Genetic diversity and population structure of sweetpotato accessions (Ipomoea batatas [L.] Lam) revealed by single nucleotide polymorphism markers

Genetic diversity and population structure of sweetpotato accessions (Ipomoea batatas [L.] Lam) revealed by single nucleotide polymorphism markers | 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 Genetic diversity and population structure of sweetpotato accessions (Ipomoea batatas [L.] Lam) revealed by single nucleotide polymorphism markers Julia Sibiya, Nomusa Chizhande, Edmore Gasura, Bruce Mutari, Davison Chaingeni, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5960213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Use of molecular markers has improved the analysis of genetic variation by eliminating environmental influences on genotype performance. The objective of this study was to assess the genetic diversity and population structure of 327 sweetpotato genotypes sourced from the major sweetpotato-growing regions of Zimbabwe and from the International Potato Centre (CIP) in Mozambique using low-density Diversity Array Technology (DArTseq) SNP chip covering the 90 chromosomes of sweetpotato. The genotypes' genetic diversity (GD) varied from 0.12 to 0.50, with a mean of 0.36. The mean polymorphic information content (PIC) was 0.29, indicating that the markers were highly informative. There was a good representation of minor alleles within the population, with an average minor allele frequency (MAF) of 0.34. The average observed heterozygosity of 0.12 was consistent with the cross-pollinating system in sweetpotato but could perpetuate a narrow genetic base. There was limited interbreeding between the populations of sweetpotato, as indicated by a mean fixation index (F) of 0.68. The high F values indicated that most alleles per genotype were contributed by one parent, which is unusual in allogamous species as sweetpotato. The sweetpotato genotypes in this study could be clustered into two sub-populations with significant differences within the sub-populations. Genetic variation among genotypes is essential for the improvement of sweetpotato. Still, significant genetic gain could be achieved by cross-pollinating divergent genotypes with high MAF to create segregants with rare alleles. It is, thus, important to capture the rare alleles as they help adapt to current and future environmental shifts. Genetic diversity genotyping SNP markers Sweetpotato Polymorphic Information Content Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sweetpotato ( Ipomoea batatas [L.] Lam) is an important root crop used for food, feed, and biofuel. The importance of sweetpotato has increased due to the rising prices of staple food crops such as maize ( Zea mays ), wheat ( Triticum aestivum L ), rice ( Oryza sativa ), and Irish potato ( Solanum tuberosum ) (Sapakhova et al. 2023 ). Although its high biomass productivity and adaptability to diverse production environments make it ideal for production in marginal environments (Chiona 2009 ; Cole et al. 2014 ), nevertheless, several biotic (viruses and weevils) and abiotic (drought, low soil fertility) factors still challenge its production. In addition, a lack of improved varieties for production has led to low average yields of 5 t/ha attained by farmers against a potential of 23–25 t/ha (van Vugt and Franke 2018 ). Many smallholder farmers still cultivate landraces and varieties that have low yield potential (Eriksson et al. 2018 ). However, landraces are important sources of genes for adaptation to biotic and abiotic stresses compared to improved varieties. Incorporating landraces in breeding programs can broaden the gene pool for crop improvement but must be followed by effective and efficient selection for high yield and desirable agronomic performance (Lazaridi et al. 2024 ). Therefore, understanding the available genetic variation and population structure is imperative for devising suitable breeding strategies. Developing new improved varieties depends on wide genetic variation for selection (Ulukan 2009 ). To evaluate genetic variation for significant traits and assess the population structure for conservation purposes, morphological and molecular markers have been employed (Emanuelli et al. 2013 ). Molecular markers are particularly useful for evaluating genetic diversity and relatedness among various genetic resources. To assess genetic variation in sweetpotato, several marker types have been used, such as simple sequence repeats (SSRs) or microsatellites (Buteler et al. 2002 ; Gichuru et al. 2006 ; Koussao et al. 2014 ; Imamura et al. 2014 ; Ngailo et al. 2016 ), inter-simple sequence repeats (ISSRs) (Huang and Sun 2000 ), random amplified polymorphic DNA (RAPD) (Gasura and Mukasa 2011 ; Maquia et al. 2013 ), amplified fragment length polymorphism (AFLP) (Zhang et al. 2000 ; Elameen et al. 2008 ), selective amplification of microsatellite polymorphic loci (SAMPL) (Yu-Tien Tseng et al 2002) DNA amplification fingerprinting (He et al.1995) and single nucleotide polymorphism (SNPs) (Arif et al. 2010). Single nucleotide polymorphisms (SNPs) markers, in particular, facilitate genetic studies because they are amenable to automation and are highly reproducible (Jehan and Lakhanpaul 2006 ) compared to the other markers. The Crop Breeding Institute (CBI) in Zimbabwe is involved in breeding for biofortified sweetpotato cultivars to improve nutritional and processing quality, and market preference. Accordingly, the CBI often introduces sweetpotato germplasm from diverse genetic backgrounds into Zimbabwe. The introductions are not well characterised for immediate incorporation into breeding programs. It is imperative to conduct genetic studies on the introduced and local germplasm as a pre-breeding activity to devise appropriate breeding strategies. Therefore, this study aimed to assess the population structure and genetic diversity of local and introduced sweetpotato genotypes in Zimbabwe using SNP markers. The study will contribute to baseline information required to inform breeding efforts for sweetpotato improvement in Zimbabwe and its germplasm exchange partners. Materials and Methods Plant material A total of 327 sweetpotato genotypes were used in this study (S1 Table). The genotypes consisted of landraces collected from the major sweetpotato-growing regions of Zimbabwe (Fig. 1 ) and 181 genotypes from the International Potato Centre (CIP) in Mozambique. From Zimbabwe, 89 landraces were collected from Mashonaland East, 27 from Manicaland, 16 from Masvingo, and 10 from Mashonaland Central provinces. The genotypes were grouped based on the geographic region of their source for further analysis, with the groups comprising genotypes obtained from CIP-Mozambique as one group and the other group genotypes sourced in Zimbabwe. The group of genotypes from Zimbabwe was further subdivided into four sub-populations based on the province from which they were collected. Leaf sampling and shipment Fresh leaf samples were collected from 3-week-old seedlings of each genotype for deoxyribonucleic acid ( DNA) extraction. Supplementary information S3 provides the leaf sampling, drying, packaging, and shipment protocol used in this study as guided by Intertek laboratory in Sweden https://www.intertek.com/agriculture/testing/ . Four leaf disks were punched out for each accession using a leaf-cutting punch. To avoid cross-contamination between accessions, 70% alcohol was used to sterilize the puncher. Sampled leaf discs were oven-dried at 35 o C for 24 hours, and the plates were immediately secured with lids after drying. The sampling plates were packaged and shipped to the Intertek laboratory in Sweden https://www.intertek.com/contact/ema/sweden/ . The samples were genotyped on a low-density Diversity Array Technology (DArTseq) SNP chip covering the 90 chromosomes of sweetpotato. Data filtering and SNP calling The snpReady software was used to filter the first 37 SNPs from the genotyping-by-sequencing (GBS) pipeline by eliminating SNPs with 20% missing data and less than 5% minor allele frequency (MAF) (Italo 2018 ). A low-cost diagnostic SNP set (low-density high informative single nucleotide polymorphism marker set) of 30 SNPs was developed for rapid Quality Assurance and Quality Control (QA/QC) of sweetpotato populations and proved to be effective in identifying relatively similar mislabeling error rates as a high-density SNP set of 10 159 markers (Gemenet et al. 2020). Twenty-seven of these were used in this study. Individual genotypes with missing data, more than 20% were eliminated. SNPs with a MAF of 0.05 and a call rate of 80% or above were kept. As a result, analysis was conducted using 327 genotypes and 27 polymorphic SNPs (Table 1 ). Data analysis The Nei and Li ( 1979 ) technique was used to calculate the number of alleles per locus, the number of effective alleles per locus, Shannon's Information Index, and gene diversity. An analysis of molecular variance (AMOVA) was conducted to partition total genetic variation within and among the different groups of the genotypes using GenAlex version 6.5 (Peakall and Smouse 2012). Population structure analysis was performed using the Structure software version 2.3.4 (Pritchard et al. 2009 ). The burn-in period and the Markov Chain Monte Carlo (MCMC) replications were set at 10,000. The Structure analysis was done for K ranging from 1 to 10 with 5 iterations at each K to determine the optimum number of clusters. The ideal K value was identified using the Evanno method, which relies on ΔK (Evanno et al. 2005 ). The genetic diversity parameters including expected heterozygosity (He), overall genetic diversity (GD), polymorphic information content (PIC), the main allele frequency, and the breeding coefficient (Fis) were calculated as well as the pairwise genetic distances and kinship matrix among the 327 sweetpotato genotypes were calculated using GenAlex version 6.5. R studio version 4.3.0. The principal coordinate and cluster analysis were carried out using the KDCompute platform. A dendrogram was then generated on the dissimilarity matrix using the Gower’s Ward.D2 hierarchical clustering method. Results Genetic parameters of the markers and genotypes The PIC for the markers ranged from 0.11 to 0.37, with an average of 0.29 for the 27 polymorphic SNP markers (Table 1 ). The most informative markers were snpIB00001, snpIB000010, snpIB00012, snpIB00032, and snpIB00051, while snpIB00057 was the least informative. The minor allele frequency ranged between 0.3 and 0.5, averaging 0.26. The average expected heterozygosity (He) and observed heterozygosity (Ho) were 0.12 and 0.36, respectively. The marker snpIB00032 had the maximum heterozygosity of 0.69. The maximum Nei genetic diversity index was 0.5 and the minimum was 0.12, with an average of 0.36 (Table 1 ). Table 1 Genetic diversity parameters were revealed by analysing 30 polymorphic SNP markers among the 327 sweet potato accessions. Marker MAF PIC value Allele No Ho He Nei snpIB00001 0.42 0.37 2 0.01 0.49 0.49 snpIB00010 0.5 0.37 2 0.03 0.5 0.5 snpIB00012 0.47 0.37 2 0.61 0.5 0.5 snpIB00021 0.19 0.26 2 0.09 0.3 0.3 snpIB00026 0.29 0.33 1 0 0.41 0.41 snpIB00027 0.24 0.3 2 0.02 0.37 0.37 snpIB00029 0.2 0.27 2 0.1 0.32 0.32 snpIB00030 0.09 0.16 2 0.08 0.17 0.17 snpIB00031 0.22 0.28 2 0.02 0.34 0.34 snpIB00032 0.49 0.37 2 0.69 0.5 0.5 snpIB00035 0.07 0.12 2 0.02 0.13 0.13 snpIB00037 0.14 0.21 2 0.01 0.24 0.24 snpIB00038 0.1 0.16 2 0.02 0.17 0.17 snpIB00040 0.37 0.36 2 0.05 0.47 0.47 snpIB00042 0.39 0.36 1 0 0.48 0.48 snpIB00043 0.27 0.32 2 0.06 0.39 0.39 snpIB00044 0.34 0.35 2 0.47 0.45 0.45 snpIB00045 0.19 0.26 2 0.05 0.31 0.31 snpIB00046 0.23 0.29 2 0.03 0.36 0.36 snpIB00049 0.23 0.29 2 0.02 0.36 0.36 snpIB00050 0.11 0.18 1 0 0.2 0.2 snpIB00051 0.47 0.37 2 0.03 0.5 0.5 snpIB00052 0.28 0.32 2 0.06 0.4 0.4 snpIB00053 0.25 0.3 2 0.01 0.37 0.37 snpIB00056 0.3 0.33 2 0.53 0.42 0.42 snpIB00057 0.06 0.11 2 0.02 0.12 0.12 snpIB00058 0.2 0.27 2 0.02 0.32 0.32 snpIB00060 0.34 0.35 2 0.25 0.45 0.45 snplB00054 0.23 0.29 2 0.04 0.36 0.36 snpIB00036 0.25 0.31 2 0.13 0.38 0.38 Minimum 0.06 0.11 1 0.23 0.12 0.12 Maximum 0.50 0.37 2 0.00 0.5 0.50 Mean 0.26 0.29 0.12 0.36 0.36 MAF = Minor allele frequency, PIC = Polymorphic information content, Ho = Observed heterozygosity, He = Expected heterozygosity, Nei = Nei genetic distance The genotypes' genetic diversity (GD) varied from 0.12 to 0.50, averaging 0.36 (Table 1 ). The polymorphic information content (PIC) showed a broad range from 0.11 to 0.37, with a mean of 0.29. The minor allele frequency (MAF) spanned from 0.06 to 0.50, averaging 0.34. Observed heterozygosity ranged from 0.00 to 0.23, with a mean of 0.12. The Fixation index (F) had a mean of 0.68, ranging from 0.36 to 1.00 (Table 2 ). Table 2 Genetic parameters of 327 sweetpotato genotypes measured with 30 SNP markers. Statistics GD PIC MAF Ho F Mean 0.36 0.29 0.26 0.12 0.68 Lower 0.12 0.11 0.06 0.00 0.36 Upper 0.50 0.37 0.50 0.23 1.00 MAF = minor allele frequency, GD = gene diversity, PIC = polymorphic information content, Ho = observed heterozygosity, F = Fixation Index. Population structure as revealed by SNP markers Evanno’s method deduced that the highest ΔK value occurred at K = 2 (Fig. 2 ). The scree plot shows a significant drop in ΔK after K = 2 and all the other values of K show no change in ΔK in the scree plot, confirming that K was optimal at 2. The two populations were divided into the largest group with 250 genotypes and the smaller group with 77 genotypes (Fig. 3 ). The distribution of the genotypes into clusters was based on kinship. Very few individual genotypes exhibited kinship to both groups. Different colours represent separate clusters, while the length of the coloured segment shows the estimated proportion of the membership of a genotype in a particular population. Cluster analysis of the genotypic data for the 327 genotypes The neighbor-joining dendrogram clustered the 327 genotypes into five clusters: C1, C2, C3, C4, and C5 based on the SNP markers (Fig. 4 ). The biggest cluster was Cluster 2, which comprised 52% of the genotypes. The next biggest cluster was Cluster 3, with 22%, and Cluster 1, with 13%. Clusters 4 and 5 were the smallest, with 10 and 3% membership, respectively. The names of individuals belonging to each cluster are provided in Supplementary Table S2. Principal coordinate clustering The principal coordinate analysis (PCoA) showed variability among sweetpotato accessions based on the 30 SNP markers. The thirty SNP markers were used as a cost effective rapid QA/QC set for sweetpotato (Gemenet et al. 2020). The two PCs on the PCoA biplot accounted for 90% of the genotype variation (Fig. 5 ). The genotypes from CIP-Mozambique were mostly clustered in the bottom two quadrants, while the genotypes from Zimbabwe were scattered in the top quadrants. The genotypes from Zimbabwe did not show further clustering by their source province. Analysis of molecular variance The analysis of molecular variance (AMOVA) among the 327 sweetpotato genotypes showed that all the variance observed was due to genotypic differences within the two populations (Table 3 ). The expected heterozygosity of genes within populations was 0.09 for population I and 0.15 for population II (Table 4 ). The corresponding fixation indices (F ST ) for both populations I and II were 0.08. The estimated Wright’s fixation index (PhiPT max) of 0.591 and 0.291 were obtained for sub-populations I and II, respectively. Table 3 Analysis of molecular variance of sweet potato populations based on 30 SNPs Source Df SS MS Est. Var. % Among Pops 1 9.391 9.391 0.029 0% Within Pops 324 1951.367 6.023 6.023 100% Total 325 1960.758 6.052 100% df = degree of freedom, SS = sum of squares, Est.Var = estimated variance, % = percentage variation Table 4 Expected heterozygosity, fixation indices and membership of the two populations found among the 327 genotypes assessed with SNP markers FST He Mean Wright Fst No of genotypes Membership I - 0.08 0.09 0.59 250 0.75 II 0.08 - 0.15 0.29 77 0.25 FST = allele frequency divergence among subpopulations; He = expected heterozygosity Discussion The average PIC value of 0.29 found in this study was high for SNP markers, showing that overall; the SNP markers could effectively distinguish the genotypes or the genetic diversity among the genotypes. The PIC values and expected heterozygosity (He) are indicators of genetic diversity within breeding populations, evolutionary pressures, and mutation rates at specific gene locations (Botstein et al. 1980 ; Shete et al. 2000 ). PIC values are particularly helpful in linkage analysis, indicating how well markers track inheritance between parents and offspring (Zhang et al. 2016 ). Though SNPs have lower PIC values due to their bi-allelic nature compared to SSR markers, they offer higher resolutions for genetic differentiation among genotypes because they detect differences at specific loci (Helyar et al. 2011 ; Suvi et al. 2020 ). The three markers that showed higher polymorphisms can be used in the future genotyping of sweetpotato clones to identify distant parents for developing and deploying varieties. The markers with low PIC may still be linked to important traits or regions of interest. Low PIC of a marker does not discount its potential value in genetic studies (Gupta et al. 2001 ). This study's heterozygosity (Ho) mean of 0.12 was very low, suggesting that the sweetpotato genotypes were highly homozygous. This is unusual for sweetpotato since it is allogamous. Sweetpotato is a hexaploidy with generally high heterozygosity for most loci (Monden and Tahara 2017 ; Lindqvist-Kreuze et al. 2024 ). However, most sweetpotato genotypes in Zimbabwe produce sterile flowers due to unfavorable photoperiodicity and altitude. As such, sweetpotato is predominantly reproduced vegetatively and cloned, which could have caused the low level of heterozygosity (Gurmu et al. 2013 ). In contrast, other reports have shown high heterozygosity in sweetpotato (Monden and Tahara 2017 ; Lindqvist et al. 2024) Ultimately, the lack of heterozygosity in the study genotypes could pose a serious bottleneck for breeding and can threaten sweetpotato production should new biotic and abiotic stresses emerge. Low levels of heterozygosity are often associated with limited genetic variation for resistance to emerging biotic or abiotic stresses (Baafi et al. 2016 ). The study showed that the inbreeding coefficient (F) varied widely and had a high mean value, indicating that the genotypes in the population underwent significant inbreeding. The F value estimates the likelihood that two alleles at every locus within an organism are descendants of the same parent (Wright and Adams 2012 ). High F values in the study indicated that one parent contributed the most alleles per genotype. This further emphasizes that most genotypes were clones or developed from self-pollination, where the same parent contributes all the chromosomes to the offspring. This is common in sweetpotato, which suffers from a lot of cross-incompatibility. Ultimately, there are high levels of inbreeding (Gurmu et al. 2013 ). Due to a lack of diversity within individual genotypes, inbreeding could lead to suppressed yield potential or unfavourable nutritional content. Generally, with some exceptions, genotypes were randomly allocated to different groups and subgroups. Some closely related genotypes were grouped into the same cluster or sub-cluster (cluster 2), confirming the existence of a relationship between the pedigree and SNP marker groupings in this study. Some of these genotypes appeared to be clustered according to the pedigree group (DOF/31, DOF/34, DOF/52, DOF/53, DOF/56, DOF/61, DOF/63, DOF/64), but there were some inconsistencies. For instance, Chemugomo, Masvingo 8, Hwedza 2, DOF/37, and Murehwa clustered together in cluster 5 despite being unrelated by pedigree. Similar findings of genotypes clustering according to their pedigree grouping were earlier reported (Dhliwayo et al. 2009 ; Semagn et al. 2012 ; Yang et al. 2017 ). Genotypes were not clustered based on geographical location, which could be due to the exchange of genetic material in geographical regions. Therefore, crossing more distant sweetpotato genotypes might create wider genetic variation and exploit heterosis during breeding for new varieties (Molin et al. 2013 ). The molecular variability indicated that genetic variation existed only between genotypes within populations, and no variation was observed between different populations. This might be attributed to the limited number of markers that were used in this study. On the other hand, the exchange of genetic material among the sources means that there is potentially no differentiation based on the collection areas. Consequently, the observed relationships among genotypes within each sub-population could be exploited to design an effective sweetpotato breeding program in Zimbabwe. Conclusion and recommendations The underlying genetic diversity and population structure in the sweetpotato introductions from CIP and local germplasm can potentially be exploited for improving sweetpotato cultivars in Zimbabwe and other countries with similar germplasm. The SNP markers used in this study were polymorphic and exhibited discriminating capability to diagnose allelic differences among the 327 genotypes. The sweetpotato genotypes were genetically divergent, providing adequate allelic diversity for recombination. Genotypes such as G40 and G9, G142 and G73, G114 and G73, I40 and G210, and G18 and G114 were distantly related and could be useful parents for recombination and developing breeding populations. Declarations Conflicts of Interest: The authors declare no conflict of interest. Funding: This work was supported by the Accelerated Breeding Initiative (ABI)-Transform ABI. Author Contribution N.C. performed the research and wrote the original draft of the manuscript; J.S. was the main supervisor for the research, E.G. was the co-supervisor involved in designing and supervising the research work; J.S., B.M., D.C., D.K. L.M., and I.M. were involved in reviewing the manuscript; E. D. was involved in data analysis and reviewing the manuscript. All authors have read and agreed to the published version of the manuscript. 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(2016) “Carbon Science in 2016: Status, Challenges and Perspectives.” Carbon 98(May 2018): 708–32. http://dx.doi.org/10.1016/j.carbon.2015.11.060. Zhang, Zheng, Scott Schwartz, Lukas Wagner, and Webb Miller (2000) “A Greedy Algorithm for Aligning DNA Sequences.” Journal of Computational Biology 7(1–2): 203–14. Supplementary Information Supplementary information item S3: Intertek sampling, drying and shipment protocol is not available with this version. Additional Declarations No competing interests reported. Supplementary Files S1ListofGenotypes.xlsx S2Listofgenotypesinsubtypes.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5960213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":413352121,"identity":"5c5cb29a-5e95-4c59-bdad-2a2ba101b13a","order_by":0,"name":"Julia Sibiya","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Sibiya","suffix":""},{"id":413352122,"identity":"56706cd8-e9a8-462b-9bcc-98494de89f89","order_by":1,"name":"Nomusa Chizhande","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYPCCAzwMEkCKh8EGSDI2HiBFSxpISwNRWhigWg7DuLiBOXt38ocfNXdk5Gc3P3vwpuK83dr2w0BbamyicWmx7Dm7TbLn2DMexjnHzA3nnLmdvO1MIlDLsbTcBhxaDG7kbmPgbTjMwyyRYCbN23Y72ewAUAtjw2F8WjZ//AvUwiaR/k2a99+5ZLPzDwlq2SANsoVHIgdoS8MBO7MbhGw5c3abtMyxwzwSEjllknOOJSeY3QDakoDPL8d7N398U3PYXn5G+jaJNzV29mbn0x8++FBjg1MLBkgEq0wgVjkI2JOieBSMglEwCkYGAAC0zGZP9R1evAAAAABJRU5ErkJggg==","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":true,"prefix":"","firstName":"Nomusa","middleName":"","lastName":"Chizhande","suffix":""},{"id":413352123,"identity":"70e2b669-0078-4200-bcb3-6b8d179fca41","order_by":2,"name":"Edmore Gasura","email":"","orcid":"","institution":"University of Zimbabwe","correspondingAuthor":false,"prefix":"","firstName":"Edmore","middleName":"","lastName":"Gasura","suffix":""},{"id":413352124,"identity":"4b814633-5534-41d3-8665-6dc95741bd18","order_by":3,"name":"Bruce Mutari","email":"","orcid":"","institution":"Alliance for a Green Revolution in Africa,Nairobi.","correspondingAuthor":false,"prefix":"","firstName":"Bruce","middleName":"","lastName":"Mutari","suffix":""},{"id":413352125,"identity":"daf1f344-b86d-463f-bcf6-898d63745f9b","order_by":4,"name":"Davison Chaingeni","email":"","orcid":"","institution":"Department of Research and Specialist Services, Crops Research Division","correspondingAuthor":false,"prefix":"","firstName":"Davison","middleName":"","lastName":"Chaingeni","suffix":""},{"id":413352127,"identity":"a1375a47-6275-4d01-a915-013530cac293","order_by":5,"name":"Dumisani Kutywayo","email":"","orcid":"","institution":"Department of Research and Specialist Services, Crops Research Division","correspondingAuthor":false,"prefix":"","firstName":"Dumisani","middleName":"","lastName":"Kutywayo","suffix":""},{"id":413352128,"identity":"518435cf-fca9-4160-82eb-74198e27f1b1","order_by":6,"name":"Lennin Musundire","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center, (CIMMYT)","correspondingAuthor":false,"prefix":"","firstName":"Lennin","middleName":"","lastName":"Musundire","suffix":""},{"id":413352129,"identity":"80fae215-f870-48c9-9e20-3e45a0a20aaa","order_by":7,"name":"Isack Mathew","email":"","orcid":"","institution":"University of Venda, FESA","correspondingAuthor":false,"prefix":"","firstName":"Isack","middleName":"","lastName":"Mathew","suffix":""},{"id":413352131,"identity":"2fb28936-5fcb-4e0e-b21b-777d201b2b5e","order_by":8,"name":"Emeline Dossa","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Emeline","middleName":"","lastName":"Dossa","suffix":""}],"badges":[],"createdAt":"2025-02-04 18:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5960213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5960213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76006888,"identity":"745018a3-fce5-48ac-bad4-5193c3f7d061","added_by":"auto","created_at":"2025-02-11 11:29:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105626,"visible":true,"origin":"","legend":"\u003cp\u003eSweetpotato Growing regions in Zimbabwe\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/59d5ed87d01ba31329ca870f.png"},{"id":76005219,"identity":"86e96426-2cb0-4f3f-b13f-7f50d6565d6b","added_by":"auto","created_at":"2025-02-11 11:13:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12908,"visible":true,"origin":"","legend":"\u003cp\u003eA scree plot showing the Delta K values for the number of clusters determined among 327 sweetpotato genotypes measured using 27 SNP markers\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/799bbd0c48959615d25ca158.png"},{"id":76006881,"identity":"65a3b3f8-41be-48cb-8f41-fce090555c65","added_by":"auto","created_at":"2025-02-11 11:29:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44285,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure of 327 sweetpotato genotypes based on SNP markers\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/e57f8437bba73cdc78134b34.png"},{"id":76005815,"identity":"13a57686-5311-44e5-acb5-59d3f5832aa4","added_by":"auto","created_at":"2025-02-11 11:21:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":231972,"visible":true,"origin":"","legend":"\u003cp\u003eNeighbor-joining dendrogram showing the clustering of 327 accessions genotyped with SNP markers. C1-5=cluster 1 to 5\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/497ad625de24fe2efc851f47.png"},{"id":76005807,"identity":"ee4a50c7-ac57-49ce-b494-9146b7f63d6b","added_by":"auto","created_at":"2025-02-11 11:21:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":75597,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinate analysis showing variability among sweet potato accessions from different regions in Zimbabwe based on 30 SNP markers.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/a6d114f531648de4e009e1b6.png"},{"id":79135152,"identity":"9c59acb7-bd41-426c-bcd0-0f691192bdfd","added_by":"auto","created_at":"2025-03-24 21:16:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/ebe6a77a-f251-4a38-9a1a-72d0b5239f7e.pdf"},{"id":76005221,"identity":"cbb1abec-274e-4bc9-9d0d-5c1953383eb7","added_by":"auto","created_at":"2025-02-11 11:13:19","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16658,"visible":true,"origin":"","legend":"","description":"","filename":"S1ListofGenotypes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/31fe5e09e1daf1aad61e6377.xlsx"},{"id":76005223,"identity":"7a751d75-7edb-499b-a618-d2cb761fb69e","added_by":"auto","created_at":"2025-02-11 11:13:19","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":41472,"visible":true,"origin":"","legend":"","description":"","filename":"S2Listofgenotypesinsubtypes.xls","url":"https://assets-eu.researchsquare.com/files/rs-5960213/v1/4268d1b4187afd454266dff0.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic diversity and population structure of sweetpotato accessions (Ipomoea batatas [L.] Lam) revealed by single nucleotide polymorphism markers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSweetpotato (\u003cem\u003eIpomoea batatas\u003c/em\u003e [L.] Lam) is an important root crop used for food, feed, and biofuel. The importance of sweetpotato has increased due to the rising prices of staple food crops such as maize (\u003cem\u003eZea mays\u003c/em\u003e), wheat (\u003cem\u003eTriticum aestivum L\u003c/em\u003e), rice (\u003cem\u003eOryza sativa\u003c/em\u003e), and Irish potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e) (Sapakhova et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although its high biomass productivity and adaptability to diverse production environments make it ideal for production in marginal environments (Chiona \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cole et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), nevertheless, several biotic (viruses and weevils) and abiotic (drought, low soil fertility) factors still challenge its production.\u003c/p\u003e \u003cp\u003eIn addition, a lack of improved varieties for production has led to low average yields of 5 t/ha attained by farmers against a potential of 23\u0026ndash;25 t/ha (van Vugt and Franke \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Many smallholder farmers still cultivate landraces and varieties that have low yield potential (Eriksson et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, landraces are important sources of genes for adaptation to biotic and abiotic stresses compared to improved varieties. Incorporating landraces in breeding programs can broaden the gene pool for crop improvement but must be followed by effective and efficient selection for high yield and desirable agronomic performance (Lazaridi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, understanding the available genetic variation and population structure is imperative for devising suitable breeding strategies.\u003c/p\u003e \u003cp\u003eDeveloping new improved varieties depends on wide genetic variation for selection (Ulukan \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). To evaluate genetic variation for significant traits and assess the population structure for conservation purposes, morphological and molecular markers have been employed (Emanuelli et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Molecular markers are particularly useful for evaluating genetic diversity and relatedness among various genetic resources. To assess genetic variation in sweetpotato, several marker types have been used, such as simple sequence repeats (SSRs) or microsatellites (Buteler et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gichuru et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Koussao et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Imamura et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ngailo et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), inter-simple sequence repeats (ISSRs) (Huang and Sun \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), random amplified polymorphic DNA (RAPD) (Gasura and Mukasa \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Maquia et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), amplified fragment length polymorphism (AFLP) (Zhang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Elameen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), selective amplification of microsatellite polymorphic loci (SAMPL) (Yu-Tien Tseng et al 2002) DNA amplification fingerprinting (He et al.1995) and single nucleotide polymorphism (SNPs) (Arif et al. 2010). Single nucleotide polymorphisms (SNPs) markers, in particular, facilitate genetic studies because they are amenable to automation and are highly reproducible (Jehan and Lakhanpaul \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) compared to the other markers.\u003c/p\u003e \u003cp\u003eThe Crop Breeding Institute (CBI) in Zimbabwe is involved in breeding for biofortified sweetpotato cultivars to improve nutritional and processing quality, and market preference. Accordingly, the CBI often introduces sweetpotato germplasm from diverse genetic backgrounds into Zimbabwe. The introductions are not well characterised for immediate incorporation into breeding programs. It is imperative to conduct genetic studies on the introduced and local germplasm as a pre-breeding activity to devise appropriate breeding strategies. Therefore, this study aimed to assess the population structure and genetic diversity of local and introduced sweetpotato genotypes in Zimbabwe using SNP markers. The study will contribute to baseline information required to inform breeding efforts for sweetpotato improvement in Zimbabwe and its germplasm exchange partners.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003ePlant material\u003c/p\u003e \u003cp\u003eA total of 327 sweetpotato genotypes were used in this study (S1 Table). The genotypes consisted of landraces collected from the major sweetpotato-growing regions of Zimbabwe (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and 181 genotypes from the International Potato Centre (CIP) in Mozambique. From Zimbabwe, 89 landraces were collected from Mashonaland East, 27 from Manicaland, 16 from Masvingo, and 10 from Mashonaland Central provinces. The genotypes were grouped based on the geographic region of their source for further analysis, with the groups comprising genotypes obtained from CIP-Mozambique as one group and the other group genotypes sourced in Zimbabwe. The group of genotypes from Zimbabwe was further subdivided into four sub-populations based on the province from which they were collected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLeaf sampling and shipment\u003c/p\u003e \u003cp\u003eFresh leaf samples were collected from 3-week-old seedlings of each genotype for deoxyribonucleic acid \u003cb\u003e(\u003c/b\u003eDNA) extraction. Supplementary information S3 provides the leaf sampling, drying, packaging, and shipment protocol used in this study as guided by Intertek laboratory in Sweden \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.intertek.com/agriculture/testing/\u003c/span\u003e\u003cspan address=\"https://www.intertek.com/agriculture/testing/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Four leaf disks were punched out for each accession using a leaf-cutting punch. To avoid cross-contamination between accessions, 70% alcohol was used to sterilize the puncher. Sampled leaf discs were oven-dried at 35\u003csup\u003eo\u003c/sup\u003eC for 24 hours, and the plates were immediately secured with lids after drying. The sampling plates were packaged and shipped to the Intertek laboratory in Sweden \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.intertek.com/contact/ema/sweden/\u003c/span\u003e\u003cspan address=\"https://www.intertek.com/contact/ema/sweden/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The samples were genotyped on a low-density Diversity Array Technology (DArTseq) SNP chip covering the 90 chromosomes of sweetpotato.\u003c/p\u003e \u003cp\u003eData filtering and SNP calling\u003c/p\u003e \u003cp\u003eThe snpReady software was used to filter the first 37 SNPs from the genotyping-by-sequencing (GBS) pipeline by eliminating SNPs with 20% missing data and less than 5% minor allele frequency (MAF) (Italo \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A low-cost diagnostic SNP set (low-density high informative single nucleotide polymorphism marker set) of 30 SNPs was developed for rapid Quality Assurance and Quality Control (QA/QC) of sweetpotato populations and proved to be effective in identifying relatively similar mislabeling error rates as a high-density SNP set of 10 159 markers (Gemenet et al. 2020). Twenty-seven of these were used in this study. Individual genotypes with missing data, more than 20% were eliminated. SNPs with a MAF of 0.05 and a call rate of 80% or above were kept. As a result, analysis was conducted using 327 genotypes and 27 polymorphic SNPs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe Nei and Li (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) technique was used to calculate the number of alleles per locus, the number of effective alleles per locus, Shannon's Information Index, and gene diversity. An analysis of molecular variance (AMOVA) was conducted to partition total genetic variation within and among the different groups of the genotypes using GenAlex version 6.5 (Peakall and Smouse 2012). Population structure analysis was performed using the Structure software version 2.3.4 (Pritchard et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe burn-in period and the Markov Chain Monte Carlo (MCMC) replications were set at 10,000. The Structure analysis was done for K ranging from 1 to 10 with 5 iterations at each K to determine the optimum number of clusters. The ideal K value was identified using the Evanno method, which relies on ΔK (Evanno et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe genetic diversity parameters including expected heterozygosity (He), overall genetic diversity (GD), polymorphic information content (PIC), the main allele frequency, and the breeding coefficient (Fis) were calculated as well as the pairwise genetic distances and kinship matrix among the 327 sweetpotato genotypes were calculated using GenAlex version 6.5. R studio version 4.3.0. The principal coordinate and cluster analysis were carried out using the KDCompute platform. A dendrogram was then generated on the dissimilarity matrix using the Gower\u0026rsquo;s Ward.D2 hierarchical clustering method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eGenetic parameters of the markers and genotypes\u003c/p\u003e \u003cp\u003eThe PIC for the markers ranged from 0.11 to 0.37, with an average of 0.29 for the 27 polymorphic SNP markers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most informative markers were snpIB00001, snpIB000010, snpIB00012, snpIB00032, and snpIB00051, while snpIB00057 was the least informative. The minor allele frequency ranged between 0.3 and 0.5, averaging 0.26. The average expected heterozygosity (He) and observed heterozygosity (Ho) were 0.12 and 0.36, respectively. The marker snpIB00032 had the maximum heterozygosity of 0.69. The maximum Nei genetic diversity index was 0.5 and the minimum was 0.12, with an average of 0.36 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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 parameters were revealed by analysing 30 polymorphic SNP markers among the 327 sweet potato accessions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIC value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllele No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNei\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnplB00054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnpIB00036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.12\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.50\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e \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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMAF\u0026thinsp;=\u0026thinsp;Minor allele frequency, PIC\u0026thinsp;=\u0026thinsp;Polymorphic information content, Ho\u0026thinsp;=\u0026thinsp;Observed heterozygosity, He\u0026thinsp;=\u0026thinsp;Expected heterozygosity, Nei\u0026thinsp;=\u0026thinsp;Nei genetic distance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe genotypes' genetic diversity (GD) varied from 0.12 to 0.50, averaging 0.36 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The polymorphic information content (PIC) showed a broad range from 0.11 to 0.37, with a mean of 0.29. The minor allele frequency (MAF) spanned from 0.06 to 0.50, averaging 0.34. Observed heterozygosity ranged from 0.00 to 0.23, with a mean of 0.12. The Fixation index (F) had a mean of 0.68, ranging from 0.36 to 1.00 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eGenetic parameters of 327 sweetpotato genotypes measured with 30 SNP markers.\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\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\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\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\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\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\u003eMAF\u0026thinsp;=\u0026thinsp;minor allele frequency, GD\u0026thinsp;=\u0026thinsp;gene diversity, PIC\u0026thinsp;=\u0026thinsp;polymorphic information content, Ho\u0026thinsp;=\u0026thinsp;observed heterozygosity, F\u0026thinsp;=\u0026thinsp;Fixation Index.\u003c/p\u003e \u003cp\u003ePopulation structure as revealed by SNP markers\u003c/p\u003e \u003cp\u003eEvanno\u0026rsquo;s method deduced that the highest ΔK value occurred at K\u0026thinsp;=\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The scree plot shows a significant drop in ΔK after K\u0026thinsp;=\u0026thinsp;2 and all the other values of K show no change in ΔK in the scree plot, confirming that K was optimal at 2. The two populations were divided into the largest group with 250 genotypes and the smaller group with 77 genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The distribution of the genotypes into clusters was based on kinship. Very few individual genotypes exhibited kinship to both groups. Different colours represent separate clusters, while the length of the coloured segment shows the estimated proportion of the membership of a genotype in a particular population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster analysis of the genotypic data for the 327 genotypes\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe neighbor-joining dendrogram clustered the 327 genotypes into five clusters: C1, C2, C3, C4, and C5 based on the SNP markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The biggest cluster was Cluster 2, which comprised 52% of the genotypes. The next biggest cluster was Cluster 3, with 22%, and Cluster 1, with 13%. Clusters 4 and 5 were the smallest, with 10 and 3% membership, respectively. The names of individuals belonging to each cluster are provided in Supplementary Table S2.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal coordinate clustering\u003c/p\u003e \u003cp\u003eThe principal coordinate analysis (PCoA) showed variability among sweetpotato accessions based on the 30 SNP markers. The thirty SNP markers were used as a cost effective rapid QA/QC set for sweetpotato (Gemenet et al. 2020). The two PCs on the PCoA biplot accounted for 90% of the genotype variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The genotypes from CIP-Mozambique were mostly clustered in the bottom two quadrants, while the genotypes from Zimbabwe were scattered in the top quadrants. The genotypes from Zimbabwe did not show further clustering by their source province.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of molecular variance\u003c/p\u003e \u003cp\u003eThe analysis of molecular variance (AMOVA) among the 327 sweetpotato genotypes showed that all the variance observed was due to genotypic differences within the two populations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The expected heterozygosity of genes within populations was 0.09 for population I and 0.15 for population II (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The corresponding fixation indices (F \u003csub\u003eST\u003c/sub\u003e) for both populations I and II were 0.08. The estimated Wright\u0026rsquo;s fixation index (PhiPT max) of 0.591 and 0.291 were obtained for sub-populations I and II, respectively.\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 sweet potato populations based on 30 SNPs\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=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEst. Var.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmong Pops\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithin Pops\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1951.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1960.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003edf\u0026thinsp;=\u0026thinsp;degree of freedom, SS\u0026thinsp;=\u0026thinsp;sum of squares, Est.Var\u0026thinsp;=\u0026thinsp;estimated variance, % = percentage variation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExpected heterozygosity, fixation indices and membership of the two populations found among the 327 genotypes assessed with SNP markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Wright Fst\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo of genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMembership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFST\u0026thinsp;=\u0026thinsp;allele frequency divergence among subpopulations; He\u0026thinsp;=\u0026thinsp;expected heterozygosity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe average PIC value of 0.29 found in this study was high for SNP markers, showing that overall; the SNP markers could effectively distinguish the genotypes or the genetic diversity among the genotypes. The PIC values and expected heterozygosity (He) are indicators of genetic diversity within breeding populations, evolutionary pressures, and mutation rates at specific gene locations (Botstein et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Shete et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). PIC values are particularly helpful in linkage analysis, indicating how well markers track inheritance between parents and offspring (Zhang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Though SNPs have lower PIC values due to their bi-allelic nature compared to SSR markers, they offer higher resolutions for genetic differentiation among genotypes because they detect differences at specific loci (Helyar et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Suvi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The three markers that showed higher polymorphisms can be used in the future genotyping of sweetpotato clones to identify distant parents for developing and deploying varieties. The markers with low PIC may still be linked to important traits or regions of interest. Low PIC of a marker does not discount its potential value in genetic studies (Gupta et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study's heterozygosity (Ho) mean of 0.12 was very low, suggesting that the sweetpotato genotypes were highly homozygous. This is unusual for sweetpotato since it is allogamous. Sweetpotato is a hexaploidy with generally high heterozygosity for most loci (Monden and Tahara \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lindqvist-Kreuze et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, most sweetpotato genotypes in Zimbabwe produce sterile flowers due to unfavorable photoperiodicity and altitude. As such, sweetpotato is predominantly reproduced vegetatively and cloned, which could have caused the low level of heterozygosity (Gurmu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, other reports have shown high heterozygosity in sweetpotato (Monden and Tahara \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lindqvist et al. 2024) Ultimately, the lack of heterozygosity in the study genotypes could pose a serious bottleneck for breeding and can threaten sweetpotato production should new biotic and abiotic stresses emerge. Low levels of heterozygosity are often associated with limited genetic variation for resistance to emerging biotic or abiotic stresses (Baafi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The study showed that the inbreeding coefficient (F) varied widely and had a high mean value, indicating that the genotypes in the population underwent significant inbreeding. The F value estimates the likelihood that two alleles at every locus within an organism are descendants of the same parent (Wright and Adams \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). High F values in the study indicated that one parent contributed the most alleles per genotype. This further emphasizes that most genotypes were clones or developed from self-pollination, where the same parent contributes all the chromosomes to the offspring. This is common in sweetpotato, which suffers from a lot of cross-incompatibility. Ultimately, there are high levels of inbreeding (Gurmu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Due to a lack of diversity within individual genotypes, inbreeding could lead to suppressed yield potential or unfavourable nutritional content.\u003c/p\u003e \u003cp\u003eGenerally, with some exceptions, genotypes were randomly allocated to different groups and subgroups. Some closely related genotypes were grouped into the same cluster or sub-cluster (cluster 2), confirming the existence of a relationship between the pedigree and SNP marker groupings in this study. Some of these genotypes appeared to be clustered according to the pedigree group (DOF/31, DOF/34, DOF/52, DOF/53, DOF/56, DOF/61, DOF/63, DOF/64), but there were some inconsistencies. For instance, Chemugomo, Masvingo 8, Hwedza 2, DOF/37, and Murehwa clustered together in cluster 5 despite being unrelated by pedigree. Similar findings of genotypes clustering according to their pedigree grouping were earlier reported (Dhliwayo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Semagn et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenotypes were not clustered based on geographical location, which could be due to the exchange of genetic material in geographical regions. Therefore, crossing more distant sweetpotato genotypes might create wider genetic variation and exploit heterosis during breeding for new varieties (Molin et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The molecular variability indicated that genetic variation existed only between genotypes within populations, and no variation was observed between different populations.\u003c/p\u003e \u003cp\u003eThis might be attributed to the limited number of markers that were used in this study. On the other hand, the exchange of genetic material among the sources means that there is potentially no differentiation based on the collection areas. Consequently, the observed relationships among genotypes within each sub-population could be exploited to design an effective sweetpotato breeding program in Zimbabwe.\u003c/p\u003e"},{"header":"Conclusion and recommendations","content":"\u003cp\u003eThe underlying genetic diversity and population structure in the sweetpotato introductions from CIP and local germplasm can potentially be exploited for improving sweetpotato cultivars in Zimbabwe and other countries with similar germplasm. The SNP markers used in this study were polymorphic and exhibited discriminating capability to diagnose allelic differences among the 327 genotypes. The sweetpotato genotypes were genetically divergent, providing adequate allelic diversity for recombination. Genotypes such as G40 and G9, G142 and G73, G114 and G73, I40 and G210, and G18 and G114 were distantly related and could be useful parents for recombination and developing breeding populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the Accelerated Breeding Initiative (ABI)-Transform ABI.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.C. performed the research and wrote the original draft of the manuscript; J.S. was the main supervisor for the research, E.G. was the co-supervisor involved in designing and supervising the research work; J.S., B.M., D.C., D.K. L.M., and I.M. were involved in reviewing the manuscript; E. D. was involved in data analysis and reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors thank the staff at Marondera Horticultural Research Institute and the Roots and Tuber Crops technical team at Harare Research Station for their technical assistance. They also thank the International Potato Center (CIP) for providing some of the material.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArif, Ibrahim A et al. 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Li (1979) \u0026ldquo;Mathematical Model for Studying Genetic Variation in Terms of Restriction Endonucleases.\u0026rdquo; \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 76(10): 5269\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eNgailo, S. et al (2016)\u0026ldquo;Genetic Diversity Assessment of Tanzanian Sweetpotato Genotypes Using Simple Sequence Repeat Markers.\u0026rdquo; \u003cem\u003eSouth African Journal of Botany\u003c/em\u003e 102: 40\u0026ndash;45. http://dx.doi.org/10.1016/j.sajb.2015.08.001.\u003c/li\u003e\n\u003cli\u003ePeakall, Rod, and Peter E. Smouse (2012) \u0026ldquo;GenALEx 6.5: Genetic Analysis in Excel. 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Elston (2000) \u0026ldquo;On Estimating the Heterozygosity and Polymorphism Information Content Value.\u0026rdquo; \u003cem\u003eTheoretical Population Biology\u003c/em\u003e 57(3): 265\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eSuvi, William Titus et al (2020) \u0026ldquo;Assessment of the Genetic Diversity and Population Structure of Rice Genotypes Using SSR Markers.\u0026rdquo; \u003cem\u003eActa Agriculturae Scandinavica Section B: Soil and Plant Science\u003c/em\u003e 70(1): 76\u0026ndash;86. https://doi.org/10.1080/09064710.2019.1670859.\u003c/li\u003e\n\u003cli\u003eUlukan, Hakan (2009) \u0026ldquo;The Evolution of Cultivated Plant Species: Classical Plant Breeding versus Genetic Engineering.\u0026rdquo; \u003cem\u003ePlant Systematics and Evolution\u003c/em\u003e 280(3\u0026ndash;4): 133\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003evan Vugt, D., and A. C. Franke (2018) \u0026ldquo;Exploring the Yield Gap of Orange-Fleshed Sweet Potato Varieties on Smallholder Farmers\u0026rsquo; Fields in Malawi.\u0026rdquo; \u003cem\u003eField Crops Research\u003c/em\u003e 221(36): 245\u0026ndash;56. http://dx.doi.org/10.1016/j.fcr.2017.11.028.\u003c/li\u003e\n\u003cli\u003eWright, Ian, and Phil Adams (2012) \u0026ldquo;Changing History.\u0026rdquo; \u003cem\u003eDrapers\u003c/em\u003e (21-APRIL-2012): 18\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eYang, Jinliang et al (2017) \u0026ldquo;Incomplete Dominance of Deleterious Alleles Contributes Substantially to Trait Variation and Heterosis in Maize.\u0026rdquo; : 1\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eYu-Tien Tseng 1, Hsiao-Feng Lo2, and Shih-Ying Hwang1 (2002) \u0026ldquo;No TitleTseng YT, Lo HF, Hwang SY (2002) Genotyping and Assessment of Genetic Relationships in Elite Polycross Breeding Cultivars of Sweet Potato in Taiwan Based on SAMPL Polymorphisms. Bot Bull Acad Sinica 43:99\u0026ndash;105.\u0026rdquo; \u003cem\u003ebotanical bulletin\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eZhang, Jin et al. (2016) \u0026ldquo;Carbon Science in 2016: Status, Challenges and Perspectives.\u0026rdquo; \u003cem\u003eCarbon\u003c/em\u003e 98(May 2018): 708\u0026ndash;32. http://dx.doi.org/10.1016/j.carbon.2015.11.060.\u003c/li\u003e\n\u003cli\u003eZhang, Zheng, Scott Schwartz, Lukas Wagner, and Webb Miller (2000) \u0026ldquo;A Greedy Algorithm for Aligning DNA Sequences.\u0026rdquo; \u003cem\u003eJournal of Computational Biology\u003c/em\u003e 7(1\u0026ndash;2): 203\u0026ndash;14.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Information","content":"\u003cp\u003eSupplementary information item S3: Intertek sampling, drying and shipment protocol is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genetic diversity, genotyping, SNP markers, Sweetpotato, Polymorphic Information Content","lastPublishedDoi":"10.21203/rs.3.rs-5960213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5960213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUse of molecular markers has improved the analysis of genetic variation by eliminating environmental influences on genotype performance. The objective of this study was to assess the genetic diversity and population structure of 327 sweetpotato genotypes sourced from the major sweetpotato-growing regions of Zimbabwe and from the International Potato Centre (CIP) in Mozambique using low-density Diversity Array Technology (DArTseq) SNP chip covering the 90 chromosomes of sweetpotato. The genotypes' genetic diversity (GD) varied from 0.12 to 0.50, with a mean of 0.36. The mean polymorphic information content (PIC) was 0.29, indicating that the markers were highly informative. There was a good representation of minor alleles within the population, with an average minor allele frequency (MAF) of 0.34. The average observed heterozygosity of 0.12 was consistent with the cross-pollinating system in sweetpotato but could perpetuate a narrow genetic base. There was limited interbreeding between the populations of sweetpotato, as indicated by a mean fixation index (F) of 0.68. The high F values indicated that most alleles per genotype were contributed by one parent, which is unusual in allogamous species as sweetpotato. The sweetpotato genotypes in this study could be clustered into two sub-populations with significant differences within the sub-populations. Genetic variation among genotypes is essential for the improvement of sweetpotato. Still, significant genetic gain could be achieved by cross-pollinating divergent genotypes with high MAF to create segregants with rare alleles. It is, thus, important to capture the rare alleles as they help adapt to current and future environmental shifts.\u003c/p\u003e","manuscriptTitle":"Genetic diversity and population structure of sweetpotato accessions (Ipomoea batatas [L.] Lam) revealed by single nucleotide polymorphism markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-11 11:13:14","doi":"10.21203/rs.3.rs-5960213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3123038a-ef2b-484b-b9e0-882f7184dd31","owner":[],"postedDate":"February 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-15T03:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-11 11:13:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5960213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5960213","identity":"rs-5960213","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}