Early Potatoes in Bangladesh: Morphological and Molecular Evaluation

Early Potatoes in Bangladesh: Morphological and Molecular Evaluation | 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 Early Potatoes in Bangladesh: Morphological and Molecular Evaluation Md Abdullah Yousuf Akhond, Kamrun Nahar, Muhammed Rezwan Kabir, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6917339/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Local potato varieties are important in the local cuisine and economy of Bangladesh for their better taste, texture and high price. However, limited information associated with characterization of local potatoes is known. Therefore, this study was conducted to determine the similarity and differences and to estimate the genetic relationship of local potato varieties in Bangladesh using morphological traits and molecular markers. A total of 16 potato varieties were evaluated for 20 qualitative traits using cluster and principal component analysis (PCA). The PCA revealed that the first seven principal components (PCs) with eigenvalue > 1 contributed for 88.01% of the total variation. The most important characters for PC1 were plant height, adaxial leaf pubescence, depth of tuber eyes, secondary tuber skin colour, abaxial leaf pubescence, number of eyes per tuber, leaf character and distribution of secondary tuber colour whereas variations for PC2 mostly related to stem colour, secondary tuber skin colour, number of eyes per tuber, stem cross section and growth habit. The important characters for PC3 were number of eyes per tuber, depth of tuber eyes, predominant tuber flesh colour, predominant tuber skin colour, stem cross section and unusual tuber shape. Cluster analysis grouped the 16 potato varieties into two clusters: six varieties in cluster I and 10 in cluster II. The dendrogram generated by cluster analysis based on DNA profiling and simple sequence repeat (SSR) screening classified most varieties into two groups: A and B. Both groups contained six samples with identical profiles. Two further varieties BD-3 and BD-12 differed by changes in STM0019. In addition, BD-15 differed from Kufri Sindhuri by a single allele. BD-13 was unique and did not form a close cluster with any variety in the database. The dendrogram revealed the Bangladeshi varieties have closest matches to European heritage varieties with a similarity between 60 and 70% when compared to the European database. The results obtained in this study will break barriers in terms of identification and conservation of the local potatoes by their phenotypic and genotypic variation. Cluster analysis Local potato Molecular marker Morphological traits Principal Component Analysis SSR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Potato ( Solanum tuberosum L.) is the third most important food crop worldwide and its total production was estimated at 383.08 million tonnes in 2023 from 16.8 million ha (FAOSTAT 2025 ). The area and production of potato in Bangladesh during 2023–2024 were 0.46 million ha and 10.6 million metric tonnes, respectively (BBS 2024 ) making it the seventh largest producer of the crop in the world. Earlier it was believed that potato had multiple independent origins in South America, but modern molecular studies support its single origin in northern Peru (Spooner et al. 2005 ). It is generally assumed that 1570 is the approximate time when potato first arrived in Spain and a second introduction was thought to be later in England from a different source in around 1590 (Hawkes 1992 ). The present day modern European varieties resemble the primitive varieties of S. tuberosum subsp. Tuberosum grown in Chile, however, the first European potatoes were short-day adapted varieties of subsp. Andigena from the Northern highlands of South America and likely to be from Colombia (Burton 1989 ; Hawkes 1990 ). Potatoes were said to have been brought to India by British missionaries in the late seventeenth century (Hawkes 1990 ). However, it is also possible that potatoes were first introduced in India by Portuguese explorers during early seventeenth century and were grown or at least known in India as early as 1615 (Swaminathan 1958 ). It is probably through this route early potatoes arrived in Bangladesh and might have changed identity over a period of about three centuries because of artificial selection and diversification. However, their genetic composition is assumed to have remained largely unchanged due to the vegetative nature of propagation. Although most of the potato crops grown in Bangladesh are modern high-yielding European varieties or their derivatives, a significant portion comes from the early varieties known as “Deshi” or “Local varieties”. In 2013–2014, local varieties were cultivated in about 0.07 million hectares of land producing 0.84 million tons of tubers which were 16% and 9% of total potato growing area and production, respectively (BBS 2016 ). Although, these varieties are naturally low yielding, they require less input for cultivation and are generally perceived to have better taste and texture. Moreover, their low yield is somewhat compensated by high market price which is usually double that of the high-yielding varieties. In the face of increasing competition from the modern varieties over the years, these varieties are holding their place due to consumer preference. Despite their importance in the local cuisine and economy, studies on these potatoes are very limited (Siddique 1995 ; Islam et al. 1998 ). Twenty-seven local varieties have been reported to be cultivated in Bangladesh (Khalil et al. 2013 ), but a closer observation might show the number to be less as a single variety is known by different local names in different regions. Therefore, it is necessary to characterize and identify the varieties, determine the genetic relationships and distinguish duplicates in the locally cultivated potato collections for conservation purposes. Hierarchical Cluster analysis has been suggested for classifying the germplasm or varieties based on degree of similarity and dissimilarity (Rathinavel 2017 ). Moreover, principal component analysis (PCA) is used to study patterns of variation in a set of interrelated traits through the identification of subsets of these traits, called factors, which are substantially correlated to each other (Pluta et al. 2012 ). However, both Cluster analysis and PCA are useful methods for revealing the patterns of phenotypic variation in the collection. Genetic variation for morphological traits has been estimated using PCA in many crops (Janmohammadi et al. 2014 ; Placide et al. 2015 ; Yuan et al. 2016 ; Rathinavel 2017 ; Tekeu et al. 2021; Seid et al. 2021 ). Beside morphological traits, molecular markers have also been used for identification of variety, cultivar and genotype. Several researchers successfully used simple sequence repeat (SSR) markers to identify potato varieties and accessions (Milbourne et al. 1998 ; Fu et al. 2009 ; Favoretto et al. 2011 Duan et al. 2019 ; Wang et al. 2019 and Lee et al. 2021 ). SSR markers have some advantages such as co-dominance, locus specific, highly reproducible, highly polymorphic and widely dispersed across the genome and were shown to be highly efficient for identification of potato varieties (Ghislain et al. 2004 ). We therefore set out to determine the similarity and differences, estimate the genetic relationship and identify local potato varieties in Bangladesh using morphological traits and SSR markers. Materials and Methods Plant Material Tubers of sixteen local potato varieties viz. BD-02, BD-03, BD-04, BD-05, BD-06, BD-07, BD-08, BD-10, BD-12, BD-13, BD-14, BD-15, BD-16, BD-17, BD-18, BD-19 were collected from Tuber Crops Research Centre of Bangladesh Agricultural Research Institute (BARI), the markets as well as from the farmers of different regions of Bangladesh (Table 1 ). The variation of tuber colours and shapes of the varieties are shown in Fig. 1 . Table 1 List of the local potato varieties with their accession numbers Accession number Local variety name BD-02 Indurkani BD-03 Shada gootee BD-04 Shilbilati BD-05 Hagrai BD-06 Ausha BD-07 Lalpakri BD-08 Zhaubilati BD-10 Sabrai BD-12 Rangpur Lal BD-13 Festashil BD-14 Challisha BD-15 Surjamukhi BD-16 Jam Aloo BD-17 Sindur Kauta BD-18 Patnai BD-19 Dohazari Morphological Characterization Tubers were planted in the field of Biotechnology Division, BARI. Morphological data were recorded according to standard descriptors (Huaman et al. 1977 ) for evaluating the substantial variation and relationship among the potato varieties and photographs were taken as required for documentation. Morphological characters of the plants and tubers are presented in Supplementary Tables 1 and 2, respectively. Twenty qualitative traits were observed, and their codes and descriptors are listed in Table 2 . Table 2 Description of the characters used in the morphological study of potato varieties No. Characters Code Description 1 Growth habit GH Erect = 1, Semi–erect = 2, Decumbent = 3, Prostrate = 4, Semi–rosette = 5, Rosette = 6 2 Plant height PH Short = 3, Medium = 5, Tall = 7 3 Stem color SC Green = 1, Red–brown = 2, Purple = 3, Cream with some red–brown = 4, Cream with purple = 5, Red–brown with some green = 6, Purple with some green = 7, Other = 8 4 Stem wing SW Absent = 0, Straight = 1, Undulate = 2, Dentate = 3 5 Stem cross section SCS Round = 1 Angular = 2 6 Leaf character LC Undissected = 1, Pinnatilobed = 2, Scarcely dissected = 3, Weakly dissected = 4, Medium dissected = 5, Strongly dissected = 6, Very strong dissected = 7, Other = 8 7 Abaxial leaf pubescence ALP Glabrous = 0, Glabrescent = 1, Pubescence = 2, Strongly pubescent = 3 8 Adaxial leaf pubescence ADLP Glabrous = 0, Glabrescent = 1, Pubescence = 2, Strongly pubescent = 3 9 Predominant tuber skin color PTSC White–cream = 1, Yellow = 2, Orange = 3, Brownish = 4, Pink = 5, Red = 6, Purplish–red = 7, Purple = 8, Dark purple-black = 9 10 Secondary tuber skin color STSC Absent = 0, White–cream = 1, Yellow = 2, Orange = 3, Brownish = 4, Pink = 5, Red = 6, Purplish–black = 7, Purple = 8, Dark purple–black = 9 11 Distribution of secondary tuber color DSTC Absent = 0, Eyes = 1, Eyebrows = 2, Splashed = 3, Scattered = 4, Spectacled = 5, Stippled = 6, Other = 7 12 Tuber skin type TST Smooth = 1, Rough (flaky) = 2, Partially netted = 3 Totally netted = 4 Very heavily netted = 5, Other = 6 13 Predominant tuber flesh color PTFC White = 1, Cream = 2, Yellow–cream = 3, Yellow = 4, Red = 5, Violet = 6, Purple = 7 Other = 8 14 Secondary tuber flesh color STFC Absent = 0, White = 1, Cream = 2, Yellow–cream = 3, Yellow = 4, Red = 5, Violet = 6, Purple = 7, Other = 8 15 Distribution of secondary tuber flesh color DSTFC Absent = 0, Scattered spots = 1, Scattered areas = 2, Narrow vascular ring = 3, Broad vascular ring = 4, Vascular ring medulle (pith) = 5, All flesh exept medulla (pith) = 6, Other = 7 16 General tuber shape GTS Compressed (oblate) = 1, Round = 2, Ovate = 3, Obovate = 4, Elliptic = 5, Oblong = 6, Long-Oblong = 7, Elongate = 8 17 Unusual tuber shape UTS Absent = 0, Flattened = 1, Clavate = 2, Reniform = 3, Fusiform = 4, Falcate = 5, Spiral = 6, Digitate = 7, Concertina-shaped = 8, Tuberosed = 9 18 Depth of tuber eyes DTE Protruding = 1, Shallow = 2, Medium = 3, Deep = 4, Very deep = 5 19 No. of eyes per tuber NEPT Few (less than 5) = 1, Intermediate = 5, Many (more than 20) = 9 20 Distribution of tuber eyes DITE Predominantly apical = 1, Evenly distributed = 2 Data Analysis Cluster and PCA were done by using complete linkage method and Gower distance. The dendrogram was constructed based on the Jaccard similarity co-efficient and clustered using the Unweighted Pair group Method with Arithmetic Mean (UPGMA). The data were analyzed by Statistical Tool for Agricultural Research (STAR) version 2.0.1. Molecular Characterization DNA Extraction and Quantification Tubers of sixteen local potato varieties were grown in pots and genomic DNA was extracted from leaf tissue using standard protocol (DNEasy plant mini kit, QIAGEN). The DNA A 260 /A 280 ratio and the concentrations were recorded by using a spectrophotometer (ThermoScientific NanoDrop ND-1000 Spectrophotometer). DNA integrity was verified by agarose gel (0.8% w/v) electrophoresis. PCR Analysis Twelve microsatellite regions were amplified in four multiplex reactions each containing primers for three markers (Table 3 ). All markers were developed by Millbourne et al. (1998), with the exception of SSR1 (Kawchuk et al. 1996 ) and STMS 5136 and STMS 5148 (Ghislain et al. 2004 ). Amplifications were carried out in 10 µL volumes with 10 ng DNA, 1 µL of primer mixes using Type-it Microsatellite PCR Kit (QIAGEN). Forward primers were labelled with either 6FAM, VIC or NED (Applied Biosystems), reverse primers had a 5´pigtail sequence of TTCTTTG (Brownstein et al. 1996 ) to help reduce plus A effects. Cycling conditions were 94ºC for 5 minutes then 30 cycles of 94ºC for 30 seconds, ramp at 1ºC/second to 50ºC for 30 seconds, ramp at 1ºC/second to 72ºC for 2 minutes, followed by a hold step at 60ºC for 30 minutes and an infinite hold at 25ºC. After amplification, 1 µL of each product was added to 9 µL HiDi formamide (Applied Biosystems) containing GS 500 LIZ size standard (Applied Biosystems), denatured at 95ºC for 5 minutes, quenched on ice and ran on an ABI 3500xL (Applied Biosystems) with a 50 cm array and POP-7. Samples were analysed using GeneMapper v4.0 (Applied Biosystems). Further details of the conditions for both PCR amplification and electrophoresis of the samples can be found in Reid et al. ( 2008 ). Resulting profiles were stored to a BioNumerics database containing over 3000 varieties. The database is a continuation of that described in Reid et al. 2011 . Dendrograms were generated using Jaccard co-efficient and Unweighted Pair group Method with Arithmetic Mean (UPGMA). Table 3 Multiplex marker set information showing repeat motif, dye and concentration in the primer multiplex mix Multiplex set Marker Repeat motif Dye Concentration µM 1 STMS 0019 (AT) 7 (GT) 10 (AT) 4 (GT) 5 (GC) 4 (GT) 4 VIC 4 1 STMS 3009 (TC) 13 NED 1 1 SSR1 (TCAC) n FAM 2 2 STMS 2005 (CTGTTG) 3 NED 1 2 STMS 3012 (CT) 4 .(CT) 8 FAM 4 2 STMS 3023 (GA) 9 .(GA) 8 .(GA) 4 VIC 1 3 STMS 2028 (TAC) 5 .(TA) 3 .(CAT) 3 NED 4 3 STMS 5136 (AGA) 5 VIC 1 3 STMS 5148 (GAA) 17 FAM 4 4 STMS 1016 (TCT) 9 VIC 1 4 STMS 1024 (TTG) 6 FAM 1 4 STMS 2022 (CAA) 3 .(CAA) 3 NED 2 Results and Discussions Principal Component Analysis (PCA) Collection, characterisation and evaluation of the local potatoes in Bangladesh could be prominent for future breeding before they disappear. In light of their significance, we started with investigation of the phenotypic variation using PCA in sixteen local potato varieties in Bangladesh. Seven PCs with eigenvalues greater than 1 were identified based on the analysis of qualitative traits (Table 4 ). The contribution of these seven PCs was 88.01% in the total variation among the potato varieties. Yuan et al. ( 2016 ) assessed 179 potato clones and found that the first five components accounted for 74.9% of the total phenotypic variation. Ahmadizadeh and Felenji ( 2011 ) reported that first three PCA explained 80.1% total variation among 22 potato variates. We found PC1 contributed 31.51% in the total variability of the studied potato varieties, in which the major contributing traits were plant height (0.334), adaxial leaf pubescence (0.196), depth of tuber eyes (0.182), secondary tuber skin colour (0.181), abaxial leaf pubescence (0.180), number of eyes per tuber (0.178), leaf character (0.162) and distribution of secondary tuber colour (0.148). In addition, stem cross section and tuber skin type also contributed positively but with low intensity. The PC2 illustrated 13.25% of the total variation found in morphological traits in potato which ascertained the pattern of variation in stem colour (0.303), secondary tuber skin colour (0.272), number of eyes per tuber (0.264), stem cross section (0.231) and growth habit (0.218). The traits distribution of secondary tuber colour (0.134), stem wing (0.131) and adaxial leaf pubescence (0.009) also added positively but with low magnitude. While predominant tuber flesh colour (-0.410), predominant tuber skin colour (-0.375), tuber skin type (-0.272), secondary tuber flesh colour (-0.255), distribution of secondary tuber flesh colour (-0.255), plant height (-0.218) and leaf character (-0.182) were having negative weights. The PC3 explained 11.81% of the total variations found in morphological traits which described the variations in number of eyes per tuber (0.386), depth of tuber eyes (0.379), predominant tuber flesh colour (0.310), predominant tuber skin colour (0.235), stem cross section (0.182) and unusual tuber shape (0.177). In contrast, adaxial leaf pubescence (-0.393) abaxial leaf pubescence (-0.313), secondary tuber skin colour (-0.272), secondary tuber flesh colour (-0.233), distribution of secondary tuber flesh colour (-0.233), were negative to add to this component. The contribution of PC4 was 9.57% in total variations which portrayed the variation in unusual tuber shape (0.383), stem wing (0.358), abaxial leaf pubescence (0.323), general tuber shape (0.322), adaxial leaf pubescence (0.312), number of eyes per tuber (0.263), distribution of tuber eyes (0.237), depth of tuber eyes (0.217), stem cross section (0.202) and secondary tuber skin colour (0.167). On the other hand, the trait stem colour (-0.313) and distribution of secondary tuber colour (-0.181) contributed negatively to this component. PC5 accounted for 8.75% of total variability mainly depicted the variations in abaxial leaf pubescence (0.355), tuber skin type (0.339), stem wing (0.337), stem colour (0.319), predominant tuber skin colour (0.245) and growth habit (0.226) with positive load, however distribution of tuber eyes (-0.440), stem cross section (-0.369) and plant height (-0.226) were negative load on this component. PC6 contributed 6.85% to the total variation and it was related to diversity for distribution of secondary tuber colour (0.541), tuber skin type (0.473), predominant tuber skin colour (0.300) and stem cross section (0.294) with positive loadings and leaf character (-0.459) and depth of tuber eyes (-0.177) with negative loadings. Lastly, PC7 comprised of 6.26% and mainly illuminated the distribution of secondary tuber colour (0.465), secondary tuber skin colour (0.443), leaf character (0.343), predominant tuber skin colour (0.291), depth of tuber eyes (0.271), secondary tuber flesh colour (0.183), distribution of secondary tuber flesh colour (0.183) and stem cross section (0.154) with positive load whereas negative loadings exhibited by tuber skin type (-0.323), number of eyes per tuber (-0.213) and distribution of tuber eyes (-0.199). The results indicated that traits which contributed more to different PCs had higher contribution to the total variation of the varieties. Similar results were observed by Seid et al. ( 2021 ) who reported that plant height, tuber skin colour, depth of tuber eyes had higher contribution to the total variation among potato genotypes. Table 4 Principal Components with Eigenvalues > 1.0 for various traits in 16 local potato varieties Characters Principal Components PC1 PC2 PC3 PC4 PC5 PC6 PC7 Growth habit -0.334 0.218 0.084 -0.083 0.226 -0.022 0.098 Plant height 0.334 -0.218 -0.084 0.083 -0.226 0.022 -0.098 Stem color -0.210 0.303 0.089 -0.313 0.319 -0.050 -0.011 Stem wing -0.061 0.131 0.097 0.358 0.337 -0.078 0.057 Stem cross section 0.003 0.231 0.182 0.202 -0.369 0.294 0.154 Leaf character 0.162 -0.182 0.101 -0.026 -0.114 -0.459 0.343 Abaxial leaf pubescence 0.180 -0.079 -0.313 0.323 0.355 -0.119 -0.048 Adaxial leaf pubescence 0.196 0.009 -0.393 0.312 0.133 0.119 0.027 Predominant tuber skin color -0.110 -0.375 0.235 0.073 0.245 0.300 0.291 Secondary tuber skin color 0.181 0.272 -0.272 0.167 0.002 0.032 0.443 Distribution of secondary tuber color 0.148 0.134 0.016 -0.181 -0.027 0.541 0.465 Tuber skin type 0.112 -0.272 0.007 -0.024 0.339 0.473 -0.323 Predominant tuber flesh color 0.175 -0.410 0.310 -0.156 -0.004 -0.020 0.035 Secondary tuber flesh color -0.320 -0.255 -0.233 -0.011 -0.033 -0.015 0.183 Distribution of secondary tuber flesh color -0.320 -0.255 -0.233 -0.011 -0.033 -0.015 0.183 General tuber shape -0.330 -0.103 0.092 0.322 -0.022 0.014 0.043 Unusual tuber shape -0.294 -0.046 0.177 0.383 -0.015 0.021 -0.002 Depth of tuber eyes 0.182 -0.100 0.379 0.217 0.115 -0.177 0.271 No. of eyes per tuber 0.178 0.264 0.386 0.263 0.028 0.031 -0.213 Distribution of tuber eyes -0.231 -0.048 -0.045 0.237 -0.440 0.146 -0.199 Eigenvalues 6.30 2.65 2.36 1.91 1.75 1.37 1.25 Variability (%) 31.51 13.25 11.81 9.57 8.75 6.85 6.26 Cumulative (%) 31.51 44.76 56.57 66.14 74.89 81.74 88.01 Biplot graph was created by using PC1 and PC2 of PCA (Fig. 2 ). Variables are overlayed as vectors in biplot graph. The length of vector depicts the amount of variation existed in each variable for differentiation of varieties. The distance of each variable with respect to PC1 and PC2 showed the contribution of these variables towards the variation of the studied varieties. Adaxial leaf pubescence mostly contributed to PC1. On the other hand, stem cross section and predominant tuber skin colour mostly contributed to PC2. The variables with a wider angle such as secondary tuber skin colour, leaf character, stem colour and distribution of secondary tuber flesh colour contributed partly to PC1 and PC2. The loading plots also indicate how variables correlate with one another. A small angle implies positive correlation and a large one suggested negative correlation. The traits secondary tuber skin colour and number of eyes per tuber were positively correlated and the traits general tuber shape, secondary tuber flesh colour and predominant tuber skin colour were negatively correlated. Cluster Analysis Qualitative data cluster analysis was performed using agglomerative, Gower distance and complete linkage method and the resultant dendrogram is presented in Fig. 3 . The dendrogram showed the relationship among potato varieties. Cluster analysis grouped the potato varieties in cluster I and II which consisted of six and 10 varieties, respectively (Table 5 ). Cluster I further divided into two subclusters with three varieties in each. Similarly, Cluster II split into two subclusters but with six and four varieties in each, respectively. Several previous studies grouped potato genotypes based on their qualitative and quantitative traits (Arslanoglu et al. 2011 and Seid et al. 2021 ). Table 5 Grouping of potato varieties based on qualitative traits Cluster Number of varieties Name of varieties Cluster I 6 BD-02, BD-04, BD-05, BD-08, BD-16, BD-17. Cluster II 10 BD-03, BD-06, BD-07, BD-10, BD-12, BD-13, BD-14, BD-15, BD-18, BD-19. SSR Analysis SSR or microsatellite markers are well known for their effectiveness in detecting genetic polymorphism in plant genomes (Morgan and Olivieri 1993). Therefore, we performed SSR analysis using specific primer pairs targeting known microsatellite loci. The dendrogram tree in Fig. 4 shows two major groups with 12 potato varieties. Group A contains samples 6, 7, 10, 14, 18 and 19. Two additional samples, 3 and 12, differed due to variations in STMS 0019. Sample 3 had lost an allele (possibly caused by a mutation in one of the primer sites) while sample 12 yielded a shift in size of one allele by one repeat unit (Table 6 ). Group B contains samples 2, 4, 5, 8, 16 and 17. There were two samples that did not fall into any group, sample 13 had no close match in the database while 15 differed from Kufri Sindhuri by a single allele present in Kufri Sindhuri but not in sample 15 (Table 6 ). A comprehensive comparison of the Bangladeshi local potato varieties (apart from sample 15 and Kufri Sindhuri) with the entire database revealed the closest match to European heritage varieties although these are not particularly close, at best in the low 70% similarities (Fig. 5 ). These varieties originated from either Norway (Ringerikspotet and Rød Kvæfjord), Sweden (Blå Dalsland, Blå Mandel and Röda Krokar), The Republic of Ireland (Gawkies) or the United Kingdom, in particular Scotland (Orkney Black, Shetland Black, Kepplestone Kidney and Foula Red). Table 6 Allelic profiles for each of the 12 markers for the different genotypes in this study Accessions Markers 0019 3009 SSR1 2005 3012 3023 2028 5136 5148 1016 1024 2022 BD-06 (A)* BFG DF DIK ABF BDF AD AC CEFH EIJO DHL CE BEG BD-03 BG DF DIK ABF BDF AD AC CEFH EIJO DHL CE BEG BD-12 BFP DF DIK ABF BDF AD AC CEFH EIJO DHL CE BEG Kufri Sindhuri FGK DF DI ABD B AD AE EFH FJO DL CE BEG BD-15 FG DF DI ABD B AD AE EFH FJO DL CE BEG BD-13 B DG DIK ABD B A AC DEFH EFI DHL CE BEG BD-02 (B)* BF D DGI ADF BCG AD A CH BEJM DL CE BE * Accessions within the same cluster have the same alleles and only one is represented here from each Conclusion Morphological and molecular analysis were employed to assess similarities and dissimilarities between local potato varieties in Bangladesh. The hierarchical cluster analysis grouped six varieties in Cluster I and 10 in Cluster II. Similarly, the DNA profiling and cluster analysis also grouped most of the varieties into two distinct groups: A and B with similar accessions. Both morpho-physiological and molecular characterization exhibited that six varieties viz; BD-02, BD-04, BD-05, BD-08, BD-16 and BD-17 were grouped in a single cluster (Cluster I) and same group (Group B), respectively. This indicates though they are slightly different in their colour and shape, they have close genetic relationship which might be due to their close genetic bases. The dendrogram analysis indicated that Bangladeshi varieties share 70% genetic similarity with European heritage varieties, based on comparisons with the European database. Most of these varieties look like Andean varieties and some strikingly like Canary Island Varieties. However, it may be inferred from the grouping that only limited number of genotypes were initially introduced in this part of the world which later diversified morphologically due to spontaneous mutation and subsequent selection by the farmers over three centuries. Declarations Acknowledgements This research was partially funded by NATP-SPGR, Phase-I, PIU-BARC and BARI (International Development Association, The International Fund for Agricultural Development and The Government of Bangladesh). We thank Dr. M. A. A. Mondal , Ex. CSO, BARI for providing some of the accessions used in the present study. Author Contribution Md Abdullah Yousuf Akhond:conceptualisation, conducted research, writing, data analysis, supervision. Kamrun Nahar: methodology, morphological data analysis, writing. Muhammed Rezwan Kabir: conducted lab and field experiments, writing, submission. AlexReid: experimentation, marker data analysis, writing. Competing Interests The authors declare no competing interests. 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Molecular and General Genetics 259:233–245. https://doi.org/10.1007/s004380050809 Morgante M, Olivieri AM (1993) PCR‐amplified microsatellites as markers in plant genetics. Plant J 3(1):175–182. https://doi.org/10.1046/j.1365-313X.1993.t01-9-00999.x Placide R, Shimelis H, Laing M, Gahakwa D (2015) Application of principal component analysis to yield and yield related traits to identify sweet potato breeding parents. Trop Agric 92(1):1–15. Pluta S, Madry W, Sieczko L (2012) Phenotypic diversity for agronomic traits in a collection of blackcurrant ( Ribes nigrum L.) cultivars evaluated in Poland. Scientia Horticulturae 145:136–144. https://doi.org/10.1016/j.scienta.2012.07.036 Rathinavel K (2017) Exploration of genetic diversity for qualitative traits among the extant upland cotton ( Gossypium hirsutum L.) varieties and parental Lines. Int J Curr Microbiol App Sci 6(8):2407–2421. https://doi.org/10.20546/ijcmas.2017.608.285 Reid A, Hof L, Esselink D, Vosman B (2008) Potato cultivar genome analysis. In: Burns, R. (ed) Plant pathology–– techniques and protocols. Springer, New York Reid A, Hof L, Felix G, Rücker B, Tams S, Milczynska E. Esselink D, Uenk G, Vosman B Weitz A (2011) Construction of an integrated microsatellite and key morphological characteristic database of potato varieties on the EU common catalogue. Euphytica 182:239–249. https://doi.org/10.1007/s10681-011-0462-6 Seid E, Mohammed W, Abebe T (2021) Genetic diversity based on cluster and principal component analyses in potato ( Solanum Tuberosum L.) for yield and processing attributes. J Hortic 8(3):1–6. Siddique MA (1995) Indigenous potato varieties of Bangladesh. Dutch executing agency, Crop Diversification Programme, Khamarbari, Dhaka. Bangladesh. Spooner DM, McLean K, Ramsay G, Waugh R, Bryan GJ (2005) A single domestication for potato based on multilocus amplified fragment length polymorphism genotyping. Proc Natl Acad Sci 102(41):14694–14699. https://doi.org/10.1073/pnas.0507400102 STAR, version 2.0.1 2014. Biometrics and Breeding Informatics, PBGB Division, International Rice Research Institute, Los Baños, Laguna. Swaminathan MS (1958) The origin of early European potato-evidence from Indian varieties. Ind J Genet 18:8–15. Tekeu H, Ngonkeu MEL, Tandzi LN, Tagne A, Djocgoué PF 2021 Agro-morphological characterization of maize ( Zea mays L.) hybrids under acid soils in two contrasting environments. J Agric Sci 13(3):32–45. https://doi.org/10.5539/jas.v13n3p32 Wang Y, Rashid MAR, Li X, Yao C, Lu L, Bai J, Li Y, Xu N, Yang Q, Zhang L, Bryan GJ, Sui Q, Pan Z (2019) Collection and evaluation of genetic diversity and population structure of potato landraces and varieties in China. Front Plant Sci 10:139. https://doi.org/10.3389/fpls.2019.00139 Yuan J, Murphy A, Koeyer D. De, Lague M Bizimungu B (2016) Effectiveness of the field selection parameters on potato yield in Atlantic Canada. Can J Plant Sci 96(4):701–710. https://doi.org/10.1139/cjps-2015-0267 Supplementary Files Supplementaryfile1.pdf Supplementaryfile2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6917339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473883228,"identity":"a995f118-0377-49c1-b794-85423c004c90","order_by":0,"name":"Md Abdullah Yousuf Akhond","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Abdullah Yousuf","lastName":"Akhond","suffix":""},{"id":473883229,"identity":"c4f15df7-c9d6-4993-9742-13f481a68b52","order_by":1,"name":"Kamrun Nahar","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kamrun","middleName":"","lastName":"Nahar","suffix":""},{"id":473883230,"identity":"7abb1f1f-6815-4ad2-b231-2759d7a3a19c","order_by":2,"name":"Muhammed Rezwan Kabir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACHgY2hgQGGzkIj414LWnGJGphYDiU2EC0Ft2ew88ePNxzIH3D7eYDDB/KDjPwz0jAr8XsbJu5QcKzO7kb7hxLYJxx7jCDxA1CWs4zmEkkHHiWu+FGjgEzb9thBgbCWti/AbUcTje4kf+B+S9QizxBLWd7QLYcTjC4kcPAzAjUYkBQy5kz5QYJB9IMZ95IMzjYcy6dx/DMA0Ja0rc9/HHARp7vRvLDBz/KrOXkjhOwBQUcYADF0ygYBaNgFIwCygEAx7NMENL6fNMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8115-7725","institution":"University of New England School of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Muhammed","middleName":"Rezwan","lastName":"Kabir","suffix":""},{"id":473883231,"identity":"42ccc8ee-efa1-4858-98de-83be333fa423","order_by":3,"name":"Alex Reid","email":"","orcid":"","institution":"Science and Advice for Scottish Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Reid","suffix":""}],"badges":[],"createdAt":"2025-06-17 20:46:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6917339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6917339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85290959,"identity":"5cf749a3-804f-4f28-80d3-d56e190ea339","added_by":"auto","created_at":"2025-06-24 09:57:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":850480,"visible":true,"origin":"","legend":"\u003cp\u003eTubers of the potato genotypes used in the study [a = BD-02; b = BD-03; c = BD-04; d = BD-05; e = BD-06; f = BD-07; g = BD-08; h = BD-10; i = BD-12; j = BD-13; k = BD-14; l = BD-15; m = BD-16; n = BD-17; o = BD-18; p = BD-19]\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/782239223b7e96d13f925330.png"},{"id":85290962,"identity":"86b6e738-f126-46db-aca0-9031df2d0d28","added_by":"auto","created_at":"2025-06-24 09:57:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175655,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot from principal component analysis of 16 potato genotypes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/00530d850d3e28e1c6ca09c4.png"},{"id":85290961,"identity":"1c1ebf71-e8ae-4cf8-969f-6de974544e05","added_by":"auto","created_at":"2025-06-24 09:57:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116308,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram representing the clustering of potato varieties based on morphological characters. The dendrogram was generated by UPGMA cluster analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/3eb9eaf584757a3a183d849e.png"},{"id":85292440,"identity":"307acd3b-6976-455a-8706-68327b1cebe3","added_by":"auto","created_at":"2025-06-24 10:13:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35888,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram depicting similarities of 16 local potato genotypes by UPGMA clustering method from Euclidean distances matrix\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/11deee43042ecae82754f783.png"},{"id":85291315,"identity":"b44cfb39-34b7-4b74-8fb9-55db38702196","added_by":"auto","created_at":"2025-06-24 10:05:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40689,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of Bangladeshi varieties with the most closely related varieties in the European database\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/1de7b446561f320824cd1e13.png"},{"id":90249597,"identity":"a6814845-ad21-4045-b097-f02d7a1f42cf","added_by":"auto","created_at":"2025-08-31 07:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2255939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/88e9fe9a-0627-4fa7-849a-c0e034f31cdd.pdf"},{"id":85290960,"identity":"452ae016-8c18-4a41-a681-e57cc1c5d5be","added_by":"auto","created_at":"2025-06-24 09:57:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47500,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/cda931f5bd7ff1e1279372f7.pdf"},{"id":85292439,"identity":"3610089b-3d85-4c6e-8e65-907928b67a82","added_by":"auto","created_at":"2025-06-24 10:13:08","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":64339,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6917339/v1/fd0ad9fd1d5aac5bd8213a01.pdf"}],"financialInterests":"","formattedTitle":"Early Potatoes in Bangladesh: Morphological and Molecular Evaluation","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePotato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is the third most important food crop worldwide and its total production was estimated at 383.08\u0026nbsp;million tonnes in 2023 from 16.8\u0026nbsp;million ha (FAOSTAT \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The area and production of potato in Bangladesh during 2023\u0026ndash;2024 were 0.46\u0026nbsp;million ha and 10.6\u0026nbsp;million metric tonnes, respectively (BBS \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) making it the seventh largest producer of the crop in the world.\u003c/p\u003e \u003cp\u003eEarlier it was believed that potato had multiple independent origins in South America, but modern molecular studies support its single origin in northern Peru (Spooner et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It is generally assumed that 1570 is the approximate time when potato first arrived in Spain and a second introduction was thought to be later in England from a different source in around 1590 (Hawkes \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The present day modern European varieties resemble the primitive varieties of \u003cem\u003eS. tuberosum\u003c/em\u003e subsp. \u003cem\u003eTuberosum\u003c/em\u003e grown in Chile, however, the first European potatoes were short-day adapted varieties of subsp. \u003cem\u003eAndigena\u003c/em\u003e from the Northern highlands of South America and likely to be from Colombia (Burton \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Hawkes \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Potatoes were said to have been brought to India by British missionaries in the late seventeenth century (Hawkes \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). However, it is also possible that potatoes were first introduced in India by Portuguese explorers during early seventeenth century and were grown or at least known in India as early as 1615 (Swaminathan \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). It is probably through this route early potatoes arrived in Bangladesh and might have changed identity over a period of about three centuries because of artificial selection and diversification. However, their genetic composition is assumed to have remained largely unchanged due to the vegetative nature of propagation.\u003c/p\u003e \u003cp\u003eAlthough most of the potato crops grown in Bangladesh are modern high-yielding European varieties or their derivatives, a significant portion comes from the early varieties known as \u0026ldquo;Deshi\u0026rdquo; or \u0026ldquo;Local varieties\u0026rdquo;. In 2013\u0026ndash;2014, local varieties were cultivated in about 0.07\u0026nbsp;million hectares of land producing 0.84\u0026nbsp;million tons of tubers which were 16% and 9% of total potato growing area and production, respectively (BBS \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although, these varieties are naturally low yielding, they require less input for cultivation and are generally perceived to have better taste and texture. Moreover, their low yield is somewhat compensated by high market price which is usually double that of the high-yielding varieties. In the face of increasing competition from the modern varieties over the years, these varieties are holding their place due to consumer preference.\u003c/p\u003e \u003cp\u003eDespite their importance in the local cuisine and economy, studies on these potatoes are very limited (Siddique \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Islam et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Twenty-seven local varieties have been reported to be cultivated in Bangladesh (Khalil et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but a closer observation might show the number to be less as a single variety is known by different local names in different regions. Therefore, it is necessary to characterize and identify the varieties, determine the genetic relationships and distinguish duplicates in the locally cultivated potato collections for conservation purposes. Hierarchical Cluster analysis has been suggested for classifying the germplasm or varieties based on degree of similarity and dissimilarity (Rathinavel \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, principal component analysis (PCA) is used to study patterns of variation in a set of interrelated traits through the identification of subsets of these traits, called factors, which are substantially correlated to each other (Pluta et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, both Cluster analysis and PCA are useful methods for revealing the patterns of phenotypic variation in the collection. Genetic variation for morphological traits has been estimated using PCA in many crops (Janmohammadi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Placide et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yuan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rathinavel \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tekeu et al. 2021; Seid et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Beside morphological traits, molecular markers have also been used for identification of variety, cultivar and genotype. Several researchers successfully used simple sequence repeat (SSR) markers to identify potato varieties and accessions (Milbourne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Favoretto et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e Duan et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e and Lee et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). SSR markers have some advantages such as co-dominance, locus specific, highly reproducible, highly polymorphic and widely dispersed across the genome and were shown to be highly efficient for identification of potato varieties (Ghislain et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe therefore set out to determine the similarity and differences, estimate the genetic relationship and identify local potato varieties in Bangladesh using morphological traits and SSR markers.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant Material\u003c/h2\u003e \u003cp\u003eTubers of sixteen local potato varieties viz. BD-02, BD-03, BD-04, BD-05, BD-06, BD-07, BD-08, BD-10, BD-12, BD-13, BD-14, BD-15, BD-16, BD-17, BD-18, BD-19 were collected from Tuber Crops Research Centre of Bangladesh Agricultural Research Institute (BARI), the markets as well as from the farmers of different regions of Bangladesh (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The variation of tuber colours and shapes of the varieties are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of the local potato varieties with their accession numbers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccession number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal variety name\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndurkani\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShada gootee\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShilbilati\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHagrai\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAusha\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLalpakri\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhaubilati\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSabrai\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRangpur Lal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFestashil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChallisha\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurjamukhi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJam Aloo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSindur Kauta\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatnai\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDohazari\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMorphological Characterization\u003c/h3\u003e\n\u003cp\u003eTubers were planted in the field of Biotechnology Division, BARI. Morphological data were recorded according to standard descriptors (Huaman et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) for evaluating the substantial variation and relationship among the potato varieties and photographs were taken as required for documentation. Morphological characters of the plants and tubers are presented in Supplementary Tables\u0026nbsp;1 and 2, respectively. Twenty qualitative traits were observed, and their codes and descriptors are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the characters used in the morphological study of potato varieties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eErect\u0026thinsp;=\u0026thinsp;1, Semi\u0026ndash;erect\u0026thinsp;=\u0026thinsp;2, Decumbent\u0026thinsp;=\u0026thinsp;3, Prostrate\u0026thinsp;=\u0026thinsp;4, Semi\u0026ndash;rosette\u0026thinsp;=\u0026thinsp;5, Rosette\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShort =\u0026thinsp;3, Medium\u0026thinsp;=\u0026thinsp;5, Tall\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStem color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen\u0026thinsp;=\u0026thinsp;1, Red\u0026ndash;brown\u0026thinsp;=\u0026thinsp;2, Purple\u0026thinsp;=\u0026thinsp;3, Cream with some red\u0026ndash;brown\u0026thinsp;=\u0026thinsp;4, Cream with purple\u0026thinsp;=\u0026thinsp;5, Red\u0026ndash;brown with some green\u0026thinsp;=\u0026thinsp;6, Purple with some green\u0026thinsp;=\u0026thinsp;7, Other\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStem wing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u0026thinsp;=\u0026thinsp;0, Straight\u0026thinsp;=\u0026thinsp;1, Undulate\u0026thinsp;=\u0026thinsp;2, Dentate\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStem cross section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRound\u0026thinsp;=\u0026thinsp;1 Angular\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf character\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUndissected\u0026thinsp;=\u0026thinsp;1, Pinnatilobed\u0026thinsp;=\u0026thinsp;2, Scarcely dissected\u0026thinsp;=\u0026thinsp;3, Weakly dissected\u0026thinsp;=\u0026thinsp;4, Medium dissected\u0026thinsp;=\u0026thinsp;5, Strongly dissected\u0026thinsp;=\u0026thinsp;6, Very strong dissected\u0026thinsp;=\u0026thinsp;7, Other\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbaxial leaf pubescence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlabrous\u0026thinsp;=\u0026thinsp;0, Glabrescent\u0026thinsp;=\u0026thinsp;1, Pubescence\u0026thinsp;=\u0026thinsp;2, Strongly pubescent\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdaxial leaf pubescence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlabrous\u0026thinsp;=\u0026thinsp;0, Glabrescent\u0026thinsp;=\u0026thinsp;1, Pubescence\u0026thinsp;=\u0026thinsp;2, Strongly pubescent\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredominant tuber skin color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePTSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhite\u0026ndash;cream\u0026thinsp;=\u0026thinsp;1, Yellow\u0026thinsp;=\u0026thinsp;2, Orange\u0026thinsp;=\u0026thinsp;3, Brownish\u0026thinsp;=\u0026thinsp;4, Pink\u0026thinsp;=\u0026thinsp;5, Red\u0026thinsp;=\u0026thinsp;6, Purplish\u0026ndash;red\u0026thinsp;=\u0026thinsp;7, Purple\u0026thinsp;=\u0026thinsp;8, Dark purple-black\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary tuber skin color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u0026thinsp;=\u0026thinsp;0, White\u0026ndash;cream\u0026thinsp;=\u0026thinsp;1, Yellow\u0026thinsp;=\u0026thinsp;2, Orange\u0026thinsp;=\u0026thinsp;3, Brownish\u0026thinsp;=\u0026thinsp;4, Pink\u0026thinsp;=\u0026thinsp;5, Red\u0026thinsp;=\u0026thinsp;6, Purplish\u0026ndash;black\u0026thinsp;=\u0026thinsp;7, Purple\u0026thinsp;=\u0026thinsp;8, Dark purple\u0026ndash;black\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistribution of secondary tuber color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u0026thinsp;=\u0026thinsp;0, Eyes\u0026thinsp;=\u0026thinsp;1, Eyebrows\u0026thinsp;=\u0026thinsp;2, Splashed\u0026thinsp;=\u0026thinsp;3, Scattered\u0026thinsp;=\u0026thinsp;4, Spectacled\u0026thinsp;=\u0026thinsp;5, Stippled\u0026thinsp;=\u0026thinsp;6, Other\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTuber skin type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmooth\u0026thinsp;=\u0026thinsp;1, Rough (flaky)\u0026thinsp;=\u0026thinsp;2, Partially netted\u0026thinsp;=\u0026thinsp;3 Totally netted\u0026thinsp;=\u0026thinsp;4 Very heavily netted\u0026thinsp;=\u0026thinsp;5, Other\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredominant tuber flesh color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhite\u0026thinsp;=\u0026thinsp;1, Cream\u0026thinsp;=\u0026thinsp;2, Yellow\u0026ndash;cream\u0026thinsp;=\u0026thinsp;3, Yellow\u0026thinsp;=\u0026thinsp;4, Red\u0026thinsp;=\u0026thinsp;5, Violet\u0026thinsp;=\u0026thinsp;6, Purple\u0026thinsp;=\u0026thinsp;7 Other\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary tuber flesh color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u0026thinsp;=\u0026thinsp;0, White\u0026thinsp;=\u0026thinsp;1, Cream\u0026thinsp;=\u0026thinsp;2, Yellow\u0026ndash;cream\u0026thinsp;=\u0026thinsp;3, Yellow\u0026thinsp;=\u0026thinsp;4, Red\u0026thinsp;=\u0026thinsp;5, Violet\u0026thinsp;=\u0026thinsp;6, Purple\u0026thinsp;=\u0026thinsp;7, Other\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistribution of secondary tuber flesh color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u0026thinsp;=\u0026thinsp;0, Scattered spots\u0026thinsp;=\u0026thinsp;1, Scattered areas\u0026thinsp;=\u0026thinsp;2, Narrow vascular ring\u0026thinsp;=\u0026thinsp;3, Broad vascular ring\u0026thinsp;=\u0026thinsp;4, Vascular ring medulle (pith)\u0026thinsp;=\u0026thinsp;5, All flesh exept medulla (pith)\u0026thinsp;=\u0026thinsp;6, Other\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral tuber shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompressed (oblate)\u0026thinsp;=\u0026thinsp;1, Round\u0026thinsp;=\u0026thinsp;2, Ovate\u0026thinsp;=\u0026thinsp;3, Obovate\u0026thinsp;=\u0026thinsp;4, Elliptic\u0026thinsp;=\u0026thinsp;5, Oblong\u0026thinsp;=\u0026thinsp;6, Long-Oblong\u0026thinsp;=\u0026thinsp;7, Elongate\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnusual tuber shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u0026thinsp;=\u0026thinsp;0, Flattened\u0026thinsp;=\u0026thinsp;1, Clavate\u0026thinsp;=\u0026thinsp;2, Reniform\u0026thinsp;=\u0026thinsp;3, Fusiform\u0026thinsp;=\u0026thinsp;4, Falcate\u0026thinsp;=\u0026thinsp;5, Spiral\u0026thinsp;=\u0026thinsp;6, Digitate\u0026thinsp;=\u0026thinsp;7, Concertina-shaped\u0026thinsp;=\u0026thinsp;8, Tuberosed\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth of tuber eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtruding\u0026thinsp;=\u0026thinsp;1, Shallow\u0026thinsp;=\u0026thinsp;2, Medium\u0026thinsp;=\u0026thinsp;3, Deep\u0026thinsp;=\u0026thinsp;4, Very deep\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of eyes per tuber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNEPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFew (less than 5)\u0026thinsp;=\u0026thinsp;1, Intermediate\u0026thinsp;=\u0026thinsp;5, Many (more than 20)\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistribution of tuber eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDITE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredominantly apical\u0026thinsp;=\u0026thinsp;1, Evenly distributed\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eCluster and PCA were done by using complete linkage method and Gower distance. The dendrogram was constructed based on the Jaccard similarity co-efficient and clustered using the Unweighted Pair group Method with Arithmetic Mean (UPGMA). The data were analyzed by Statistical Tool for Agricultural Research (STAR) version 2.0.1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular Characterization\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDNA Extraction and Quantification\u003c/h2\u003e \u003cp\u003eTubers of sixteen local potato varieties were grown in pots and genomic DNA was extracted from leaf tissue using standard protocol (DNEasy plant mini kit, QIAGEN). The DNA A\u003csub\u003e260\u003c/sub\u003e/A\u003csub\u003e280\u003c/sub\u003e ratio and the concentrations were recorded by using a spectrophotometer (ThermoScientific NanoDrop ND-1000 Spectrophotometer). DNA integrity was verified by agarose gel (0.8% w/v) electrophoresis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePCR Analysis\u003c/h2\u003e \u003cp\u003eTwelve microsatellite regions were amplified in four multiplex reactions each containing primers for three markers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All markers were developed by Millbourne et al. (1998), with the exception of SSR1 (Kawchuk et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and STMS 5136 and STMS 5148 (Ghislain et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Amplifications were carried out in 10 \u0026micro;L volumes with 10 ng DNA, 1 \u0026micro;L of primer mixes using Type-it Microsatellite PCR Kit (QIAGEN). Forward primers were labelled with either 6FAM, VIC or NED (Applied Biosystems), reverse primers had a 5\u0026acute;pigtail sequence of TTCTTTG (Brownstein et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) to help reduce plus A effects. Cycling conditions were 94\u0026ordm;C for 5 minutes then 30 cycles of 94\u0026ordm;C for 30 seconds, ramp at 1\u0026ordm;C/second to 50\u0026ordm;C for 30 seconds, ramp at 1\u0026ordm;C/second to 72\u0026ordm;C for 2 minutes, followed by a hold step at 60\u0026ordm;C for 30 minutes and an infinite hold at 25\u0026ordm;C. After amplification, 1 \u0026micro;L of each product was added to 9 \u0026micro;L HiDi formamide (Applied Biosystems) containing GS 500 LIZ size standard (Applied Biosystems), denatured at 95\u0026ordm;C for 5 minutes, quenched on ice and ran on an ABI 3500xL (Applied Biosystems) with a 50 cm array and POP-7. Samples were analysed using GeneMapper v4.0 (Applied Biosystems). Further details of the conditions for both PCR amplification and electrophoresis of the samples can be found in Reid et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Resulting profiles were stored to a BioNumerics database containing over 3000 varieties. The database is a continuation of that described in Reid et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e. Dendrograms were generated using Jaccard co-efficient and Unweighted Pair group Method with Arithmetic Mean (UPGMA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiplex marker set information showing repeat motif, dye and concentration in the primer multiplex mix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiplex set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepeat motif\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDye\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003cp\u003e\u0026micro;M\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(AT)\u003csub\u003e7\u003c/sub\u003e (GT)\u003csub\u003e10\u003c/sub\u003e (AT)\u003csub\u003e4\u003c/sub\u003e (GT)\u003csub\u003e5\u003c/sub\u003e (GC)\u003csub\u003e4\u003c/sub\u003e (GT)\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 3009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(TC)\u003csub\u003e13\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(TCAC)\u003csub\u003en\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(CTGTTG)\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 3012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(CT)\u003csub\u003e4\u003c/sub\u003e.(CT)\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 3023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(GA)\u003csub\u003e9\u003c/sub\u003e.(GA)\u003csub\u003e8\u003c/sub\u003e.(GA)\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 2028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(TAC)\u003csub\u003e5\u003c/sub\u003e.(TA)\u003csub\u003e3\u003c/sub\u003e.(CAT)\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 5136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(AGA)\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 5148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(GAA)\u003csub\u003e17\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 1016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(TCT)\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 1024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(TTG)\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTMS 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(CAA)\u003csub\u003e3\u003c/sub\u003e.(CAA)\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussions","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003eCollection, characterisation and evaluation of the local potatoes in Bangladesh could be prominent for future breeding before they disappear. In light of their significance, we started with investigation of the phenotypic variation using PCA in sixteen local potato varieties in Bangladesh. Seven PCs with eigenvalues greater than 1 were identified based on the analysis of qualitative traits (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The contribution of these seven PCs was 88.01% in the total variation among the potato varieties. Yuan et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) assessed 179 potato clones and found that the first five components accounted for 74.9% of the total phenotypic variation. Ahmadizadeh and Felenji (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported that first three PCA explained 80.1% total variation among 22 potato variates. We found PC1 contributed 31.51% in the total variability of the studied potato varieties, in which the major contributing traits were plant height (0.334), adaxial leaf pubescence (0.196), depth of tuber eyes (0.182), secondary tuber skin colour (0.181), abaxial leaf pubescence (0.180), number of eyes per tuber (0.178), leaf character (0.162) and distribution of secondary tuber colour (0.148). In addition, stem cross section and tuber skin type also contributed positively but with low intensity. The PC2 illustrated 13.25% of the total variation found in morphological traits in potato which ascertained the pattern of variation in stem colour (0.303), secondary tuber skin colour (0.272), number of eyes per tuber (0.264), stem cross section (0.231) and growth habit (0.218). The traits distribution of secondary tuber colour (0.134), stem wing (0.131) and adaxial leaf pubescence (0.009) also added positively but with low magnitude. While predominant tuber flesh colour (-0.410), predominant tuber skin colour (-0.375), tuber skin type (-0.272), secondary tuber flesh colour (-0.255), distribution of secondary tuber flesh colour (-0.255), plant height (-0.218) and leaf character (-0.182) were having negative weights. The PC3 explained 11.81% of the total variations found in morphological traits which described the variations in number of eyes per tuber (0.386), depth of tuber eyes (0.379), predominant tuber flesh colour (0.310), predominant tuber skin colour (0.235), stem cross section (0.182) and unusual tuber shape (0.177). In contrast, adaxial leaf pubescence (-0.393) abaxial leaf pubescence (-0.313), secondary tuber skin colour (-0.272), secondary tuber flesh colour (-0.233), distribution of secondary tuber flesh colour (-0.233), were negative to add to this component. The contribution of PC4 was 9.57% in total variations which portrayed the variation in unusual tuber shape (0.383), stem wing (0.358), abaxial leaf pubescence (0.323), general tuber shape (0.322), adaxial leaf pubescence (0.312), number of eyes per tuber (0.263), distribution of tuber eyes (0.237), depth of tuber eyes (0.217), stem cross section (0.202) and secondary tuber skin colour (0.167). On the other hand, the trait stem colour (-0.313) and distribution of secondary tuber colour (-0.181) contributed negatively to this component. PC5 accounted for 8.75% of total variability mainly depicted the variations in abaxial leaf pubescence (0.355), tuber skin type (0.339), stem wing (0.337), stem colour (0.319), predominant tuber skin colour (0.245) and growth habit (0.226) with positive load, however distribution of tuber eyes (-0.440), stem cross section (-0.369) and plant height (-0.226) were negative load on this component. PC6 contributed 6.85% to the total variation and it was related to diversity for distribution of secondary tuber colour (0.541), tuber skin type (0.473), predominant tuber skin colour (0.300) and stem cross section (0.294) with positive loadings and leaf character (-0.459) and depth of tuber eyes (-0.177) with negative loadings. Lastly, PC7 comprised of 6.26% and mainly illuminated the distribution of secondary tuber colour (0.465), secondary tuber skin colour (0.443), leaf character (0.343), predominant tuber skin colour (0.291), depth of tuber eyes (0.271), secondary tuber flesh colour (0.183), distribution of secondary tuber flesh colour (0.183) and stem cross section (0.154) with positive load whereas negative loadings exhibited by tuber skin type (-0.323), number of eyes per tuber (-0.213) and distribution of tuber eyes (-0.199). The results indicated that traits which contributed more to different PCs had higher contribution to the total variation of the varieties. Similar results were observed by Seid et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who reported that plant height, tuber skin colour, depth of tuber eyes had higher contribution to the total variation among potato genotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal Components with Eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1.0 for various traits in 16 local potato varieties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003ePrincipal Components\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePC6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePC7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem wing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem cross section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf character\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbaxial leaf pubescence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaxial leaf pubescence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredominant tuber skin color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary tuber skin color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution of secondary tuber color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuber skin type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredominant tuber flesh color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary tuber flesh color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution of secondary tuber flesh color\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral tuber shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnusual tuber shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth of tuber eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of eyes per tuber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution of tuber eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBiplot graph was created by using PC1 and PC2 of PCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Variables are overlayed as vectors in biplot graph. The length of vector depicts the amount of variation existed in each variable for differentiation of varieties. The distance of each variable with respect to PC1 and PC2 showed the contribution of these variables towards the variation of the studied varieties. Adaxial leaf pubescence mostly contributed to PC1. On the other hand, stem cross section and predominant tuber skin colour mostly contributed to PC2. The variables with a wider angle such as secondary tuber skin colour, leaf character, stem colour and distribution of secondary tuber flesh colour contributed partly to PC1 and PC2. The loading plots also indicate how variables correlate with one another. A small angle implies positive correlation and a large one suggested negative correlation. The traits secondary tuber skin colour and number of eyes per tuber were positively correlated and the traits general tuber shape, secondary tuber flesh colour and predominant tuber skin colour were negatively correlated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCluster Analysis\u003c/h2\u003e \u003cp\u003eQualitative data cluster analysis was performed using agglomerative, Gower distance and complete linkage method and the resultant dendrogram is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The dendrogram showed the relationship among potato varieties. Cluster analysis grouped the potato varieties in cluster I and II which consisted of six and 10 varieties, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Cluster I further divided into two subclusters with three varieties in each. Similarly, Cluster II split into two subclusters but with six and four varieties in each, respectively. Several previous studies grouped potato genotypes based on their qualitative and quantitative traits (Arslanoglu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e and Seid et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrouping of potato varieties based on qualitative traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of varieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName of varieties\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD-02, BD-04, BD-05, BD-08, BD-16, BD-17.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD-03, BD-06, BD-07, BD-10, BD-12, BD-13, BD-14, BD-15, BD-18, BD-19.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSSR Analysis\u003c/h2\u003e \u003cp\u003eSSR or microsatellite markers are well known for their effectiveness in detecting genetic polymorphism in plant genomes (Morgan and Olivieri 1993). Therefore, we performed SSR analysis using specific primer pairs targeting known microsatellite loci. The dendrogram tree in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows two major groups with 12 potato varieties. Group A contains samples 6, 7, 10, 14, 18 and 19. Two additional samples, 3 and 12, differed due to variations in STMS 0019. Sample 3 had lost an allele (possibly caused by a mutation in one of the primer sites) while sample 12 yielded a shift in size of one allele by one repeat unit (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Group B contains samples 2, 4, 5, 8, 16 and 17. There were two samples that did not fall into any group, sample 13 had no close match in the database while 15 differed from Kufri Sindhuri by a single allele present in Kufri Sindhuri but not in sample 15 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A comprehensive comparison of the Bangladeshi local potato varieties (apart from sample 15 and Kufri Sindhuri) with the entire database revealed the closest match to European heritage varieties although these are not particularly close, at best in the low 70% similarities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These varieties originated from either Norway (Ringerikspotet and R\u0026oslash;d Kv\u0026aelig;fjord), Sweden (Bl\u0026aring; Dalsland, Bl\u0026aring; Mandel and R\u0026ouml;da Krokar), The Republic of Ireland (Gawkies) or the United Kingdom, in particular Scotland (Orkney Black, Shetland Black, Kepplestone Kidney and Foula Red).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAllelic profiles for each of the 12 markers for the different genotypes in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccessions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3009\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSSR1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3012\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2028\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5136\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5148\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-06 (A)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCEFH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEIJO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBEG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCEFH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEIJO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBEG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCEFH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEIJO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBEG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKufri Sindhuri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEFH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFJO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBEG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEFH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFJO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBEG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDEFH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBEG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-02 (B)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBEJM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eBE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e* Accessions within the same cluster have the same alleles and only one is represented here from each\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMorphological and molecular analysis were employed to assess similarities and dissimilarities between local potato varieties in Bangladesh. The hierarchical cluster analysis grouped six varieties in Cluster I and 10 in Cluster II. Similarly, the DNA profiling and cluster analysis also grouped most of the varieties into two distinct groups: A and B with similar accessions. Both morpho-physiological and molecular characterization exhibited that six varieties viz; BD-02, BD-04, BD-05, BD-08, BD-16 and BD-17 were grouped in a single cluster (Cluster I) and same group (Group B), respectively. This indicates though they are slightly different in their colour and shape, they have close genetic relationship which might be due to their close genetic bases. The dendrogram analysis indicated that Bangladeshi varieties share 70% genetic similarity with European heritage varieties, based on comparisons with the European database. Most of these varieties look like Andean varieties and some strikingly like Canary Island Varieties. However, it may be inferred from the grouping that only limited number of genotypes were initially introduced in this part of the world which later diversified morphologically due to spontaneous mutation and subsequent selection by the farmers over three centuries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partially funded by NATP-SPGR, Phase-I, PIU-BARC and BARI (International Development Association, The International Fund for Agricultural Development and The Government of Bangladesh). We thank Dr. M. A. A. Mondal\u003cem\u003e, Ex. CSO, BARI for providing some of the accessions used in the present study.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor Contribution \u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMd Abdullah Yousuf Akhond:conceptualisation, conducted research, writing, data analysis, supervision. Kamrun Nahar: methodology, morphological data analysis, writing. Muhammed Rezwan Kabir: conducted lab and field experiments, writing, submission. AlexReid: experimentation, marker data analysis, writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmadizadeh M, Felenji H (2011) Evaluating diversity among potato cultivars using agro-morphological and yield components in fall cultivation of Jiroft area. 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De, Lague M Bizimungu B (2016) Effectiveness of the field selection parameters on potato yield in Atlantic Canada. Can J Plant Sci 96(4):701\u0026ndash;710. https://doi.org/10.1139/cjps-2015-0267\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cluster analysis, Local potato, Molecular marker, Morphological traits, Principal Component Analysis, SSR ","lastPublishedDoi":"10.21203/rs.3.rs-6917339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6917339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLocal potato varieties are important in the local cuisine and economy of Bangladesh for their better taste, texture and high price. However, limited information associated with characterization of local potatoes is known. Therefore, this study was conducted to determine the similarity and differences and to estimate the genetic relationship of local potato varieties in Bangladesh using morphological traits and molecular markers. A total of 16 potato varieties were evaluated for 20 qualitative traits using cluster and principal component analysis (PCA). The PCA revealed that the first seven principal components (PCs) with eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1 contributed for 88.01% of the total variation. The most important characters for PC1 were plant height, adaxial leaf pubescence, depth of tuber eyes, secondary tuber skin colour, abaxial leaf pubescence, number of eyes per tuber, leaf character and distribution of secondary tuber colour whereas variations for PC2 mostly related to stem colour, secondary tuber skin colour, number of eyes per tuber, stem cross section and growth habit. The important characters for PC3 were number of eyes per tuber, depth of tuber eyes, predominant tuber flesh colour, predominant tuber skin colour, stem cross section and unusual tuber shape. Cluster analysis grouped the 16 potato varieties into two clusters: six varieties in cluster I and 10 in cluster II. The dendrogram generated by cluster analysis based on DNA profiling and simple sequence repeat (SSR) screening classified most varieties into two groups: A and B. Both groups contained six samples with identical profiles. Two further varieties BD-3 and BD-12 differed by changes in STM0019. In addition, BD-15 differed from Kufri Sindhuri by a single allele. BD-13 was unique and did not form a close cluster with any variety in the database. The dendrogram revealed the Bangladeshi varieties have closest matches to European heritage varieties with a similarity between 60 and 70% when compared to the European database. The results obtained in this study will break barriers in terms of identification and conservation of the local potatoes by their phenotypic and genotypic variation.\u003c/p\u003e","manuscriptTitle":"Early Potatoes in Bangladesh: Morphological and Molecular Evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 09:57:03","doi":"10.21203/rs.3.rs-6917339/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4a82e92-fd65-4991-9171-29f7227d9ce6","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-31T07:09:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-24 09:57:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6917339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6917339","identity":"rs-6917339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}