicates the introduction of seeds of unique landraces from distant localities. Furthermore, some of local accessions collected from Kurunegala district in Sri Lanka with vernacular name \u003cem\u003eEdal Eringu\u003c/em\u003e, grouped into different two clusters I and III. Generally, farmers save seeds from previous harvest and in cases of insufficiency they tend to obtain seeds from neighboring farmers or markets. As a consequence, there would be a lot of variation derived from generations of selection even within the varieties having the same vernacular name. This also indicates the high stepwise mutation rates of SSR (Dje et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Despite being a predominantly inbreeding species, outcrossing rates as high as 0.10% \u0026minus;\u0026thinsp;0.15% (Doggett, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) and spontaneous hybridization are reported in sorghum. This might possibly explain the molecular level differences that exist between other studied sorghum germplasm accessions as well.\u003c/p\u003e \u003cp\u003eUnder K\u0026thinsp;=\u0026thinsp;3 in population structure analysis, SP1comprised of 28 germplasm accessions including six local, 21 of unknown origin and one exotic (ETHIOPIA 6014) accession. Purelines of 15 local accessions assigned into SP2 while purelines of five local and eight exotic accessions of European origin (Italian and French) assigned into SP3 along with two accessions of unknown origin. The results thus provide further evidence for the genetic similarity of certain accessions irrespective of geographical origin spanning across three continents. The formation of SP2 solely with local accessions indicates their genetic uniqueness which makes them even more exclusive. The admixtures of all three subpopulations were observed in two germplasm accessions with unknown origins (UNKNOWN 8918 and UNKNOWN 8942). This observation could be due to the heterogonous nature of the two accessions, sorghum being a cross-pollinating crop. In parallel to the present study, Nemera et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) revealed two sub-populations irrespective to the geographical origin of 80 Ethiopian sorghum accessions studied at 15 SSR loci. Similarly, population structure of 100 genotypes of Ethiopian sorghum profiled at 15 SSR loci, revealed three sub-populations with no clear geographical origin-based structure Mamo et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, a total of evenly distributed 108,107 high-quality single-nucleotide polymorphism (SNPs) markers differentiated six sub-populations within 304 Ethiopian sorghum accessions (Wondimu et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Enyew et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified two sub-populations within 24 Ethiopian sorghum accessions using 5,000 SNPs. Interestingly, Chakrabarty et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) recently inferred five sub-populations within 3333 Ugandan sorghum accessions using 12,742 SNP markers. However, Menamo et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that agro-ecology is more important than the administrative region in defining the genetic variation in sorghum based on redundancy analysis.\u003c/p\u003e \u003cp\u003eResults revealed the inclusion of certain morphologically different local accessions with different vernacular names such as \u0026lsquo;\u003cem\u003eWaguru\u003c/em\u003e\u0026rsquo; (MONARAGALA 106), \u0026lsquo;\u003cem\u003eEdal Iringu\u003c/em\u003e\u0026rsquo; (MATALE 285) and \u0026lsquo;\u003cem\u003eKaral Iringu\u003c/em\u003e\u0026rsquo; (HAMABANTOTA 971) in the same subpopulation (SP2). Similarly, SP3 contained morphologically different exotic germplasm accessions collected from Italy (\u0026lsquo;Roce\u0026rsquo; [ITALY 6006]; \u0026lsquo;\u003cem\u003eVespa\u003c/em\u003e\u0026rsquo; [ITALY 6008]; \u0026lsquo;Wonder Dwarf\u0026rsquo; [ITALY 6013]) and from France (\u0026lsquo;\u003cem\u003eArval\u003c/em\u003e\u0026rsquo; [FRANCE 6004]; \u0026lsquo;\u003cem\u003eArgence\u003c/em\u003e\u0026rsquo; [FRANCE 6005]). The occurrence of morphologically distinct accessions having different vernacular names in the same sub-population could be due to a multitude of reasons; narrow genetic base despite the morphological differences, farmers\u0026rsquo; directional selection for different morphological traits and non-detectable nature of observed morphological differences with neutral genetic markers being the most probable reasons. This observation is consistent with the observations made by Sagnard et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Labeyrie et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt K\u0026thinsp;=\u0026thinsp;4 of population structure analysis, SP1 and SP3 remained unchanged from that of K\u0026thinsp;=\u0026thinsp;3 confirming their genetic sub-populations. At K\u0026thinsp;=\u0026thinsp;4, SP2 contained 15 accessions including only two accessions (RATNAPURA 8638 and KEGALLA 8867) with local origin while the rest had exotic and unknown origins. SP4 included purelines of two local germplasm accessions (MONARAGALA 9002 and KEGALLA 9012) and nine accessions of unknown origin.\u003c/p\u003e \u003cp\u003ePopulation structure of common self-pollinated field crops such as wheat (Vanzetti et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and barley (Naeem et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) have been shown to be strongly influenced by geographic adaptation. Previous studies have reported sorghum population structure based on botanical races and geography (Deu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Maina et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It was not possible to relate the observed genetic structure with racial category because the germplasm accessions used in the current study have not been characterized for racial groups. Of the different sorghum growing agroecological regions in Sri Lanka, the country\u0026rsquo;s \u0026lsquo;wet zone\u0026rsquo;, the western face of the mountain range, and the southwest windy slopes receive an average of 2500 mm of rainfall each year whereas the \u0026lsquo;dry zone\u0026rsquo; receives a rainfall between 1200 to 1900 mm, mainly through the northeast monsoon (Alahacoon and Edirisinghe, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This spatial analysis separated SP1 which consisted of purelines of local germplasm accessions inhabiting contrasting environments, at least in terms of rainfall. In contrast, two local germplasm accessions (MATALE 310 and KURUNEGALA 380) and two accessions of unknown origin (UNKNOWN 8953 and UNKNOWN 8956) were observed with admixtures of all four subpopulations suggesting that the population structure may also be affected by other factors such as human activities including seed exchange and food preferences. Deu et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) reported similar observation on spatio-temporal dynamics of genetic diversity in \u003cem\u003eSorghum bicolor\u003c/em\u003e in Niger.\u003c/p\u003e \u003cp\u003eSorghum is a globally widely cultivated crop which has been distributed through transboundary seed exchanges across continents and within different areas within countries. In addition, the crop is cultivated in diverse agro-climatological areas and the cultivars may have genetic adaptations specific to diverse localities. Sorghum displays varying degrees of outcrossing leading to the creation of novel genotypes and getting them genetically fixed upon selfing in subsequent generations. Accordingly, the wide scale cultivation across continents coupled with the creation of novel genotypes have led to a huge genetic variation of the global sorghum germplasm. Sorghum being an ideal crop candidate in marginal areas especially in facing the adverse effects of global climate change the vast diversity unveiled in the current study as well as in other parts of the world should be appropriately utilized in breeding programs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe current study attempted to elucidate the genetic diversity and population structure of a collection of 60, Sri Lankan \u003cem\u003eex-situ\u003c/em\u003e conserved sorghum germplasm accessions comprising of Sri Lankan, South Asian, African and European collections. The collection was observed to be highly polymorphic at 16 SSR loci that were tested giving evidence for high allelic richness and the availability of three to four genetic subpopulations within the collection. The revealed genetic diversity and population structure did not exactly depict the geographical origin of the germplasm accessions. Furthermore, the majority of local sorghum germplasm accessions tested in this study were genetically distinct. The study gave evidence for the importance of systematic studies of sorghum genetic resources for better decision making for sustainable cultivation, further collection and conservation and in utilization of conserved material in genetic improvement of the sorghum crop as a candidate for marginal areas and as a climate resilient crop. The findings will be of high importance with regard to decision making on sustainability of cultivation, further collection and conservation of genetic resources and genetic improvement of sorghum at a global level. The findings of the study reiterate the importance of systematic evaluation of the genetic diversity and genetic structures of populations for better decision making in selection for cultivation and more importantly in genetic improvement programmes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026nbsp;gratefully acknowledge Plant Genetic Resources Center, Gannoruwa, Sri Lanka for providing seeds and the Institute of Biochemistry, Molecular Biology and Biotechnology (IBMBB), Sri Lanka for providing the laboratory facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD. V. S. Kaluthanthri contributed to the Conceptualization, Data curation, Formal analysis, Software, Writing \u0026ndash; original draft. S. A. C. N. Perera contributed to the Conceptualization, Methodology, Supervision, Writing \u0026ndash; review and editing. P. N. Dasanayaka contributed to the Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by\u0026nbsp;University of Sri Jayewardenepura, Sri Lanka.\u0026nbsp;(Grant No.\u0026nbsp;ASP/01/RE/SCI/2017/07, ASP/01/RE/SCI/2021/08). University of Sri Jayewardenepura, Sri Lanka had no role in the design, analysis or writing of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical standards\u003c/strong\u003e: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eAdugna, A.\u003c/strong\u003e (2014). Analysis of \u003cem\u003ein-situ\u0026nbsp;\u003c/em\u003ediversity and population structure in Ethiopian cultivated \u003cem\u003eSorghum bicolor\u0026nbsp;\u003c/em\u003e(L.) landraces using phenotypic traits and SSR markers. \u003cem\u003eSpringerplus\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 212.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAgrama, H. A.\u0026nbsp;\u003c/strong\u003eand\u0026nbsp;\u003cstrong\u003eTuinstra, M. R\u003c/strong\u003e. (2003). 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Genetic diversity, structure, gene flow and evolutionary relationships within the \u003cem\u003eSorghum bicolor\u003c/em\u003e wild-weedy-crop complex in a western African region. \u003cem\u003eTheoretical Applied Genetics,\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e123\u003c/strong\u003e(7), 1231-1246.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSapey, E., Offei, S. K., Danquah, E. Y\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eAsante, I. K\u0026nbsp;\u003c/strong\u003e(2012). Assessment of genetic diversity among 60 sorghum accessions in Ghana using microsatellites. \u003cem\u003eElixir Bio Technology\u003c/em\u003e, \u003cstrong\u003e44\u003c/strong\u003e, 7171-7177.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSchloss, S. J., Mitchell, S. E., White, G. M., Kukatla, R., Bowers, J. E., Paterson, A. H.\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eKresovich, S.\u0026nbsp;\u003c/strong\u003e(2002). Characterization of RFLP probe sequences for gene discovery and SSR development in \u003cem\u003eSorghum bicolor\u003c/em\u003e L. Moench. \u003cem\u003eTheoretical and Applied Genetics,\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 912-920.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSmith, J. S. C., Kresovich, S., Hopkins, M. S., Mitchell, S. E., Dean, R. E., Woodman, W. L., Lee, M.\u003c/strong\u003e and \u003cstrong\u003ePorter, K.\u0026nbsp;\u003c/strong\u003e(2000). Genetic diversity among elite sorghum inbred lines assessed with simple sequence repeats. \u003cem\u003eCrop Science Journal,\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 226-232.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStatista\u003c/strong\u003e (2023). \u003cem\u003eWorldwide production of grain in 2022/23, by type (in million metric tons)*| Statista\u003c/em\u003e. https://www.statista.com/statistics/263977/world-grain-production-by-type/\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTaylor, B.\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003ePowell, A.\u003c/strong\u003e (1982). Isolation of Plant DNA and RNA. \u003cem\u003eFocus\u003c/em\u003e, \u003cstrong\u003e4\u003c/strong\u003e, 4-6\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVanzetti, L.S., Yerkovich, N., Chialvo, E., Lombardo, L., Vaschetto, L.\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eHelguer, M.\u0026nbsp;\u003c/strong\u003e(2013). Genetic structure of Argentinean hexaploid wheat germplasm. \u003cem\u003eGenetics and Molecular Biology\u003c/em\u003e, \u003cstrong\u003e36\u003c/strong\u003e(3), 391-399.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWondimu, Z., Dong, H., Paterson, A. H., Worku, W.,\u003c/strong\u003e and \u003cstrong\u003eBantte, K.\u0026nbsp;\u003c/strong\u003e(2021). Genetic diversity, population structure and selection signature in Ethiopian Sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e L.[Moench]) germplasm. \u003cem\u003eGenes, Genomes, Genetics\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e(6), 87-97.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eYahaya ,M.A., Shimelis, H., Nebie, B., Ojiewo, C.O., Rathore, A.\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eDas, R.\u0026nbsp;\u003c/strong\u003e2023. Genetic Diversity and Population Structure of African Sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e L. Moench) Accessions Assessed through Single Nucleotide Polymorphisms Markers. \u003cem\u003eGenes\u003c/em\u003e.\u003cstrong\u003e14\u003c/strong\u003e(7):1480. https://doi.org/10.3390/genes14071480\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eDetails of studied sorghum germplasm accessions of local origin\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccession number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccession Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrigin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene bank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eIDAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eANURADHAPURA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003ePOTH IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eWAGURU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eKARAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eRATHU THIRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eSUDU THIRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eWAGURU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eBADULLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eIDAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eHAMBANTOTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMATALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eSORGHUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMATALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eKARAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMATALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKURUNEGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKURUNEGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKURUNEGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eKARAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKURUNEGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eRATA KURAKKAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMATALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eKARAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMATALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eKARALLIYA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eGALLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eSORGHUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eGALLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e000971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eKARAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eHAMBANTOTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003eRSRS/AK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e001532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eRICE SORGHUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eANURADHAPURA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e001546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eNUWARA ELIYA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e001701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eSORGHUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eSRI LANKA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003eICRISAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e001824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eEDAL ERINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKANDY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e002971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eIDAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e003015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eBATH IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eHAMBANTOTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e007969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eSHAN JATHAKA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKANDY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003eNYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e008638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eRATNAPURA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e008867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eIDAL IRINGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKEGALLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e009002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eRED SORGHUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eMONARAGALA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964349376114082%\" valign=\"bottom\"\u003e\n \u003cp\u003e009012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.163992869875223%\" valign=\"bottom\"\u003e\n \u003cp\u003eNot Given\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\" valign=\"bottom\"\u003e\n \u003cp\u003eKEGALLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71657754010695%\" valign=\"bottom\"\u003e\n \u003cp\u003ePGRC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eDetails of sorghum germplasm accessions of known and unknown exotic origins\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcc. \u0026nbsp; \u0026nbsp; No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccession Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrigin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene bank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e001700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eRICE SORGHUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eICRISAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e005363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/AR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e005364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/AR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e005365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/AR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eARVAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eFRANCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eARGENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eFRANCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eROCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eSOAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eVESPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eMN 150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eSOAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eWONDER DWARF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e006014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eGAMBELLA 107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eETHIOPIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 1159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 2311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 2341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 2432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 3525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 4010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 8344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 9371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eOOO234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003ePJH 53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003ePJH 62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003e93064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003e93050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003e94002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003e94004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003ePSLL 8320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eOOO745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eIS 2941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV - 112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV - 745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV 239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV 93050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV 758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV 93028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV 93046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV 96007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eICSV 95076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.43598615916955%\" valign=\"bottom\"\u003e\n \u003cp\u003e008961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.719723183391004%\" valign=\"bottom\"\u003e\n \u003cp\u003eA-2267-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.98961937716263%\" valign=\"bottom\"\u003e\n \u003cp\u003eUNKNOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.85467128027682%\" valign=\"bottom\"\u003e\n \u003cp\u003eRARS/MI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003eDetails of SSR primers used to determine the genetic diversity and population structure of sorghum.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Sequence of forward primer (5\u0026apos;to 3\u0026apos;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence of reverse primer (5\u0026apos; to 3\u0026apos;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnealing Temp. (\u003csup\u003e0\u003c/sup\u003eC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp302\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eTAGGTTCTGGACCACTTTTCTTTTTGTGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eGAATCAACTATGTGCTTGCATTGTGCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eAGAAATTCACCATGCTGCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eACCTCACAGGCCATGTCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp46\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eGGGCAATCTTGATGGCGACAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eAGGTGTGGCTCGGGGAGAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp297\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eGACCCATATGTGGTTTAGTCGCAAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eGCACAATCTTCGCCTAAATCAACAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp266\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eGTTGTCTAGTATAGCAAGGTGGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eATAATAGTAGATGCGTGTCAAAAGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eAGATCTGGCGGCAACG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eAGTCACCCATCGATCATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp21\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eGAGCTGCCATAGATTTGGTCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eACCTCGTCCCACCTTTGTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp136\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eGCGAATAGCATCTTACAACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eACTGATCATTGGCAGGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp265\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eGTCTACAGGCGTGCAAATAAAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eTTACCATGCTACCCCTAAAAGTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eCGGACCAACGACGATTATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eACTCGTCTCACTGCAATACTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp295\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eAAATCATGCATCCATGTTCGTCTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eCTCCCGCTACAAGAGTACATTCATAGCTTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp210\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eCGCTTTTCTGAAAATATTAAGGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eGATGAGCGATGGAGGAGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp321\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eTAACCCAAGCCTGAGCATAAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eCCCATTCACACATGAGACGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp258\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eCACCAAGTGTCGCGAACTGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eGCTTAGTGTGAGCGCTGACCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp230\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eGCTACCGCTGCTGCTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eAGGGGGCATCCAAGAAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.198757763975156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp141\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.267080745341616%\" valign=\"top\"\u003e\n \u003cp\u003eTGTATGGCCTAGCTTATCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.50931677018634%\" valign=\"top\"\u003e\n \u003cp\u003eCAACAAGCCAACCTAAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003eSummary statistics of SSR markers used to study sorghum germplasm accessions.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor Allele Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Genotypes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of Alleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Diversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeterozygosity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e7.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp46\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e12.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e10.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp302\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e17.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e11.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp297\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e20.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e14.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp266\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e22.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e13.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp21\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e17.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e12.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e17.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e12.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp136\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e11.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e7.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e19.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e9.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp265\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e29.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e17.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp295\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e4.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e4.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp321\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e18.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e11.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp210\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e14.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e10.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp258\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e19.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e13.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp230\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e13.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e10.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\"\u003e\n \u003cp\u003e\u003cem\u003eXtxp141\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e22.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e10.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.024844720496894%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.509316770186336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.257\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.354037267080745%\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.800\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.600\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.596273291925465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.806\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167701863354036%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.797\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp; Abbreviation: PIC \u0026ndash; polymorphic information content\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eSummary of Nei\u0026rsquo;s standard genetic distances with respect to clusters resulted in cluster analysis of sorghum accessions (SD=Standard deviation).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"575\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.52173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Genetic distance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.434782608695652%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster IV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.52173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.434782608695652%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.52173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.434782608695652%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.52173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.434782608695652%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.52173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.434782608695652%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.52173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.434782608695652%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.52173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.434782608695652%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Ex-situ conservation, Genetic variation, Molecular characterization, Population structure, Sorghum bicolor","lastPublishedDoi":"10.21203/rs.3.rs-4332824/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4332824/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e (L.) Moench) is one of the most important cereal crops occupying the fifth position based on the cultivated extent among the cereal crops in the world. Characterization of genetic resources is a pre-requisite for utilization of conserved genetic resources in breeding programmes and cultivation. The present study was carried out to reveal the genetic variation and population structure of local and exotic sorghum germplasm collection conserved in \u003cem\u003eex-situ\u003c/em\u003e seed gene bank at the Plant Genetic Resources Centre, Sri Lanka. Total genomic DNA was extracted from 60 germplasm accessions using CTAB miniprep DNA extraction protocol. A two-step PCR amplification was performed at 16 Simple Sequence Repeat (SSR) loci. Four differentially labeled PCR products were multiplexed and size-fractioned using capillary electrophoresis. Data analyses were performed using GeneMapper 4.0, OSIRIS, PowerMarker 3.25, Structure 2.2 and STRUCTURE HARVESTER. The 16 SSR loci recorded polymorphism and the dendrogram revealed four distinct clusters. The optimum number of subpopulations was three in addition to two admixture subpopulations. The revealed population structure did not depict the geographical origin of the germplasm accessions. The present study confirmed that the majority of local sorghum germplasm accessions tested were genetically distinct. Varying degrees of outcrossing selfing in subsequent generations may have led to the creation of novel sorghum genotypes at global level.\u003c/p\u003e","manuscriptTitle":"Simple Sequence Repeats (SSR) marker-based analysis on the genetic variation and population structure of local and exotic sorghum germplasm collection conserved ex-situ in Sri Lanka","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-03 08:00:37","doi":"10.21203/rs.3.rs-4332824/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2024-08-13T17:16:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"76257407016495902994859011060229241322","date":"2024-05-09T03:00:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-08T21:14:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220185088499736062556473692922058536652","date":"2024-04-29T13:47:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-27T09:51:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-27T09:37:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-27T09:37:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2024-04-27T07:21:45+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":"3fb36c3e-9c55-47d9-b561-427562cd2e03","owner":[],"postedDate":"May 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-26T16:06:00+00:00","versionOfRecord":{"articleIdentity":"rs-4332824","link":"https://doi.org/10.1007/s10722-024-02128-7","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2024-08-22 15:58:03","publishedOnDateReadable":"August 22nd, 2024"},"versionCreatedAt":"2024-05-03 08:00:37","video":"","vorDoi":"10.1007/s10722-024-02128-7","vorDoiUrl":"https://doi.org/10.1007/s10722-024-02128-7","workflowStages":[]},"version":"v1","identity":"rs-4332824","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4332824","identity":"rs-4332824","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}