Genetic diversity and population structure of Uganda cassava germplasm

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Dramadri, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3944682/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jul, 2024 Read the published version in Journal of Applied Genetics → Version 1 posted 3 You are reading this latest preprint version Abstract The genetic diversity and population structure were assessed in 155 Uganda cassava genotypes using 5,247 single nucleotide polymorphism (SNP) markers which had an average call rate of 96%. Polymorphic information content values of the markers ranged from 0.1 to 0.5 with an average of 0.4 which was considered to be moderately high. The Principal Component analysis (PCA) showed that the first two components captured ~ 24.2% of the genetic variation. The average genetic diversity was 0.3. The analysis of Molecular Variance (AMOVA) indicated that 66.02% and 33.98% of the total genetic variation occurred within accessions and between sub-populations, respectively. Five sub-populations were identified based on ADMIXTURE structure analysis (K = 5). Neighbor-joining tree and hierarchical clustering tree revealed the presence of three different groups which were primarily based on the source of the genotypes. The results suggested that there was considerable genetic variation among the cassava genotypes which is useful in cassava improvement and conservation efforts. Genetic variation Single nucleotide polymorphism polymorphic information content hierarchical clustering Figures Figure 1 Figure 2 Figure 3 Introduction Cassava ( Manihot esculenta Crantz) stands as one of the most important staple crops, providing sustenance and livelihood to millions of people globally, especially in tropical regions (Ceballos et al. 2020 ). This crop serves as the primary source of calories and income for small-scale farmers with its starchy roots providing carbohydrates and leaves offering vitamins, proteins and minerals (Bayata 2019 ). Cassava's unique ability to thrive in marginal ecologies with low soil fertility and rainfall makes it a crucial player in global agriculture, food security and economic growth in such ecologies (Ngongo et al. 2022 ). Advancements in breeding programs heavily rely on gaining a deeper understanding of the genetic diversity within populations which functions as a repository of diverse genes with significant potential for enhancing productivity and adapting to both abiotic and biotic stress (Adu et al. 2021 ). The relevance is particularly pronounced in the context of ongoing climate change and global warming as genetic variation serves as a foundation upon which breeding efforts are built, providing the raw material for developing improved varieties. Variations in traits such as yield, disease resistance, drought tolerance, and nutritional content are essential for enhancing cassava resilience and nutritional value, making them fundamental prerequisites for sustainable cassava cultivation, crop improvement, and conservation. The analysis of genetic diversity and population structure plays a crucial role in understanding the historical patterns of natural selection and the genetic connections among different accessions (Luo et al. 2019 ). A considerable number of molecular markers such as SSR (Adjebeng-Danquah et al. 2020 ), DarT (Adu et al. 2021 ) and ISSR (Tiago et al. 2016 ) have been employed to assess genetic variation in cassava accessions. The utilization of SNP has largely been used to assess the diversity of genetic information between and within populations because of their abundance, stability, polymorphism, and automation compatibility. The study aims to address the historical challenges posed by Cassava Mosaic Disease (CMD) and Cassava Brown Disease (CBSD) to cassava production by utilizing the resistant cassava populations to elucidate the genetic characteristics, population structure, and gene flow patterns. Therefore, the objectives of this study are 1. To unravel allelic diversity and heterozygosity levels 2. To explore population structure 3. To detect potential admixture and, 4. To identify gene flow patterns. The ultimate goal includes contributing valuable information to cassava breeding programs in Uganda for the development of more resistant and disease-resistant varieties. Materials and methods Plant material and populations development One hundred and fifty-five cassava genotypes were used in the study, with 80 genotypes being collected from existing germplasm at National Agriculture Semi-Arid Research Resources Institute (NaSARRI) in Eastern Uganda and 75 accession which were obtained from International Institute of Tropical Agriculture (IITA), Sendusu in Central Uganda. These genotypes were developed as biparental populations using parents originating from West Africa, East Africa and Southern America. Genotyping A total of 155 cassava leaf samples were collected from a single representative plant, dried at room temperature and shipped in silica gel to Intertek, Australia Lab for DNA sequencing. High molecular weight DNA was extracted from the leaf samples and subjected to quality control before Diversity Array Technology sequencing (DarTseq™) (Kilian et al. 2012 ). The PCR products of each sample were sequenced using Hiseq2500 (Illumina Inc. San Diego, CA, USA). The resultant identical sequences were collapsed into FASTQCOL from which the software package DArTsoft14 was used for markers discovery and scoring. The Single nucleotide polymorphism (SNP) markers were scored and converted to HapMap format after mapping them to the cassava ( Manihot esculenta ) reference genome v8.1 available in Phytozome (Goodstein et al. 2012 ). Genotype data processing The data in HapMap format was converted to variant call format (VCF) using TASSEL (Bradbury et al. 2007). The genotype data was filtered by removing SNPs with less than 80% call rate and less than 5% minor allele frequency (MAF) using VCFtools (Danecek et al. 2011 ). The filtered markers were used for subsequent analysis. The marker characteristics such as polymorphic information content (PIC), reproducibility, and call rate were determined in the dartR package of R (Gruber et al. 2018 ). Population structure and diversity analysis of cassava genotypes Population structure analysis and admixed ancestry were estimated using a model-based clustering method implemented in ADMIXTURE software (Alexander et al. 2009 ). To determine the actual number of populations, ten-fold cross-validation (CV) procedure for K1 to K10 was run in ADMIXTURE, and the K value with the lowest CV error was selected as the optimal number of sub-populations. Principal component analysis (PCA) was performed in dartR package of R (Gruber et al. 2018 ) and the first two principal components were plotted based on the sub-populations pre-determined by ADMIXTURE to visualize structure stratification. The genetic diversity indices including observed heterozygosity (H o ), expected heterozygosity (H e ), and fixation index (F st ) for the sub-populations were calculated using adegenet package in R (Jombart 2008 ). Analysis of molecular variance (AMOVA) was determined using the poppr package of R (Kamvar et al. 2014 ). The genetic differentiation among the sub-populations identified in population structure analysis, was assessed using the Nei’s pairwise fixation indices (F st ) using the hierfstat package in R (GOUDET 2005 ). Phylogenetic analysis A neighbor-joining phylogenetic tree showing the different sub-populations from ADMIXTURE was constructed based on the Nei’s pairwise fixation indices (F st ) generated from hierfstat package of R (GOUDET 2005 ). The relationship among individuals was shown by generating a Euclidian distance matrix in R (R Core Team) (R Foundation for Statistical Computing 2021) which was further subjected to hierarchical clustering with the Unweighted Pair-Group Method with Arithmetic Means (UPGMA). The resultant phylogenetic tree was exported in Newick format using the ape package of R (Paradis et al 2004 ) for visualization and annotation in the interactive tree of life (iTOL) Version 6.8 ( https://itol.embl.de/ , accessed on 5th September, 2023) (Letunic & Bork 2016 ). Results Characterization of SNP markers In this study, a comprehensive analysis of single nucleotide polymorphism (SNPs) within the cassava (Manihot esculenta) genome utilizing the reference genome v8.1 was conducted. Initially, a total of 12,841 SNPs was identified, and through the application of stringent filtration criteria (retaining markers with a minor allele frequency > 0.05 and a call rate > 80), 5,247 SNPs were retained for further investigation. The outcome of this filtration process is visually represented in Fig. 1 , where the average call rate for the genotypes was determined to be 96%, exhibiting a narrow range of 92–98% (Fig. 1 A). The distribution across chromosomes was analyzed, and it was noted that chromosome 1 contained the highest number of SNPs, amounting to 477, while chromosome 18 displayed the lowest count with 200 SNPs. The average SNP count per chromosome was calculated to be 292, providing a comprehensive overview of the genomic landscape (Fig. 1 B Furthermore, the 5,247 retained SNPs exhibited an average polymorphic information content (PIC) value of 0.4, indicating a moderate level of diversity. The PIC values ranged from 0.10 to 0.5, highlighting the variability in informativeness among the identified SNPs (Fig. 1 C). Population structure analysis of the cassava genotypes Population structure based on 5,247 (MAF > 0.05 and 80% call rate) identified 5 sub-populations across the 155 cassava genotypes (Fig. 2 ). The 5 sub-populations (pop1, pop2, pop3, pop4 and pop5) were pre-defined by K value of five which showed the least cross-validation error in ADMIXTURE (Fig. 2 A). Pop1 (10) and Pop2 (38) were composed of materials from a cross between a variety from Ibadan Nigeria, TME14 and Ugandan genotype MM160128. Most of the samples clustered in pop3 (75 genotypes) were from a cross between a Columbian variety COL40 and Ibadan, Nigeria variety TME14. Pop4 (18) was composed of materials from a cross between a Ugandan variety MM160128 and Ibadan, Nigeria variety TME14. The genotypes in pop5 (14) were highly admixed and were derived from a cross between Ugandan and Ibadan varieties, Variety TME14 and Variety MM060128, respectively. Much of the genetic makeup of individuals in pop5 was from pop2. Furthermore, the genetic makeup of pop3 was from pop5. In the investigation of genotypic relationship, principal component analysis (PCA) was employed as shown in Fig. 2 B. The examination of the first two principal components, PC1 and PC2, revealed that they collectively accounted for approximately 24.2% of the total genetic variation. The resultant biplot of PC1 and PC2 exhibited a discernible clustering pattern among the samples, mirroring the trends observed in the structure analysis conducted through ADMIXTURE. This alignment with the source-based structure analysis underscored the reliability of the findings. To further understand the genetic relationship among the subpopulations, a neighbor-joining tree based on the Nei’s pairwise fixation indices (F st ) was constructed (Fig. 2 C). The analysis identified three major groups within the studied population. Group 1 which was made of pop1, pop3 and pop5 suggesting a close genetic affinity among these subpopulations. In contrast, pop2 and pop4 each formed distinct clusters, indicating a genetic distinctiveness that sets apart from the aforementioned Group 1. This tree-based approach provided a complimentary perspective, further enriching our understanding of the intricate genetic structure within the studied populations. The genetic relationship among the individuals in the 5 sub-populations was determined by hierarchical clustering of the Euclidean distance matrix using UPGMA method. This resulted into individuals in the five sub-populations to cluster into three major clades (Fig. 3 ). Majority of individuals in pop1, pop3 and pop5 shared one clade while pop2 and pop4 each was on its own clade. This clustering was similar to that of the neighbor-joining phylogenetic tree in Fig. 2 C above. Genetic diversity indices The mean values for expected (H e ), observed (H o ) and unbiased expected heterozygosity (uHe) were 0.30, 0.32 and 0.31, respectively (Table 1 ). The gene diversity values represented by He ranged from 0.28 in pop1 to 0.31 in pop2. The Ho was between 0.3 (pop4) and 0.33 (pop2, pop3 and pop5). Table 1 Genetic diversity indices of the 5 sub-populations based on SNP markers Sub-population No. of Individuals H e H o uH e pop1 10 0.28 0.31 0.3 pop2 38 0.31 0.33 0.32 pop3 75 0.29 0.33 0.29 pop4 18 0.3 0.3 0.31 pop5 14 0.3 0.33 0.31 Minimum 0.29 0.3 0.29 Maximum 0.31 0.33 0.31 Mean - 0.3 0.32 0.31 H e expected heterozygosity, H o observed heterozygosity, uH e , unbiased expected heterozygosity. The population fixation indices were used to estimate the genetic differentiation among the five sub-populations due to genetic structure (Table 2 ). Pop3 and pop4 showed the greatest genetic distance (F st = 0.23) while pop1 and pop5, and pop2 and pop4 had the least genetic distance of F st = 0.12. Table 2 Sub-population’s pairwise genetic differentiation index (F st ). Sub-population pop1 pop2 pop3 pop4 pop5 pop1 NA pop2 0.14 NA pop3 0.21 0.22 NA pop4 0.13 0.12 0.23 NA pop5 0.12 0.08 0.15 0.13 NA Analysis of molecular variance (AMOVA) showed that the overall fixation index of the whole population was 0.13 (Table 3 ). The variation between sub-populations was ~ 34% while variation within sub-populations was 66%. Table 3 Analysis of molecular variance of all genotypes using 5,247 SNPs as markers Source of variance Df Sum Mean Variation (%) Between sub-populations 4 26795.68 6698.92 33.98 Within genotypes 150 69002.98 460.02 66.02 Total 154 95798.66 622.07 100 Fixation index (F st ) 0.13 *Fixation index (Fst) for the whole population based on Nei’s distance matrix. Discussion In this study, a total of 5,247 SNP markers were generated from 155 cassava genotypes with moderate polymorphism that revealed moderate genetic diversity These results were inconsistent with the results by Soro et al. ( 2023 ) and Ferguson et al. ( 2019 ) that obtained lower number of SNPs; 36 SNPs from 184 cassava genotypes (Soro et al. 2023 ) and 1,124 SNPs from 522 genotypes respectively. In addition, the contrary results were obtained in the study by Ogbonna et al. ( 2021 ) and Rabbi et al. ( 2017 ) that obtained higher 27,045 SNPs from 3,354 genotypes and 72,279 genome-wide SNP markers from 672 cassava accessions respectively. These findings suggests that the number and distribution of markers across the chromosomes were linked to the level of genetic diversity (Ogbonna et al. 2021 ). The average PIC value of 0.4 for SNPs showed that they were moderately informative with ~ 65% of the markers having PIC values of 0.4–0.5 which were higher than for previous studies in cassava, 0.26 (de Oliveira et al. 2014 ), 0.18 (Ogbonna et al. 2021 ) and 0.24 (de Albuquerque et al. 2018 ) using SNP markers but similar results were observed with SNPs by Eltaher et al. ( 2018 ), indicating the suitability of these SNP markers for the genetic diversity study of cassava accessions. The population structure analysis resulted into5 sub-populations which was an indication of differentiation within the cassava panel. It was observed that the composition of each sub-population was based on the source of materials and/or pedigree as all the genotypes were originally from biparental populations whose parents were from West Africa, East Africa and Southern America. Such kind of genetic divergence based on the crop origin was reported by Adjebeng-Danquah et al. ( 2020 ) and Adu et al. ( 2021 ). Principal component analysis (PCA) confirmed the genetic relationship amongst accessions in the diversity panel. The clustering of accessions in the PCA biplots was similar to that of structure analysis in ADMIXTURE which was consistent with other reports (Adu et al. 2021 ; Soro et al. 2023 ). However, the total genetic variation explained by the first two principal components (PCs) was lower in this study (~ 24.2%) compared to 38.2% and 48.13% obtained in the study by Adu et al. ( 2021 ) and Soro et al. ( 2023 ) respectively. These results could be due to the narrow genetic background of the population derived from biparental populations. The NJ and hierarchical classified grouped the 155 cassava genotypes into three major groups revealing a shared gene pool within each cluster with Cluster 1 comprising of pop1, pop3 and pop5 whose cassava genotypes shared common parents. The average observed heterozygosity (H O ) was determined to be 0.32, aligning with findings from similar studies, which reported Ho of 0.33 (Ferguson et al. 2019 ) and 0.32 (Ogbonna et al. 2021 ) respectively. Analysis of heterozygosity within distinct sub-populations revealed significantly diversity, suggesting potential hybridization and selective processes among the genotypes. This diversity underscores the adaptability and resilience of the species to environmental changes, diseases, and pests, as proposed by Goulet et al. ( 2017 ). Such genetic diversity serves as a valuable resource for breeders, offering to develop new varieties with enhanced traits and increased resistance to various stresses. The differentiation among the groups was assessed using Fst index, which estimates genetic differentiation among groups. According to Wright (1968), Fst values are categorized as high (> 0.25), moderate (0.15–0.25), and low (< 0.05). This index serves as an estimate of genetic flow between sub-populations and, when considered alongside heterozygosity, can directly influence genetic differentiation. In this study, the differentiation between pop 1 and 3 (F st = 0.21), 2 and 3 (F st = 0.22), and 3 and 4 (F st = 0.23) was observed to be moderate. However, the lowest differentiation was noted between pop 2 and pop 5 (F st = 0.08). This could be attributed to the fact that pop 2 and pop 5 were derived from the same parents. Furthermore, the sources of parents for pop 1 and 3 were geographically isolated (Africa and South America), potentially limiting the exchange of materials. The results of AMOVA indicated that there was higher level of diversity within individual accessions compared to variation observed between sub-populations suggesting the presence of various genetic variants, and alleles within the accessions studied. On the other hand, diversity between sub-populations implied the gene flow of genetic exchange between these populations, preventing them from becoming distinct from each other (Adu et al. 2021 ). Conclusion In conclusion, the detailed findings regarding the distribution of SNPs across chromosomes and the assessment of polymorphic information content (PIC) significantly contribute to a more profound understanding of the genetic architecture of cassava. This lays a foundation for future investigations into its diversity and potential applications in breeding programs or genetic studies. The markers successfully clustered cassava populations into distinct sub-populations based on their allele’s genetic variants. The genotypes exhibited a range of moderate to high genetic diversity, suggesting the presence of valuable alleles for desirable traits. The incorporation of these genotypes into cassava improvement efforts holds the potential to yield improved varieties within breeding programs. Declarations Acknowledgments The research grant was provided by NextGen cassava Project to Makerere Regional Centre for Crop improvement (MaRCCI) for the research and training of the first author. The International Institute for Tropical Agriculture (IITA) Uganda hosted field experiments at their experimental field stations and provided field assistance and INTERTEK Australia Lab for sequencing the cassava genotypes. Funding Funding for this work was provided by Cornell University through a sub-award agreement (N0.84941-11056 between TARI and Cornell University through Next Generation Cassava Breeding Project Authors Information Makerere University, Department of Agricultural Production, College of Agricultural and Environmental Sciences, P.O Box 7062, Kampala, Uganda Karoline L. Sichalwe, Richard Edema, Isaac O. Dramadri. Patrick Rubahaiyo Tanzania Agricultural Research Institute, P.O Box 30031, Kibaha,Tanzania Karoline Sichalwe, Doreen Mgonja International Institute of Tropical Agriculture, Plot 25 Mikocheni Industrial Area, Mwenge Coca-Cola Road, Mikocheni Dar es Salaam, Tanzania Ismail Kayondo, Edward Kanju CSIR- Savannah Agriculture Research Institute, Tamale, Ghana Emmanuel A. Adjei Tanzania Agricultural Research Institute, P.O Box 1433, Mwanza, Tanzania Heneriko Kulembeka International Livestock Research Institute (ILRI), P.O Box 30709, Nairobi, Kenya Wilson Kimani Authors contributions The authors made the following contributions to the study: Conceptualization was led by KLS, IK and WK; study design involved KLS, EK, IK; data collection was carried out by KLS: analysis and investigation were conducted by KLS and WK; the writing of the draft and final manuscript was primarily the responsibility of KLS, with contributions from EAA, PR, IOD, EK, DM and IK; the final manuscript was reviewed and approved by all authors; funding acquisition was managed by RE, HK, EK, and ISK; and supervision was provided by PR, EK, and ISK. Conflicts of interest The authors declare no conflict of interest with any one and the funders had no role in the design of the study, data collection, analysis, or interpretation of the results or in the writing of the manuscript. 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Genet Mol Res: GMR 15(3). https://doi.org/10.4238/gmr.15038615 Cite Share Download PDF Status: Published Journal Publication published 16 Jul, 2024 Read the published version in Journal of Applied Genetics → Version 1 posted Reviewers agreed at journal 16 Feb, 2024 Reviewers invited by journal 16 Feb, 2024 First submitted to journal 08 Feb, 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-3944682","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273396164,"identity":"3c1837ee-7191-4d7b-bf53-f63c71cc23bb","order_by":0,"name":"KAROLINE LEONARD SICHALWE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYNACA4YEBvYGEMOCFC08B0AMCeLtSWCQSADRRGjhbz/78OGPArs8/pnPr274USABFOlOwKtF4ky6sTGPQXKxxO2csps9QIdJnDm7Aa8WA4Y0NmkGgwOJDbdz0m7wALUYSOQS0ML/jE3yB1DL/Jtn0m7+IUqLRBqbBA9Qy4Yb7MduE2WLxI1nzCC/JG48k8N2W8ZAgoegX/j70xgf/vhjlzjv+PFnN9/8sZHjb+/FrwUJ8BiASWKVgwD7A1JUj4JRMApGwQgCAKR+RehazEgdAAAAAElFTkSuQmCC","orcid":"","institution":"Makerere University College of Agricultural and Environmental Sciences","correspondingAuthor":true,"prefix":"","firstName":"KAROLINE","middleName":"LEONARD","lastName":"SICHALWE","suffix":""},{"id":273396165,"identity":"ed506e86-3f2a-440a-b172-eb1d48e135fd","order_by":1,"name":"Ismail Kayondo","email":"","orcid":"","institution":"IITA: International Institute of Tropical Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Ismail","middleName":"","lastName":"Kayondo","suffix":""},{"id":273396166,"identity":"7ed63c4d-d7e2-4e1e-8a7d-b4311c70edb1","order_by":2,"name":"Richard Edema","email":"","orcid":"","institution":"Makerere University College of Agricultural and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Edema","suffix":""},{"id":273396167,"identity":"9df71243-d5ac-4198-9b81-f9169e26e4d7","order_by":3,"name":"Isaac O. Dramadri","email":"","orcid":"","institution":"Makerere University College of Agricultural and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"O.","lastName":"Dramadri","suffix":""},{"id":273396168,"identity":"f28b01f5-e69f-4b82-81f7-54ca76ed4da8","order_by":4,"name":"Heneriko Kulembeka","email":"","orcid":"","institution":"Tanzania Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Heneriko","middleName":"","lastName":"Kulembeka","suffix":""},{"id":273396169,"identity":"0f0db639-a3b8-46b2-a2c3-98096b98a868","order_by":5,"name":"Wilson Kimani","email":"","orcid":"","institution":"ILRI: International Livestock Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Wilson","middleName":"","lastName":"Kimani","suffix":""},{"id":273396170,"identity":"ed328a37-0fcb-4afa-af57-a4489cf12343","order_by":6,"name":"Doreen Mgonja","email":"","orcid":"","institution":"Tanzania Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Doreen","middleName":"","lastName":"Mgonja","suffix":""},{"id":273396171,"identity":"816ffa37-8617-461e-b88b-2c2cf1d6ef75","order_by":7,"name":"Patrick Rubahaiyo","email":"","orcid":"","institution":"Makerere University College of Agricultural and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Rubahaiyo","suffix":""},{"id":273396172,"identity":"190458e6-a444-4898-8a6c-8b032356a38d","order_by":8,"name":"Edward Kanju","email":"","orcid":"","institution":"IITA: International Institute of Tropical Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"","lastName":"Kanju","suffix":""}],"badges":[],"createdAt":"2024-02-10 02:40:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3944682/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3944682/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13353-024-00892-x","type":"published","date":"2024-07-16T16:13:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51377153,"identity":"03ad4e85-d472-40c6-99fd-42a6cb53bd22","added_by":"auto","created_at":"2024-02-20 14:26:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90179,"visible":true,"origin":"","legend":"\u003cp\u003eSNP markers characteristics.\u003cstrong\u003e A\u003c/strong\u003e Call rate of the SNP markers, \u003cstrong\u003eB\u003c/strong\u003e distribution of SNP markers across the 18 chromosomes of cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e), and \u003cstrong\u003eC\u003c/strong\u003ePolymorphic information content (PIC) range values of the 5,247 SNP markers.\u003c/p\u003e","description":"","filename":"floatimage158.png","url":"https://assets-eu.researchsquare.com/files/rs-3944682/v1/d026dd5de6ebca563999e927.png"},{"id":51377154,"identity":"acc31148-1d32-455e-9a6e-f5c78eadbfa6","added_by":"auto","created_at":"2024-02-20 14:26:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278363,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure analysis of 155 cassava genotypes based on 5,247 genome-wide SNP markers with K=5. \u003cstrong\u003eA\u003c/strong\u003e Hierarchical organization of the genetic relatedness of the 155 cassava genotypes. Each bar represents a single genotype and the coloured segments within each bar represents the proportional contribution of each sub- population to that accession. \u003cstrong\u003eB\u003c/strong\u003e A biplot of the first two principal components with colours based on the 5 sub-populations. \u003cstrong\u003eC\u003c/strong\u003e Neighbor-joining tree showing the genetic differentiation among the five sub-populations from ADMIXTURE analysis.\u003c/p\u003e","description":"","filename":"floatimage253.png","url":"https://assets-eu.researchsquare.com/files/rs-3944682/v1/6b023083df987ae43e6fd58a.png"},{"id":51377155,"identity":"513c51d1-9955-4a6e-9854-215ac44d1df1","added_by":"auto","created_at":"2024-02-20 14:26:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1363691,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree for 155 cassava genotypes based on 5,247 SNP markers. The colour of each of the five sub-populations is based on the ADMIXTURE results. The individuals clustered into three major groups (I, II and III).\u003c/p\u003e","description":"","filename":"floatimage351.png","url":"https://assets-eu.researchsquare.com/files/rs-3944682/v1/a1d75a931c4d600467bdac47.png"},{"id":61596716,"identity":"2bc91a83-0417-40d7-bba7-c3b1ab07c5ac","added_by":"auto","created_at":"2024-08-01 17:29:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2233170,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3944682/v1/452fa54f-b8da-44a6-9bba-65d9385b1224.pdf"}],"financialInterests":"","formattedTitle":"Genetic diversity and population structure of Uganda cassava germplasm","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCassava (\u003cem\u003eManihot esculenta\u003c/em\u003e Crantz) stands as one of the most important staple crops, providing sustenance and livelihood to millions of people globally, especially in tropical regions (Ceballos et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This crop serves as the primary source of calories and income for small-scale farmers with its starchy roots providing carbohydrates and leaves offering vitamins, proteins and minerals (Bayata \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Cassava's unique ability to thrive in marginal ecologies with low soil fertility and rainfall makes it a crucial player in global agriculture, food security and economic growth in such ecologies (Ngongo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdvancements in breeding programs heavily rely on gaining a deeper understanding of the genetic diversity within populations which functions as a repository of diverse genes with significant potential for enhancing productivity and adapting to both abiotic and biotic stress (Adu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The relevance is particularly pronounced in the context of ongoing climate change and global warming as genetic variation serves as a foundation upon which breeding efforts are built, providing the raw material for developing improved varieties. Variations in traits such as yield, disease resistance, drought tolerance, and nutritional content are essential for enhancing cassava resilience and nutritional value, making them fundamental prerequisites for sustainable cassava cultivation, crop improvement, and conservation. The analysis of genetic diversity and population structure plays a crucial role in understanding the historical patterns of natural selection and the genetic connections among different accessions (Luo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA considerable number of molecular markers such as SSR (Adjebeng-Danquah et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), DarT (Adu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and ISSR (Tiago et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) have been employed to assess genetic variation in cassava accessions. The utilization of SNP has largely been used to assess the diversity of genetic information between and within populations because of their abundance, stability, polymorphism, and automation compatibility.\u003c/p\u003e \u003cp\u003eThe study aims to address the historical challenges posed by Cassava Mosaic Disease (CMD) and Cassava Brown Disease (CBSD) to cassava production by utilizing the resistant cassava populations to elucidate the genetic characteristics, population structure, and gene flow patterns. Therefore, the objectives of this study are 1. To unravel allelic diversity and heterozygosity levels 2. To explore population structure 3. To detect potential admixture and, 4. To identify gene flow patterns. The ultimate goal includes contributing valuable information to cassava breeding programs in Uganda for the development of more resistant and disease-resistant varieties.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and populations development\u003c/h2\u003e \u003cp\u003eOne hundred and fifty-five cassava genotypes were used in the study, with 80 genotypes being collected from existing germplasm at National Agriculture Semi-Arid Research Resources Institute (NaSARRI) in Eastern Uganda and 75 accession which were obtained from International Institute of Tropical Agriculture (IITA), Sendusu in Central Uganda. These genotypes were developed as biparental populations using parents originating from West Africa, East Africa and Southern America.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eA total of 155 cassava leaf samples were collected from a single representative plant, dried at room temperature and shipped in silica gel to Intertek, Australia Lab for DNA sequencing. High molecular weight DNA was extracted from the leaf samples and subjected to quality control before Diversity Array Technology sequencing (DarTseq\u0026trade;) (Kilian et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The PCR products of each sample were sequenced using Hiseq2500 (Illumina Inc. San Diego, CA, USA). The resultant identical sequences were collapsed into FASTQCOL from which the software package DArTsoft14 was used for markers discovery and scoring. The Single nucleotide polymorphism (SNP) markers were scored and converted to HapMap format after mapping them to the cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e) reference genome v8.1 available in Phytozome (Goodstein et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenotype data processing\u003c/h2\u003e \u003cp\u003eThe data in HapMap format was converted to variant call format (VCF) using TASSEL (Bradbury et al. 2007). The genotype data was filtered by removing SNPs with less than 80% call rate and less than 5% minor allele frequency (MAF) using VCFtools (Danecek et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The filtered markers were used for subsequent analysis. The marker characteristics such as polymorphic information content (PIC), reproducibility, and call rate were determined in the \u003cem\u003edartR\u003c/em\u003e package of R (Gruber et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure and diversity analysis of cassava genotypes\u003c/h2\u003e \u003cp\u003ePopulation structure analysis and admixed ancestry were estimated using a model-based clustering method implemented in ADMIXTURE software (Alexander et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). To determine the actual number of populations, ten-fold cross-validation (CV) procedure for K1 to K10 was run in ADMIXTURE, and the K value with the lowest CV error was selected as the optimal number of sub-populations. Principal component analysis (PCA) was performed in \u003cem\u003edartR\u003c/em\u003e package of R (Gruber et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the first two principal components were plotted based on the sub-populations pre-determined by ADMIXTURE to visualize structure stratification.\u003c/p\u003e \u003cp\u003eThe genetic diversity indices including observed heterozygosity (H\u003csub\u003eo\u003c/sub\u003e), expected heterozygosity (H\u003csub\u003ee\u003c/sub\u003e), and fixation index (F\u003csub\u003est\u003c/sub\u003e) for the sub-populations were calculated using \u003cem\u003eadegenet\u003c/em\u003e package in R (Jombart \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Analysis of molecular variance (AMOVA) was determined using the \u003cem\u003epoppr\u003c/em\u003e package of R (Kamvar et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The genetic differentiation among the sub-populations identified in population structure analysis, was assessed using the Nei\u0026rsquo;s pairwise fixation indices (F\u003csub\u003est\u003c/sub\u003e) using the \u003cem\u003ehierfstat\u003c/em\u003e package in R (GOUDET \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic analysis\u003c/h2\u003e \u003cp\u003eA neighbor-joining phylogenetic tree showing the different sub-populations from ADMIXTURE was constructed based on the Nei\u0026rsquo;s pairwise fixation indices (F\u003csub\u003est\u003c/sub\u003e) generated from \u003cem\u003ehierfstat\u003c/em\u003e package of R (GOUDET \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The relationship among individuals was shown by generating a Euclidian distance matrix in R (R Core Team) (R Foundation for Statistical Computing 2021) which was further subjected to hierarchical clustering with the Unweighted Pair-Group Method with Arithmetic Means (UPGMA). The resultant phylogenetic tree was exported in Newick format using the \u003cem\u003eape\u003c/em\u003e package of R (Paradis et al \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) for visualization and annotation in the interactive tree of life (iTOL) Version 6.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de/\u003c/span\u003e\u003cspan address=\"https://itol.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 5th September, 2023) (Letunic \u0026amp; Bork \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of SNP markers\u003c/h2\u003e \u003cp\u003eIn this study, a comprehensive analysis of single nucleotide polymorphism (SNPs) within the cassava (Manihot esculenta) genome utilizing the reference genome v8.1 was conducted. Initially, a total of 12,841 SNPs was identified, and through the application of stringent filtration criteria (retaining markers with a minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and a call rate\u0026thinsp;\u0026gt;\u0026thinsp;80), 5,247 SNPs were retained for further investigation. The outcome of this filtration process is visually represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, where the average call rate for the genotypes was determined to be 96%, exhibiting a narrow range of 92\u0026ndash;98% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe distribution across chromosomes was analyzed, and it was noted that chromosome 1 contained the highest number of SNPs, amounting to 477, while chromosome 18 displayed the lowest count with 200 SNPs. The average SNP count per chromosome was calculated to be 292, providing a comprehensive overview of the genomic landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003c/p\u003e \u003cp\u003eFurthermore, the 5,247 retained SNPs exhibited an average polymorphic information content (PIC) value of 0.4, indicating a moderate level of diversity. The PIC values ranged from 0.10 to 0.5, highlighting the variability in informativeness among the identified SNPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure analysis of the cassava genotypes\u003c/h2\u003e \u003cp\u003ePopulation structure based on 5,247 (MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and 80% call rate) identified 5 sub-populations across the 155 cassava genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The 5 sub-populations (pop1, pop2, pop3, pop4 and pop5) were pre-defined by K value of five which showed the least cross-validation error in ADMIXTURE (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Pop1 (10) and Pop2 (38) were composed of materials from a cross between a variety from Ibadan Nigeria, TME14 and Ugandan genotype MM160128. Most of the samples clustered in pop3 (75 genotypes) were from a cross between a Columbian variety COL40 and Ibadan, Nigeria variety TME14. Pop4 (18) was composed of materials from a cross between a Ugandan variety MM160128 and Ibadan, Nigeria variety TME14. The genotypes in pop5 (14) were highly admixed and were derived from a cross between Ugandan and Ibadan varieties, Variety TME14 and Variety MM060128, respectively. Much of the genetic makeup of individuals in pop5 was from pop2. Furthermore, the genetic makeup of pop3 was from pop5.\u003c/p\u003e \u003cp\u003eIn the investigation of genotypic relationship, principal component analysis (PCA) was employed as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. The examination of the first two principal components, PC1 and PC2, revealed that they collectively accounted for approximately 24.2% of the total genetic variation. The resultant biplot of PC1 and PC2 exhibited a discernible clustering pattern among the samples, mirroring the trends observed in the structure analysis conducted through ADMIXTURE. This alignment with the source-based structure analysis underscored the reliability of the findings.\u003c/p\u003e \u003cp\u003eTo further understand the genetic relationship among the subpopulations, a neighbor-joining tree based on the Nei\u0026rsquo;s pairwise fixation indices (F\u003csub\u003est\u003c/sub\u003e) was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The analysis identified three major groups within the studied population. Group 1 which was made of pop1, pop3 and pop5 suggesting a close genetic affinity among these subpopulations. In contrast, pop2 and pop4 each formed distinct clusters, indicating a genetic distinctiveness that sets apart from the aforementioned Group 1. This tree-based approach provided a complimentary perspective, further enriching our understanding of the intricate genetic structure within the studied populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe genetic relationship among the individuals in the 5 sub-populations was determined by hierarchical clustering of the Euclidean distance matrix using UPGMA method. This resulted into individuals in the five sub-populations to cluster into three major clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Majority of individuals in pop1, pop3 and pop5 shared one clade while pop2 and pop4 each was on its own clade. This clustering was similar to that of the neighbor-joining phylogenetic tree in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC above.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity indices\u003c/h2\u003e \u003cp\u003eThe mean values for expected (H\u003csub\u003ee\u003c/sub\u003e), observed (H\u003csub\u003eo\u003c/sub\u003e) and unbiased expected heterozygosity (uHe) were 0.30, 0.32 and 0.31, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The gene diversity values represented by He ranged from 0.28 in pop1 to 0.31 in pop2. The Ho was between 0.3 (pop4) and 0.33 (pop2, pop3 and pop5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity indices of the 5 sub-populations based on SNP markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of Individuals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003euH\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epop1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epop2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epop3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epop4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epop5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eH\u003csub\u003ee\u003c/sub\u003e expected heterozygosity, H\u003csub\u003eo\u003c/sub\u003e observed heterozygosity, uH\u003csub\u003ee\u003c/sub\u003e, unbiased expected heterozygosity.\u003c/p\u003e \u003cp\u003eThe population fixation indices were used to estimate the genetic differentiation among the five sub-populations due to genetic structure (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pop3 and pop4 showed the greatest genetic distance (F\u003csub\u003est\u003c/sub\u003e = 0.23) while pop1 and pop5, and pop2 and pop4 had the least genetic distance of F\u003csub\u003est\u003c/sub\u003e = 0.12.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSub-population\u0026rsquo;s pairwise genetic differentiation index (F\u003csub\u003est\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epop1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epop2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epop3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epop4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epop5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epop1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epop2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epop3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epop4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epop5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalysis of molecular variance (AMOVA) showed that the overall fixation index of the whole population was 0.13 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The variation between sub-populations was ~\u0026thinsp;34% while variation within sub-populations was 66%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular variance of all genotypes using 5,247 SNPs as markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariation (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween sub-populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26795.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6698.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin genotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69002.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e460.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e154\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95798.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e622.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixation index (F\u003csub\u003est\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Fixation index (Fst) for the whole population based on Nei\u0026rsquo;s distance matrix.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, a total of 5,247 SNP markers were generated from 155 cassava genotypes with moderate polymorphism that revealed moderate genetic diversity These results were inconsistent with the results by Soro et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Ferguson et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) that obtained lower number of SNPs; 36 SNPs from 184 cassava genotypes (Soro et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and 1,124 SNPs from 522 genotypes respectively. In addition, the contrary results were obtained in the study by Ogbonna et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Rabbi et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that obtained higher 27,045 SNPs from 3,354 genotypes and 72,279 genome-wide SNP markers from 672 cassava accessions respectively. These findings suggests that the number and distribution of markers across the chromosomes were linked to the level of genetic diversity (Ogbonna et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe average PIC value of 0.4 for SNPs showed that they were moderately informative with ~\u0026thinsp;65% of the markers having PIC values of 0.4\u0026ndash;0.5 which were higher than for previous studies in cassava, 0.26 (de Oliveira et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), 0.18 (Ogbonna et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and 0.24 (de Albuquerque et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) using SNP markers but similar results were observed with SNPs by Eltaher et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), indicating the suitability of these SNP markers for the genetic diversity study of cassava accessions.\u003c/p\u003e \u003cp\u003eThe population structure analysis resulted into5 sub-populations which was an indication of differentiation within the cassava panel. It was observed that the composition of each sub-population was based on the source of materials and/or pedigree as all the genotypes were originally from biparental populations whose parents were from West Africa, East Africa and Southern America. Such kind of genetic divergence based on the crop origin was reported by Adjebeng-Danquah et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Adu et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Principal component analysis (PCA) confirmed the genetic relationship amongst accessions in the diversity panel. The clustering of accessions in the PCA biplots was similar to that of structure analysis in ADMIXTURE which was consistent with other reports (Adu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soro et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the total genetic variation explained by the first two principal components (PCs) was lower in this study (~\u0026thinsp;24.2%) compared to 38.2% and 48.13% obtained in the study by Adu et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Soro et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) respectively. These results could be due to the narrow genetic background of the population derived from biparental populations.\u003c/p\u003e \u003cp\u003eThe NJ and hierarchical classified grouped the 155 cassava genotypes into three major groups revealing a shared gene pool within each cluster with Cluster 1 comprising of pop1, pop3 and pop5 whose cassava genotypes shared common parents.\u003c/p\u003e \u003cp\u003eThe average observed heterozygosity (H\u003csub\u003eO\u003c/sub\u003e) was determined to be 0.32, aligning with findings from similar studies, which reported Ho of 0.33 (Ferguson et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and 0.32 (Ogbonna et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) respectively. Analysis of heterozygosity within distinct sub-populations revealed significantly diversity, suggesting potential hybridization and selective processes among the genotypes. This diversity underscores the adaptability and resilience of the species to environmental changes, diseases, and pests, as proposed by Goulet et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such genetic diversity serves as a valuable resource for breeders, offering to develop new varieties with enhanced traits and increased resistance to various stresses.\u003c/p\u003e \u003cp\u003eThe differentiation among the groups was assessed using Fst index, which estimates genetic differentiation among groups. According to Wright (1968), Fst values are categorized as high (\u0026gt;\u0026thinsp;0.25), moderate (0.15\u0026ndash;0.25), and low (\u0026lt;\u0026thinsp;0.05). This index serves as an estimate of genetic flow between sub-populations and, when considered alongside heterozygosity, can directly influence genetic differentiation. In this study, the differentiation between pop 1 and 3 (F\u003csub\u003est\u003c/sub\u003e = 0.21), 2 and 3 (F\u003csub\u003est\u003c/sub\u003e = 0.22), and 3 and 4 (F\u003csub\u003est\u003c/sub\u003e = 0.23) was observed to be moderate. However, the lowest differentiation was noted between pop 2 and pop 5 (F\u003csub\u003est\u003c/sub\u003e = 0.08). This could be attributed to the fact that pop 2 and pop 5 were derived from the same parents. Furthermore, the sources of parents for pop 1 and 3 were geographically isolated (Africa and South America), potentially limiting the exchange of materials.\u003c/p\u003e \u003cp\u003eThe results of AMOVA indicated that there was higher level of diversity within individual accessions compared to variation observed between sub-populations suggesting the presence of various genetic variants, and alleles within the accessions studied. On the other hand, diversity between sub-populations implied the gene flow of genetic exchange between these populations, preventing them from becoming distinct from each other (Adu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the detailed findings regarding the distribution of SNPs across chromosomes and the assessment of polymorphic information content (PIC) significantly contribute to a more profound understanding of the genetic architecture of cassava. This lays a foundation for future investigations into its diversity and potential applications in breeding programs or genetic studies.\u003c/p\u003e \u003cp\u003eThe markers successfully clustered cassava populations into distinct sub-populations based on their allele\u0026rsquo;s genetic variants. The genotypes exhibited a range of moderate to high genetic diversity, suggesting the presence of valuable alleles for desirable traits. The incorporation of these genotypes into cassava improvement efforts holds the potential to yield improved varieties within breeding programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe research grant was provided by NextGen cassava Project to Makerere Regional Centre for Crop improvement (MaRCCI) for the research and training of the first author. The International Institute for Tropical Agriculture (IITA) Uganda hosted field experiments at their experimental field stations and provided field assistance and INTERTEK Australia Lab for sequencing the cassava genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this work was provided by Cornell University through a sub-award agreement (N0.84941-11056 between TARI and Cornell University through Next Generation Cassava Breeding Project\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthors Information\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eMakerere University, Department of Agricultural Production, College of Agricultural and Environmental Sciences, P.O Box 7062, Kampala, Uganda\u003c/p\u003e\n\u003cp\u003eKaroline L. Sichalwe, Richard Edema, Isaac O. Dramadri. Patrick Rubahaiyo\u003c/p\u003e\n\u003cp\u003eTanzania Agricultural Research Institute, P.O Box 30031, Kibaha,Tanzania\u003c/p\u003e\n\u003cp\u003eKaroline Sichalwe, Doreen Mgonja\u003c/p\u003e\n\u003cp\u003eInternational Institute of Tropical Agriculture, Plot 25 Mikocheni Industrial Area, Mwenge Coca-Cola Road, Mikocheni Dar es Salaam, Tanzania\u003c/p\u003e\n\u003cp\u003eIsmail Kayondo, Edward Kanju\u003c/p\u003e\n\u003cp\u003eCSIR- Savannah Agriculture Research Institute, Tamale, Ghana\u003c/p\u003e\n\u003cp\u003eEmmanuel A. Adjei\u003c/p\u003e\n\u003cp\u003eTanzania Agricultural Research Institute, P.O Box 1433, Mwanza, Tanzania \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHeneriko Kulembeka\u003c/p\u003e\n\u003cp\u003eInternational Livestock Research Institute (ILRI), P.O Box 30709, Nairobi, Kenya\u003c/p\u003e\n\u003cp\u003eWilson Kimani\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors made the following contributions to the study: Conceptualization was led by KLS, IK and WK; study design involved KLS, EK, IK; data collection was carried out by KLS: analysis and investigation were conducted by KLS and WK; the writing of the draft and final manuscript was primarily the responsibility of KLS, with contributions from EAA, PR, IOD, EK, DM and IK; the final manuscript was reviewed and approved by all authors; funding acquisition was managed by RE, HK, EK, and ISK; and supervision was provided by PR, EK, and ISK.\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest with any one and the funders had no role in the design of the study, data collection, analysis, or interpretation of the results or in the writing of the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdjebeng-Danquah J, Manu-Aduening J, Asante IK, Agyare RY, Gracen V, Offei SK (2020) Genetic diversity and population structure analysis of Ghanaian and exotic cassava accessions using simple sequence repeat (SSR) markers. \u003cem\u003eHeliyon 6\u003c/em\u003e(1), e03154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2019.e03154\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2019.e03154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdu BG, Akromah R, Amoah S, Nyadanu D, Yeboah A, Aboagye LM, Amoah RA, Owusu EG (2021) High-density DArT-based SilicoDArT and SNP markers for genetic diversity and population structure studies in cassava (Manihot esculenta Crantz). 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Genet Mol Res: GMR 15(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4238/gmr.15038615\u003c/span\u003e\u003cspan address=\"10.4238/gmr.15038615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic variation, Single nucleotide polymorphism, polymorphic information content, hierarchical clustering","lastPublishedDoi":"10.21203/rs.3.rs-3944682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3944682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe genetic diversity and population structure were assessed in 155 Uganda cassava genotypes using 5,247 single nucleotide polymorphism (SNP) markers which had an average call rate of 96%. Polymorphic information content values of the markers ranged from 0.1 to 0.5 with an average of 0.4 which was considered to be moderately high. The Principal Component analysis (PCA) showed that the first two components captured\u0026thinsp;~\u0026thinsp;24.2% of the genetic variation. The average genetic diversity was 0.3. The analysis of Molecular Variance (AMOVA) indicated that 66.02% and 33.98% of the total genetic variation occurred within accessions and between sub-populations, respectively. Five sub-populations were identified based on ADMIXTURE structure analysis (K\u0026thinsp;=\u0026thinsp;5). Neighbor-joining tree and hierarchical clustering tree revealed the presence of three different groups which were primarily based on the source of the genotypes. The results suggested that there was considerable genetic variation among the cassava genotypes which is useful in cassava improvement and conservation efforts.\u003c/p\u003e","manuscriptTitle":"Genetic diversity and population structure of Uganda cassava germplasm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 14:26:26","doi":"10.21203/rs.3.rs-3944682/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-16T13:20:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-16T12:43:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Genetics","date":"2024-02-08T20:48:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1c8d3b2-a5f5-40f2-8882-2617ccd3aad3","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T17:13:54+00:00","versionOfRecord":{"articleIdentity":"rs-3944682","link":"https://doi.org/10.1007/s13353-024-00892-x","journal":{"identity":"journal-of-applied-genetics","isVorOnly":false,"title":"Journal of Applied Genetics"},"publishedOn":"2024-07-16 16:13:23","publishedOnDateReadable":"July 16th, 2024"},"versionCreatedAt":"2024-02-20 14:26:26","video":"","vorDoi":"10.1007/s13353-024-00892-x","vorDoiUrl":"https://doi.org/10.1007/s13353-024-00892-x","workflowStages":[]},"version":"v1","identity":"rs-3944682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3944682","identity":"rs-3944682","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}