Genetic Diversity of BAG’s cassava clones of the Embrapa Amapá obtained by Diversity Arrays Technology (DArT)

Genetic Diversity of BAG’s cassava clones of the Embrapa Amapá obtained by Diversity Arrays Technology (DArT) | 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 of BAG’s cassava clones of the Embrapa Amapá obtained by Diversity Arrays Technology (DArT) Gilberto Ken Iti Yokomizo, Ana Flávia Francisconi, Daniela Loschtschagina Gonzaga, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7013079/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 9 You are reading this latest preprint version Abstract Cassava farming is the main agricultural economic activity in Amapá, being a rustic crop with low use of agricultural inputs, adapted to the soil and climate conditions of northern Brazil. In Amapá there is an active cassava active germplasm bank (BAG) that requires characterization and differentiation. To overcome this problem, molecular markers can be used. Therefore, the study objective was to identify different cassava clones groups in the BAG of the Embrapa Amapá to exclude duplicate materials and study genomic diversity. The BAG is located in Mazagão municipality, containing 62 clones, whose leaves were collected and sent for molecular analysis using the Diversity Arrays Technology (DArT) technique. The main conclusions are: four main groups is differenced by allelic similarity; in the graphical analysis, group 2 is less similar because the distance from the other groups; there are graphically overlapping clones with numbers 030 and 032; 019 and 036; 024 and 035; 054 and 055; 014 and 015; 020 and 022; 050 and 052, possibly being repeated materials; group 2 presents an absence of inbreeding, genetic drift and erosion, greater genetic variability or allelic diversity, greater heterozygosity compared to the other groups; groups 1 and 4 present a greater number of alleles and private alleles, with greater genetic variation, but has manifested inbreeding, where the variability must run through the quantity of individuals and; the structuring of the groups occurs through intragroup inbreeding with moderate differentiation between them. Manihot sculenta Krantz north Brazil molecular markers genetic variability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Cassava ( Manihot esculenta Krantz) belongs to the Manihot genus, which currently includes at least 97 known species, distributed throughout all Brazilian states (Jabot 2020). It is a crop with important prominence in Brazil due to indications that it originated in the southwest region of the Amazon basin, with the domestication process occurring between 8,000 and 10,000 years ago and which subsequently spread to other locations. Currently, the cassava can be found between latitudes 30° N and 30° S, in tropical and subtropical regions, and is responsible for one third of the daily calories consumed by 800 million people (Vidigal Filho et al. 2022 ). Cassava farming is a prominent social and economic activity, providing an important part of the livelihood of family farmers through its by-products (flour, gum and tucupi) at fairs in nearby cities and directly to middlemen on the property (Souza and Bezerra 2003 ; Modesto Júnior 2016 ). In addition to being a prominent food source, cassava also has agronomic characteristics that allow it to be cultivated in conditions with low agricultural inputs use, especially fertilizers and chemical pesticides, being a rustic crop that is well adapted to the soil and climate conditions of the northern region of Brazil (Silva and Ferreira Filho 2007 ). Specifically for Amapá state, cassava farming represents the main agricultural activity economically, totaling around 50% of the total agricultural production in the state, and is an important food security source for the local population, especially for the low-income population (Costa et al. 2017 ). According to the latest data from the Brazilian Institute of Geography and Statistics (IBGE 2025 ), in 2023, production was 95,110 thousand tons of the root in an area of ​​9,007 ha, with a yield of 10,560 kg.ha − 1 . There are genetic variability reservoirs in crops, including cassava, forming collections that keep genes or germplasm, called active germplasm banks (BAG) and producer areas, which is particularly relevant in the current context of climate change and global warming, with the potential to improve productivity and adaptation or tolerance to abiotic and biotic stresses (Swaminathan 2009 ). In addition to the function of attempting to conserve genetic variability, BAGs in general also prevent the genetic erosion effect, a anthropic effects reflex, and conserve them over time, ensuring that there is a sufficient genetic base for breeding programs, allowing the new genotypes development that meet the producers, industries, and consumers demands (Swarup et al. 2021 ). For the cassava BAG effective use, it is essential to obtain information of the genetic variability size, collaborating for better management of the bank and helping to understand in genetic terms. Therefore, according to Oliveira et al. ( 2014 ), it is essential to know the genetic structure of the accessions. In order to effectively conserve these genetic resources, attention must be paid to efficient collection, introduction, exchange, conservation, characterization, and evaluation, for perfect distinction between individuals (Oliveira et al. 2020 ; Faleiro et al. 2020). The BAG characterization generally involves agro morphological aspects in the field, preliminary effectuated in the evaluation phase because it is a faster and easier approach to estimate diversity (Asare et al. 2011 ; Pinton and Emperaire 2001 ). Therefore, this procedure based on morphological descriptors is important; however, in situations where cassava genetic collections are very large, this methodology has not been effective, reflecting the interaction with environmental factors (Benesi et al. 2008 ; Kawuki et al. 2009 ). To overcome this problem, the use of data from morphological descriptors associated with DNA markers has shown greater efficiency and reliability for genetic diversity studies (Asare et al. 2011 ; Jansky et al. 2015 ). A modern technique with markers called Diversity Arrays Technology (Dart) was proposed by Jaccoud et al. ( 2001 ), whose procedure allows the simultaneous analysis of several hundred polymorphic loci dispersed throughout the genome without requiring DNA sequence information or specific primers, which has reduced the genotyping cost per locus (Ferguson et al. 2021b). DarT markers are biallelic, behaving in a dominant (presence or absence) or co-dominant (double dose, single dose or absence) manner. The generation of DArT markers basically consists of six steps: complexity reduction of genomic DNA, genomic library creation (genomic representation), microarrays preparation of genomic representations on glass slides, labeled DNA hybridization, fluorescent signal reading emitted by hybridization, extraction and analysis of data. For cassava, the first DArT array developed had almost 1,000 polymorphic clones with 99.8% reproducibility (Xia et al. 2005 ), offering a high-throughput marker screening system at low cost. In the Mazagão municipality, belonging to the Amapá state, there is an cassava Active Germplasm Bank (BAG), where conservation and characterization are essential to avoid the loss of these essential resources. Based on these aspects, the study objectives were to identify groups of cassava clones existing in the BAG of Embrapa Amapá and additionally to enable the exclusion of duplicated genetic material and to genetic variability verify within this BAG, with an molecular markers analysis. MATERIAL AND METHODS The genetic material was obtained from the BAG conducted in the experimental field of Embrapa Amapá, located in the Mazagão municipality, part of the Brazilian continental biome, called Amazon, consisting of 49.29% of the contiguous vegetation grouping of the national territory (IBGE 2004 ), approximately 36 km from the state capital, with an altitude of 10 m, at the geographic coordinates of 0º 07’ 19” S and 51º 17” 57” W. According to the Brazilian soil classification system, the location presents a Yellow Latosol (Santos et al. 2018 ). The region climate is of the Ami type, according to the Köppen classification, which is hot and humid, with a small thermal amplitude and by having three months of the year with precipitation less than 60 mm. The average annual precipitation is 2,250 mm, with a period of less precipitation from August to November. The average annual temperature is 27.4 ºC, the average minimum temperature is 20.1 ºC and the average maximum temperature is 35.2 ºC (Rabelo 2005 ). Table 1 Identification of BAG’s cassava clones existing belonging to Embrapa Amapá. Mazagão, AP. Code CPAF-AP Common name Code CPAF-AP Common name Code CPAF-AP Common name 001 Maria Pretinha 022 Cidade Rica 043 2002-41-10 002 Farias 023 Marí 044 2003-14-11 003 Seis Meses 024 Mulatinha 045 98-54-04 004 Chico vara 025 BRS Poti 046 Cigana preta 005 Miriti 026 Prata 047 Boi Cajari 006 Pai Lourenço 027 Faria Itaubal 048 98-102-02 007 97-152-01 028 Soin 049 Amarelinha 2 008 97-89-13 029 Ouro negro I 050 Caíra 009 97-85-04 030 16–96 051 96-207-05 010 MX vermelha 031 Tapioqueira 052 Kiriris 011 MX preta 032 Rocha da Rurap 053 Faria Covad 012 MX pirarucunema 033 Amarelinha 054 97-54-04 013 Ajuruxi 034 Faria da Rurap 055 Itaubal 014 Tapioqueira 035 Santa Maria 056 Caipira 015 Ouro Negro II 036 Sem nome 057 99-75-01 016 Tucunaré 037 Faria do Indio 058 Pecuí 017 V 20-06-36 038 95-115-38 059 98-61-04 018 98-145-03 039 95-93-37 060 MX Dourada 019 Poti branca 040 95-99-37 061 Formosa 020 98-137-03 041 Verdinha 062 Manivão 021 98-64-04 042 2022-35 Samples for DNA extraction from each cassava clone were collected in the field, with 20 to 40 leaf discs of approximately 5 mm in diameter from fully opened young fresh leaves, later packaged in Eppendorf tubes, kept inside a thermal box with ice. Genotyping plates were prepared in the laboratory, after were freeze-dried for 48 h, but were first kept for 2 h in an ultra-freezer. After this process, these plates were sent to Intertek in Australia for DNA extraction. Libraries were constructed at Diversity Arrays Technology in Canberra, Australia, according to the DArTseq methodology of complexity reduction through digestion of genomic DNA and ligation indexed by short sequences called “barcode” adapters, according to Kilian et al. ( 2012 ). This genotyping provides fast, high quality and accessible genomic profiles, even of the most complex polyploid genomes (Kilian et al. 2012 ; Raman et al. 2014 ). After the entire process of preparation and alignment of reads, called SNPs, and filtering of low-quality SNPs carried out by the company Diversity Arrays Technology, 13,604 high-quality SNPs were obtained. Initial analyzes The SNP analysis process results was carried out by structuring the heatmap, based on the Euclidean distance, in addition to the application of Principal Component Analysis (PCA). According to Galili ( 2015 ), Gu et al. ( 2016 ) and Gu ( 2022 ), the heatmap is a data visualization method that takes colors as differentiating elements, adjusting them in a similar way to a matrix to reveal patterns shared by subsets. Additionally, according to Fernandez et al. ( 2017 ), it is the representation through a two-dimensional image, in which the participation of each data line with each column of variables is represented, through the indication by color blocks, i.e., the color intensity is proportional to the variable level (Lee et al. 2016 ). In the R platform, to construct the heatmap, the “pheatmap” package (Kolde et al. 2015) was used, with the Euclidean distance from the “stats” package. Principal component analysis (PCA) was used to reduce the data set original dimensions. The method seeks to find the variables smallest number that can describe the maximum original variability data. To do this, it identifies the axes (principal components) that capture most of the variation present in the data. Consequently, the data grouping, through linear combinations, provides information on the similarity between these variables or axes (Ferreira 2015 ). The principal components (PC) were obtained with the “ADE4” package, and the “factoextra” package (Kassambra and Mundt 2020) was used to obtain the graphs. The Scree Plot was structured to verify the variance explained by each component in the PCA. The curve slope in the graph is usually higher at the beginning, explaining a large amount of variance, and then a gradual decline or leveling off is seen, indicating that the remaining components explain little variance. The Scree plot is an instrument to assist in determining the ideal number of components to be retained for later analysis (Donald et al. 2009 ). Later, with these results, a biplot graph was structured, separating the clones existing in the BAG’s cassava clones of the Embrapa Amapá into distinct groupings. Analysis of genomic diversity of BAG Genomic diversity analysis was performed to quantify genomic variation in order to distinguish each of the clone clusters from the BAG. Using the “hierfstat” package (Goudet 2005 ) for R, the following parameters were estimated: observed heterozygosity (HO), an index that compares genetic diversity, affected by inbreeding and other processes involved, such as mutation, selection, and genetic drift, reflecting the heterozygotes frequency for each cluster of cassava clones; genetic diversity (HS), which is the average genetic diversity within each cluster of clones in the population existing in the BAG; allelic richness proportion (AR), which measures the average number of alleles regardless of sample size; and inbreeding ( f ). Using the “Adegenet” package (Jombart 2008 ), the alleles number (A) was estimated, which indicates the alleles number found for each group of clones in the population, and the proven alleles number (Ap), which identifies the number of exclusive alleles in each group existing in the BAG. Population structure analysis in the BAG Using the “hierfstat” package (Goudet 2005 ), the F test statistics were calculated, which use the inbreeding coefficients to measure the variability within and between groups. Thus, the FIS, inbreeding coefficient or fixation index, was determined within the groups related to the general population, which measures the reduction in heterozygosity of a group due to non-random matings in the groups (Hartl et al. 2010 ; Holsinger and Weir 2009 ). The inbreeding coefficient of individuals in relation to the total population (FIT) was calculated, which considers, in addition to random matings, the differentiation in the genetic scope between the groups (Holsinger and Weiir 2009). Finally, the inbreeding coefficient within the groups in relation to the total (FST) provides the total percentage of genetic diversity that is distributed among the groups (Wright 1965 ) based on observed (HO) and expected (HE) heterozygosity or, reflects the genetic variability proportion found between the groups due to the subdivision between the clones (genetic structuring) (Solferini and Selivon 2004 ). With this package, a pairwise FST was also performed between the four groups identified in the initial analyses. Genetic differentiation between genetic groups was analyzed using Nei's distance (1987), which allows heterozygosity levels comparison between and within populations, as well as obtaining a divergence estimate. The data were presented in a new heatmap created with the “pheatmap” package (Kolde et al. 2015). The molecular variance analysis (AMOVA) (Excoffier et al. 1992 ) was performed between and within groups of cassava clones to estimate the genetic structure. The analysis identifies the genetic diversity levels existing between populations, between individuals and between individuals within populations (Excoffier et al. 1992 ; Schneider, 2000). This analysis was performed with the “poppr” package (Kamvar et al. 2014 ), using 20,000 replicates. To identify genetic groups (clusters), Discriminant Analysis of Principal Components (DAPC) was performed, available in the Adegenet package (Jombart et al., 2010 ; Jombart and Collins, 2015 ). DAPC is based on the genetic relationship between individuals. In this analysis, the data are first transformed and subjected to a Principal Component Analysis (PCA). Then, the principal components are subjected to a Linear Discriminant Analysis (Jombart et al. 2010 ). The methodology identifies the ideal number of clusters (K) through the Bayesian Information Criterion (BIC). The optimal value of K is usually determined by the lowest BIC value or by the inflection point of the generated graphical curve. RESULT AND DISCUSSION Prior structuring into groups With the next-generation sequencing technologies advent is possible to discover thousands of markers throughout the genome of interest, even for individuals such as this cassava BAG from Embrapa Amapá for which there is no previous genetic information, as cited by Davey et al. ( 2011 ), serving to distinguish individuals, with the possibility of use in any species or population. This procedure is used here due to the need to identify whether there are plots with a similar genetic origin, aiming to reduce maintenance efforts and costs. According to Gonçalves and Lima ( 2021 ), the heatmap generates results that are more robust and efficient for genetic materials discrimination from any crop; also is easier to visually understand and to interpreting the results. Therefore, a heatmap was initially structured (Fig. 1 ) with a one measure of dissimilarity for quantitative characters in genetic studies, which is the Euclidean distance (Silva 2012), being the most recommended in experiments with multivariate data (Wickelmaier 2003 ; De Castro and Ferrari 2016 ), which in this case are the thousands of markers used in each clone and presented in Fig. 1 . It is important to mention that the grouped heatmap (Fig. 1 ) is a variant of the standard grid heatmap, where hierarchical information is represented in addition to the usual numerical values. A color scale once again codes the numerical value of each cell, but the rows and columns are classified according to their correlations, and a dendrogram-type tree is added to show the hierarchical relationships between the rows and columns. These types of graphs are often used in biological sciences to show relationships in genetic data (Gehleborg and Wong 2012), such as those being studied here with the different clones of the cassava BAG from Embrapa Amapá. Ling ( 1973 ) proposed the including cluster trees procedure or dendrograms associated with the rows and columns of the heatmap. Based on these aspects, the heatmap allows us to distinguish, based on the correlations and the dendrogram structuring, the formation of four main groups, which are therefore considered to group the clones that are most similar allelically to each other. The largest contain 21 and 27 clones, and the smallest contain 4 and 8 clones. Unfortunately, consistent results were not obtained for clones 002 and 027, and therefore these were not included in the analysis. In the Scree Plot (Fig. 2 ), the first component (PC1) explained 8% of the total variation in the data, while the second (PC2) had a variance of 5.8%. It is important to highlight that data provided by molecular markers in principal component analyses are highly dimensional, since each individual marker behaves as a variable. Therefore, it is not expected that PC1 and PC2 together explain a high percentage of the total variance, since the greater the information amount, the more difficult it becomes to aggregate the data into a small number of dimensions (Teich et al. 2014 ). Thus, the results presented are fully consistent with the behavior existing in the study with molecular markers of this type. Regarding the inflection point, the local at which the graph begins to become horizontal is considered indicative of the maximum number of components to be extracted (Hair et al. 2005 ). However, Ruscio and Roche ( 2012 ) and Streiner et al. ( 2015 ) argue that the existence of this point cannot always be clearly visualized in visual inspection, suggesting a high patterns number what make difficult to results understand. Therefore, the subjective judgment use in identifying the cutoff point is justified, fixing the number of factors here on two PC axes in a similar way to that cited by Gomes ( 2016 ). In Fig. 2 , despite the high number of alleles that contributed to the Scree Plot, only the first 10 axes explained just over 50%, below the value found by Soro et al. ( 2023 ), where was captured 82.6%. However, in studies of this type with numerous SNPs, this sum is accepted. With the subjective selection of two PC axes, being sufficient to explain the cassava clones behavior, Principal Component Analysis (PCA) was performed, which represents a key tool in the study and multivariate data analysis. In practice, PCA results in the original data projection into a lower dimensional space, capturing as much possible information in the data, that is, the observed variation (Saccenti et al. 2014 ). With this, it was possible to create the scatter plot of the 60 cassava genotypes (Fig. 3 ), except for materials 002 and 027. Based on the heatmap, Scree Plot and PCA results, the four dispersed clusters can be seen in the structured Biplot, where the fourth cluster was more concentrated in the center, being surrounded by clusters first and third, while second cluster was shown to be less close (similar) to these other clusters. In first cluster, the clones CPAF-AP 030 and 032; 019 and 036; 024 and 035; in third cluster, the clones CPAF-AP 054 and 055; in fourth cluster, the clones CPAF-AP 014 and 015; 020 and 022; in third group, clones CPAF-AP 050 and 052 were plotted very close together, indicating repeated materials and for some reason due to an error in the clone maintenance and multiplication procedures, with installation in duplicate. Genomic diversity Using the four genetic clusters identified in the previous analyses, genomic diversity analyses were performed. Thus, the parameters of observed heterozygosity (H O ), gene diversity (H S ), number of alleles (A), allelic richness proportion (AR), private alleles (Ap) and inbreeding ( f ) were estimated according to Table 2 . Table 2 Genomic diversity indices obtained in four clusters containing 60 cassava clones from the BAG belonging to Embrapa Amapá. Group H O H S A AR Ap f 1 0.116 0.187 20888.000 1.179 3360 0.311 2 0.137 0.089 13847.000 1.094 427 -0.604 3 0.120 0.163 18217.000 1.155 438 0.179 4 0.127 0.219 22820.000 1.213 6304 0.358 H O : observed heterozygosity; H S : genetic diversity; A: number of alleles; Ap: private alleles; AR: allelic richness proportion; f : inbreeding. The presence of H O lower than H S in groups 1, 3 and 4 was similar to that observed by Neim Semman et al. ( 2024 ), with values within the same range but different from that found by Abadura et al. ( 2025 ) and Ferguson et al. ( 2024 ) studying cassava origins and for this second study all f were negative. Therefore, here there is low heterozygosity, different from that observed by Paredes et al. ( 2021 ). The highest observed heterozygosity was in second group, which was also the only group with no inbreeding. According to previous analyses, this group is the most isolated (Fig. 3 ) and this is indicative of higher genetic variability in relation to the others, since this higher value compared to the other groups indicates the existence of high allelic diversity according to Butler (2005), that is, low inbreeding. Next come group 4 and finally group 1. As for genetic diversity (HS), which would be the heterozygotes number that should actually exist in accordance with Hardy-Weinberg equilibrium, according to Butler (2005), the highest value was estimated for cluster 4 and the lowest for 2. Thus, a higher number of heterozygotes would be expected in cluster 4 and a lower number in 2. However, based on H O , there is evidence of the genetic base loss in cluster 4, while in 2 there were no signs of genetic drift or erosion. When there is excess homozygosity in the cluster, the observed heterozygosity is lower than expected. This heterozygotes deficit can be caused by several factors, the main one being inbreeding in addition to situations of effective size reduction. Under these conditions, the original allele frequencies do not always correspond to the original population (Futuyma and Kirkpatrick 2022). The largest number of alleles and private alleles is concentrated in fourth cluster, which was identified in previous analyses as the group with the largest number of individuals (Figs. 1 and 3 ). First cluster also presented a high alleles number and private alleles. The greater private alleles number found in the cluster, the more divergent its origin (Leberg 2002 ), and the identification is of special interest for genetic improvement programs (Frankham et al. 2010 ). It is noteworthy that, in places with smaller population fluctuations (genetic drift), is expected to find populations with greater heterozygosity and a greater private alleles number, such as in the centers of origin of the species (Alves et al. 2007 ). Thus, the clusters 1 and 4 differ intra-group in relation to the others. Clusters with lower diversity values indicate greater similarity among their individuals. Although group 2 presents greater distance in the biplot graph, within the group the clones are more similar to each other. In group 4 there is the greatest intra-group genetic variation. Despite the private alleles quantity, the presence of intra-group inbreeding was observed in clusters 4 and 1. This indicates that despite the genetic variability present, which may then be the greater number effect of distinct clones in relation to cluster 1, these undergo a greater process of inbreeding. Furthermore, f indicates that there is pressure in favor of heterozygotes in cluster 2 and homozygotes in the others. Population structure To understand the population structure of the cassava clone clusters, Wright's F statistics were calculated considering the four genetic groups. Initially, the F IT (Table 3 ) was estimated, which is the average inbreeding coefficient of the clusters set and measures of the genotypic frequencies deviation in relation to the Hardy-Weinberg equilibrium (Cruz et al 2011 ). There is a predominance of inbreeding, indicating that the materials existing in each cluster are very similar, with a reduction in the heterozygosity of each individual in relation to the entire population (Barros 2009 ). Table 3. Estimates of differentiation between four clusters containing 60 cassava clones from the BAG belonging to Embrapa Amapá. Estimates Average F IT 0.425 F ST 0.146 F IS 0.332 For F IS , which is the fixation index or intrapopulation inbreeding coefficient, to put it simply, within the population, if FIS > 0, then real inbreeding exceeds the expected level under random mating, implying that there is greater homozygosity among clones than the average. Consequently, the population will be divided into clusters and there will be inbreeding. When FIS < 0, inbreeding is avoided or mating between subpopulations predominates (Carneiro et al. 2009 ) or between unrelated individuals (Barros, 2009 ). The value obtained in Table 3 indicates that the structuring of the populations is mainly due to internal inbreeding of the populations, so there really is a subdivision into clusters of cassava clones from the BAG. Genetic divergence between populations (F ST ) has parameters such that values close to zero indicate that the groups have equal allele frequencies (Oliveira 2007 ). F ST values between 0.05 and 0.15 would be moderate; 0.15 to 0.25 would be high; and above 0.25, very high differentiation (Carneiro et al. 2010). For Holsinger et al. (2009), the closer to zero there are no restrictions between two groups, with alleles being freely exchanged. Close to unity, it follows that all genetic variation is explained by the population structure and that there is no sharing of genetic diversity between two groups. The estimated value in Table 3 indicates moderate differentiation between the groups, differing from that observed in the study by Soro et al. ( 2023 ), whose germplasm results in Burkina Faso indicated little differentiation. To verify the correlations between the clusters based on genetic divergence, the pairwise F ST was calculated, generating a heatmap associated with the similarity dendrogram (Fig. 4 ). The analysis was based on the probability calculation of encountering two identical alleles by descent (Nei 1977). Thus, the higher the observed F ST value, the lower the allelic coincidence between the clusters. According to the pairwise F ST (Fig. 4 ), the highest values were found in group 2 when paired with the others, which is the one with the greatest differentiation. Coincidentally, it is also the group that is furthest apart in the PCA analysis (Fig. 3 ). The greatest differentiation was between groups 2 and 3 (0.44). Compared with the PCA performed previously, the results here confirm the performance of these groups that are furthest apart on the Cartesian plane. Analysis of Molecular Variance (AMOVA) was used to quantify genetic variability between and within clusters of cassava clones from the BAG. The results (Table 4 ) showed that, a total of 25.61% of the genetic variability occurs due to differences between clusters and 74.39% due to the effect within each cluster, thus indicating that the structuring occurs mainly due to factors internal to the groups, similar to that cited by Adjebeng-Danquah et al. ( 2020 ), Soro et al. ( 2023 ), Neim Semman et al. ( 2024 ) and Abadura et al. ( 2025 ) where the greatest contribution was due to variations within each cluster. The PhiF ST value was higher than that found by Wright's F ST , but both indicate the existence of differentiation between the clusters. Table 4 Analysis of molecular variance (AMOVA) for four clusters involving 60 different cassava clones of the BAG belonging from Embrapa Amapá. Variation Source Df SQ QM Sigma % PhiFST Between clusters 3 7093.88 2364.63 48.16 25.61 0,26 Within cluster 56 24100.52 430.37 430.37 74.39 Total 59 31194.4 528.72 578.52 100 P-valor = 0.00005 Another way to individually evaluate the clusters was by Discriminant Analysis of Principal Components (DAPC). Initially, the adequacy statistic (Bayesian Information Criterion, BIC) presented in Fig. 5 was calculated, which allowed choosing the best K (Jombart et al. 2010 ), defining the genomic groups number. From Fig. 5 , the number of clusters (K) varies between 1 and 5, based on the generated curve, with K = 4 being adopted. This choice is justified by the fact that four genomic groups had already been identified in previous analysis and is associated with the fact that this value was also one of the lowest identified by BIC. Thus, there is previous analyses ratification in relation to the clusters number. Then, the best number of PCs retained for DAPC was defined by the α-score function presented in Fig. 6 . Where the difference between the proportion of successful reassignment of individuals to groups (observed discrimination) and the values obtained using random groups (random discrimination) was measured. Thus, 5 PCs were retained for DA transformation, being the highest value in the figure. In this way, the ideal groups number (K = 4), principal components (PCs = 5) and the discriminant analyses amount (DA = 3) to be retained in the DAPC were used. This analysis presented in Fig. 7 was performed to identify similar allelic groups. This methodology uses PCA as the first step for Discriminant Analysis (DA), ensuring that the variables are not correlated (Jombart et al. 2010 ), maximizing the variation between groups and decreasing the variation within groups. The first two linear discriminant axes (DL1 and DL2) totaled 29.67%, close to the value achieved by Abadura et al. ( 2025 ), but the genetic materials division was in six clusters, with only four here, indicating that there is less diversity compared to the clones studied by Abadura et al. ( 2025 ) and in relation to that cited by Sichalwe et al. ( 2024 ). Cluster 2, with only four clones, presented greater allelic diversity in relation to the others. Clusters 3 and 4 contain seven clones, in the case of CPAF-AP 12, CPAF-AP 21, CPAF-AP 50, CPAF-AP 56, with a higher proportion of alleles in cluster 3 and a lower proportion in cluster 4; while CPAF-AP 20, CPAF-AP 23, CPAF-AP 59, showed a higher proportion in cluster 4 and a lower proportion in cluster 3. This pattern suggests an interconnection between clusters, with clones possessing greater characteristics of one, but still retaining alleles of the other. The clone CPAF-AP 25 with greater similarity to cluster 1 and less with 4. Based on the obtained results, an assessment of the genetic differentiation between and within populations, origins, types and varieties preserved in germplasm banks or collections requires specific studies so that these populations can be properly managed (Cardoso et al. 2000 ). By identifying the different groupings and the genetic diversity held in the BAGs, similar maintenance of the accessions can be carried out as that carried out in the field by the farmers themselves, which is invaluable in the conservation of any species (Montero-Rojas et al. 2011 ). It is thus possible to differentiate clones, avoiding waste of area, inputs and labor by discarding similar materials and avoiding the use of related materials. Because each different clones or cultivars set obtains different behaviors for each situation and edaphoclimatic condition. CONCLUSIONS Four main groups are formed considering the clones that are most similar allelically to each other, with two containing a higher number of clones and two with fewer clones. There are clones plotted almost overlapping in the graphs, possibly repeated materials, as in the case of the CPAF-AP clones with numbers 030 and 032; 019 and 036; 024 and 035; 054 and 055; 014 and 015; 020 and 022; 050 and 052. Group 4 is in a central position of the graphical analysis surrounded by groups 1 and 3, while group 2 is less similar because it is distant from the other groups. Despite the low heterozygosity present in all clusters, group 2 had greater heterozygosity compared to the other groups, without presenting inbreeding, which reflects the number of clones contained, without evidence of genetic drift and erosion, but being more similar to each other. In cluster 1, there is evidence of the genetic base loss. Groups 1 and 4 have a greater number of alleles and private alleles, with greater genetic variation, but they manifested inbreeding, thus the variability may be due to the number of individuals. The structuring is mainly due to the internal clusters inbreeding with moderate differentiation between them, ratifying the subdivision of the BAG’s cassava clones into groups. Declarations Funding: This work was supported by resources to project conduction by Embrapa in Mazagão Experimental Field. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection was perfomed by Daniela Loschtschagina Gonzaga; sending for Dart analysis to the appropriate laboratory was perfomed by Eder Jorge de Oliveira; analysis with computational packages for generating tables, figures and graphs was perfomed by Ana Flávia Francisconi. The first draft of the manuscript was written by Gilberto Ken Iti Yokomizo, the first correction and initial theoretical knowledge was perfomed by Ana Flávia Francisconi; suggestions, comments and important theoretical knowledge were perfomed by Maria Imaculada Zucchi, José Baldin Pinheiro. Finally all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability: The datasets generated during and/or analysed during the current study are not publicly available due to because they are specific materials from the active germplasm bank of Embrapa but are available from the Deputy Head of R&D at Embrapa Amapá on reasonable request. References Abadura NS, Abebe AT, Rabbi IY, Beyene TM, Abtew WG (2025) DArTSNP based genetic diversity analyses in cassava (Manihote esculenta [Cranz]) genotypes sourced from different regions revealed high level of diversity within population. 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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-7013079","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485877084,"identity":"38acdcfc-62b1-48df-92e4-15677dc5a324","order_by":0,"name":"Gilberto Ken Iti Yokomizo","email":"data:image/png;base64,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","orcid":"","institution":"Embrapa Amapá","correspondingAuthor":true,"prefix":"","firstName":"Gilberto","middleName":"Ken Iti","lastName":"Yokomizo","suffix":""},{"id":485877087,"identity":"96cdedac-b816-45eb-b089-67382a5348bf","order_by":1,"name":"Ana Flávia Francisconi","email":"","orcid":"","institution":"ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Flávia","lastName":"Francisconi","suffix":""},{"id":485877089,"identity":"7baa212f-b1fb-400f-bf4f-8cce27db0a2e","order_by":2,"name":"Daniela Loschtschagina Gonzaga","email":"","orcid":"","institution":"Embrapa Amapá","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"Loschtschagina","lastName":"Gonzaga","suffix":""},{"id":485877090,"identity":"05557342-2706-46fe-b25a-04907806b7f4","order_by":3,"name":"Maria Imaculada Zucchi","email":"","orcid":"","institution":"ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Imaculada","lastName":"Zucchi","suffix":""},{"id":485877091,"identity":"76e329bf-4fd1-495b-83d3-a0b39aefce7e","order_by":4,"name":"José Baldin Pinheiro","email":"","orcid":"","institution":"ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Baldin","lastName":"Pinheiro","suffix":""},{"id":485877092,"identity":"4148fc59-446c-4e6c-8167-d6a356ccd50e","order_by":5,"name":"Eder Jorge Oliveira","email":"","orcid":"","institution":"Embrapa Mandioca e Fruticultura, Cruz das Almas","correspondingAuthor":false,"prefix":"","firstName":"Eder","middleName":"Jorge","lastName":"Oliveira","suffix":""}],"badges":[],"createdAt":"2025-06-30 17:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7013079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7013079/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-025-02702-7","type":"published","date":"2026-01-05T15:58:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86966331,"identity":"d6421e99-7bb1-462e-bdf3-0cd6b219bbe3","added_by":"auto","created_at":"2025-07-17 17:25:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":610921,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic kinship heatmap for 62 cassava genotypes, components in the BAG’s cassava clones of the Embrapa Amapá, based on an analysis of 13,603 single nucleotide polymorphism (SNP) markers; the four groups formed by the main components of the discriminant analysis are represented in the Figure by the white division between the blocks.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/1915d97bdbf006533da1d939.png"},{"id":86965780,"identity":"e493a770-872e-41e8-918b-49fe34ae57c6","added_by":"auto","created_at":"2025-07-17 17:17:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164464,"visible":true,"origin":"","legend":"\u003cp\u003eScree Plot of the first 10 principal components and their respective percentages of explained variance of 60 cassava genotypes, components of the BAG’s cassava clones from Embrapa Amapá, based on an analysis of 13,603 single nucleotide polymorphism (SNP) markers.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/66e8493fc8ff17c10663d07e.png"},{"id":86965312,"identity":"3bf05bf7-a566-4411-a418-a6355e1416c9","added_by":"auto","created_at":"2025-07-17 17:09:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":389647,"visible":true,"origin":"","legend":"\u003cp\u003eFirst and second principal components of Principal Component Analysis (PCA) scatter plot based on the analysis of 62 cassava genotypes with 13,603 single nucleotide polymorphism (SNP) markers; The analyzed accessions that were most similar are clustered within the circles represented by different colors.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/fff4f5655d08d31bf1b8a2de.png"},{"id":86965782,"identity":"cb5484e4-2091-4f03-912d-5667767e39be","added_by":"auto","created_at":"2025-07-17 17:17:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":217425,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise F\u003csub\u003eST\u003c/sub\u003e comparison between cassava clone clusters from BAG belonging from Embrapa Amapá, defined based on correlations of similar alleles.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/9402efa0bcc9c166faa173f0.png"},{"id":86965315,"identity":"f16414ad-8485-4754-90ce-8428baaace0b","added_by":"auto","created_at":"2025-07-17 17:09:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37363,"visible":true,"origin":"","legend":"\u003cp\u003eBIC adequacy statistics for K identification.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/e63b45d6d6ed34dd250f9a31.png"},{"id":86965316,"identity":"63375537-0687-4c53-9e3a-c775c737afc6","added_by":"auto","created_at":"2025-07-17 17:09:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53811,"visible":true,"origin":"","legend":"\u003cp\u003eGraph showing the results of the optimization of the α-score value in cassava clones of the BAG belonging from Embrapa Amapá. The optimal α-score value was obtained by retaining a number of 5 principal components.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/d323037b24be2939c74503bc.png"},{"id":86965318,"identity":"e1d36b1a-82c1-4264-85e7-0a47a43855da","added_by":"auto","created_at":"2025-07-17 17:09:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":293221,"visible":true,"origin":"","legend":"\u003cp\u003eADCP scatter plot of the first two linear discriminant axes (LD1 and LD2) based on the analysis of 60 individuals of cassava clones from the BAG belonging from Embrapa Amapá using SNP markers. The groups are represented by different colors and numbers, each delimited bar represents a distinct clone. In the upper left corner, the number of retained principal components (PCs eigenvalues) and in the upper right corner, the number of retained discriminant analyses (DA eigenvalues).\u003c/p\u003e","description":"","filename":"Picture7.png","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/7cc9a67746e7a17a0bd053b1.png"},{"id":100070896,"identity":"0657d572-a944-4cd9-9fe0-acfb4c81f840","added_by":"auto","created_at":"2026-01-12 16:18:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2503739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7013079/v1/ea053fdb-209d-4256-9071-6cc1e1ce8c9d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Diversity of BAG’s cassava clones of the Embrapa Amapá obtained by Diversity Arrays Technology (DArT)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCassava (\u003cem\u003eManihot esculenta\u003c/em\u003e Krantz) belongs to the Manihot genus, which currently includes at least 97 known species, distributed throughout all Brazilian states (Jabot 2020). It is a crop with important prominence in Brazil due to indications that it originated in the southwest region of the Amazon basin, with the domestication process occurring between 8,000 and 10,000 years ago and which subsequently spread to other locations. Currently, the cassava can be found between latitudes 30\u0026deg; N and 30\u0026deg; S, in tropical and subtropical regions, and is responsible for one third of the daily calories consumed by 800\u0026nbsp;million people (Vidigal Filho et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCassava farming is a prominent social and economic activity, providing an important part of the livelihood of family farmers through its by-products (flour, gum and tucupi) at fairs in nearby cities and directly to middlemen on the property (Souza and Bezerra \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Modesto J\u0026uacute;nior \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition to being a prominent food source, cassava also has agronomic characteristics that allow it to be cultivated in conditions with low agricultural inputs use, especially fertilizers and chemical pesticides, being a rustic crop that is well adapted to the soil and climate conditions of the northern region of Brazil (Silva and Ferreira Filho \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpecifically for Amap\u0026aacute; state, cassava farming represents the main agricultural activity economically, totaling around 50% of the total agricultural production in the state, and is an important food security source for the local population, especially for the low-income population (Costa et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to the latest data from the Brazilian Institute of Geography and Statistics (IBGE \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), in 2023, production was 95,110 thousand tons of the root in an area of ​​9,007 ha, with a yield of 10,560 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThere are genetic variability reservoirs in crops, including cassava, forming collections that keep genes or germplasm, called active germplasm banks (BAG) and producer areas, which is particularly relevant in the current context of climate change and global warming, with the potential to improve productivity and adaptation or tolerance to abiotic and biotic stresses (Swaminathan \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to the function of attempting to conserve genetic variability, BAGs in general also prevent the genetic erosion effect, a anthropic effects reflex, and conserve them over time, ensuring that there is a sufficient genetic base for breeding programs, allowing the new genotypes development that meet the producers, industries, and consumers demands (Swarup et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For the cassava BAG effective use, it is essential to obtain information of the genetic variability size, collaborating for better management of the bank and helping to understand in genetic terms. Therefore, according to Oliveira et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), it is essential to know the genetic structure of the accessions. In order to effectively conserve these genetic resources, attention must be paid to efficient collection, introduction, exchange, conservation, characterization, and evaluation, for perfect distinction between individuals (Oliveira et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Faleiro et al. 2020).\u003c/p\u003e\u003cp\u003eThe BAG characterization generally involves agro morphological aspects in the field, preliminary effectuated in the evaluation phase because it is a faster and easier approach to estimate diversity (Asare et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pinton and Emperaire \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Therefore, this procedure based on morphological descriptors is important; however, in situations where cassava genetic collections are very large, this methodology has not been effective, reflecting the interaction with environmental factors (Benesi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kawuki et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). To overcome this problem, the use of data from morphological descriptors associated with DNA markers has shown greater efficiency and reliability for genetic diversity studies (Asare et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jansky et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA modern technique with markers called Diversity Arrays Technology (Dart) was proposed by Jaccoud et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), whose procedure allows the simultaneous analysis of several hundred polymorphic loci dispersed throughout the genome without requiring DNA sequence information or specific primers, which has reduced the genotyping cost per locus (Ferguson et al. 2021b). DarT markers are biallelic, behaving in a dominant (presence or absence) or co-dominant (double dose, single dose or absence) manner. The generation of DArT markers basically consists of six steps: complexity reduction of genomic DNA, genomic library creation (genomic representation), microarrays preparation of genomic representations on glass slides, labeled DNA hybridization, fluorescent signal reading emitted by hybridization, extraction and analysis of data.\u003c/p\u003e\u003cp\u003eFor cassava, the first DArT array developed had almost 1,000 polymorphic clones with 99.8% reproducibility (Xia et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), offering a high-throughput marker screening system at low cost.\u003c/p\u003e\u003cp\u003eIn the Mazag\u0026atilde;o municipality, belonging to the Amap\u0026aacute; state, there is an cassava Active Germplasm Bank (BAG), where conservation and characterization are essential to avoid the loss of these essential resources. Based on these aspects, the study objectives were to identify groups of cassava clones existing in the BAG of Embrapa Amap\u0026aacute; and additionally to enable the exclusion of duplicated genetic material and to genetic variability verify within this BAG, with an molecular markers analysis.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003eThe genetic material was obtained from the BAG conducted in the experimental field of Embrapa Amapá, located in the Mazagão municipality, part of the Brazilian continental biome, called Amazon, consisting of 49.29% of the contiguous vegetation grouping of the national territory (IBGE \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), approximately 36 km from the state capital, with an altitude of 10 m, at the geographic coordinates of 0º 07’ 19” S and 51º 17” 57” W. According to the Brazilian soil classification system, the location presents a Yellow Latosol (Santos et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe region climate is of the Ami type, according to the Köppen classification, which is hot and humid, with a small thermal amplitude and by having three months of the year with precipitation less than 60 mm. The average annual precipitation is 2,250 mm, with a period of less precipitation from August to November. The average annual temperature is 27.4 ºC, the average minimum temperature is 20.1 ºC and the average maximum temperature is 35.2 ºC (Rabelo \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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\u003eIdentification of BAG’s cassava clones existing belonging to Embrapa Amapá. Mazagão, AP.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003cp\u003eCPAF-AP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommon\u003c/p\u003e\u003cp\u003ename\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003cp\u003eCPAF-AP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCommon\u003c/p\u003e\u003cp\u003ename\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003cp\u003eCPAF-AP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCommon\u003c/p\u003e\u003cp\u003ename\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaria Pretinha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCidade Rica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2002-41-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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char=\".\" colname=\"c4\"\u003e\u003cp\u003e024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMulatinha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98-54-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChico vara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBRS Poti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCigana preta\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiriti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBoi Cajari\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePai Lourenço\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFaria Itaubal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98-102-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97-152-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAmarelinha 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97-89-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOuro negro I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCaíra\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97-85-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16–96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96-207-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMX vermelha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTapioqueira\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKiriris\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMX preta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRocha da Rurap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFaria Covad\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMX pirarucunema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmarelinha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97-54-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAjuruxi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFaria da Rurap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eItaubal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTapioqueira\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSanta Maria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCaipira\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOuro Negro II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSem nome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e99-75-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTucunaré\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFaria do Indio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePecuí\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eV 20-06-36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95-115-38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98-61-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98-145-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95-93-37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMX Dourada\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoti branca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95-99-37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFormosa\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98-137-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVerdinha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eManivão\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98-64-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2022-35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSamples for DNA extraction from each cassava clone were collected in the field, with 20 to 40 leaf discs of approximately 5 mm in diameter from fully opened young fresh leaves, later packaged in Eppendorf tubes, kept inside a thermal box with ice. Genotyping plates were prepared in the laboratory, after were freeze-dried for 48 h, but were first kept for 2 h in an ultra-freezer. After this process, these plates were sent to Intertek in Australia for DNA extraction. Libraries were constructed at Diversity Arrays Technology in Canberra, Australia, according to the DArTseq methodology of complexity reduction through digestion of genomic DNA and ligation indexed by short sequences called “barcode” adapters, according to Kilian et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis genotyping provides fast, high quality and accessible genomic profiles, even of the most complex polyploid genomes (Kilian et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Raman et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). After the entire process of preparation and alignment of reads, called SNPs, and filtering of low-quality SNPs carried out by the company Diversity Arrays Technology, 13,604 high-quality SNPs were obtained.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInitial analyzes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe SNP analysis process results was carried out by structuring the heatmap, based on the Euclidean distance, in addition to the application of Principal Component Analysis (PCA).\u003c/p\u003e\u003cp\u003eAccording to Galili (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Gu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Gu (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the heatmap is a data visualization method that takes colors as differentiating elements, adjusting them in a similar way to a matrix to reveal patterns shared by subsets. Additionally, according to Fernandez et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), it is the representation through a two-dimensional image, in which the participation of each data line with each column of variables is represented, through the indication by color blocks, i.e., the color intensity is proportional to the variable level (Lee et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the R platform, to construct the heatmap, the “pheatmap” package (Kolde et al. 2015) was used, with the Euclidean distance from the “stats” package.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) was used to reduce the data set original dimensions. The method seeks to find the variables smallest number that can describe the maximum original variability data. To do this, it identifies the axes (principal components) that capture most of the variation present in the data. Consequently, the data grouping, through linear combinations, provides information on the similarity between these variables or axes (Ferreira \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The principal components (PC) were obtained with the “ADE4” package, and the “factoextra” package (Kassambra and Mundt 2020) was used to obtain the graphs.\u003c/p\u003e\u003cp\u003eThe Scree Plot was structured to verify the variance explained by each component in the PCA. The curve slope in the graph is usually higher at the beginning, explaining a large amount of variance, and then a gradual decline or leveling off is seen, indicating that the remaining components explain little variance. The Scree plot is an instrument to assist in determining the ideal number of components to be retained for later analysis (Donald et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Later, with these results, a biplot graph was structured, separating the clones existing in the BAG’s cassava clones of the Embrapa Amapá into distinct groupings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of genomic diversity of BAG\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic diversity analysis was performed to quantify genomic variation in order to distinguish each of the clone clusters from the BAG. Using the “hierfstat” package (Goudet \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) for R, the following parameters were estimated: observed heterozygosity (HO), an index that compares genetic diversity, affected by inbreeding and other processes involved, such as mutation, selection, and genetic drift, reflecting the heterozygotes frequency for each cluster of cassava clones; genetic diversity (HS), which is the average genetic diversity within each cluster of clones in the population existing in the BAG; allelic richness proportion (AR), which measures the average number of alleles regardless of sample size; and inbreeding (\u003cem\u003ef\u003c/em\u003e). Using the “Adegenet” package (Jombart \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), the alleles number (A) was estimated, which indicates the alleles number found for each group of clones in the population, and the proven alleles number (Ap), which identifies the number of exclusive alleles in each group existing in the BAG.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation structure analysis in the BAG\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the “hierfstat” package (Goudet \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), the F test statistics were calculated, which use the inbreeding coefficients to measure the variability within and between groups. Thus, the FIS, inbreeding coefficient or fixation index, was determined within the groups related to the general population, which measures the reduction in heterozygosity of a group due to non-random matings in the groups (Hartl et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Holsinger and Weir \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The inbreeding coefficient of individuals in relation to the total population (FIT) was calculated, which considers, in addition to random matings, the differentiation in the genetic scope between the groups (Holsinger and Weiir 2009). Finally, the inbreeding coefficient within the groups in relation to the total (FST) provides the total percentage of genetic diversity that is distributed among the groups (Wright \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1965\u003c/span\u003e) based on observed (HO) and expected (HE) heterozygosity or, reflects the genetic variability proportion found between the groups due to the subdivision between the clones (genetic structuring) (Solferini and Selivon \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). With this package, a pairwise FST was also performed between the four groups identified in the initial analyses.\u003c/p\u003e\u003cp\u003eGenetic differentiation between genetic groups was analyzed using Nei's distance (1987), which allows heterozygosity levels comparison between and within populations, as well as obtaining a divergence estimate. The data were presented in a new heatmap created with the “pheatmap” package (Kolde et al. 2015).\u003c/p\u003e\u003cp\u003eThe molecular variance analysis (AMOVA) (Excoffier et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) was performed between and within groups of cassava clones to estimate the genetic structure. The analysis identifies the genetic diversity levels existing between populations, between individuals and between individuals within populations (Excoffier et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Schneider, 2000). This analysis was performed with the “poppr” package (Kamvar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), using 20,000 replicates.\u003c/p\u003e\u003cp\u003eTo identify genetic groups (clusters), Discriminant Analysis of Principal Components (DAPC) was performed, available in the Adegenet package (Jombart et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jombart and Collins, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). DAPC is based on the genetic relationship between individuals. In this analysis, the data are first transformed and subjected to a Principal Component Analysis (PCA). Then, the principal components are subjected to a Linear Discriminant Analysis (Jombart et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The methodology identifies the ideal number of clusters (K) through the Bayesian Information Criterion (BIC). The optimal value of K is usually determined by the lowest BIC value or by the inflection point of the generated graphical curve.\u003c/p\u003e"},{"header":"RESULT AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003ePrior structuring into groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the next-generation sequencing technologies advent is possible to discover thousands of markers throughout the genome of interest, even for individuals such as this cassava BAG from Embrapa Amap\u0026aacute; for which there is no previous genetic information, as cited by Davey et al. (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), serving to distinguish individuals, with the possibility of use in any species or population. This procedure is used here due to the need to identify whether there are plots with a similar genetic origin, aiming to reduce maintenance efforts and costs.\u003c/p\u003e\n\u003cp\u003eAccording to Gon\u0026ccedil;alves and Lima (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), the heatmap generates results that are more robust and efficient for genetic materials discrimination from any crop; also is easier to visually understand and to interpreting the results. Therefore, a heatmap was initially structured (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) with a one measure of dissimilarity for quantitative characters in genetic studies, which is the Euclidean distance (Silva 2012), being the most recommended in experiments with multivariate data (Wickelmaier \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; De Castro and Ferrari \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), which in this case are the thousands of markers used in each clone and presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIt is important to mention that the grouped heatmap (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) is a variant of the standard grid heatmap, where hierarchical information is represented in addition to the usual numerical values. A color scale once again codes the numerical value of each cell, but the rows and columns are classified according to their correlations, and a dendrogram-type tree is added to show the hierarchical relationships between the rows and columns. These types of graphs are often used in biological sciences to show relationships in genetic data (Gehleborg and Wong 2012), such as those being studied here with the different clones of the cassava BAG from Embrapa Amap\u0026aacute;. Ling (\u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e) proposed the including cluster trees procedure or dendrograms associated with the rows and columns of the heatmap.\u003c/p\u003e\n\u003cp\u003eBased on these aspects, the heatmap allows us to distinguish, based on the correlations and the dendrogram structuring, the formation of four main groups, which are therefore considered to group the clones that are most similar allelically to each other. The largest contain 21 and 27 clones, and the smallest contain 4 and 8 clones. Unfortunately, consistent results were not obtained for clones 002 and 027, and therefore these were not included in the analysis.\u003c/p\u003e\n\u003cp\u003eIn the Scree Plot (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the first component (PC1) explained 8% of the total variation in the data, while the second (PC2) had a variance of 5.8%. It is important to highlight that data provided by molecular markers in principal component analyses are highly dimensional, since each individual marker behaves as a variable. Therefore, it is not expected that PC1 and PC2 together explain a high percentage of the total variance, since the greater the information amount, the more difficult it becomes to aggregate the data into a small number of dimensions (Teich et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, the results presented are fully consistent with the behavior existing in the study with molecular markers of this type.\u003c/p\u003e\n\u003cp\u003eRegarding the inflection point, the local at which the graph begins to become horizontal is considered indicative of the maximum number of components to be extracted (Hair et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, Ruscio and Roche (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Streiner et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) argue that the existence of this point cannot always be clearly visualized in visual inspection, suggesting a high patterns number what make difficult to results understand. Therefore, the subjective judgment use in identifying the cutoff point is justified, fixing the number of factors here on two PC axes in a similar way to that cited by Gomes (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, despite the high number of alleles that contributed to the Scree Plot, only the first 10 axes explained just over 50%, below the value found by Soro et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), where was captured 82.6%. However, in studies of this type with numerous SNPs, this sum is accepted.\u003c/p\u003e\n\u003cp\u003eWith the subjective selection of two PC axes, being sufficient to explain the cassava clones behavior, Principal Component Analysis (PCA) was performed, which represents a key tool in the study and multivariate data analysis.\u003c/p\u003e\n\u003cp\u003eIn practice, PCA results in the original data projection into a lower dimensional space, capturing as much possible information in the data, that is, the observed variation (Saccenti et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). With this, it was possible to create the scatter plot of the 60 cassava genotypes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), except for materials 002 and 027.\u003c/p\u003e\n\u003cp\u003eBased on the heatmap, Scree Plot and PCA results, the four dispersed clusters can be seen in the structured Biplot, where the fourth cluster was more concentrated in the center, being surrounded by clusters first and third, while second cluster was shown to be less close (similar) to these other clusters. In first cluster, the clones CPAF-AP 030 and 032; 019 and 036; 024 and 035; in third cluster, the clones CPAF-AP 054 and 055; in fourth cluster, the clones CPAF-AP 014 and 015; 020 and 022; in third group, clones CPAF-AP 050 and 052 were plotted very close together, indicating repeated materials and for some reason due to an error in the clone maintenance and multiplication procedures, with installation in duplicate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the four genetic clusters identified in the previous analyses, genomic diversity analyses were performed. Thus, the parameters of observed heterozygosity (H\u003csub\u003eO\u003c/sub\u003e), gene diversity (H\u003csub\u003eS\u003c/sub\u003e), number of alleles (A), allelic richness proportion (AR), private alleles (Ap) and inbreeding (\u003cem\u003ef\u003c/em\u003e) were estimated according to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenomic diversity indices obtained in four clusters containing 60 cassava clones from the BAG belonging to Embrapa Amap\u0026aacute;.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003csub\u003eS\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20888.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13847.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18217.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22820.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eH\u003csub\u003eO\u003c/sub\u003e: observed heterozygosity; H\u003csub\u003eS\u003c/sub\u003e: genetic diversity; A: number of alleles; Ap: private alleles; AR: allelic richness proportion; \u003cem\u003ef\u003c/em\u003e: inbreeding.\u003c/p\u003e\n\u003cp\u003eThe presence of H\u003csub\u003eO\u003c/sub\u003e lower than H\u003csub\u003eS\u003c/sub\u003e in groups 1, 3 and 4 was similar to that observed by Neim Semman et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), with values within the same range but different from that found by Abadura et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Ferguson et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) studying cassava origins and for this second study all \u003cem\u003ef\u003c/em\u003e were negative. Therefore, here there is low heterozygosity, different from that observed by Paredes et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe highest observed heterozygosity was in second group, which was also the only group with no inbreeding. According to previous analyses, this group is the most isolated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and this is indicative of higher genetic variability in relation to the others, since this higher value compared to the other groups indicates the existence of high allelic diversity according to Butler (2005), that is, low inbreeding. Next come group 4 and finally group 1.\u003c/p\u003e\n\u003cp\u003eAs for genetic diversity (HS), which would be the heterozygotes number that should actually exist in accordance with Hardy-Weinberg equilibrium, according to Butler (2005), the highest value was estimated for cluster 4 and the lowest for 2. Thus, a higher number of heterozygotes would be expected in cluster 4 and a lower number in 2. However, based on H\u003csub\u003eO\u003c/sub\u003e, there is evidence of the genetic base loss in cluster 4, while in 2 there were no signs of genetic drift or erosion. When there is excess homozygosity in the cluster, the observed heterozygosity is lower than expected. This heterozygotes deficit can be caused by several factors, the main one being inbreeding in addition to situations of effective size reduction. Under these conditions, the original allele frequencies do not always correspond to the original population (Futuyma and Kirkpatrick 2022).\u003c/p\u003e\n\u003cp\u003eThe largest number of alleles and private alleles is concentrated in fourth cluster, which was identified in previous analyses as the group with the largest number of individuals (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). First cluster also presented a high alleles number and private alleles. The greater private alleles number found in the cluster, the more divergent its origin (Leberg \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), and the identification is of special interest for genetic improvement programs (Frankham et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is noteworthy that, in places with smaller population fluctuations (genetic drift), is expected to find populations with greater heterozygosity and a greater private alleles number, such as in the centers of origin of the species (Alves et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Thus, the clusters 1 and 4 differ intra-group in relation to the others. Clusters with lower diversity values indicate greater similarity among their individuals. Although group 2 presents greater distance in the biplot graph, within the group the clones are more similar to each other. In group 4 there is the greatest intra-group genetic variation.\u003c/p\u003e\n\u003cp\u003eDespite the private alleles quantity, the presence of intra-group inbreeding was observed in clusters 4 and 1. This indicates that despite the genetic variability present, which may then be the greater number effect of distinct clones in relation to cluster 1, these undergo a greater process of inbreeding. Furthermore, \u003cem\u003ef\u003c/em\u003e indicates that there is pressure in favor of heterozygotes in cluster 2 and homozygotes in the others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the population structure of the cassava clone clusters, Wright\u0026apos;s F statistics were calculated considering the four genetic groups. Initially, the F\u003csub\u003eIT\u003c/sub\u003e (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) was estimated, which is the average inbreeding coefficient of the clusters set and measures of the genotypic frequencies deviation in relation to the Hardy-Weinberg equilibrium (Cruz et al \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). There is a predominance of inbreeding, indicating that the materials existing in each cluster are very similar, with a reduction in the heterozygosity of each individual in relation to the entire population (Barros \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Estimates of differentiation between four clusters containing 60 cassava clones from the BAG belonging to Embrapa Amap\u0026aacute;.\u003c/div\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"321\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.5327%;\"\u003e\n \u003cp\u003e\u0026nbsp;Estimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50.4673%;\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.5327%;\"\u003e\n \u003cp\u003eF\u003csub\u003eIT\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50.4673%;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.5327%;\"\u003e\n \u003cp\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50.4673%;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.5327%;\"\u003e\n \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50.4673%;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eFor F\u003csub\u003eIS\u003c/sub\u003e, which is the fixation index or intrapopulation inbreeding coefficient, to put it simply, within the population, if FIS\u0026thinsp;\u0026gt;\u0026thinsp;0, then real inbreeding exceeds the expected level under random mating, implying that there is greater homozygosity among clones than the average. Consequently, the population will be divided into clusters and there will be inbreeding. When FIS\u0026thinsp;\u0026lt;\u0026thinsp;0, inbreeding is avoided or mating between subpopulations predominates (Carneiro et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) or between unrelated individuals (Barros, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The value obtained in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that the structuring of the populations is mainly due to internal inbreeding of the populations, so there really is a subdivision into clusters of cassava clones from the BAG.\u003c/p\u003e\n\u003cp\u003eGenetic divergence between populations (F\u003csub\u003eST\u003c/sub\u003e) has parameters such that values close to zero indicate that the groups have equal allele frequencies (Oliveira \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). F\u003csub\u003eST\u003c/sub\u003e values between 0.05 and 0.15 would be moderate; 0.15 to 0.25 would be high; and above 0.25, very high differentiation (Carneiro et al. 2010). For Holsinger et al. (2009), the closer to zero there are no restrictions between two groups, with alleles being freely exchanged. Close to unity, it follows that all genetic variation is explained by the population structure and that there is no sharing of genetic diversity between two groups. The estimated value in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e indicates moderate differentiation between the groups, differing from that observed in the study by Soro et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), whose germplasm results in Burkina Faso indicated little differentiation.\u003c/p\u003e\n\u003cp\u003eTo verify the correlations between the clusters based on genetic divergence, the pairwise F\u003csub\u003eST\u003c/sub\u003e was calculated, generating a heatmap associated with the similarity dendrogram (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The analysis was based on the probability calculation of encountering two identical alleles by descent (Nei 1977). Thus, the higher the observed F\u003csub\u003eST\u003c/sub\u003e value, the lower the allelic coincidence between the clusters.\u003c/p\u003e\n\u003cp\u003eAccording to the pairwise F\u003csub\u003eST\u003c/sub\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), the highest values were found in group 2 when paired with the others, which is the one with the greatest differentiation. Coincidentally, it is also the group that is furthest apart in the PCA analysis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The greatest differentiation was between groups 2 and 3 (0.44). Compared with the PCA performed previously, the results here confirm the performance of these groups that are furthest apart on the Cartesian plane.\u003c/p\u003e\n\u003cp\u003eAnalysis of Molecular Variance (AMOVA) was used to quantify genetic variability between and within clusters of cassava clones from the BAG. The results (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that, a total of 25.61% of the genetic variability occurs due to differences between clusters and 74.39% due to the effect within each cluster, thus indicating that the structuring occurs mainly due to factors internal to the groups, similar to that cited by Adjebeng-Danquah et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), Soro et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Neim Semman et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Abadura et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) where the greatest contribution was due to variations within each cluster. The PhiF\u003csub\u003eST\u003c/sub\u003e value was higher than that found by Wright\u0026apos;s F\u003csub\u003eST\u003c/sub\u003e, but both indicate the existence of differentiation between the clusters.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of molecular variance (AMOVA) for four clusters involving 60 different cassava clones of the BAG belonging from Embrapa Amap\u0026aacute;.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariation Source\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhiFST\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBetween clusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7093.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2364.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithin cluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24100.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e430.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e430.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31194.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e528.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e578.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eP-valor\u0026thinsp;=\u0026thinsp;0.00005\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAnother way to individually evaluate the clusters was by Discriminant Analysis of Principal Components (DAPC). Initially, the adequacy statistic (Bayesian Information Criterion, BIC) presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e was calculated, which allowed choosing the best K (Jombart et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), defining the genomic groups number.\u003c/p\u003e\n\u003cp\u003eFrom Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the number of clusters (K) varies between 1 and 5, based on the generated curve, with K\u0026thinsp;=\u0026thinsp;4 being adopted. This choice is justified by the fact that four genomic groups had already been identified in previous analysis and is associated with the fact that this value was also one of the lowest identified by BIC. Thus, there is previous analyses ratification in relation to the clusters number.\u003c/p\u003e\n\u003cp\u003eThen, the best number of PCs retained for DAPC was defined by the \u0026alpha;-score function presented in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Where the difference between the proportion of successful reassignment of individuals to groups (observed discrimination) and the values obtained using random groups (random discrimination) was measured. Thus, 5 PCs were retained for DA transformation, being the highest value in the figure.\u003c/p\u003e\n\u003cp\u003eIn this way, the ideal groups number (K\u0026thinsp;=\u0026thinsp;4), principal components (PCs\u0026thinsp;=\u0026thinsp;5) and the discriminant analyses amount (DA\u0026thinsp;=\u0026thinsp;3) to be retained in the DAPC were used. This analysis presented in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e was performed to identify similar allelic groups. This methodology uses PCA as the first step for Discriminant Analysis (DA), ensuring that the variables are not correlated (Jombart et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), maximizing the variation between groups and decreasing the variation within groups.\u003c/p\u003e\n\u003cp\u003eThe first two linear discriminant axes (DL1 and DL2) totaled 29.67%, close to the value achieved by Abadura et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), but the genetic materials division was in six clusters, with only four here, indicating that there is less diversity compared to the clones studied by Abadura et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and in relation to that cited by Sichalwe et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCluster 2, with only four clones, presented greater allelic diversity in relation to the others. Clusters 3 and 4 contain seven clones, in the case of CPAF-AP 12, CPAF-AP 21, CPAF-AP 50, CPAF-AP 56, with a higher proportion of alleles in cluster 3 and a lower proportion in cluster 4; while CPAF-AP 20, CPAF-AP 23, CPAF-AP 59, showed a higher proportion in cluster 4 and a lower proportion in cluster 3. This pattern suggests an interconnection between clusters, with clones possessing greater characteristics of one, but still retaining alleles of the other. The clone CPAF-AP 25 with greater similarity to cluster 1 and less with 4.\u003c/p\u003e\n\u003cp\u003eBased on the obtained results, an assessment of the genetic differentiation between and within populations, origins, types and varieties preserved in germplasm banks or collections requires specific studies so that these populations can be properly managed (Cardoso et al. \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). By identifying the different groupings and the genetic diversity held in the BAGs, similar maintenance of the accessions can be carried out as that carried out in the field by the farmers themselves, which is invaluable in the conservation of any species (Montero-Rojas et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is thus possible to differentiate clones, avoiding waste of area, inputs and labor by discarding similar materials and avoiding the use of related materials. Because each different clones or cultivars set obtains different behaviors for each situation and edaphoclimatic condition.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eFour main groups are formed considering the clones that are most similar allelically to each other, with two containing a higher number of clones and two with fewer clones.\u003c/p\u003e\u003cp\u003eThere are clones plotted almost overlapping in the graphs, possibly repeated materials, as in the case of the CPAF-AP clones with numbers 030 and 032; 019 and 036; 024 and 035; 054 and 055; 014 and 015; 020 and 022; 050 and 052.\u003c/p\u003e\u003cp\u003eGroup 4 is in a central position of the graphical analysis surrounded by groups 1 and 3, while group 2 is less similar because it is distant from the other groups.\u003c/p\u003e\u003cp\u003eDespite the low heterozygosity present in all clusters, group 2 had greater heterozygosity compared to the other groups, without presenting inbreeding, which reflects the number of clones contained, without evidence of genetic drift and erosion, but being more similar to each other. In cluster 1, there is evidence of the genetic base loss.\u003c/p\u003e\u003cp\u003eGroups 1 and 4 have a greater number of alleles and private alleles, with greater genetic variation, but they manifested inbreeding, thus the variability may be due to the number of individuals.\u003c/p\u003e\u003cp\u003eThe structuring is mainly due to the internal clusters inbreeding with moderate differentiation between them, ratifying the subdivision of the BAG\u0026rsquo;s cassava clones into groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by resources to project conduction by Embrapa in Mazagão Experimental Field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection was perfomed by Daniela Loschtschagina Gonzaga; sending for Dart analysis to the appropriate laboratory was perfomed by Eder Jorge de Oliveira; analysis with computational packages for generating tables, figures and graphs was perfomed by Ana Flávia Francisconi. The first draft of the manuscript was written by Gilberto Ken Iti Yokomizo, the first correction and initial theoretical knowledge was perfomed by Ana Flávia Francisconi; suggestions, comments and important theoretical knowledge were perfomed by Maria Imaculada Zucchi, José Baldin Pinheiro. Finally all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to because they are specific materials from the active germplasm bank of Embrapa but are available from the Deputy Head of R\u0026amp;D at Embrapa Amapá on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbadura NS, Abebe AT, Rabbi IY, Beyene TM, Abtew WG (2025) DArTSNP based genetic diversity analyses in cassava (Manihote esculenta [Cranz]) genotypes sourced from different regions revealed high level of diversity within population. 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Nat Genet 42(7): 565-569.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Manihot sculenta Krantz, north Brazil, molecular markers, genetic variability","lastPublishedDoi":"10.21203/rs.3.rs-7013079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7013079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCassava farming is the main agricultural economic activity in Amap\u0026aacute;, being a rustic crop with low use of agricultural inputs, adapted to the soil and climate conditions of northern Brazil. In Amap\u0026aacute; there is an active cassava active germplasm bank (BAG) that requires characterization and differentiation. To overcome this problem, molecular markers can be used. Therefore, the study objective was to identify different cassava clones groups in the BAG of the Embrapa Amap\u0026aacute; to exclude duplicate materials and study genomic diversity. The BAG is located in Mazag\u0026atilde;o municipality, containing 62 clones, whose leaves were collected and sent for molecular analysis using the Diversity Arrays Technology (DArT) technique. The main conclusions are: four main groups is differenced by allelic similarity; in the graphical analysis, group 2 is less similar because the distance from the other groups; there are graphically overlapping clones with numbers 030 and 032; 019 and 036; 024 and 035; 054 and 055; 014 and 015; 020 and 022; 050 and 052, possibly being repeated materials; group 2 presents an absence of inbreeding, genetic drift and erosion, greater genetic variability or allelic diversity, greater heterozygosity compared to the other groups; groups 1 and 4 present a greater number of alleles and private alleles, with greater genetic variation, but has manifested inbreeding, where the variability must run through the quantity of individuals and; the structuring of the groups occurs through intragroup inbreeding with moderate differentiation between them.\u003c/p\u003e","manuscriptTitle":"Genetic Diversity of BAG’s cassava clones of the Embrapa Amapá obtained by Diversity Arrays Technology (DArT)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 17:01:11","doi":"10.21203/rs.3.rs-7013079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-22T09:15:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T08:30:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T03:15:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214920354305329999857462994204224103181","date":"2025-07-15T15:46:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153299700213376273408003970353159725231","date":"2025-07-15T15:20:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T15:10:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-01T13:56:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-01T13:55:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2025-06-30T17:15:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6f3d2dcb-d597-4b2c-ad80-b4116ce250e5","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:15:38+00:00","versionOfRecord":{"articleIdentity":"rs-7013079","link":"https://doi.org/10.1007/s10722-025-02702-7","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2026-01-05 15:58:46","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-07-17 17:01:11","video":"","vorDoi":"10.1007/s10722-025-02702-7","vorDoiUrl":"https://doi.org/10.1007/s10722-025-02702-7","workflowStages":[]},"version":"v1","identity":"rs-7013079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7013079","identity":"rs-7013079","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}