Molecular Insight into the Genetic structure and Banding pattern Analysis of Bambara groundnut (Vigna subterranea L.) with Random Amplified Microsatellites (RAMs)

Molecular Insight into the Genetic structure and Banding pattern Analysis of Bambara groundnut (Vigna subterranea L.) with Random Amplified Microsatellites (RAMs) | 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 Molecular Insight into the Genetic structure and Banding pattern Analysis of Bambara groundnut (Vigna subterranea L.) with Random Amplified Microsatellites (RAMs) Md Mahmudul Hasan Khan, Mohd Y. Rafii, Shairul Izan Ramlee, Mashitah Jusoh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2678771/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Aug, 2023 Read the published version in Molecular Biology Reports → Version 1 posted 4 You are reading this latest preprint version Abstract Background A set of 44 selected Bambara groundnut ( Vigna subterranea L. Verdc.) accessions was sampled from 11 distinct populations of four geographical zones to assess the genetic drift, population structure, phylogenetic relationship, and genetic differentiation linked with ISSR primers. In Malaysia, this is an exotic legume introduced from Africa and having tremendous nutritional values and diverse usages. Methods and Results The amplification of genomic DNA with 32 ISSR markers detected an average of 97.64% polymorphism while 35.15% and 51.08% polymorphism per population and geographical zone, respectively. Genetic diversity estimated by Shannon’s information index ( I ) = 0.177 (average) and populations under Gombe showed maximum diversity ( I = 0.271) with 90.98% polymorphism. Analysis of molecular variance revealed significant variation within population 75% and between population 25% whereas within region 84% and between region 16%. The study also divulged total genetic variation Ht = 0.1781 closer to within population diversity ( Hs = 0.1155). Among the population, Cancaraki revealed 40.39% polymorphism while the average polymorphism was 35.15%. The Bidillali exposed greater number of locally common band i.e., NLCB (≤ 25%) = 25 and NLCB (≤ 50%) = 115 were shown by Cancaraki while the lowest was recorded as NLCB (≤ 25%) = 6 and NLCB (≤ 50%) = 72 for Roko and Maibergo, accordingly. The highest PhiPT value was noted between Roko and Katawa (0.405*) whereas Nei’s genetic distance was maximum between Roko and Karu (0.124). The genetic differentiation among population Gst = 0.3514 (35.14%) leaving 65.86% of genetic variation leads to within-population with gene flow of Nm = 0.9229. Based on Nei’s genetic distance, a radial phylogenetic tree was constructed that assembled the entire accessions into 3 major clusters for further confirmation unrooted NJ vs NNet split tree analysis based on uncorrected P distance exposed the similar result. Principal coordinate analysis showed variation as PC1 (15.04%) > PC2 (5.81%). Mantel test exposed a significant correlation among genetic and geographic distance of accessions. STRUCTURE analysis (Bayesian) grouped the accessions into 3 major genetic components based on best ΔK = 3 and admixture population. Conclusions The current study leads to prompting the genetic improvement and future breeding program by maximum utilization and better conservation of existing V. subterranea accessions in this subtropical environment. Vigna subterranea L. Banding pattern Population structure Phylogenetic linkage and Genetic diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1.0 Introduction Bambara groundnut ( Vigna subterranea [L.] Verdc. Syn. Voandzeia subterranean [L.] Thouars ex DC. 2n = 2x = 22) belongs to an important legume taxon: the genus Vigna, family Leguminosae. It was previously classified into the genus Voandzeia later included as a member of the genus Vigna (Verdcourt 1980 ), though it has significant differences in morphological features from other species of Vigna . The genus Vigna is comprised of ninety species among them seven species (major) commercially grown in several countries while other species are grown as minor or underutilized legume at pocket areas of different countries for local food and feed supply (Rungnoi et al. 2012 ). The minor or underutilized species such as Kersting groundnut ( Kerstingiella geocarpa ), Marma bean ( Tylosema esculentum ), Rice bean ( Vigna angularies ), Mung bean ( Vigna mungo ) and Cowpea ( Vigna unguiculata ) have a wide spectrum of genetic variation all over the world, either as cultivated or wild races (Khan et al. 2021a ). One such imperative underutilized legume is Bambara groundnut (Molosiwa et al. 2015 ) occupy 3rd position after groundnut and cowpea production in Africa (Olukolu et al. 2012 ). It is highly tolerant to water deficit, infertile soil, and widely grown by marginal farmers of semiarid African region as monoculture or intercropped with cereals, root, and tuber crops (Olukolu et al. 2012 ). Besides Africa, it has been successfully cultivated in Asian regions such as Malaysia, Indonesia, Philippines, Thailand, and India [2, 6]. Consumption of agri-based food reduces the mortality resulting from coronary heart diseases (Khan et al. 2022a ) so, Bambara groundnut can be an agri-based protein source for resource-limited people who are unaffordable to precious animal protein (Khan et al. 2021c ). The current scarcity and malnutrition, particularly in low-income countries can be mitigated by giving emphasis on underutilized such legume research and expansion resulting in gradually getting the planetary food and nutritional security (Molosiwa et al. 2015 ). Bambara groundnut is hardy crop that have been noted as a lucrative and nutritive food source when food is under threat (Mbosso et al. 2021) also improves the soil profiles via fixing atmospheric Nitrogen (Paliwal et al. 2020). Due to balanced macronutrients viz. carbohydrates (64.4%), protein (23.6%), oil (6.5%), fiber (5.5%), and a significant amount of trace minerals (Halimi et al. 2019 ) Bambara groundnut remarked as “Complete Food”, having potentiality in reducing gaps in the food scheme to assure food sustainability and nutrient sanctuary (Lin Tan et al. 2020 ). In the case of world Bambara groundnut production Nigeria and Burkina faso hold the first position (Hillocks et al. 2012 , Khan et al. 2022b ]. The major obstacles to large scale farming of Bambara ground are its low yield which is recorded as low as 650–850 kg ha − 1 (Olukolu et al. 2012 ), though this crop has enabled to produce up to 4.0 t ha − 1 (Kouassi and Bi. 2010) whereas 0.38–1.6 t ha − 1 reported by Khan et al. ( 2020 ) by ensuring the optimal growing environment. The main point of low yield of Bambara groundnut is the use of local land races in addition to little or no attention to genetic improvement, lack of improving varieties with production technologies, lack of effective research and resources, less research interest by scientific personals (Mohammed et al. 2019 ). The autogamous and cleistogamous nature of Bambara groundnut is the major hindrance of its varietal development through hybridization (Molosiwa et al. 2015 ). Information on genetic makeup and parental survey is imperative for varietal advancement in all crop species, predominantly in neglected crops such as Bambara groundnut (Bamshaiye et al. 2011 ). Characterization at the genomic level is highly authoritative to counterpart the morphologic characterization (Fatimah and Ardiarini 2018 ) as a reason it is mostly influenced by environmental factors (Massawe et al. 2003 ; Amzeri 2015 ). Genomic markers are the powerful tools for sensing the genetic inconsistency over the traditional breeding approaches (Gupta and Varshney 2000 ), which can be used for sketching the germplasm's origin and breeding scheme to genomic upgrading (Khan et al. 2021b ). Going by the available literature, a few kinds of research has been conducted on Bambara groundnut (inter and intra species) at a molecular level using genomic markers. The first initiative was taken by Odeigah and Osanyinpeju ( 1998 ) using the SDS-polyacrylamide electrophoresis technique. The researchers Molosiwa et al. ( 2015 ), Mohammed et al. ( 2019 ), SiiseAliyu and Massawe (2013), and Odongo et al. ( 2015 ) reported genetic diversity has existed in the evaluated genotypes of Bambara groundnut applying SSR markers because of their duplicability, co-dominant nature, and richness in the genome. In literature, negligible evidence was found related to the use of ISSRs marker in Bambara groundnut. Although, Rungnoi et al. ( 2012 ) noted only three ISSR markers in their study and recently a statement is available on ISSR genomic marker-based population genetic structure of this crop in Malaysia (Khan et al. 2021b ). Among the PCR-based marker, ISSR is the most frequently used for population genetic structure portrayal in different crop species, which generate highly steadfast and reproducible bands over RAPD tools (Nilkanta et al. 2017 ). ISSR markers are also popularly known as random amplified microsatellites (RAMs). Moreover, ISSRs are technically easier, rapid, more economical related to AFLP, SSR, and RFLP genomic tools as it requires a small amount of DNA fragment, as well as no anterior sequence data, are needed to engendering DNA amplified products (Oumer et al. 2020 ). To identify genetically similar lines, ISSRs have the potentiality to generate very repeatable bands on identical genotypes (Fang and Roose 1997 ). It has been successfully applied for landraces characterization, valuation of genetic variation and phylogenetic relation, documentation of DNA primers associated with agro-morphic features, and for crop breeding schemes (Reddy et al. 2002 ; Alansi et al. 2016 ). As a new crop in Malaysia, there is an absence and/or gap of research at a molecular level, information on crop’s botany, genetics, farming techniques, economic value addition, and diversity of Bambara groundnut that prompted the inauguration of this research. The current investigation is designed to explore genetic disparity, gene flow, and population genetic makeup of Bambara groundnut using ISSR primers. Additionally, this investigation contributes to the policy making for actual conservation and justifiable usages of this legume. Contrarily, findings from this study may deepen the Bambara groundnut gene pool, backing the upcoming breeding program, assuring economic and ecological gains by divulging the genetic structure of current Vigna subterranea [L.] accessions. A better understanding of their genetic relationship may expand the Bambara groundnut accessions' conservation, maintenance, application, and leads to the development of new cultivar through a proper breeding scheme. 2.0 Materials And Methods 2.1 Plant materials From June to December 2020, the present research was conducted at the Laboratory of Climate-Smart Food Crop Production, Institute of Tropical Agriculture and Food Security (ITAFoS), Universiti Putra Malaysia (UPM). For this study, a set of 44 Bambara groundnut accessions were sampled from 11 distinct populations (Table 1 ). Healthy and fresh leaves were taken from individual plants of 44 accessions of selfed generation S 4 that was two weeks old for DNA extraction. Leafy samples were collected and preserved at -80°C temperature until genomic DNA extraction was completed. Table 1 List of accessions and geographic coordinates of 44 V. subterranea accessions Population Geo.Location ID Accessions Latitude (N) Longitude (E) Elevation (m) Population 1: Duna Gombe G1 DunP2-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G2 DunP8-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G3 DunP9-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G4 DunP6-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 Population 2: Maikai Gombe G5 MaikP11-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G6 Maik12-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G7 MaikP3-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G8 MaikP6-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 Population 3: Cancaraki Gombe G9 CancP1-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G10 CancP2-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G11 CancP4-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G12 CancP3-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 Population 4: Roko Kwami G13 RokP6-18 10° 50ˊ85˝ 11°25ˊ24˝ 503 G14 RokP9-18 10° 50ˊ85˝ 11°25ˊ24˝ 503 G15 RokP1-18 10° 50ˊ85˝ 11°25ˊ24˝ 503 G16 RokP3-18 10° 50ˊ85˝ 11°25ˊ24˝ 503 Population 5: Bidilalle Akko G17 BdilaP10-18 10° 11ˊ86˝ 11°02ˊ59˝ 446 G18 BdilaP8-18 10° 11ˊ86˝ 11°02ˊ59˝ 446 G19 BdilaP11-18 10° 11ˊ86˝ 11°02ˊ59˝ 446 G20 BdilaP5-18 10° 11ˊ86˝ 11°02ˊ59˝ 446 Population 6: Jatau Gombe G21 JataP3-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G22 JataP5-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G23 JataP4-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G24 JataP1-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 Population 7: Maibargo Sokoto G25 MaibP3-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 G26 MaibP8-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 G27 MaibP9-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 G28 MaibP6-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 Population 8: Katawa Gombe G29 KataP4-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G30 KataP1-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G31 KataP5-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G32 KataP8-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 Population 9: Giiwa Gombe G33 GiiwP12-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G34 GiiwP11-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G35 GiiwP9-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G36 GiiwP1-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 Population 10: Karu Gombe G37 KarP3-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G38 KarP10-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G39 KarP9-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 G40 KarP8-18 10° 27ˊ91˝ 11°17ˊ31˝ 449 Population 11: Exsokoto Sokoto G41 ExSokP4-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 G42 ExSokP3-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 G43 ExSokP10-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 G44 ExSokP5-18 13° 00ˊ59˝ 5°24ˊ76˝ 450 Legend: Dun = Duna; Maik = Maikai, Canc = Cancaraki; Rok = Roko; Bdila = Bidilalli; Jata = Jatau; Maib = Maibargo; Kata = Katawa; Giiw = Giiwa; Kar = Karu; Exsok = Exsokoto 2.2 DNA Extraction, PCR amplification and band scoring The updated Zheng ( 1995 ) protocol was used to extract the genomic DNA from the plant sample. For this purpose, 2.5g of fresh foliar tissue from a 14 day-aged seedling that was healthy and free of mechanical injury was selected. Young stable leaves tissues are milled into fine powder in the presence of liquid nitrogen using a mortar and pestle. All the steps of the “Zheng protocol” were carefully followed (details are presented as supplementary Table S1 ). The concentration and quality of the genomic DNA solution were tested using the Thermo ScientificTM NanoDrop Lite Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The ratio absorbance 260/280 nm and 260/230 nm of more than 1.8 was used as a criterion for the next steps to ensure DNA purity. As a working sample, a part of the DNA template was diluted to a concentration of 40 ng/𝜇l. To run the PCR, following settings were performed: 95°C for initial denaturation aimed at 3 minutes, afterward 35 cycles of denaturation at 94°C for 45 seconds, one minutes for primer specific annealing temperature, extension at 72°C for 2 minutes, and final extension was adjusted at 72°C for 10 minutes before being saturated at 4°C. Using horizontal gel electrophoresis (400A with 80V for 75 mins) approach the amplified products were separated on 1.5% (w/v) agarose gel. To get exact band size based on the standard molecular weight of 100 bp DNA ladder was used. The data was scored into binary data as present (1) and absent (0) for each locus by using UVIDoc software. Figure 1 represent the banding pattern of some ISSR markers amplified from PCR reaction. 2.3 Statistical analysis Thirty-two ISSR primers were considered to polymorphism analysis across the 44 Bambara groundnut genotypes (Table 2 ). POPGENE version 1.32 (Yeh et al. 1999 ) was used to calculate the primer polymorphism index and Polymorphic Information Content ( PIC ), for each primer was measured by Botstein et al. ( 1980 ) formula PIC = 1 – Σ p i 2 – Σ Σ p i 2 p j 2 where p i and p j are the population frequency of the ith and jth allele. POPGENE version 1.32 was used to analyse the genetic diversity among the populations based on G ST = (H T − H S )/H T and gene flow (𝑁m) between populations Nm = 0.5 (1- Gst )/ Gst was calculated as per McDermott and McDonald ( 1993 ). To estimate the banding patterns on its frequency and polymorphism GenAlEx (genetic analysis in excel) version 6.5 (Peakall and Smouse 2006 ) was performed. Nie’s (1978) unbiased genetic distances and genetic identity matrix performed by GenAlEx 6.5 software. The population-based genetic diversity indices were estimated on the following heads such as observed number of alleles per locus ( Na ), number of effective alleles per locus ( Ne ) = 1/(p 2 + q 2 ), Shannon’s information index ( I ) = -1 (p Ln (p) + q Ln (q)), the expected heterozygosity ( He ) = 2pq, the unbiased expected heterozygosity ( uHe ) = (2N / (2N-1)) He and percent of polymorphic loci (%P) using GenAlEx 6.5 software (Peakall and Smouse 2006 ). The principal coordinate analysis (PCoA) was performed by MVSP and NCSS 2021 software and a correlation study: Mantel ( 1967 ) test between genetic (linear based) and geographic distance (log-transformed) among the Bambara groundnut populations was performed to assess whether there is a significant relationship between the matrix of pairwise genetic distances and geographical distances between overall populations using GenAlEx 6.5 with 999 random permutations. The geographical region-based factorial analysis (PCoA) was analyzed by DARWin 6 program. To estimate the judicial relationship of PC1 and PC2, Bland-Altman (1986) test was performed using NCSS 2021 program. A dendrogram or phylogenetic (radial) tree was constructed based on the Unweighted Pair Group Arithmetic Mean (UPMGA) method in POPGENE software ver. 1.32 followed by MEGA version 6.10 (Tamura et al. 2013 ). Moreover, an unrooted tree was constructed based on Neighbour-joining (NJ) and a split network phylogenetic tree (NNet) using Splitstreev.4.6 (Huson et al. 2006). The distribution of incompatible splits based on uncorrected p distance was inferred, which provided a split graph through NNet analysis. An analysis of molecular variance (AMOVA) was performed to estimate the variance components and their significance levels [P (rand > = data)] of genetic variation within and among populations using GenALEx version 6.5 (Peakall and Smouse 2006 ) with a permutation number of 999. Estimation of PhiPT (based on standard permutation across the full data set) distance was performed by the formula of PhiPT = AP / (WP + AP) = AP / TOT, where AP = est. var. among populations, WP = est. var. within populations, TOT = sum square total using GenALEx version 6.5. STRUCTURE ver. 2.3.4 (Pritchard et al. 2010 ) was run based on the ISSR binary data of 44 BG genotypes to determine the pattern of population structure. Structure analysis is one of the most ideal techniques for crop diversity study to perceive the patterns of population genetic structure using molecular markers (Wu et al. 2019 ; Pritchard et al. 2000 ; Zimisuhara et al. 2015 ). The burn-in time of 5.0×10 4 followed by 1.0 ×10 6 m MCMC simulations at 4 iterations (Welt et al. 2015 ) with ten independent repetitions were done to determine the optimal genetic unit, K value (Evanno et al. 2005 ). The Structure Harvester 0.6.93 version" ( http://taylor0.biology.ucla.edu/Structure ) (Earl 2012 ) and CLUMPAK (Cluster Markov Packager Across K) beta ver. (Kopelman et al. 2015 ) to determine the average Log-likelihood, Ln P(D), probability by K-graph, the most provable K value using ΔK method by Evanno et al. ( 2005 ). The standard Q value (Q > 0.60 < Q) representing relationship coefficient (%) value of assigning accessions to a certain population. The maximum (K) likelihood value was used to assign the accessions to the appropriate cluster ((Wu et al. 2019 ). Table 2 Amplified polymorphic indices of selected 32 ISSR primers on 44 V. subterranea accessions Markers Sequences Ta (°C) ISSR 11 AGCAGCAGCAGCAGC 59.7 ISSR 18 CACACACACACACACA 58.6 UBC 807 AGAGAGAGAGAGAGAGT 48.1 UBC 808 AGAGAGAGAGAGAGAGC 49.6 UBC 809 AGAGAGAGAGAGAGAGG 51.6 UBC 810 GAGAGAGAGAGAGAGAT 46.3 UBC 816 CACACACACACACACAT 50.8 UBC 836 AGAGAGAGAGAGAGAGYA 47.3 UBC 841 GAGAGAGAGAGAGAGAYC 49.8 UBC 844 CTCTCTCTCTCTCTCTRC 49.8 UBC-815 CTCTCTCTCTCTCTCTG 51.4 UBC 817 CACACACACACACACAA 51.5 UBC 873 GACA GACA GACA GACA 42.4 ISSR 811 ACACACACACACACT 48.3 ISSR 901 AGAGAGAGAGAGAGAGYC 49.1 UBC 835 CTCTCTCTCTCTCTCAT 44.9 ISSR 889 GAGAGAGAGAGAGAGATT 42.6 ISSR 812 GAGAGAGAGAGAGAGAA 41.4 ISSR 842 GAGAGAGAGAGAGAGACTG 51.5 A-856 ACACACACACACACACYA 57.8 I-825 ACACACACACACACACAT 57.8 ISSR 10 AGA GAG AGA GAG AGA GYC 47.4 ISSR 17 TCT CTC TCT CTC TCT CRG 49.1 PRIMER 9 AGAGAGAGAGAGAGAGAGAGT 47 ISSR 856 ACCATGGCTACCACCGAC 52.3 ISSR 2M CACACACACACACACAAAGCT 61.3 ISSR 835 AGAGAGAGAGAGAGAGYC 41..4 UBC 813 CTCTCTCTCTCTCTCTT 41.7 PRIMER 3 CTCCTCCTCCTCCTCCTC 54.2 ISSR 848 CACACACACACACACAAAGG 57.4 UBC 825 ACACACACACACACACT 53.1 UBC 830 TGTGTGTGTGTGTGTGG 56 Legend: R = A, G (Purine); Y = C, T (Pyrimidine); Ta = annealing temperature. Sources of primers: (Khan et al. 2021b ) 3.0 Result 3.1 Genetic diversity among the population and geographical units Within population an average percent of polymorphism was 35.15% with a range between 33.92% for Roko and 41.57% for Jatau. The diversity parameters among population as revealed by observed number of alleles ( Na ), Number of effective alleles ( Ne ), expected heterozygosity ( He ), and unbiased heterozygosity ( uHe ) observed that the greater degree of variability for Na = 0.833 ± 0.013 and Ne = 1.208 ± 0.013 possessed by Cancaraki population with a mean of 0.732 ± 0.013 and 1.187 ± 0.004, respectively. The population Jatau occupied higher index for Shannon information ( I ) = 0.202 ± 0.011, uHe = 0.148 ± 0.008 while the population Cancaraki and Jatau possessed greater He = 0.130 ± 0.008 with an average of I = 0.177 ± 0.003, uHe = 0.132 ± 0.003 and He = 0.116 ± 0.002, respectively (Table 3 ). The lowest variation was owned by the population Katawa with heading of I = 0.158 ± 0.011, He = 0.103 ± 0.007, and uHe = 0.118 ± 0.008 while for Karu and Exsokoto population it was Na = 0.657 ± 0.041 and Ne = 0.116 ± 0.013, respectively. However, average genetic variation at population level was comparatively lower related to the variation exposed at species level which was recorded as Na = 1.973, Ne = 1.382, I = 0.395 and He = 0.248. Based on geographical group of population (Table 4 ), percent of polymorphism spanned from 30.98% (Kwami) to 90.98% for Gombe state with a mean of 51.08% per geographical location. Among the geographical unit, population with Gombe state had highest Na = 1.82 ± 0.025, I = 0.271 ± 0.008, He = 0.157 ± 0.006, and uHe = 0.160 ± 0.006 with an average variation of 1.041 ± 0.022, 0.2 ± 0.005, 0.124 ± 0.003, and 0.135 ± 0.004, respectively. Moreover, maximum expected heterozygosity was shown by the population of Gombe state of Ne = 1.218 ± 0.01 with a mean of 1.191 ± 0.006 per geographical unit. Table 3 Diversity parameters within 11 populations over the loci of V. subterranea species Population M & SE N Na Ne I He uHe %P DUNA Mean 4 0.724 1.191 0.176 0.116 0.132 34.12% SE 0.042 0.014 0.011 0.008 0.009 Maikai Mean 4 0.763 1.201 0.188 0.123 0.141 36.67% SE 0.042 0.014 0.011 0.008 0.009 Cancaraki Mean 4 0.833 1.208 0.201 0.130 0.149 40.39% SE 0.043 0.013 0.011 0.008 0.009 Roko Mean 4 0.722 1.179 0.170 0.111 0.127 33.92% SE 0.042 0.013 0.011 0.007 0.008 Bidillali Mean 4 0.784 1.204 0.192 0.125 0.143 38.04% SE 0.043 0.014 0.011 0.008 0.009 Jatau Mean 4 0.831 1.202 0.202 0.130 0.148 41.57% SE 0.044 0.012 0.011 0.007 0.008 Maibergo Mean 4 0.663 1.185 0.167 0.110 0.126 31.76% SE 0.041 0.014 0.011 0.008 0.009 Katawa Mean 4 0.661 1.168 0.158 0.103 0.118 30.98% SE 0.041 0.013 0.011 0.007 0.008 Giiwa Mean 4 0.731 1.185 0.176 0.115 0.131 35.29% SE 0.042 0.013 0.011 0.007 0.009 Karu Mean 4 0.657 1.172 0.159 0.104 0.119 30.98% SE 0.041 0.013 0.011 0.007 0.009 Exsokoto Mean 4 0.680 1.166 0.161 0.104 0.119 32.94% SE 0.041 0.013 0.011 0.007 0.008 Grand mean 4 0.732 1.187 0.177 0.116 0.132 35.15% SE over loci and populations 0.013 0.004 0.003 0.002 0.003 1.10% Legend: M = Mean; N = number of accessions; SE = standard error; Na = observed number of alleles per locus; Ne = number of effective alleles per locus; I = Shannon’s information index; He = expected heterozygosity; uHe = unbaised heterozygosity; %P = percent polymorphism. Table 4 Diversity parameters within 4 geographical unit over the loci of V. subterranea Geo. L M & SE N Na Ne I He uHe %P Gombe Mean 28 1.820 1.218 0.271 0.157 0.160 90.98% SE 0.025 0.010 0.008 0.006 0.006 Kwami Mean 4 0.661 1.168 0.158 0.103 0.118 30.98% SE 0.041 0.013 0.011 0.007 0.008 Akko Mean 4 0.731 1.185 0.176 0.115 0.131 35.29% SE 0.042 0.013 0.011 0.007 0.009 Sokoto Mean 8 0.951 1.192 0.196 0.123 0.131 47.06% SE 0.044 0.013 0.011 0.007 0.008 Grand Mean 11 1.041 1.191 0.200 0.124 0.135 51.08% SE 0.022 0.006 0.005 0.003 0.004 13.73% Legend: Geo.L = geographical location; M = Mean; N = number of accessions; SE = standard error; Na = observed number of alleles per locus; Ne = number of effective alleles per locus; I = Shannon’s information index; He = expected heterozygosity; uHe = unbaised heterozygosity; %P = percent polymorphism 3.2 Banding pattern analysis The ISSR primer-based banding patterns and heterozygosity were demonstrated in Table 5 and Fig. 2 . In the population three (Cancaraki) we detected the maximum number of band patterns (NDB = 219) afterwards the population six (NDB = 212) and population five (NBD = 206) whereas minimum was recorded as NDB = 177 for population 10 (Karu). The enormously highest number of private bands (NPB = 10) were detected in population one (Duna) after that population five (Bidillali) with NPB = 9, while the lowest number of private bands were accounted for population eight (NPB = 1). Most of the bands were obtained from the number of different bands with a frequency of ≥ 5% (NDBF ≥ 5%). The Population five (Bidillali) exposed greater number of locally common band i.e. NLCB (≤ 25%) = 25 alongside the NLCB (≤ 50%) = 115 were shown by the population three (Cancaraki) while the lowest was recorded as NLCB (≤ 25%) = 6 and NLCB (≤ 50%) = 72 for the population four (Roko) and population seven (Maibergo), respectively. The mean of expected heterozygosity ( He ) and mean of unbiased expected heterozygosity ( uHe ) together with standard errors were observed higher in the population three ( He = 0.130 ± 0.008; uHe = 0.149 ± 0.009) subsequently the population six ( He = 0.130 ± 0.007; uHe = 0.148 ± 0.008). Table 5 Estimated banding pattern across 11 V. subterranea populations using ISSR assay Population NDB NDBF ≥ 5% NBSP NLCB ≤ (25%) NLCB ≤ (50%) Mean He ± SE Mean u He ± SE Pop1 195 195 10 9 92 0.116 ± 0.008 0.132 ± 0.009 Pop2 202 202 6 10 98 0.123 ± 0.008 0.141 ± 0.009 Pop3 219 219 7 17 115 0.130 ± 0.008 0.149 ± 0.009 Pop4 195 195 7 6 101 0.111 ± 0.007 0.127 ± 0.008 Pop5 206 206 9 25 100 0.125 ± 0.008 0.143 ± 0.009 Pop6 212 212 6 20 96 0.130 ± 0.007 0.148 ± 0.008 Pop7 176 176 5 11 72 0.110 ± 0.008 0.126 ± 0.009 Pop8 179 179 1 10 80 0.103 ± 0.007 0.118 ± 0.008 Pop9 193 193 6 9 81 0.115 ± 0.007 0.131 ± 0.009 Pop10 177 177 3 9 76 0.104 ± 0.007 0.119 ± 0.009 Pop11 179 179 6 12 86 0.104 ± 0.007 0.119 ± 0.008 Legend: NDB = number of different bands; NDBF ≥ 5% = number of different bands with a frequency ≥ 5%; NBSP = number of bands unique to a single population; NLCB (≤ 25%) = number of locally common bands (Freq ≥ 5%) found in 25% or fewer populations; NLCB (≤ 50%) = number of locally common bands (Freq ≥ 5%) found in 50% or fewer populations; He = expected heterozygosity = 2*p*q; uHe = unbiased expected heterozygosity = (2N/(2N-1)) * He where for diploid binary data an assuming Hardy-Weinberg Equilibrium, q = (1- band Freq.)^ 0.5 and p = 1-q. 3.3 Molecular variance study among the subdivided populations and geographical units For better interpretation, analysis of molecular variance (AMOVA) was performed in two phases; 1st emphasized the entire genotypes subdivided into 11 populations, and the 2nd phase focused on the entire populations considering into 4 geographical zones using overall loci (Table 6 ). Partitioning genetic diversity by AMOVA revealed that a greater portion of diversity was covered within a population (75%) and within geographical units (84%), while the variation among populations and geographical units was 25% and 16%, respectively (Fig. 3 a and Fig. 3 b). The genetic differentiation by AMOVA based on PhiPT distances showed significant variation among the accessions (PhiPT = 0.248; p ≤ 0.001) and among the geographical zones (PhiPT = 0.163; p ≤ 0.001) indicating that the accessions are genetically distinct from each other as well as geographically. Table 6 Analysis of molecular variance (AMOVA) for 11 populations and 4 geographical zones of V. subterranea Source df SS MS Est. Var % P-Value PhiPT With 11 populations Among Pops 10 1140.00 114.00 16.20 25% < 0.001 0.248 Within Pops 33 1623.50 49.20 49.20 75% < 0.001 Total 43 2763.50 65.40 100% With 4 Geographical units Among Geo. units 3 444.48 148.16 11.27 16% < 0.001 0.163 Within Geo. units 40 2319.02 57.98 57.98 84% < 0.001 Total 43 2763.50 69.25 100% Legend: df = degree of freedom, SS = sum square total, MS = Mean square total, Est. Var. = estimated variance 3.4 Genetic differentiation and gene flow estimation The inter and intra-population structure analysis revealed that the overall genetic diversity ( Ht ) was estimated as Ht = 0.178 ± 0.017 whereas within-population genetic diversity was Hs = 0.115 ± 0.006. The genetic differentiation ( Gst ) among the Bambara groundnut population was recorded as Gst = 0.3514, signifying the incidence of 35.14% genetic differentiation between the populations. This result was constant with the output of molecular variance analysis which revealed 25% and 75% genetic variation existing among populations and within-population, respectively (Fig. 3 a). The unveiled differences between and among the populations were found to be highly significant (p < 0.001) (Table 6 ). The finding was additionally authenticated by the presence of a significant level of gene flow ( Nm ) among the populations, estimated as Nm = 0.9229 (Table 7 ). Table 7 Genetic differentiation stricture of 11 population based on Nei’s analysis Ht Hs Gst Nm (Gst) Mean 0.1781 0.1155 0.3514 0.9229 St. Dev 0.0171 0.006 Ht = Total gene diversity; Hs = gene diversity within populations; GSt = the relative magnitude of genetic differentiation among populations (coefficient of gene differentiation); Nm ( Gst ) = estimate of gene flow from Gst (among populations). 3.4.1 Mantel tests To determine the correlation between genetic distances among the population and geographic distances among the population, a mantel test was performed. The tested results exposed a significant correlation between the genetic and geographic distance (R 2 = 0.511; P = 0.031) with 9999 permutation (Fig. 4 ). To run this test geographical distances were transformed by Log (1 + x) with the unit of a kilometer (Km). Nilkanta et al. ( 2017 ) reported moderately non-significant correlation between the genetic and geographic distances (R = 0.311; P = 0.240) while Barbosa et al. ( 2019 ) accounted no correlation between genetic and geographic distances (R 2 = 0.011; P = 0.004) among populations revealed by Mantel test using ISSR primers. 3.5 Population genetic distances vs PhiPT analysis The genetic differentiation among the V. subterranea population and their engaged geographical zones were revealed by pairwise Nei’s genetic distances and PhiPT values. PhiPT Values below diagonal with probability, P (rand ≥ data), and Nei’s genetic distance shown above diagonal based on 999 permutations. The PhiPT values showed significant differences for all populations except populations between 5 & 6 (Table 8 ) and populations 7 & 6. Besides, the population under Kwami and Akko have not significantly variation whereas a highly significant variation was found in the population with all other geographical zones (Table 9 ). In the Matrix, the highest PhiPT value between population 4 and population 8 (0.405*) and Nei’s genetic distance between population 4 and population 10 (0.124) was observed. The population under Gombe and Kwami found a maximum PhiPT value (0.178**) with higher (0.064) Nei’s genetic distances indicating the genotypes under these two regions are not closely related (Table 9 ). The lowest Nei’s distances (0.041) were found between populations 8 and 9 is the sign of these two populations are closely related while populations 9 and 10 had lower PhiPT (0.049*) value. The population under Sokoto and Gombe were closer (Nei’s distances = 0.036) than other geographic populations whereas the population from Kwami and Akko zones showed a lower (0.061) PhiPT value. This observation is also evidenced by the UPMGA clustering result (Fig. 5 ) which grouped all populations into two major clusters assembling the relatively closer populations in the same group based on genetic and geographic origin. Table 8 Nei’s genetic distances (above diagonal) and pairwise PhiPT values (below diagonal) among 11 populations Pop1 Pop2 Pop3 Pop4 Pop5 Pop6 Pop7 Pop8 Pop9 Pop10 Pop11 Pop1 0.000 0.051 0.067 0.080 0.085 0.067 0.100 0.117 0.105 0.123 0.097 Pop2 0.097* 0.000 0.057 0.079 0.070 0.060 0.100 0.108 0.098 0.107 0.095 Pop3 0.150* 0.085* 0.000 0.050 0.061 0.060 0.101 0.111 0.095 0.108 0.087 Pop4 0.239* 0.208* 0.076* 0.000 0.062 0.068 0.117 0.130 0.116 0.124 0.102 Pop5 0.241* 0.183* 0.152* 0.141* 0.000 0.049 0.098 0.114 0.096 0.109 0.086 Pop6 0.192* 0.166* 0.163* 0.186* 0.104ns 0.000 0.048 0.061 0.052 0.068 0.056 Pop7 0.338* 0.324* 0.322* 0.376* 0.322* 0.100ns 0.000 0.054 0.050 0.074 0.073 Pop8 0.373* 0.339* 0.338* 0.405* 0.356* 0.145* 0.119* 0.000 0.041 0.051 0.079 Pop9 0.326* 0.305* 0.280* 0.352* 0.300* 0.1212* 0.123* 0.061* 0.000 0.042 0.059 Pop10 0.379* 0.332* 0.329* 0.392* 0.352* 0.203* 0.243* 0.124* 0.049* 0.000 0.051 Pop11 0.319* 0.314* 0.287* 0.338* 0.294* 0.180* 0.252* 0.246* 0.145* 0.118* 0.000 Table 9 Nei’s genetic distances (above diagonal) and pairwise PhiPT values (below diagonal) between the population of 4 geographical units Geo. location Gombe Kwami Akko Sokoto Gombe 0.000 0.064 0.051 0.036 Kwami 0.178** 0.000 0.041 0.052 Akko 0.150** 0.061ns 0.000 0.037 Sokoto 0.173** 0.154* 0.070** 0.000 3.5.1 Population genetic distance vs genetic identity analysis The Nei’s unbiased genetic distance and identity (Table 10 ) revealed that greater variation (0.113) accounted for population 4 (Roko) and population 8 (Katawa) followed by population 4 (Roko) and population 10 (Karu) (0.107). Population 1 (Duna) also distantly (0.105) related to population 10 (Karu). However, the population Karu and Katawa belong to the same zones of Gombe while the population Roko is under the Kwami region. Population 8 (Katawa) was closely related to population 9 (Giiwa) considering low genetic distances (0.023) with a higher genetic identity of (0.977) between them. The next low genetic distance was marked between population 9 (Giiwa) and 10 (Karu) (0.024) followed by the population 5 (Bidillali) with 6 (Jatau) (0.028) and population 6 (Jatau) with population 7 (Maibergo), although they are not under in the same geographic zones. Table 10 Genetic distances (below diagonal) and genetic identity (above diagonal) revealed by Nei’s unbiased measure of V. subterranea populations Pop1 Pop2 Pop3 Pop4 Pop5 Pop6 Pop7 Pop8 Pop9 Pop10 Pop11 Pop1 **** 0.969 0.954 0.940 0.937 0.954 0.922 0.906 0.918 0.900 0.923 Pop2 0.031 **** 0.965 0.942 0.952 0.962 0.922 0.914 0.924 0.915 0.926 Pop3 0.047 0.036 **** 0.970 0.961 0.963 0.922 0.912 0.928 0.915 0.934 Pop4 0.062 0.060 0.031 **** 0.958 0.953 0.906 0.893 0.907 0.899 0.919 Pop5 0.065 0.050 0.040 0.043 **** 0.973 0.924 0.909 0.927 0.914 0.935 Pop6 0.047 0.039 0.038 0.048 0.028 **** 0.972 0.959 0.969 0.952 0.964 Pop7 0.082 0.081 0.081 0.099 0.079 0.029 **** 0.964 0.969 0.945 0.946 Pop8 0.099 0.090 0.092 0.113 0.095 0.042 0.037 **** 0.977 0.966 0.939 Pop9 0.086 0.079 0.075 0.097 0.076 0.031 0.031 0.023 **** 0.976 0.959 Pop10 0.105 0.088 0.089 0.107 0.090 0.049 0.057 0.035 0.024 **** 0.966 Pop11 0.080 0.077 0.068 0.085 0.067 0.037 0.056 0.063 0.041 0.034 **** Legend: Pop =Population 3.6 Genetic Relationships Among Populations The constructed dendrogram based on Nei’s unbiased measures of genetic distances exposed two major clusters with branch lengths of 1.52 and 1.42 though considering the edge length of 1.52 (a), 0.95 (b), and 0.41 (c) we noted three major groups (Fig. 5 ). Among the entire population group (a) comprised of Maibergo, Katawa, Giiwa, Karau, and Exsokoto. The population Exsokoto was separated from other 4 population while Mibergo was fragmented from rest 3 populations of the same geographical region of Gombe but typically Maibergo and Exsokoto belongs to the same zone of Sokoto. Group b (edge length 0.95) covered two populations (Duna and Maikai) of the same geographical zones (Gombe) which is split out from groups c (edge length 0.41) consisted of 4 populations (Roko, Cancaraki, Bidillai, and Jatau). Cancaraki and Roko owned the same subgroups whereas a close association was found between Bidillali and Jatau. The population Roko and Bidillali were in different sub-clusters with different locations of Kwami and Akko, respectively while Cancaraki and Jatau were in the same location of Gombe but positioned into different sub-cluster. The population Roko (node no. = 13; edge length = 1.53) under Kwami gained distinct genetic distances (0.113) from the population Katawa (node no. = 17; edge length = 1.17) was found in separate cluster under separate zones of Gombe (Fig. 5 ). 3.6.1 Genetic relatedness among the accessions The Neighbor-joining (NJ) radial tree gained the relationship for the individual genotypes based on Nei’s genetic distance, exposed majority of the plants belonging to different population origins, separated distinctly though some individuals were partly mixed within the clustered (Fig. 6 ). The dendrogram partitioned the 44 V. subterranea accessions into three major clusters (MC I, MC II & MC III) which was further divided into six subclusters (SC I, SC II, SCIII, SC IV, SC V & SC VI). The distribution of all accessions based on the cluster was shown in Table 11 . The genotypes from each population were marked using the same-colored symbol viz. upward triangle, downward triangle, square, circle, diamond shape, etc. alongside different sub-tree branch colored. The accessions from major cluster I composed of 22 individuals under six populations. The sub-cluster I (SC I) under major cluster I comprised 11 genotypes in which 4 from Duna, 4 from Maikai, and 3 genotypes from the population Cancaraki but one genotype (Cancaraki P3-18) hold the position in subcluster (SC) II. In SC I, genotypes under Maikai and Duna fitted into the same groups whereas accessions Cancaraki P1-18 have the molecular divergence from Cancaraki P2-18 and Cancaraki P4-18. The sub-cluster II composed of 4 accessions from Bidillali (Bidillali P11-18 and Bidillali P15-18 separated from Bidillali P8-18 and Bidillali P10-18), 4 accessions from Roko, 2 accessions from Jatau, and one from accessions from the population of Cancaraki. The sub-cluster III under the major cluster II holds the 4 accessions of the same population of Exsokoto. Out of 7 genotypes in subcluster V, four individual comes from the population of Katawa in which 3 (Katawa P1-18, Katawa P8-18, and Katawa P5-18) drives in same group rest one (Katwa P4-18) constructed another group with 3 genotypes of Maibego population. However, the sole genotype Karu P8-18 belongs to the subcluster IV while the rest 3 genotypes (Karu P3-18, Karu P9-18, and Karu P10-18) derives to sub-cluster VI constructed the same group with 4 genotypes of Giiwa population. Moreover, the accessions from Jatau (Jatau P1-18 & Jatau P4-18) together with Maibergo (Maibergo P3-18) generated a distinct group remarked as a major cluster (MC) III. Table 11 Cluster-based distribution of V. subterranea accessions revealed by Neighbor-joining method. Population a MC b MC I MC III MC II SC c SC I SC II - SC III SC IV SC V SC VI Duna 4 - - - - - - Maikai 4 - - - - - - Cancaraki 3 1 - - - - - Roko - 4 - - - - - Bidillai - 4 - - - - - Jatau - 2 2 - - - Maibergo - - 1 - - 3 - Katawa - - - - - 4 - Giiwa - - - - - - 4 Karu - - - 1 - 3 Exsokoto - - - 4 - - - a Genotypes under population by geographical zones are listed in Table 12; b & c Genotypes listed by cluster in figure (dendrogram); MC = major cluster; SC = sub cluster 3.6.2 Genetic relationship based on NJ and NNet analysis The unrooted phylogenetic tree was established based on the Neighbour-Joining (NJ) method using 44 accessions of V. subterranea (Fig. 7 A). The NJ phylogenetic tree exposed that the accessions clustered into three (A, B, and C) main groups though group A had two subgroups (a and b) whereas group B composed of 2 distinct subgroups of (e and f) and group C divided into subgroup (c and d) which mostly coincided with the four regions (Gombe, Akko, Kwami, and Sokoto) from where the accessions were collected. There were 11 accessions in subgroup a, which were belongs to the population of Duna (4 accessions), Maikai (4 accessions), and Cancaraki (3 accessions) collected from the same region of Gombe. The one genotype (G12) from the Cancaraki population together with 4 accessions from Roko, 4 accessions from Bidillali, 2 accessions from Jatau, constructed subgroup b. Here the accessions under Jatau and Cancaraki had the common region (Gombe), while the accessions under Roko and Bidillali hold the different regions of Kwami and Akko, respectively (Table 1 ). The major groups B and C consisted of 22 accessions and contained a mixture of varieties from Sokoto and Gombe. The subgroup c captured 4 accessions of Exsokoto collected from the Sokoto region. There were 7 accessions in subgroup d: two (G23 & G24) from Jatau population and one (G29) came from Katawa population belongs to a common area of Gombe, other four (G25, G26, G27, and G28) accessions from Sokoto, these all were in Maibergo population. Ten accessions were in subgroup e which were assembled by Katawa: 3 accessions (G30, G31, & G32), Giiwa: 3 accessions (G33, G35, & G36), and Karu: 4 accessions (G37, G38, G39 & G40) having a common region of collection Gombe. The lone accession G34 of the Giiwa population was isolated from other accessions forming a distinct subgroup f which was native to Gombe. Based on split networks, the NNet (Neighbour network) analysis delivers additional network topology associated with NJ phylogenetic relationship. The NNet analysis (Fig. 7 B) partitioned a total of 44 accessions into three main groups (A, B, and C) in which group A further fragmented into subgroup (a and b) whereas group B divided into subgroup (e and f) and group C segmented into subgroup (c and d). Group B and C jointly dominated by 22 accessions of Gombe and Sokoto region while subgroup d dominated by a maximum of 8 accessions (G27, G26, G28 from Maibergo; G29, G30, G31, G32 from Katawa; G33 from Giiwa) population. A broad-spectrum similarity was observed between the result of NNet analysis to those of NJ analysis (Fig. 7 A vs Fig. 7 B). However, the accession G34 from Giiwa was clustered with G23 and G24 from Jatau and G25 from Maibergo population combinedly created subgroup c despite it took position alone in subgroup f by NJ analysis. 3.7 Factorial analysis: Principal coordinate (PCoA) Ordination, also known as multivariate gradient analysis, is a collective term for multivariate analysis that adapts a multidimensional set of data in such a way that identical species or samples are plotted close together when dissimilar ones have plotted far apart (Arolu et al. 2012 ). The considerable degree of intra and inter-genotypic diversity was shown by factorial analysis of the ISSR data set based on geographical location (Fig. 8 ). There were 28 accessions from seven populations under the Gombe region (Fig. 8 A) mainly separated into groups a’, ‘b’, and ‘c’. In group ‘a’ genotype G23, G24, and G34 were isolated from the rest of the accessions whereas in group b genotype G12 was separated from others. Group c had two accessions (G21 & G22) that were placed far apart from group ‘a’ and ‘b’. Four accessions from the Bidillali population in Akko zones (Fig. 8 c) showed divergence from each other while the accessions G14 and G15 were closer than other accessions G13 and G16 under Roko in the Kwami zone (Fig. 8 B). In zone Sokoto (Fig. 8 D), the accession (G41) of the Exsokoto population remarkably separated from the other three (G40, G42, & G43) accessions, on the other hand, accession G25 was detached from the rest of the accessions (G22, G23, G24) under Maibergo. In the case of all accessions, the factorial analysis clustered the 44 Bambara groundnut accessions into four diverse groups in which within accessions under group ‘a’ and ‘d’ were showed more genetic variability than the group ‘b’ and ‘c’ (Fig. 8 ). To lead the clustering investigation eigenvalues and total percentages of principal component case scores were used. The distribution of eigenvalues, percent of genetic variation, and cumulative percent of genetic variation based on 1st three axes (PCs) were displayed in Table 12 . The first three principal components covered 26.15% of cumulative variation which is portioned by 15.05%, 5.81%, and 5.29% variation for PC1, PC2, and PC3, accordingly. However, in PCoA analysis, accessions considering axis 1 vs axis 3 were distributed into two groups that were not associated with the accessions distributed by axis 1 vs axis 2 (Fig. 9 ) indicate that maximum variation reflected by axis 1 vs. axis 2. Two dimensional (2D) (Fig. 10 A) and three-dimensional (3D) (Fig. 10 B) visual illustrations of PCoA analysis exposed the substantial level of genetic divergence among the V. subterranea accessions. PCoA analysis sharply assembled the accessions into three major groups based on Euclidian distance. Genetically related accessions within and among population were placed closer together, whereas distant accessions were placed wide away. The contribution of accessions is further established by the PCoA contour plot (Fig. 10 C) in which the intensity of red and blue clour indicating the magnitude of the contribution of accessions on total genetic variation. Table 12 Percentage of total variation contributed by 1st three components revealed by PCoA. Parameters PC1 PC2 PC3 Eigenvalue 415.99 160.49 146.2 Percent of variance (%) 15.05 5.81 5.29 Cumulative percent of variance (%) 15.05 20.86 26.15 3.7.1 Bland-Altman agreement analysis Bland-Altman plots were also used to explore any possible relationship of the inconsistencies of the true values of two axes (Bland and Altman 1986 ). To evaluate the agreement among two axes (1 and 2) Bland and Altman regression analysis was performed (Fig. 10 D) which is tremendously used to estimate the agreement among two dissimilar measurements. The difference between axis 1 and axis 2 spanned from − 2.80 to + 2.80 and with a mean difference of 1.86 ± 1.42. If the difference, follows a normal distribution the 95% differences are expected to place in-between the average differences of the two-axis with values of ± 1.96 times the SD of difference in the axis. The upper and lower limit of agreements with 95.0% confidence level (CLs) ranged from 2.05 to 3.55 and − 3.55 to – 2.05, respectively for BG accessions. The observed mean difference of 1.86 (P = 0.0005) indicates the presence of fixed bias moreover as the differences within mean ± 1.96 SD, two axes may be used interchangeably (Carkeet 2015 ). 3.8 Population genetic structure The structure analysis assessed the most likely number of cluster (K) by calculating the Ln probability of data for each value of K = -8053.2, probability (ProbK) = 1.00 is highest at K = 3 (Fig. 11 C) and ΔK = 104.97 (Fig. 11 B) approached by Evanno et al. [46] generated from STRUCTURE version 2.2.3 [43] inferred by CLUMPAK beta ver. [48]. The K is a simple concept in theory, very effective practice in population genetic structure analysis and K means clusters. It is a way to analyze genetic data and the degree to which genetic variation can be partitioned into a small number of groups or clusters. Estimating K is arbitrary and involves Bayes’ (Admixture) theorem. The admixture results elucidated a sharp peak ΔK at K = 3 (Fig. 11 B) suggesting the 3 genetic groups, which ensure the reasonableness for clustering the 44 V. subterranea accessions into three distinct populations. Considering K value (K = 1 to 10) bar plot based on original population order (Fig. 11 A), estimated membership coefficients (Q) values of each accession were displayed in Fig. 11 D, inferred by the Structure harvester [47]. Each vertical black line and color (red, yellow, and purple) visualized the magnitude or fraction of membership of each accession to the 3 clusters. Among the three clusters, the red color cluster mainly captured 22 accessions from the population Duna, Maikai, Cancaraki, Roko, and Bidillali, whereas the yellow color cluster majorly belonged to Exsokoto, Katawa, Karu, and Giiwa population and purple color cluster mainly occupied Jatau and Maibergo populations. Associating the result of structural analysis with phylogenetic tree (NJ and NNet) analysis (Fig. 11 D), we observed accessions that assembled in major group A in NJ and NNet method were mainly positioned into the red color cluster, major group B in the yellow color cluster and major group C in the purple color cluster in the bar plot inferred by structure harvester. The accessions with the higher membership coefficient (Q > 0.60) were documented as pure one while the Q < 0.60 recognized as admixture one (Wu et al. 2019 ). In the three major groups, out of 44 accessions, the red color cluster consists of 22 standard or pure accessions, the yellow color cluster consisted of 6 pure or standard accessions whereas 7 pure accessions and 9 admixture accessions combinedly generated the purple color cluster. 4.0 Discussion 4.1 Genetic diversity among the population and geographical units Study based on molecular tools such as ISSR primers will be efficient and reliable for genetic differentiation by the selection and application of primers which provide clear and sufficient knowledge needed to investigate the diversity that happens within the crop. Amplification of ISSR primers on 44 V. subterranea accession representing 11 distinct populations sampled from 4 geographical regions and revealing a high degree of polymorphism. The average percent of polymorphism within the population 35.15% and the geographical region (51.08%) is lower as compared to the species level polymorphism (97.64%). The results of current study has strong evidence with the findings of Rungnoi et al . [2] recorded ( He = 0.179, I = 0.227) using ISSR and RAPD; Nilkanta et al. ( 2017 ) noted ( Na = 1.88, Ne = 1.27, He = 0.193, and I = 0.321) ; Ismail et al. ( 2019 ) recorded ( Na = 1.09, Ne = 1.26, He = 0.14, and I = 0.24); Arolu et al. ( 2012 ) recorded ( Na = 0.03, Ne = 0.025, He = 0.01, and I = 0.014) using ISSR primers. 4.2 Banding pattern and heterozygosity In terms of band privacy, the maximum number of private bands was found for the population 1 (Duna), a moderate number of bands in Bidillali, and none in other populations makes Duna population isolated and different from other populations. The findings of this current study were an agreement with Oumer et al. ( 2020 ) who analysed the banding pattern and heterozygosity of Oxytenanthera abyssinica using ISSR primers and noted the highest number of band patterns as 227 for the Koyshe population and expected heterozygosity as 0.219 ± 0.013 for Guba with unbiased heterozygosity 0.231 ± 0.013. 4.3 Molecular variance study among the subdivided populations and geographical units To partition the variation within and among the populations, AMOVA was executed, revealed higher variation present within the population and the region compared to among populations and the geographical region. This is the indication of significant divergence exist in Bambara groundnut species and the trends of our findings was advocated by Arolu et al. ( 2012 ) stated 94% within and 6% among the population, Alansi et al. ( 2016 ) accounted for within (90%) and among (9%) population and region (1%), Nilkanta et al. ( 2017 ) noted 78% within and 22% among the population, Oumer et al. ( 2020 ) reported 75% within and 25% among population using ISSR primers. In the current study, our observation was a greater portion of variation avail by the genotype within populations (75%) compared to among the populations (25%) which was lower than the previous observation of 98% (within populations) and 2% (among populations) was reported by Odongo et al. ( 2015 ) in 105 Bambara groundnut genotypes using SSR markers. AMOVA leads to conclude that maximum variation spanned by within population of any species. However, estimation of genetic variation among the populations is crucial for V. subterranean using genomic markers to select the parental material purpose to development of elite genotype through crossing (hybridization) and improvement of the breeding program, supported by Arolu et al. ( 2012 ) and Mastan et al. ( 2012 ). 4.3.1 Genetic differentiation and gene flow The value of calculated gene flow is categorized by Kumar et al. ( 2014 ) as Nm 1 for medium, and Nm > 4 for greater indices of gene flow. Based on G st mean, our observed gene flow is (Nm = 0.9212) which was higher than gene flow Nm = 0.0101 recorded by Tian et al. (2012) and Nm = 0.1885 noted by Asra et al. ( 2014 ) but lower than Nm = 2.545 by Nilkanta et al. ( 2017 ) and Nm = 2.375 by Alansi et al. ( 2016 ). Our estimated low gene flow is the indication of low genetic migration among Bambara groundnut accessions. In our study gene flow less than 1 (Nm = 0.9212) also indicating that our populations are subjected to low genetic drift, so if the gene flow Nm < 1 (i.e., no migration) is rational to extensive differentiation due to low genetic drift (Slatkin, and Barton 1989 ). Isolation of populations leads to divergence due to genetic drift or migration lessens divergence and the harmful effects of inbreeding can be ameliorated when gene flow happens due to migration (Frankham et al. 2002 ). However, low genetic differentiation among the Bambara groundnut population may be due to the nature of the extremely self-crossing phenomenon. The lower estimate of gene flow is the indication of two populations are genetically differentiated because it has a homogenizing effect (Frankham et al. 2002 ). The gene flow is negatively correlated with genetic differentiation within populations also influenced by the pollens and seed dispersal (Nilkanta et al. 2017 ). Nei, ( 1978 ) has been categorized genetic differentiation (G st ) as low when G st ≤ 0.05, intermediate when 0.05 ≤ G st ≤ 0.15, and high when G st ≥ 0.15. Subsequently, as per our findings, the G st coefficient of Bambara accessions (G st = 0.351) indicating greater differentiation presences among the population and this result is consistent with Alansi et al. ( 2016 ) recoded G st = 0.17 in Ziziphus spina-christi L, Nilkanta et al. ( 2017 ) observed G st = 0.19 in M. baccifera , and Kumar et al. ( 2014 ) observed G st = 0.31 in Justicia adhatoda L. In present study the observed Ht = 0.178 and Hs = 0.115 of Bambara groundnut is supported by similar trends of Ht = 01961; Hs = 0.1639 stated by Nilkanta et al. ( 2017 ) and Ht = 0.2708; Hs = 0.2047 reported by Oumer et al. ( 2020 ). The geographical isolation of populations strongly influenced the genetic differentiation among populations by preventing the degree of gene flow through seeds, pollen as well as human movements (Pfeifer and Jetschke 2006 ). Hypothetically, the gene flow of more than 4 migrants per generation is enough to avoid genetic differentiation among populations caused by genetic drift only (Nilkanta et al. 2017 ). In the present study, estimated low gene flow (Nm = 0.9229) < 1 ruling out the probability of inducing genetic variation among Bambara groundnut populations due to geographic and genetic distances. The above observation is also supported by the findings from the Mantel test as it exposed a significant correlation between genetic and geographic distances among the Bambara groundnut populations. However, Nilkanta et al. ( 2017 ) stated the genetic differentiation among populations due to geographic distances in M. baccifera . The cleistogamous flowering nature and geographic isolation are the major issues of having high genetic variation in Bambara groundnut. Geographical isolation hindered the genetic migration or allele flow i.e., higher geographical distance resulting in the lower gene or allele flow (Asra et al. 2014 ; Fischer and Matthies, 1998 , and this condition is evidenced by our estimated lower gene flow Nm = 0.9229. 4.4 Population genetic distance vs PhiPT analysis PhiPT is a measure of estimation of intra genotypic variation as well as determination of genetic differentiation among the population (Teixeira et al. 2014 ). Our observation is consistent with Nei’s genetic distances among Bambara groundnut population from 6 different geographic regions was calculated by Rungnoi et al. ( 2012 ) using ISSR and RAPD as well as Arolu et al. ( 2012 ) in Jatropha curcas using ISSR. Alansi et al. ( 2016 ) reported the highest PhiPT (0.138) value and Nei’s genetic distances (0.0908) between populations 1 and 3 using ISSR. Nair et al. ( 2014 ) in Rauvolfia serpentina using RAPD and Teixeira et al. ( 2014 ) in Juniperus thurifera L. noted the incidence of higher PhiPT genetic distances using SSR. 4.4.1 Population genetic distance vs genetic identity analysis Observation of extremely low genetic distances (closer to 0.00) highlights dimensions of ISSR markers to differentiate among Bambara groundnut genotypes even there is the existence of extreme close association among the populations. Henceforth, these current findings also confirmed the efficiency of ISSR primers to discriminate among the populations that are distinct, similar, or closely associated. Our findings have reliable evidence with the result of Rungnoi et al. ( 2012 ) reported Nei’s genetic distances among the populations of 14 countries using ISSR and RAPD; Nilkanta et al. ( 2017 ) using ISSR, Mohammed et al. ( 2019 ) stated genetic distances of 50 Bambara groundnut varied from 0.0 to 3.8 using SSR, A similar extent of variation among the populations was reported in the previous study that related to our current genetic analysis. Massawe et al. ( 2003 ) found an identical trend of association in the Bambara groundnut diversity study using RAPD and suggested that such nature of association among the landraces indicating the genotypes were genetically identical. However, the unauthorized collection and unorganized categorize of Bambara groundnut germplasms may be the influential factors of a unique accession holding different names. The estimated genetic distances in the present study revealed minimum, medium, and maximum values related to the report of Massawe et al. ( 2003 ) by RAPD and Mohammed et al. ( 2019 ) using SSR in V. subterranea . These differentiations are caused by the profiles of landraces evaluated in this research which are comprised of pure lines developed from single plant selection and use of germplasms embraced to the mixture of different seed morphotypes. 4.5 Genetic relatedness The UPMGA method revealed two major groups of the entire population. In the present study, a substantial genetic variation was disclosed among the populations which are consistent with the result of Rungnoi et al. ( 2012 ) reported significant variation among Bambara groundnut accessions of 5 geographical origins whereas Mohammed et al. ( 2019 ) described the variation presences among 50 genotypes of Bambara groundnut. Besides this Nilkanta et al. ( 2017 ) found countable variation among 7 populations of M. baccifera ; Oumer et al. ( 2020 ) concluded significant variation exists among 13 populations of Oxytenanthera abyssinica while Alansi et al. ( 2016 ) recorded the variation of 4 population of Ziziphus spp. The information of three major clusters and six sub-clusters in this study is a decent sign of the higher richness among the accessions related to other previous studies of Bambara groundnut such as those reported by Mohammed et al. ( 2019 ) as 7 clusters of 50 accessions while Odongo et al. ( 2015 ) stated 3 major clusters of 105 accessions using SSR markers and Massawe et al. ( 2003 ) grouped the 12 landraces into two clusters using RAPD. Besides these, Olukolu et al. 2012 grouped in 4 distinct clusters of 124 Bambara groundnut accessions using DArT, Siise and Massawe, ( 2013 ) noted 17 units of 80 Bambara groundnut accessions using SSR, Rungnoi et al. ( 2012 ) classified 363 Bambara groundnut accessions into two major groups using 65 RAPD and ISSR loci, whereas Mukakalisa et al. ( 2011 ) illustrated genetic relationship by clustering 13 landraces of V. subterranea using RAPD primers. Moreover, Nilkana et al. (2017) grouped 93 individuals of M. baccifera populations into distinct clusters while Dos Santos et al. ( 2011 ) reported 11 clusters of 45 Passiflora spp based on Nei’s distance using ISSR primers. Fatimah and Ardiarini, ( 2018 ) grouped 12 accessions of V. subterranea into two clusters based on similarity indices. Stavridou et al. ( 2020 ) used the UPMGA radial tree to illustrate the relatedness of pea populations based on ISSR primers. Our study reflected that most of the accessions under identical populations hold the same subgroup such as all 4 accessions under the population of Duna, Maikai, Roko, Exsokoto, Katawa, and Giiwa. The accessions were diverse from the major cluster in the dendrogram depicting less genetic relatedness to the remaining populations present in the major cluster. The individual genotype under the same population was more closely associated genetically than those of other close populations. Due to the cleistogamy system of Bambara groundnut, in all of the cases, we found some divergence of plants among populations but within the population, we observed a higher degree of divergence which may be pronounced in the accession with seeds collected from different agroecological zones. The accessions from Cancaraki, Maibergo, Bidillai, Jatau, and Karu showed more inconsistency due to their diverse position in the dendrogram which is uncommon, the sharing of alleles among these species is most probably due to the random effect or evolutionary aspects. Generally, the ISSR marker consent to the broad-spectrum separation among V. subterranea , indicating high genetic dissimilarity among them. 4.5.1 Genetic Relationship based on NJ and NNet analysis Genetic relatedness among 44 V. subterranea genotypes was illustrated by an unrooted phylogenetic tree based on the NJ and NNet method of analysis (Fig. 8 A and 8 B). Both the approaches clustered the accessions into distinct groups, most of which are coincident with geographical zones of collection. To infer the evolutionary bonding of accessions unrooted phylogenetic tree based on genetic dissimilarity was performed (Huson 2005 ). As a result, to elucidate the visualized contradictory indicator in the data matrix, either derives from sampling error or sincere recombination NNet phylogenetic network analysis was executed (Wu et al. 2019 ). In our study, the NNet analysis clustered the accessions into six subgroups under three main groups, a similar grouping pattern was reflected by NJ analysis. Further, we observed some discrepancies between the unrooted NJ and NNet phylogenetic tree approaches in grouping a few accessions. As for evidence, the unrooted NJ tree arrested 7 genotypes (G23, G24, G25, G26, G27, G28, and G29) constituting the subgroup ‘d’ whereas the NNet analysis captured the genotypes G23, G24, G25, and G34 together remarked as subgroup ‘c’ although G34 was taken a position in a unique subgroup by NJ method. Our findings are validated by Wu et al. ( 2019 ) when studied 33 plum varieties using ISSR primers. Additionally, the findings of NNet phylogenetic analysis are motivated to territoriality compared to NJ phylogenetic analysis, as the NNet analysis based on split networks enhances extra topology associated factors during the phylogenetic study (Huson 2005 ). Usually, genetically nearness accessions are often clustered in an identical group whereas the accessions that were not grouped may be due to geographic blockade resulting in genetic divergence which is also supported in the case of the study by Schic et al. (2015). For adaptation with genetic variation, the constant and natural selection stress could be a vital aspect within the same region. However, the association among geographical factors and genetic variation within the accessions was also elucidated via molecular tools, isozymes, as well as morphological analysis (Wu et al. 2019 ). 4.6 Factorial analysis Principal coordinates analysis (PCoA) employs eigen analysis to determine the principal axes and computes a sequence of eigenvalues and eigenvectors. Eigenvalues are commonly ordered from largest to lowest. The PCoA analysis in our study revealed that PC1 (15.05%) and PC2 (5.81%) recorded the most variance, which is corroborated by Rungnoi et al. ( 2012 ) who reported 90.3% variance headed by the top three PCs using PCoA analysis in Bambara groundnut. Using SSR markers, PCoA accounted for 84.3% covered by the first three principal components (Odongo et al. 2015 ), whereas the first two principal components showed 37.30% (DArT) and 19.50% (SSR) when PCoA analysis was applied to Bambara groundnut accessions (Molosiwa et al. 2015 ). We discovered considerable variance in accession by region-based partitioning using factorial analysis, and this inference is supported by the earlier results obtained by Odongo et al. ( 2015 ) used SSR markers to discover a similar trend of divergence in 105 Bambara groundnut populations from various regions. The principal co-ordinate analysis is a data minimization approach that groups and separates correlated variables from those that have little or no association (Khan et al. 2021d ). 4.7 Population genetic structure analysis The admixture model-based analysis was executed to illustrate the genetic structure of 44 Bambara groundnut accessions sampled from 11 populations of different agro-ecological zones. Based on the likelihood of accession sampled from a certain population our result suggested that accessions are composed of three distinct genetic components. Therefore, in the case of accessions assortment, there was little inconsistency was observed between the output of structure analysis (best delta K = 3) and grouping of accessions by phylogenetic tree (NJ and NNet) analysis. Determination of delta K is an ad hoc quantity related to the second-order rate of change of the log-likelihood of data related to the number of clusters (Zimisuhara et al. 2015 ). Bar plot depicts those 35 accessions were comparatively unique or genetically pure based on Q > 0.60 relations to that of unique standard (Wu et al. 2019 ), indicating that these accessions might have an inadequate genetic drift to other accessions. On the other hand, accessions G33 of population Giiwa and G31, G32 from Katwa had Q < 0.60 saying that they are either genetically admixed or govern by genes of other accessions. This finding is consistent with the similar trend of results reported by Olukolu et al. ( 2012 ) reported ΔK = 4 using DArT assay of 40 Bambara groundnut genotypes whereas Rungnoi et al. ( 2012 ) estimated ΔK = 2 using ISSR and RAPD in 363 Bambara groundnut genotypes. There are some other related researches such as Wu et al. ( 2019 ) found ΔK = 3 using ISSR, Nilkanta et al. ( 2017 ) found ΔK = 3 using ISSR, Zarei and Erfani-Moghadam et al. (2021) found ΔK = 3 using SCoT, Barbosa et al. ( 2019 ) found ΔK = 3 using ISSR, Zimisuhara et al. ( 2015 ) found ΔK = 2 using ISSR, Li and Zhang, (2020) found ΔK = 2 using ISSR strongly advocated our current investigation using V. subterranea accessions. The population belong to the yellow color cluster was more complex compared to that of the purple color cluster, the fact is due to the incorporation of genetic material from the population of the red color cluster (G41, G43, and G44) and purple color cluster (G36, G38, and G42). 5.0 Conclusion The present research is the first initiative on the valuation of genetic structure and differentiation in V. subterranea accessions using molecular markers in Malaysia. ISSR primers are widely used to reveal the genetic variation, structure, and relatedness of a crop species. The result of our investigation proved the efficiency of ISSR primers revealing the significant level of differentiation among the accessions of V. subterranea . The detection of polymorphism and banding patterns in our study is considerably accountable. Based on data generated from ISSR markers we found a broad-spectrum differentiation among the population sampled from the Gombe region. Accessions from the Jatau population had a maximum variation with a range from 30.98–41.57%. Neighbor-joining and split clustering result validated by Bayesian clustering based on structure analysis revealed three genetic clusters in which accessions belong to cluster 1 (red) were highly pure one at standard likelihood value Q > 0.60 though, cluster 2 (yellow) and cluster three (purple) composed of both pure and admixture accessions. In our study low genetic variation among the population compared to within-population couples with significant diversity at species level suggested that the emphasis should be needed to conserve and protect the existing accessions of V. subterranea . However, for gaining efficient, reliable, and precise inference dominant marker couple with codominant marker is strongly recommended. To broaden the genetic variation for breeding new cultivars of this crop mutation and inter- genotypic hybridization are also suggested. Furthermore, these current findings will provide a sharp knowledge of the accessions evaluated, enrich the gene pool, and strengthen the future breeding program for this crop improvement. Abbreviations DNA: Deoxy ribonucleic acid; RNA: Ribonucleic acid; ISSR = Inter simple sequence repeat; NJ: Neighbour joining; NNet: Neighbour network; PCR: Polymerase chain reaction; RAPD: Random Amplified Polymorphic DNA; RFLP: Restriction fragment length polymorphism; AFLP: Amplified fragment length polymorphism; GPS: Global positioning system; CATB: Cetyl trimethylammonium bromide; EDTA: Ethylenediaminetetraacetic acid; PCoA: Principal coordinate analysis; MVSP: Multivariate statistical packages; UPMGA: Unweighted Pair Group Arithmetic Mean, MEGA: Molecular Evolutionary Genetic Analysis; AMOVA: analysis of molecular variance; MCMC: Markov Chain Monte Carlo; CLUMPAK: Cluster Markov Packager Across K Declarations Compliance with Ethical Standards This article does not contain any studies with human participants or animals performed by any of the authors. Consent for publication Not applicable. Availability of data All data generated or analysed during this study are included in this article and full length of gel or blots are represented in supplementary file. Competing interests The authors declare that they have no conflict interests in this paper. Funding Bangladesh Agricultural Research Council (BARC- Project of NATP Phase-II), The People’s Republic of Bangladesh, World Bank, IFAD, and Universiti Putra Malaysia (research grant: vote number 6282518). Author’s contribution M.M.H.K. and M.Y.R. created the paper's concept, design, and methodology. M.M.H.K. collected the data. M.M.H.K. performed statistical analysis, used software, and interpreted the results. M.M.H.K. wrote the first draft and prepared the text. M.Y.R. is in charge of supervision. S.I.R. and M.J. investigate the situation. M.M.H.K., B.C.K., and M.A.M. wrote the article; they also reviewed and edited it. The final, published version of the paper has been reviewed and approved by all authors. 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Structure and principal components analyses reveal an intervarietal fusion in Malaysian mistletoe fig (Ficus deltoidea Jack) populations Supplementary Files SupplimentariyMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 02 Aug, 2023 Read the published version in Molecular Biology Reports → Version 1 posted Editorial decision: Minor Revisions Needed 20 Apr, 2023 Reviewers agreed at journal 22 Mar, 2023 Editor assigned by journal 14 Mar, 2023 First submitted to journal 12 Mar, 2023 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2678771","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":185505852,"identity":"e28b6310-f232-450f-a43c-ce0b4f0e8258","order_by":0,"name":"Md Mahmudul Hasan Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCQiVwMDA2HDgQwWQyczcQLSWxoMzzoC0MBKthYH5MG8biE1Ai+7s5scffu5gyOOffbjhAO+82mj+dqCWHxXbcGoxu3PMTLL3DEOxxLnEhgOS247nzjjM2MDYc+Y2bi03EswYgO5JbDgD9L7htmO5DUAtzIxt+LSkf/74F6hlPkhL4pxjufMJa8kxkAbZsgGk5WBDTe4GIrSUScu2SRQbArUcbDh2IHcjUMtB/H5J3/zxbZtNntwZ9sef/9TU5c47f/jggx8VuLVAgQSMcRhMHiCkHhnUkaJ4FIyCUTAKRggAAIEyZP//KN9eAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8195-3783","institution":"Universiti Putra Malaysia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Md","middleName":"Mahmudul Hasan","lastName":"Khan","suffix":""},{"id":185505853,"identity":"13549d8a-bee4-4c43-bb07-810658169fc6","order_by":1,"name":"Mohd Y. Rafii","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Y.","lastName":"Rafii","suffix":""},{"id":185505854,"identity":"54b3a41b-16b4-4426-aaa9-c11d98d7465a","order_by":2,"name":"Shairul Izan Ramlee","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shairul","middleName":"Izan","lastName":"Ramlee","suffix":""},{"id":185505855,"identity":"8b0ec190-0067-45f0-a942-4c3c7bfa07c0","order_by":3,"name":"Mashitah Jusoh","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mashitah","middleName":"","lastName":"Jusoh","suffix":""},{"id":185505856,"identity":"747a405d-368f-4ae5-a8a9-70f83c0795f2","order_by":4,"name":"Md Al Mamun","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Md","middleName":"Al","lastName":"Mamun","suffix":""},{"id":185505857,"identity":"a68fcf56-f9db-4747-9fbb-f97bdf4a904e","order_by":5,"name":"Bimal Chandra Kundu","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bimal","middleName":"Chandra","lastName":"Kundu","suffix":""}],"badges":[],"createdAt":"2023-03-10 16:33:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2678771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2678771/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11033-023-08693-x","type":"published","date":"2023-08-02T21:51:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":34776803,"identity":"b47e43a5-f81f-4e61-b702-c7197a5a9458","added_by":"auto","created_at":"2023-03-24 14:35:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":364240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe banding pattern of forty-four Bambara groundnut genotypes. Note: M representing a 100 bp DNA ladder. The amplified product of PCR reaction a) UBC 808 primer, b) UBC 809, and b) UBC 841 primer. We run 22 accessions at a time each column with a numeric number refers to the accession number listed in Table 1. The full length of gel or blots are given in Supplementary Figure S1.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/55def79b39c5250a88cd4e79.jpg"},{"id":34775703,"identity":"e712d453-45df-4c0c-bd58-9f667e3d6ad1","added_by":"auto","created_at":"2023-03-24 14:27:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical display of banding pattern and heterozygosity of 11 Bambara groundnut population.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/256316fc1beee4d61a880ebb.jpg"},{"id":34776804,"identity":"7a9d3917-e64a-46f3-a27c-f049a277dbb1","added_by":"auto","created_at":"2023-03-24 14:35:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFractions of molecular variance regarding a subdivided population (a) and geographical units (b).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/1d21560b2ec4cb4a04077435.jpg"},{"id":34775700,"identity":"87bf8e7f-1973-43f1-9232-1d2dffebaa7c","added_by":"auto","created_at":"2023-03-24 14:27:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between the genetic and geographic distances among 11 populations revealed by Mantel test\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/7fe1150cb23554ab390f37b9.jpg"},{"id":34778277,"identity":"01ba7cdb-ed41-44a4-b421-39ed85cc164b","added_by":"auto","created_at":"2023-03-24 14:43:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship of 11 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV. subterranea \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epopulations based on Nei’s unbiased genetic measures revealed by UPMGA methods. Note: the numeric value in parenthesis with each population is the number of populations with respective geographical zones from where genetic material was collected; values beneath each edge is toggling the display edge length; values in parenthesis upper each edge is toggling the display nodes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/b8f84c3ae0b568e8f5db87f9.jpg"},{"id":34778279,"identity":"ef7d0af2-9e56-469a-884e-5c6d99a2edad","added_by":"auto","created_at":"2023-03-24 14:43:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":536680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadial tree of genetic relatedness among 44 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV. subterranea\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e accessions revealed by Neighbor-joining method constructed from Nei’s distance using ISSR data set. Letters inside the dendrogram indicate the grouping of genotypes as MC (major cluster) I, II, \u0026amp; III and SC (sub-cluster) I, II, III, IV, V, \u0026amp; VI. Multiple plants in each population are marked with the same color symbol such as upward triangle, downward triangle, square, circle, diamond shape, etc.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/da3c85cde0f6b141c7def9c6.jpg"},{"id":34775706,"identity":"e4cc7c76-8921-43ff-ba7a-277f2f60aaf6","added_by":"auto","created_at":"2023-03-24 14:27:15","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":371819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eISSR data based phylogenetic trees of 44 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV. subterranea\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e accessions: an unrooted tree generated using the NJ method (A) and NNet method calculated from uncorrected P distance (B).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/4cc67f414a34c4c1ea9e4722.jpg"},{"id":34776807,"identity":"e2a99979-e179-44f9-919c-9c07fcd83002","added_by":"auto","created_at":"2023-03-24 14:35:15","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":659520,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical visualization of PCoA (Factorial analysis) based on Jaccard dissimilarity and NJ phylogenetic tree of 32 ISSR primer data set across 44 accessions of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV. subterranea\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e collected from different regions (A. Gombe; B Kwami; C. Akko; and D. Sokoto).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/ab792f347f6aa26203403225.jpg"},{"id":34778278,"identity":"b5f03c16-8b54-4845-82d0-d5f444962996","added_by":"auto","created_at":"2023-03-24 14:43:15","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":188772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal coordinates analysis (PCoA): A. axis 1 vs axis 2 and B. axis 1 vs axis 3 derived from ISSR analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV. subterranea \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eaccessions using NCSS 2021\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/515db5c60488041281573e48.jpg"},{"id":34775710,"identity":"033cec7e-0c11-489d-b933-467017dfbdb7","added_by":"auto","created_at":"2023-03-24 14:27:15","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":578361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-dimensional (2D) graphical display: A). PCoA (Euclidian’s measure) case scores using MVSP; B). PCoA 3D plot; C). PCoA contour plot using NCSS 2021; D). Bland and Altman regression plot using NCSS 2021.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/041c929d2b40fc9a0aabad3b.jpg"},{"id":34775707,"identity":"6fa45665-6e36-438a-90e0-a37811e8e725","added_by":"auto","created_at":"2023-03-24 14:27:15","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":715741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBayesian model-based population structure and Delta K value estimated by STRUCTURE program using Evanno et al. [46] method\u003c/strong\u003e for 44 \u003cem\u003eV. subterranea\u003c/em\u003e species based on ISSR primer data set. (A). bar graph based on the original population order (K = 1-10); (B). K = mean (|L\"(K)|)/sd(L(K)) in this case, where K = 3 denotes the highest K value; (C). Using median values of Ln (Pr data), get the maximum k for Pr (K= 1.00): 3. Take note of the population relationships of the examined group of Bambara groundnut species, as inferred by the CLUMPAK beta version by Kopelman et al. [48] for a priori distinct number of K = 1 to 10. The demographic code listed in Table 1 is indicated by the numeric number underneath the adjacent bar graph (D). Accessions are allocated to a specific population when the highest Q value is \u0026gt; 0.6. All the 44 accessions were assembled into six (a, b, c, d, e, and f) subgroups by NNet and NJ analysis are itemized under the bar plot.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/1cb3c0c280f34f40565922c6.jpg"},{"id":44735407,"identity":"8c34d2c8-9a4f-4019-a337-09824f565e7b","added_by":"auto","created_at":"2023-10-16 22:25:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2231678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/4fde6807-458b-4e47-8e6f-c621b646f57a.pdf"},{"id":34776809,"identity":"c4699f53-26fe-4bb0-a8a4-35f3bb98b34a","added_by":"auto","created_at":"2023-03-24 14:35:15","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1301753,"visible":true,"origin":"","legend":"","description":"","filename":"SupplimentariyMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-2678771/v1/c8c8b3925fc466d45cd45d4b.docx"}],"financialInterests":"","formattedTitle":"Molecular Insight into the Genetic structure and Banding pattern Analysis of Bambara groundnut (Vigna subterranea L.) with Random Amplified Microsatellites (RAMs)","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eBambara groundnut (\u003cem\u003eVigna subterranea\u003c/em\u003e [L.] Verdc. Syn. \u003cem\u003eVoandzeia subterranean\u003c/em\u003e [L.] Thouars ex DC. 2n\u0026thinsp;=\u0026thinsp;2x\u0026thinsp;=\u0026thinsp;22) belongs to an important legume taxon: the genus Vigna, family Leguminosae. It was previously classified into the genus \u003cem\u003eVoandzeia\u003c/em\u003e later included as a member of the genus Vigna (Verdcourt \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), though it has significant differences in morphological features from other species of \u003cem\u003eVigna\u003c/em\u003e. The genus \u003cem\u003eVigna\u003c/em\u003e is comprised of ninety species among them seven species (major) commercially grown in several countries while other species are grown as minor or underutilized legume at pocket areas of different countries for local food and feed supply (Rungnoi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The minor or underutilized species such as Kersting groundnut (\u003cem\u003eKerstingiella geocarpa\u003c/em\u003e), Marma bean (\u003cem\u003eTylosema esculentum\u003c/em\u003e), Rice bean (\u003cem\u003eVigna angularies\u003c/em\u003e), Mung bean (\u003cem\u003eVigna mungo\u003c/em\u003e) and Cowpea (\u003cem\u003eVigna unguiculata\u003c/em\u003e) have a wide spectrum of genetic variation all over the world, either as cultivated or wild races (Khan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). One such imperative underutilized legume is Bambara groundnut (Molosiwa et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) occupy 3rd position after groundnut and cowpea production in Africa (Olukolu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It is highly tolerant to water deficit, infertile soil, and widely grown by marginal farmers of semiarid African region as monoculture or intercropped with cereals, root, and tuber crops (Olukolu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Besides Africa, it has been successfully cultivated in Asian regions such as Malaysia, Indonesia, Philippines, Thailand, and India [2, 6]. Consumption of agri-based food reduces the mortality resulting from coronary heart diseases (Khan et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) so, Bambara groundnut can be an agri-based protein source for resource-limited people who are unaffordable to precious animal protein (Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e). The current scarcity and malnutrition, particularly in low-income countries can be mitigated by giving emphasis on underutilized such legume research and expansion resulting in gradually getting the planetary food and nutritional security (Molosiwa et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Bambara groundnut is hardy crop that have been noted as a lucrative and nutritive food source when food is under threat (Mbosso et al. 2021) also improves the soil profiles via fixing atmospheric Nitrogen (Paliwal et al. 2020). Due to balanced macronutrients viz. carbohydrates (64.4%), protein (23.6%), oil (6.5%), fiber (5.5%), and a significant amount of trace minerals (Halimi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Bambara groundnut remarked as \u0026ldquo;Complete Food\u0026rdquo;, having potentiality in reducing gaps in the food scheme to assure food sustainability and nutrient sanctuary (Lin Tan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the case of world Bambara groundnut production Nigeria and Burkina faso hold the first position (Hillocks et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Khan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e]. The major obstacles to large scale farming of Bambara ground are its low yield which is recorded as low as 650\u0026ndash;850 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Olukolu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), though this crop has enabled to produce up to 4.0 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Kouassi and Bi. 2010) whereas 0.38\u0026ndash;1.6 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e reported by Khan et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) by ensuring the optimal growing environment. The main point of low yield of Bambara groundnut is the use of local land races in addition to little or no attention to genetic improvement, lack of improving varieties with production technologies, lack of effective research and resources, less research interest by scientific personals (Mohammed et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The autogamous and cleistogamous nature of Bambara groundnut is the major hindrance of its varietal development through hybridization (Molosiwa et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Information on genetic makeup and parental survey is imperative for varietal advancement in all crop species, predominantly in neglected crops such as Bambara groundnut (Bamshaiye et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Characterization at the genomic level is highly authoritative to counterpart the morphologic characterization (Fatimah and Ardiarini \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) as a reason it is mostly influenced by environmental factors (Massawe et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Amzeri \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Genomic markers are the powerful tools for sensing the genetic inconsistency over the traditional breeding approaches (Gupta and Varshney \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), which can be used for sketching the germplasm's origin and breeding scheme to genomic upgrading (Khan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Going by the available literature, a few kinds of research has been conducted on Bambara groundnut (inter and intra species) at a molecular level using genomic markers. The first initiative was taken by Odeigah and Osanyinpeju (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) using the SDS-polyacrylamide electrophoresis technique. The researchers Molosiwa et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Mohammed et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), SiiseAliyu and Massawe (2013), and Odongo et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported genetic diversity has existed in the evaluated genotypes of Bambara groundnut applying SSR markers because of their duplicability, co-dominant nature, and richness in the genome. In literature, negligible evidence was found related to the use of ISSRs marker in Bambara groundnut. Although, Rungnoi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) noted only three ISSR markers in their study and recently a statement is available on ISSR genomic marker-based population genetic structure of this crop in Malaysia (Khan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Among the PCR-based marker, ISSR is the most frequently used for population genetic structure portrayal in different crop species, which generate highly steadfast and reproducible bands over RAPD tools (Nilkanta et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). ISSR markers are also popularly known as random amplified microsatellites (RAMs). Moreover, ISSRs are technically easier, rapid, more economical related to AFLP, SSR, and RFLP genomic tools as it requires a small amount of DNA fragment, as well as no anterior sequence data, are needed to engendering DNA amplified products (Oumer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To identify genetically similar lines, ISSRs have the potentiality to generate very repeatable bands on identical genotypes (Fang and Roose \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). It has been successfully applied for landraces characterization, valuation of genetic variation and phylogenetic relation, documentation of DNA primers associated with agro-morphic features, and for crop breeding schemes (Reddy et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Alansi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a new crop in Malaysia, there is an absence and/or gap of research at a molecular level, information on crop\u0026rsquo;s botany, genetics, farming techniques, economic value addition, and diversity of Bambara groundnut that prompted the inauguration of this research. The current investigation is designed to explore genetic disparity, gene flow, and population genetic makeup of Bambara groundnut using ISSR primers. Additionally, this investigation contributes to the policy making for actual conservation and justifiable usages of this legume. Contrarily, findings from this study may deepen the Bambara groundnut gene pool, backing the upcoming breeding program, assuring economic and ecological gains by divulging the genetic structure of current \u003cem\u003eVigna subterranea\u003c/em\u003e [L.] accessions. A better understanding of their genetic relationship may expand the Bambara groundnut accessions' conservation, maintenance, application, and leads to the development of new cultivar through a proper breeding scheme.\u003c/p\u003e"},{"header":"2.0 Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Plant materials\u003c/h2\u003e\n\u003cp\u003eFrom June to December 2020, the present research was conducted at the Laboratory of Climate-Smart Food Crop Production, Institute of Tropical Agriculture and Food Security (ITAFoS), Universiti Putra Malaysia (UPM). For this study, a set of 44 Bambara groundnut accessions were sampled from 11 distinct populations (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Healthy and fresh leaves were taken from individual plants of 44 accessions of selfed generation S\u003csub\u003e4\u003c/sub\u003e that was two weeks old for DNA extraction. Leafy samples were collected and preserved at -80\u0026deg;C temperature until genomic DNA extraction was completed.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eList of accessions and geographic coordinates of 44 \u003cem\u003eV. subterranea\u003c/em\u003e accessions\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGeo.Location\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAccessions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLatitude (N)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLongitude (E)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eElevation (m)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 1: Duna\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDunP2-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDunP8-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDunP9-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDunP6-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 2: Maikai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaikP11-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaik12-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaikP3-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaikP6-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 3: Cancaraki\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancP1-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancP2-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancP4-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancP3-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 4: Roko\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eKwami\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRokP6-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 50ˊ85˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;25ˊ24˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e503\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRokP9-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 50ˊ85˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;25ˊ24˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e503\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRokP1-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 50ˊ85˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;25ˊ24˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e503\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRokP3-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 50ˊ85˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;25ˊ24˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e503\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 5: Bidilalle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eAkko\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBdilaP10-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 11ˊ86˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;02ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e446\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBdilaP8-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 11ˊ86˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;02ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e446\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBdilaP11-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 11ˊ86˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;02ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e446\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBdilaP5-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 11ˊ86˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;02ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e446\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 6: Jatau\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJataP3-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJataP5-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJataP4-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJataP1-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 7: Maibargo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eSokoto\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaibP3-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaibP8-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaibP9-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaibP6-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 8: Katawa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKataP4-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKataP1-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKataP5-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKataP8-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 9: Giiwa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGiiwP12-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGiiwP11-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGiiwP9-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGiiwP1-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 10: Karu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKarP3-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKarP10-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKarP9-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKarP8-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026deg; 27ˊ91˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u0026deg;17ˊ31˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePopulation 11: Exsokoto\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eSokoto\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExSokP4-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExSokP3-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExSokP10-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExSokP5-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u0026deg; 00ˊ59˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026deg;24ˊ76˝\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend: Dun\u0026thinsp;=\u0026thinsp;Duna; Maik\u0026thinsp;=\u0026thinsp;Maikai, Canc\u0026thinsp;=\u0026thinsp;Cancaraki; Rok\u0026thinsp;=\u0026thinsp;Roko; Bdila\u0026thinsp;=\u0026thinsp;Bidilalli; Jata\u0026thinsp;=\u0026thinsp;Jatau; Maib\u0026thinsp;=\u0026thinsp;Maibargo; Kata\u0026thinsp;=\u0026thinsp;Katawa; Giiw\u0026thinsp;=\u0026thinsp;Giiwa; Kar\u0026thinsp;=\u0026thinsp;Karu; Exsok\u0026thinsp;=\u0026thinsp;Exsokoto\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 DNA Extraction, PCR amplification and band scoring\u003c/h2\u003e\n\u003cp\u003eThe updated Zheng (\u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e) protocol was used to extract the genomic DNA from the plant sample. For this purpose, 2.5g of fresh foliar tissue from a 14 day-aged seedling that was healthy and free of mechanical injury was selected. Young stable leaves tissues are milled into fine powder in the presence of liquid nitrogen using a mortar and pestle. All the steps of the \u0026ldquo;Zheng protocol\u0026rdquo; were carefully followed (details are presented as supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The concentration and quality of the genomic DNA solution were tested using the Thermo ScientificTM NanoDrop Lite Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The ratio absorbance 260/280 nm and 260/230 nm of more than 1.8 was used as a criterion for the next steps to ensure DNA purity. As a working sample, a part of the DNA template was diluted to a concentration of 40 ng/𝜇l. To run the PCR, following settings were performed: 95\u0026deg;C for initial denaturation aimed at 3 minutes, afterward 35 cycles of denaturation at 94\u0026deg;C for 45 seconds, one minutes for primer specific annealing temperature, extension at 72\u0026deg;C for 2 minutes, and final extension was adjusted at 72\u0026deg;C for 10 minutes before being saturated at 4\u0026deg;C. Using horizontal gel electrophoresis (400A with 80V for 75 mins) approach the amplified products were separated on 1.5% (w/v) agarose gel. To get exact band size based on the standard molecular weight of 100 bp DNA ladder was used. The data was scored into binary data as present (1) and absent (0) for each locus by using UVIDoc software. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represent the banding pattern of some ISSR markers amplified from PCR reaction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eThirty-two ISSR primers were considered to polymorphism analysis across the 44 Bambara groundnut genotypes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). POPGENE version 1.32 (Yeh et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) was used to calculate the primer polymorphism index and Polymorphic Information Content (\u003cem\u003ePIC\u003c/em\u003e), for each primer was measured by Botstein et al. (\u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e) formula \u003cem\u003ePIC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 \u0026ndash; \u0026Sigma; \u003cem\u003ep\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e \u0026ndash; \u0026Sigma; \u0026Sigma; \u003cem\u003ep\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e where p\u003csub\u003ei\u003c/sub\u003e and p\u003csub\u003ej\u003c/sub\u003e are the population frequency of the ith and jth allele. POPGENE version 1.32 was used to analyse the genetic diversity among the populations based on \u003cem\u003eG\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e= (H\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026minus; H\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)/H\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand\u003c/em\u003e gene flow (𝑁m) between populations \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5 (1- \u003cem\u003eGst\u003c/em\u003e)/\u003cem\u003eGst\u003c/em\u003e was calculated as per McDermott and McDonald (\u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e). To estimate the banding patterns on its frequency and polymorphism GenAlEx (genetic analysis in excel) version 6.5 (Peakall and Smouse \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) was performed. Nie\u0026rsquo;s (1978) unbiased genetic distances and genetic identity matrix performed by GenAlEx 6.5 software. The population-based genetic diversity indices were estimated on the following heads such as observed number of alleles per locus (\u003cem\u003eNa\u003c/em\u003e), number of effective alleles per locus (\u003cem\u003eNe\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;1/(p\u003csup\u003e2\u003c/sup\u003e + q\u003csup\u003e2\u003c/sup\u003e), Shannon\u0026rsquo;s information index (\u003cem\u003eI\u003c/em\u003e) = -1 (p Ln (p)\u0026thinsp;+\u0026thinsp;q Ln (q)), the expected heterozygosity (\u003cem\u003eHe\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;2pq, the unbiased expected heterozygosity (\u003cem\u003euHe\u003c/em\u003e) = (2N / (2N-1)) \u003cem\u003eHe\u003c/em\u003e and percent of polymorphic loci (%P) using GenAlEx 6.5 software (Peakall and Smouse \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The principal coordinate analysis (PCoA) was performed by MVSP and NCSS 2021 software and a correlation study: Mantel (\u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e) test between genetic (linear based) and geographic distance (log-transformed) among the Bambara groundnut populations was performed to assess whether there is a significant relationship between the matrix of pairwise genetic distances and geographical distances between overall populations using GenAlEx 6.5 with 999 random permutations. The geographical region-based factorial analysis (PCoA) was analyzed by DARWin 6 program. To estimate the judicial relationship of PC1 and PC2, Bland-Altman (1986) test was performed using NCSS 2021 program. A dendrogram or phylogenetic (radial) tree was constructed based on the Unweighted Pair Group Arithmetic Mean (UPMGA) method in POPGENE software ver. 1.32 followed by MEGA version 6.10 (Tamura et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, an unrooted tree was constructed based on Neighbour-joining (NJ) and a split network phylogenetic tree (NNet) using Splitstreev.4.6 (Huson et al. 2006). The distribution of incompatible splits based on uncorrected p distance was inferred, which provided a split graph through NNet analysis. An analysis of molecular variance (AMOVA) was performed to estimate the variance components and their significance levels [P (rand\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;data)] of genetic variation within and among populations using GenALEx version 6.5 (Peakall and Smouse \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) with a permutation number of 999. Estimation of PhiPT (based on standard permutation across the full data set) distance was performed by the formula of PhiPT\u0026thinsp;=\u0026thinsp;AP / (WP\u0026thinsp;+\u0026thinsp;AP)\u0026thinsp;=\u0026thinsp;AP / TOT, where AP\u0026thinsp;=\u0026thinsp;est. var. among populations, WP\u0026thinsp;=\u0026thinsp;est. var. within populations, TOT\u0026thinsp;=\u0026thinsp;sum square total using GenALEx version 6.5.\u003c/p\u003e\n\u003cp\u003eSTRUCTURE ver. 2.3.4 (Pritchard et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) was run based on the ISSR binary data of 44 BG genotypes to determine the pattern of population structure. Structure analysis is one of the most ideal techniques for crop diversity study to perceive the patterns of population genetic structure using molecular markers (Wu et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pritchard et al. \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Zimisuhara et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The burn-in time of 5.0\u0026times;10\u003csup\u003e4\u003c/sup\u003e followed by 1.0 \u0026times;10\u003csup\u003e6\u003c/sup\u003e m MCMC simulations at 4 iterations (Welt et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) with ten independent repetitions were done to determine the optimal genetic unit, K value (Evanno et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). The Structure Harvester 0.6.93 version\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://taylor0.biology.ucla.edu/Structure\u003c/span\u003e\u003c/span\u003e) (Earl \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) and CLUMPAK (Cluster Markov Packager Across K) beta ver. (Kopelman et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) to determine the average Log-likelihood, Ln P(D), probability by K-graph, the most provable K value using \u0026Delta;K method by Evanno et al. (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). The standard Q value (Q\u0026thinsp;\u0026gt;\u0026thinsp;0.60\u0026thinsp;\u0026lt;\u0026thinsp;Q) representing relationship coefficient (%) value of assigning accessions to a certain population. The maximum (K) likelihood value was used to assign the accessions to the appropriate cluster ((Wu et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAmplified polymorphic indices of selected 32 ISSR primers on 44 \u003cem\u003eV. subterranea\u003c/em\u003e accessions\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMarkers\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSequences\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTa (\u0026deg;C)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGCAGCAGCAGCAGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCACACACACACACACA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGAGAGAGAGAGAGAGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 808\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGAGAGAGAGAGAGAGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 809\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGAGAGAGAGAGAGAGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAGAGAGAGAGAGAGAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCACACACACACACACAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 836\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGAGAGAGAGAGAGAGYA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 841\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAGAGAGAGAGAGAGAYC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 844\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTCTCTCTCTCTCTCTRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC-815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTCTCTCTCTCTCTCTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 817\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCACACACACACACACAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGACA GACA GACA GACA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 811\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACACACACACACACT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 901\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGAGAGAGAGAGAGAGYC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 835\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTCTCTCTCTCTCTCAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAGAGAGAGAGAGAGATT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAGAGAGAGAGAGAGAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 842\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAGAGAGAGAGAGAGACTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA-856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACACACACACACACACYA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI-825\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACACACACACACACACAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGA GAG AGA GAG AGA GYC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTCT CTC TCT CTC TCT CRG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePRIMER 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGAGAGAGAGAGAGAGAGAGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACCATGGCTACCACCGAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 2M\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCACACACACACACACAAAGCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 835\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGAGAGAGAGAGAGAGYC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41..4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTCTCTCTCTCTCTCTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePRIMER 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTCCTCCTCCTCCTCCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISSR 848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCACACACACACACACAAAGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 825\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACACACACACACACACT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUBC 830\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGTGTGTGTGTGTGTGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eLegend: R\u0026thinsp;=\u0026thinsp;A, G (Purine); Y\u0026thinsp;=\u0026thinsp;C, T (Pyrimidine); Ta\u0026thinsp;=\u0026thinsp;annealing temperature. Sources of primers: (Khan et al. \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3.0 Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Genetic diversity among the population and geographical units\u003c/h2\u003e\n\u003cp\u003eWithin population an average percent of polymorphism was 35.15% with a range between 33.92% for Roko and 41.57% for Jatau. The diversity parameters among population as revealed by observed number of alleles (\u003cem\u003eNa\u003c/em\u003e), Number of effective alleles (\u003cem\u003eNe\u003c/em\u003e), expected heterozygosity (\u003cem\u003eHe\u003c/em\u003e), and unbiased heterozygosity (\u003cem\u003euHe\u003c/em\u003e) observed that the greater degree of variability for \u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.833\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013 and \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.208\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013 possessed by Cancaraki population with a mean of 0.732\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013 and 1.187\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004, respectively. The population Jatau occupied higher index for Shannon information (\u003cem\u003eI\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;0.202\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011, \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008 while the population Cancaraki and Jatau possessed greater \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.130\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008 with an average of \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.177\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003, \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.132\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003 and \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.116\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The lowest variation was owned by the population Katawa with heading of \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.158\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011, \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.103\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007, and \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.118\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008 while for Karu and Exsokoto population it was \u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.657\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041 and \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.116\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013, respectively. However, average genetic variation at population level was comparatively lower related to the variation exposed at species level which was recorded as \u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.973, \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.382, \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.395 and \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.248. Based on geographical group of population (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), percent of polymorphism spanned from 30.98% (Kwami) to 90.98% for Gombe state with a mean of 51.08% per geographical location. Among the geographical unit, population with Gombe state had highest \u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025, \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.271\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008, \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.157\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006, and \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.160\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006 with an average variation of 1.041\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022, 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005, 0.124\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003, and 0.135\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004, respectively. Moreover, maximum expected heterozygosity was shown by the population of Gombe state of \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.218\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 with a mean of 1.191\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006 per geographical unit.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDiversity parameters within 11 populations over the loci of \u003cem\u003eV. subterranea species\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM \u0026amp; SE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNa\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNe\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003euHe\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e%P\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDUNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e34.12%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaikai\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e36.67%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCancaraki\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40.39%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRoko\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e33.92%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBidillali\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.784\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e38.04%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJatau\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e41.57%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaibergo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.167\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e31.76%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eKatawa\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30.98%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGiiwa\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.731\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e35.29%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eKaru\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.657\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30.98%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eExsokoto\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.680\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e32.94%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGrand mean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.732\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.187\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.177\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.116\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.132\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e35.15%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSE over loci and populations\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.10%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend: M\u0026thinsp;=\u0026thinsp;Mean; N\u0026thinsp;=\u0026thinsp;number of accessions; SE\u0026thinsp;=\u0026thinsp;standard error; \u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;observed number of alleles per locus; \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;number of effective alleles per locus; I\u0026thinsp;=\u0026thinsp;Shannon\u0026rsquo;s information index; \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;expected heterozygosity; \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;unbaised heterozygosity; \u003cem\u003e%P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;percent polymorphism.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDiversity parameters within 4 geographical unit over the loci of \u003cem\u003eV. subterranea\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGeo. L\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM \u0026amp; SE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNa\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNe\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHe\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003euHe\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e%P\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGombe\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.820\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e90.98%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eKwami\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30.98%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAkko\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.731\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e35.29%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSokoto\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.951\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e47.06%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGrand Mean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.08%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.73%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend: Geo.L\u0026thinsp;=\u0026thinsp;geographical location; M\u0026thinsp;=\u0026thinsp;Mean; N\u0026thinsp;=\u0026thinsp;number of accessions; SE\u0026thinsp;=\u0026thinsp;standard error; \u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;observed number of alleles per locus; \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;number of effective alleles per locus; I\u0026thinsp;=\u0026thinsp;Shannon\u0026rsquo;s information index; \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;expected heterozygosity; \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;unbaised heterozygosity; \u003cem\u003e%P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;percent polymorphism\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Banding pattern analysis\u003c/h2\u003e\n\u003cp\u003eThe ISSR primer-based banding patterns and heterozygosity were demonstrated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. In the population three (Cancaraki) we detected the maximum number of band patterns (NDB\u0026thinsp;=\u0026thinsp;219) afterwards the population six (NDB\u0026thinsp;=\u0026thinsp;212) and population five (NBD\u0026thinsp;=\u0026thinsp;206) whereas minimum was recorded as NDB\u0026thinsp;=\u0026thinsp;177 for population 10 (Karu). The enormously highest number of private bands (NPB\u0026thinsp;=\u0026thinsp;10) were detected in population one (Duna) after that population five (Bidillali) with NPB\u0026thinsp;=\u0026thinsp;9, while the lowest number of private bands were accounted for population eight (NPB\u0026thinsp;=\u0026thinsp;1). Most of the bands were obtained from the number of different bands with a frequency of \u0026ge;\u0026thinsp;5% (NDBF\u0026thinsp;\u0026ge;\u0026thinsp;5%). The Population five (Bidillali) exposed greater number of locally common band i.e. NLCB (\u0026le;\u0026thinsp;25%)\u0026thinsp;=\u0026thinsp;25 alongside the NLCB (\u0026le;\u0026thinsp;50%)\u0026thinsp;=\u0026thinsp;115 were shown by the population three (Cancaraki) while the lowest was recorded as NLCB (\u0026le;\u0026thinsp;25%)\u0026thinsp;=\u0026thinsp;6 and NLCB (\u0026le;\u0026thinsp;50%)\u0026thinsp;=\u0026thinsp;72 for the population four (Roko) and population seven (Maibergo), respectively. The mean of expected heterozygosity (\u003cem\u003eHe\u003c/em\u003e) and mean of unbiased expected heterozygosity (\u003cem\u003euHe\u003c/em\u003e) together with standard errors were observed higher in the population three (\u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.130\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008; \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.149\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009) subsequently the population six (\u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.130\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007; \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEstimated banding pattern across 11 \u003cem\u003eV. subterranea\u003c/em\u003e populations using ISSR assay\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNDB\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNDBF\u003c/p\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;5%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNBSP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNLCB \u0026le; (25%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNLCB \u0026le; (50%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean u\u003cem\u003eHe\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.116\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.132\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.141\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.149\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.127\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.125\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.148\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.110\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.126\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.115\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.119\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.119\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLegend: NDB\u0026thinsp;=\u0026thinsp;number of different bands; NDBF\u0026thinsp;\u0026ge;\u0026thinsp;5% = number of different bands with a frequency\u0026thinsp;\u0026ge;\u0026thinsp;5%; NBSP\u0026thinsp;=\u0026thinsp;number of bands unique to a single population; NLCB (\u0026le;\u0026thinsp;25%)\u0026thinsp;=\u0026thinsp;number of locally common bands (Freq\u0026thinsp;\u0026ge;\u0026thinsp;5%) found in 25% or fewer populations; NLCB (\u0026le;\u0026thinsp;50%)\u0026thinsp;=\u0026thinsp;number of locally common bands (Freq\u0026thinsp;\u0026ge;\u0026thinsp;5%) found in 50% or fewer populations; \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;expected heterozygosity\u0026thinsp;=\u0026thinsp;2*p*q; \u003cem\u003euHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;unbiased expected heterozygosity = (2N/(2N-1)) *\u003cem\u003eHe\u003c/em\u003e where for diploid binary data an assuming Hardy-Weinberg Equilibrium, q = (1- band Freq.)^ 0.5 and p\u0026thinsp;=\u0026thinsp;1-q.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Molecular variance study among the subdivided populations and geographical units\u003c/h2\u003e\n\u003cp\u003eFor better interpretation, analysis of molecular variance (AMOVA) was performed in two phases; 1st emphasized the entire genotypes subdivided into 11 populations, and the 2nd phase focused on the entire populations considering into 4 geographical zones using overall loci (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Partitioning genetic diversity by AMOVA revealed that a greater portion of diversity was covered within a population (75%) and within geographical units (84%), while the variation among populations and geographical units was 25% and 16%, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). The genetic differentiation by AMOVA based on PhiPT distances showed significant variation among the accessions (PhiPT\u0026thinsp;=\u0026thinsp;0.248; p\u0026thinsp;\u0026le;\u0026thinsp;0.001) and among the geographical zones (PhiPT\u0026thinsp;=\u0026thinsp;0.163; p\u0026thinsp;\u0026le;\u0026thinsp;0.001) indicating that the accessions are genetically distinct from each other as well as geographically.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAnalysis of molecular variance (AMOVA) for 11 populations and 4 geographical zones of \u003cem\u003eV. subterranea\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003edf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEst. Var\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhiPT\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eWith 11 populations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmong Pops\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1140.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.248\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWithin Pops\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1623.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2763.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eWith 4 Geographical units\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmong Geo. units\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e444.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWithin Geo. units\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2319.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2763.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: df = degree of freedom, SS = sum square total, MS = Mean square total, Est. Var. = estimated variance\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Genetic differentiation and gene flow estimation\u003c/h2\u003e\n\u003cp\u003eThe inter and intra-population structure analysis revealed that the overall genetic diversity (\u003cem\u003eHt\u003c/em\u003e) was estimated as \u003cem\u003eHt\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.178\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017 whereas within-population genetic diversity was \u003cem\u003eHs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.115\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006. The genetic differentiation (\u003cem\u003eGst\u003c/em\u003e) among the Bambara groundnut population was recorded as \u003cem\u003eGst\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3514, signifying the incidence of 35.14% genetic differentiation between the populations. This result was constant with the output of molecular variance analysis which revealed 25% and 75% genetic variation existing among populations and within-population, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). The unveiled differences between and among the populations were found to be highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The finding was additionally authenticated by the presence of a significant level of gene flow (\u003cem\u003eNm\u003c/em\u003e) among the populations, estimated as \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9229 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGenetic differentiation stricture of 11 population based on Nei\u0026rsquo;s analysis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHt\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHs\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGst\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNm (Gst)\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9229\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSt. Dev\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eHt\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Total gene diversity; \u003cem\u003eHs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;gene diversity within populations; \u003cem\u003eGSt\u003c/em\u003e\u0026thinsp;=\u0026thinsp;the relative magnitude of genetic differentiation among populations (coefficient of gene differentiation); \u003cem\u003eNm\u003c/em\u003e (\u003cem\u003eGst\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;estimate of gene flow from \u003cem\u003eGst\u003c/em\u003e (among populations).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.1 Mantel tests\u003c/h2\u003e\n\u003cp\u003eTo determine the correlation between genetic distances among the population and geographic distances among the population, a mantel test was performed. The tested results exposed a significant correlation between the genetic and geographic distance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.511; P\u0026thinsp;=\u0026thinsp;0.031) with 9999 permutation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). To run this test geographical distances were transformed by Log (1\u0026thinsp;+\u0026thinsp;x) with the unit of a kilometer (Km). Nilkanta et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported moderately non-significant correlation between the genetic and geographic distances (R\u0026thinsp;=\u0026thinsp;0.311; P\u0026thinsp;=\u0026thinsp;0.240) while Barbosa et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) accounted no correlation between genetic and geographic distances (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.011; P\u0026thinsp;=\u0026thinsp;0.004) among populations revealed by Mantel test using ISSR primers.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 Population genetic distances vs PhiPT analysis\u003c/h2\u003e\n\u003cp\u003eThe genetic differentiation among the \u003cem\u003eV. subterranea\u003c/em\u003e population and their engaged geographical zones were revealed by pairwise Nei\u0026rsquo;s genetic distances and PhiPT values. PhiPT Values below diagonal with probability, P (rand\u0026thinsp;\u0026ge;\u0026thinsp;data), and Nei\u0026rsquo;s genetic distance shown above diagonal based on 999 permutations. The PhiPT values showed significant differences for all populations except populations between 5 \u0026amp; 6 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) and populations 7 \u0026amp; 6. Besides, the population under Kwami and Akko have not significantly variation whereas a highly significant variation was found in the population with all other geographical zones (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). In the Matrix, the highest PhiPT value between population 4 and population 8 (0.405*) and Nei\u0026rsquo;s genetic distance between population 4 and population 10 (0.124) was observed. The population under Gombe and Kwami found a maximum PhiPT value (0.178**) with higher (0.064) Nei\u0026rsquo;s genetic distances indicating the genotypes under these two regions are not closely related (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). The lowest Nei\u0026rsquo;s distances (0.041) were found between populations 8 and 9 is the sign of these two populations are closely related while populations 9 and 10 had lower PhiPT (0.049*) value. The population under Sokoto and Gombe were closer (Nei\u0026rsquo;s distances\u0026thinsp;=\u0026thinsp;0.036) than other geographic populations whereas the population from Kwami and Akko zones showed a lower (0.061) PhiPT value. This observation is also evidenced by the UPMGA clustering result (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) which grouped all populations into two major clusters assembling the relatively closer populations in the same group based on genetic and geographic origin.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNei\u0026rsquo;s genetic distances (above diagonal) and pairwise PhiPT values (below diagonal) among 11 populations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop5\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop6\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop7\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop8\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop9\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop10\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop11\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.097\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.097*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.150*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.085*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.239*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.208*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.076*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.241*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.152*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.141*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.192*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.166*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.163*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.186*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104ns\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.338*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.324*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.322*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.376*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.322*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100ns\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.073\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.373*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.339*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.338*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.405*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.356*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.145*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.119*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.326*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.305*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.280*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.352*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.300*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1212*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.379*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.332*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.329*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.392*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.352*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.203*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.243*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.124*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePop11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.319*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.314*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.287*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.338*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.294*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.180*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.252*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.246*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.145*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNei\u0026rsquo;s genetic distances (above diagonal) and pairwise PhiPT values (below diagonal) between the population of 4 geographical units\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGeo. location\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eKwami\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAkko\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSokoto\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGombe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKwami\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.178**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAkko\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.150**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061ns\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSokoto\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.173**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.154*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.070**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e3.5.1 Population genetic distance vs genetic identity analysis\u003c/h2\u003e\n\u003cp\u003eThe Nei\u0026rsquo;s unbiased genetic distance and identity (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e) revealed that greater variation (0.113) accounted for population 4 (Roko) and population 8 (Katawa) followed by population 4 (Roko) and population 10 (Karu) (0.107). Population 1 (Duna) also distantly (0.105) related to population 10 (Karu). However, the population Karu and Katawa belong to the same zones of Gombe while the population Roko is under the Kwami region. Population 8 (Katawa) was closely related to population 9 (Giiwa) considering low genetic distances (0.023) with a higher genetic identity of (0.977) between them. The next low genetic distance was marked between population 9 (Giiwa) and 10 (Karu) (0.024) followed by the population 5 (Bidillali) with 6 (Jatau) (0.028) and population 6 (Jatau) with population 7 (Maibergo), although they are not under in the same geographic zones.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab10\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGenetic distances (below diagonal) and genetic identity (above diagonal) revealed by Nei\u0026rsquo;s unbiased measure of \u003cem\u003eV. subterranea\u003c/em\u003e populations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop5\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop6\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop7\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop8\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop9\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop10\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePop11\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.937\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.922\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.906\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.918\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.923\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.922\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.915\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.926\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.922\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.928\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.915\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.934\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.958\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.906\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.893\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.907\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.899\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.919\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.927\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.935\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.959\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.964\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.946\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.090\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.939\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop9\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.959\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop10\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.088\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.090\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.035\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.966\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePop11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: Pop =Population\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.6 Genetic Relationships Among Populations\u003c/h2\u003e\n\u003cp\u003eThe constructed dendrogram based on Nei\u0026rsquo;s unbiased measures of genetic distances exposed two major clusters with branch lengths of 1.52 and 1.42 though considering the edge length of 1.52 (a), 0.95 (b), and 0.41 (c) we noted three major groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the entire population group (a) comprised of Maibergo, Katawa, Giiwa, Karau, and Exsokoto. The population Exsokoto was separated from other 4 population while Mibergo was fragmented from rest 3 populations of the same geographical region of Gombe but typically Maibergo and Exsokoto belongs to the same zone of Sokoto. Group b (edge length 0.95) covered two populations (Duna and Maikai) of the same geographical zones (Gombe) which is split out from groups c (edge length 0.41) consisted of 4 populations (Roko, Cancaraki, Bidillai, and Jatau). Cancaraki and Roko owned the same subgroups whereas a close association was found between Bidillali and Jatau. The population Roko and Bidillali were in different sub-clusters with different locations of Kwami and Akko, respectively while Cancaraki and Jatau were in the same location of Gombe but positioned into different sub-cluster. The population Roko (node no. = 13; edge length\u0026thinsp;=\u0026thinsp;1.53) under Kwami gained distinct genetic distances (0.113) from the population Katawa (node no. = 17; edge length\u0026thinsp;=\u0026thinsp;1.17) was found in separate cluster under separate zones of Gombe (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.6.1 Genetic relatedness among the accessions\u003c/h2\u003e\n\u003cp\u003eThe Neighbor-joining (NJ) radial tree gained the relationship for the individual genotypes based on Nei\u0026rsquo;s genetic distance, exposed majority of the plants belonging to different population origins, separated distinctly though some individuals were partly mixed within the clustered (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The dendrogram partitioned the 44 \u003cem\u003eV. subterranea\u003c/em\u003e accessions into three major clusters (MC I, MC II \u0026amp; MC III) which was further divided into six subclusters (SC I, SC II, SCIII, SC IV, SC V \u0026amp; SC VI). The distribution of all accessions based on the cluster was shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e. The genotypes from each population were marked using the same-colored symbol viz. upward triangle, downward triangle, square, circle, diamond shape, etc. alongside different sub-tree branch colored. The accessions from major cluster I composed of 22 individuals under six populations. The sub-cluster I (SC I) under major cluster I comprised 11 genotypes in which 4 from Duna, 4 from Maikai, and 3 genotypes from the population Cancaraki but one genotype (Cancaraki P3-18) hold the position in subcluster (SC) II. In SC I, genotypes under Maikai and Duna fitted into the same groups whereas accessions Cancaraki P1-18 have the molecular divergence from Cancaraki P2-18 and Cancaraki P4-18. The sub-cluster II composed of 4 accessions from Bidillali (Bidillali P11-18 and Bidillali P15-18 separated from Bidillali P8-18 and Bidillali P10-18), 4 accessions from Roko, 2 accessions from Jatau, and one from accessions from the population of Cancaraki. The sub-cluster III under the major cluster II holds the 4 accessions of the same population of Exsokoto. Out of 7 genotypes in subcluster V, four individual comes from the population of Katawa in which 3 (Katawa P1-18, Katawa P8-18, and Katawa P5-18) drives in same group rest one (Katwa P4-18) constructed another group with 3 genotypes of Maibego population. However, the sole genotype Karu P8-18 belongs to the subcluster IV while the rest 3 genotypes (Karu P3-18, Karu P9-18, and Karu P10-18) derives to sub-cluster VI constructed the same group with 4 genotypes of Giiwa population. Moreover, the accessions from Jatau (Jatau P1-18 \u0026amp; Jatau P4-18) together with Maibergo (Maibergo P3-18) generated a distinct group remarked as a major cluster (MC) III.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab11\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCluster-based distribution of \u003cem\u003eV. subterranea\u003c/em\u003e accessions revealed by Neighbor-joining method.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePopulation \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMC \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMC I\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMC III\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eMC II\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSC \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSC I\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSC II\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSC III\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSC IV\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSC V\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSC VI\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuna\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaikai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancaraki\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRoko\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBidillai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJatau\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaibergo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKatawa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGiiwa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKaru\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExsokoto\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea \u003c/sup\u003eGenotypes under population by geographical zones are listed in Table 12; \u003csup\u003eb \u0026amp; c\u003c/sup\u003e Genotypes listed by cluster in figure (dendrogram); MC = major cluster; SC = sub cluster\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e3.6.2 Genetic relationship based on NJ and NNet analysis\u003c/h2\u003e\n\u003cp\u003eThe unrooted phylogenetic tree was established based on the Neighbour-Joining (NJ) method using 44 accessions of \u003cem\u003eV. subterranea\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). The NJ phylogenetic tree exposed that the accessions clustered into three (A, B, and C) main groups though group A had two subgroups (a and b) whereas group B composed of 2 distinct subgroups of (e and f) and group C divided into subgroup (c and d) which mostly coincided with the four regions (Gombe, Akko, Kwami, and Sokoto) from where the accessions were collected. There were 11 accessions in subgroup a, which were belongs to the population of Duna (4 accessions), Maikai (4 accessions), and Cancaraki (3 accessions) collected from the same region of Gombe. The one genotype (G12) from the Cancaraki population together with 4 accessions from Roko, 4 accessions from Bidillali, 2 accessions from Jatau, constructed subgroup b. Here the accessions under Jatau and Cancaraki had the common region (Gombe), while the accessions under Roko and Bidillali hold the different regions of Kwami and Akko, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The major groups B and C consisted of 22 accessions and contained a mixture of varieties from Sokoto and Gombe. The subgroup c captured 4 accessions of Exsokoto collected from the Sokoto region. There were 7 accessions in subgroup d: two (G23 \u0026amp; G24) from Jatau population and one (G29) came from Katawa population belongs to a common area of Gombe, other four (G25, G26, G27, and G28) accessions from Sokoto, these all were in Maibergo population. Ten accessions were in subgroup e which were assembled by Katawa: 3 accessions (G30, G31, \u0026amp; G32), Giiwa: 3 accessions (G33, G35, \u0026amp; G36), and Karu: 4 accessions (G37, G38, G39 \u0026amp; G40) having a common region of collection Gombe. The lone accession G34 of the Giiwa population was isolated from other accessions forming a distinct subgroup f which was native to Gombe. Based on split networks, the NNet (Neighbour network) analysis delivers additional network topology associated with NJ phylogenetic relationship. The NNet analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB) partitioned a total of 44 accessions into three main groups (A, B, and C) in which group A further fragmented into subgroup (a and b) whereas group B divided into subgroup (e and f) and group C segmented into subgroup (c and d). Group B and C jointly dominated by 22 accessions of Gombe and Sokoto region while subgroup d dominated by a maximum of 8 accessions (G27, G26, G28 from Maibergo; G29, G30, G31, G32 from Katawa; G33 from Giiwa) population. A broad-spectrum similarity was observed between the result of NNet analysis to those of NJ analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA vs Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). However, the accession G34 from Giiwa was clustered with G23 and G24 from Jatau and G25 from Maibergo population combinedly created subgroup c despite it took position alone in subgroup f by NJ analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e3.7 Factorial analysis: Principal coordinate (PCoA)\u003c/h2\u003e\n\u003cp\u003eOrdination, also known as multivariate gradient analysis, is a collective term for multivariate analysis that adapts a multidimensional set of data in such a way that identical species or samples are plotted close together when dissimilar ones have plotted far apart (Arolu et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The considerable degree of intra and inter-genotypic diversity was shown by factorial analysis of the ISSR data set based on geographical location (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). There were 28 accessions from seven populations under the Gombe region (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA) mainly separated into groups a\u0026rsquo;, \u0026lsquo;b\u0026rsquo;, and \u0026lsquo;c\u0026rsquo;. In group \u0026lsquo;a\u0026rsquo; genotype G23, G24, and G34 were isolated from the rest of the accessions whereas in group b genotype G12 was separated from others. Group c had two accessions (G21 \u0026amp; G22) that were placed far apart from group \u0026lsquo;a\u0026rsquo; and \u0026lsquo;b\u0026rsquo;. Four accessions from the Bidillali population in Akko zones (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ec) showed divergence from each other while the accessions G14 and G15 were closer than other accessions G13 and G16 under Roko in the Kwami zone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). In zone Sokoto (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD), the accession (G41) of the Exsokoto population remarkably separated from the other three (G40, G42, \u0026amp; G43) accessions, on the other hand, accession G25 was detached from the rest of the accessions (G22, G23, G24) under Maibergo. In the case of all accessions, the factorial analysis clustered the 44 Bambara groundnut accessions into four diverse groups in which within accessions under group \u0026lsquo;a\u0026rsquo; and \u0026lsquo;d\u0026rsquo; were showed more genetic variability than the group \u0026lsquo;b\u0026rsquo; and \u0026lsquo;c\u0026rsquo; (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). To lead the clustering investigation eigenvalues and total percentages of principal component case scores were used. The distribution of eigenvalues, percent of genetic variation, and cumulative percent of genetic variation based on 1st three axes (PCs) were displayed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e. The first three principal components covered 26.15% of cumulative variation which is portioned by 15.05%, 5.81%, and 5.29% variation for PC1, PC2, and PC3, accordingly. However, in PCoA analysis, accessions considering axis 1 vs axis 3 were distributed into two groups that were not associated with the accessions distributed by axis 1 vs axis 2 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) indicate that maximum variation reflected by axis 1 vs. axis 2. Two dimensional (2D) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA) and three-dimensional (3D) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eB) visual illustrations of PCoA analysis exposed the substantial level of genetic divergence among the \u003cem\u003eV. subterranea\u003c/em\u003e accessions. PCoA analysis sharply assembled the accessions into three major groups based on Euclidian distance. Genetically related accessions within and among population were placed closer together, whereas distant accessions were placed wide away. The contribution of accessions is further established by the PCoA contour plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eC) in which the intensity of red and blue clour indicating the magnitude of the contribution of accessions on total genetic variation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab12\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercentage of total variation contributed by 1st three components revealed by PCoA.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParameters\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePC1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePC2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePC3\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEigenvalue\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e415.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e160.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePercent of variance (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative percent of variance (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n\u003ch2\u003e3.7.1 Bland-Altman agreement analysis\u003c/h2\u003e\n\u003cp\u003eBland-Altman plots were also used to explore any possible relationship of the inconsistencies of the true values of two axes (Bland and Altman \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e). To evaluate the agreement among two axes (1 and 2) Bland and Altman regression analysis was performed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eD) which is tremendously used to estimate the agreement among two dissimilar measurements. The difference between axis 1 and axis 2 spanned from \u0026minus;\u0026thinsp;2.80 to +\u0026thinsp;2.80 and with a mean difference of 1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42. If the difference, follows a normal distribution the 95% differences are expected to place in-between the average differences of the two-axis with values of \u0026plusmn;\u0026thinsp;1.96 times the SD of difference in the axis. The upper and lower limit of agreements with 95.0% confidence level (CLs) ranged from 2.05 to 3.55 and \u0026minus;\u0026thinsp;3.55 to \u0026ndash; 2.05, respectively for BG accessions. The observed mean difference of 1.86 (P\u0026thinsp;=\u0026thinsp;0.0005) indicates the presence of fixed bias moreover as the differences within mean\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 SD, two axes may be used interchangeably (Carkeet \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e3.8 Population genetic structure\u003c/h2\u003e\n\u003cp\u003eThe structure analysis assessed the most likely number of cluster (K) by calculating the Ln probability of data for each value of K = -8053.2, probability (ProbK)\u0026thinsp;=\u0026thinsp;1.00 is highest at K\u0026thinsp;=\u0026thinsp;3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eC) and \u0026Delta;K\u0026thinsp;=\u0026thinsp;104.97 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eB) approached by Evanno \u003cem\u003eet al.\u003c/em\u003e [46] generated from STRUCTURE version 2.2.3 [43] inferred by CLUMPAK beta ver. [48]. The K is a simple concept in theory, very effective practice in population genetic structure analysis and K means clusters. It is a way to analyze genetic data and the degree to which genetic variation can be partitioned into a small number of groups or clusters. Estimating K is arbitrary and involves Bayes\u0026rsquo; (Admixture) theorem. The admixture results elucidated a sharp peak \u0026Delta;K at K\u0026thinsp;=\u0026thinsp;3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eB) suggesting the 3 genetic groups, which ensure the reasonableness for clustering the 44 \u003cem\u003eV. subterranea\u003c/em\u003e accessions into three distinct populations. Considering K value (K\u0026thinsp;=\u0026thinsp;1 to 10) bar plot based on original population order (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eA), estimated membership coefficients (Q) values of each accession were displayed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eD, inferred by the Structure harvester [47]. Each vertical black line and color (red, yellow, and purple) visualized the magnitude or fraction of membership of each accession to the 3 clusters. Among the three clusters, the red color cluster mainly captured 22 accessions from the population Duna, Maikai, Cancaraki, Roko, and Bidillali, whereas the yellow color cluster majorly belonged to Exsokoto, Katawa, Karu, and Giiwa population and purple color cluster mainly occupied Jatau and Maibergo populations. Associating the result of structural analysis with phylogenetic tree (NJ and NNet) analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eD), we observed accessions that assembled in major group A in NJ and NNet method were mainly positioned into the red color cluster, major group B in the yellow color cluster and major group C in the purple color cluster in the bar plot inferred by structure harvester. The accessions with the higher membership coefficient (Q\u0026thinsp;\u0026gt;\u0026thinsp;0.60) were documented as pure one while the Q\u0026thinsp;\u0026lt;\u0026thinsp;0.60 recognized as admixture one (Wu et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the three major groups, out of 44 accessions, the red color cluster consists of 22 standard or pure accessions, the yellow color cluster consisted of 6 pure or standard accessions whereas 7 pure accessions and 9 admixture accessions combinedly generated the purple color cluster.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Genetic diversity among the population and geographical units\u003c/h2\u003e \u003cp\u003eStudy based on molecular tools such as ISSR primers will be efficient and reliable for genetic differentiation by the selection and application of primers which provide clear and sufficient knowledge needed to investigate the diversity that happens within the crop. Amplification of ISSR primers on 44 \u003cem\u003eV. subterranea\u003c/em\u003e accession representing 11 distinct populations sampled from 4 geographical regions and revealing a high degree of polymorphism. The average percent of polymorphism within the population 35.15% and the geographical region (51.08%) is lower as compared to the species level polymorphism (97.64%). The results of current study has strong evidence with the findings of Rungnoi \u003cem\u003eet al\u003c/em\u003e. [2] recorded (\u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.179, I\u0026thinsp;=\u0026thinsp;0.227) using ISSR and RAPD; Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) noted (\u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.88, \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, He\u0026thinsp;=\u0026thinsp;0.193, and I\u0026thinsp;=\u0026thinsp;0.321) ; Ismail et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) recorded (\u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09, Ne\u0026thinsp;=\u0026thinsp;1.26, \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, and \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24); Arolu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) recorded (\u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, and \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) using ISSR primers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Banding pattern and heterozygosity\u003c/h2\u003e \u003cp\u003eIn terms of band privacy, the maximum number of private bands was found for the population 1 (Duna), a moderate number of bands in Bidillali, and none in other populations makes Duna population isolated and different from other populations. The findings of this current study were an agreement with Oumer et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who analysed the banding pattern and heterozygosity of \u003cem\u003eOxytenanthera abyssinica\u003c/em\u003e using ISSR primers and noted the highest number of band patterns as 227 for the Koyshe population and expected heterozygosity as 0.219\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013 for Guba with unbiased heterozygosity 0.231\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Molecular variance study among the subdivided populations and geographical units\u003c/h2\u003e \u003cp\u003eTo partition the variation within and among the populations, AMOVA was executed, revealed higher variation present within the population and the region compared to among populations and the geographical region. This is the indication of significant divergence exist in Bambara groundnut species and the trends of our findings was advocated by Arolu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) stated 94% within and 6% among the population, Alansi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) accounted for within (90%) and among (9%) population and region (1%), Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) noted 78% within and 22% among the population, Oumer et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported 75% within and 25% among population using ISSR primers. In the current study, our observation was a greater portion of variation avail by the genotype within populations (75%) compared to among the populations (25%) which was lower than the previous observation of 98% (within populations) and 2% (among populations) was reported by Odongo et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in 105 Bambara groundnut genotypes using SSR markers. AMOVA leads to conclude that maximum variation spanned by within population of any species. However, estimation of genetic variation among the populations is crucial for \u003cem\u003eV. subterranean\u003c/em\u003e using genomic markers to select the parental material purpose to development of elite genotype through crossing (hybridization) and improvement of the breeding program, supported by Arolu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Mastan et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Genetic differentiation and gene flow\u003c/h2\u003e \u003cp\u003eThe value of calculated gene flow is categorized by Kumar et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) as Nm\u0026thinsp;\u0026lt;\u0026thinsp;1 for low, Nm\u0026thinsp;\u0026gt;\u0026thinsp;1 for medium, and Nm\u0026thinsp;\u0026gt;\u0026thinsp;4 for greater indices of gene flow. Based on G\u003cem\u003est\u003c/em\u003e mean, our observed gene flow is (Nm\u0026thinsp;=\u0026thinsp;0.9212) which was higher than gene flow Nm\u0026thinsp;=\u0026thinsp;0.0101 recorded by Tian \u003cem\u003eet al.\u003c/em\u003e (2012) and Nm\u0026thinsp;=\u0026thinsp;0.1885 noted by Asra et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) but lower than Nm\u0026thinsp;=\u0026thinsp;2.545 by Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Nm\u0026thinsp;=\u0026thinsp;2.375 by Alansi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our estimated low gene flow is the indication of low genetic migration among Bambara groundnut accessions. In our study gene flow less than 1 (Nm\u0026thinsp;=\u0026thinsp;0.9212) also indicating that our populations are subjected to low genetic drift, so if the gene flow Nm\u0026thinsp;\u0026lt;\u0026thinsp;1 (i.e., no migration) is rational to extensive differentiation due to low genetic drift (Slatkin, and Barton \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Isolation of populations leads to divergence due to genetic drift or migration lessens divergence and the harmful effects of inbreeding can be ameliorated when gene flow happens due to migration (Frankham et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, low genetic differentiation among the Bambara groundnut population may be due to the nature of the extremely self-crossing phenomenon. The lower estimate of gene flow is the indication of two populations are genetically differentiated because it has a homogenizing effect (Frankham et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The gene flow is negatively correlated with genetic differentiation within populations also influenced by the pollens and seed dispersal (Nilkanta et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nei, (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) has been categorized genetic differentiation (G\u003cem\u003est\u003c/em\u003e) as low when G\u003cem\u003est\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05, intermediate when 0.05\u0026thinsp;\u0026le;\u0026thinsp;G\u003cem\u003est\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.15, and high when G\u003cem\u003est\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.15. Subsequently, as per our findings, the G\u003cem\u003est\u003c/em\u003e coefficient of Bambara accessions (G\u003cem\u003est\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.351) indicating greater differentiation presences among the population and this result is consistent with Alansi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) recoded G\u003cem\u003est\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17 \u003cem\u003ein Ziziphus spina-christi\u003c/em\u003e L, Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed G\u003cem\u003est\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19 in \u003cem\u003eM. baccifera\u003c/em\u003e, and Kumar et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) observed G\u003cem\u003est\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31 in \u003cem\u003eJusticia adhatoda\u003c/em\u003e L. In present study the observed Ht\u0026thinsp;=\u0026thinsp;0.178 and Hs\u0026thinsp;=\u0026thinsp;0.115 of Bambara groundnut is supported by similar trends of Ht\u0026thinsp;=\u0026thinsp;01961; Hs\u0026thinsp;=\u0026thinsp;0.1639 stated by Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Ht\u0026thinsp;=\u0026thinsp;0.2708; Hs\u0026thinsp;=\u0026thinsp;0.2047 reported by Oumer et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The geographical isolation of populations strongly influenced the genetic differentiation among populations by preventing the degree of gene flow through seeds, pollen as well as human movements (Pfeifer and Jetschke \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Hypothetically, the gene flow of more than 4 migrants per generation is enough to avoid genetic differentiation among populations caused by genetic drift only (Nilkanta et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the present study, estimated low gene flow (Nm\u0026thinsp;=\u0026thinsp;0.9229)\u0026thinsp;\u0026lt;\u0026thinsp;1 ruling out the probability of inducing genetic variation among Bambara groundnut populations due to geographic and genetic distances. The above observation is also supported by the findings from the Mantel test as it exposed a significant correlation between genetic and geographic distances among the Bambara groundnut populations. However, Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) stated the genetic differentiation among populations due to geographic distances in \u003cem\u003eM. baccifera\u003c/em\u003e. The cleistogamous flowering nature and geographic isolation are the major issues of having high genetic variation in Bambara groundnut. Geographical isolation hindered the genetic migration or allele flow i.e., higher geographical distance resulting in the lower gene or allele flow (Asra et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fischer and Matthies, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, and this condition is evidenced by our estimated lower gene flow Nm\u0026thinsp;=\u0026thinsp;0.9229.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Population genetic distance vs PhiPT analysis\u003c/h2\u003e \u003cp\u003ePhiPT is a measure of estimation of intra genotypic variation as well as determination of genetic differentiation among the population (Teixeira et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our observation is consistent with Nei\u0026rsquo;s genetic distances among Bambara groundnut population from 6 different geographic regions was calculated by Rungnoi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) using ISSR and RAPD as well as Arolu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in \u003cem\u003eJatropha curcas\u003c/em\u003e using ISSR. Alansi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported the highest PhiPT (0.138) value and Nei\u0026rsquo;s genetic distances (0.0908) between populations 1 and 3 using ISSR. Nair et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in \u003cem\u003eRauvolfia serpentina\u003c/em\u003e using RAPD and Teixeira et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in \u003cem\u003eJuniperus thurifera\u003c/em\u003e L. noted the incidence of higher PhiPT genetic distances using SSR.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Population genetic distance vs genetic identity analysis\u003c/h2\u003e \u003cp\u003eObservation of extremely low genetic distances (closer to 0.00) highlights dimensions of ISSR markers to differentiate among Bambara groundnut genotypes even there is the existence of extreme close association among the populations. Henceforth, these current findings also confirmed the efficiency of ISSR primers to discriminate among the populations that are distinct, similar, or closely associated. Our findings have reliable evidence with the result of Rungnoi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported Nei\u0026rsquo;s genetic distances among the populations of 14 countries using ISSR and RAPD; Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) using ISSR, Mohammed et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) stated genetic distances of 50 Bambara groundnut varied from 0.0 to 3.8 using SSR, A similar extent of variation among the populations was reported in the previous study that related to our current genetic analysis. Massawe et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) found an identical trend of association in the Bambara groundnut diversity study using RAPD and suggested that such nature of association among the landraces indicating the genotypes were genetically identical. However, the unauthorized collection and unorganized categorize of Bambara groundnut germplasms may be the influential factors of a unique accession holding different names. The estimated genetic distances in the present study revealed minimum, medium, and maximum values related to the report of Massawe et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) by RAPD and Mohammed et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) using SSR in \u003cem\u003eV. subterranea\u003c/em\u003e. These differentiations are caused by the profiles of landraces evaluated in this research which are comprised of pure lines developed from single plant selection and use of germplasms embraced to the mixture of different seed morphotypes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Genetic relatedness\u003c/h2\u003e \u003cp\u003eThe UPMGA method revealed two major groups of the entire population. In the present study, a substantial genetic variation was disclosed among the populations which are consistent with the result of Rungnoi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported significant variation among Bambara groundnut accessions of 5 geographical origins whereas Mohammed et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) described the variation presences among 50 genotypes of Bambara groundnut. Besides this Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found countable variation among 7 populations of \u003cem\u003eM. baccifera\u003c/em\u003e; Oumer et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) concluded significant variation exists among 13 populations of \u003cem\u003eOxytenanthera abyssinica\u003c/em\u003e while Alansi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) recorded the variation of 4 population of \u003cem\u003eZiziphus spp.\u003c/em\u003e The information of three major clusters and six sub-clusters in this study is a decent sign of the higher richness among the accessions related to other previous studies of Bambara groundnut such as those reported by Mohammed et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) as 7 clusters of 50 accessions while Odongo et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) stated 3 major clusters of 105 accessions using SSR markers and Massawe et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) grouped the 12 landraces into two clusters using RAPD. Besides these, Olukolu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e grouped in 4 distinct clusters of 124 Bambara groundnut accessions using DArT, Siise and Massawe, (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted 17 units of 80 Bambara groundnut accessions using SSR, Rungnoi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) classified 363 Bambara groundnut accessions into two major groups using 65 RAPD and ISSR loci, whereas Mukakalisa et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) illustrated genetic relationship by clustering 13 landraces of \u003cem\u003eV. subterranea\u003c/em\u003e using RAPD primers. Moreover, Nilkana \u003cem\u003eet al.\u003c/em\u003e (2017) grouped 93 individuals of \u003cem\u003eM. baccifera\u003c/em\u003e populations into distinct clusters while Dos Santos et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported 11 clusters of 45 \u003cem\u003ePassiflora\u003c/em\u003e spp based on Nei\u0026rsquo;s distance using ISSR primers. Fatimah and Ardiarini, (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) grouped 12 accessions of \u003cem\u003eV. subterranea\u003c/em\u003e into two clusters based on similarity indices. Stavridou et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used the UPMGA radial tree to illustrate the relatedness of pea populations based on ISSR primers. Our study reflected that most of the accessions under identical populations hold the same subgroup such as all 4 accessions under the population of Duna, Maikai, Roko, Exsokoto, Katawa, and Giiwa. The accessions were diverse from the major cluster in the dendrogram depicting less genetic relatedness to the remaining populations present in the major cluster. The individual genotype under the same population was more closely associated genetically than those of other close populations. Due to the cleistogamy system of Bambara groundnut, in all of the cases, we found some divergence of plants among populations but within the population, we observed a higher degree of divergence which may be pronounced in the accession with seeds collected from different agroecological zones. The accessions from Cancaraki, Maibergo, Bidillai, Jatau, and Karu showed more inconsistency due to their diverse position in the dendrogram which is uncommon, the sharing of alleles among these species is most probably due to the random effect or evolutionary aspects. Generally, the ISSR marker consent to the broad-spectrum separation among \u003cem\u003eV. subterranea\u003c/em\u003e, indicating high genetic dissimilarity among them.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Genetic Relationship based on NJ and NNet analysis\u003c/h2\u003e \u003cp\u003eGenetic relatedness among 44 \u003cem\u003eV. subterranea\u003c/em\u003e genotypes was illustrated by an unrooted phylogenetic tree based on the NJ and NNet method of analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Both the approaches clustered the accessions into distinct groups, most of which are coincident with geographical zones of collection. To infer the evolutionary bonding of accessions unrooted phylogenetic tree based on genetic dissimilarity was performed (Huson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). As a result, to elucidate the visualized contradictory indicator in the data matrix, either derives from sampling error or sincere recombination NNet phylogenetic network analysis was executed (Wu et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In our study, the NNet analysis clustered the accessions into six subgroups under three main groups, a similar grouping pattern was reflected by NJ analysis. Further, we observed some discrepancies between the unrooted NJ and NNet phylogenetic tree approaches in grouping a few accessions. As for evidence, the unrooted NJ tree arrested 7 genotypes (G23, G24, G25, G26, G27, G28, and G29) constituting the subgroup \u0026lsquo;d\u0026rsquo; whereas the NNet analysis captured the genotypes G23, G24, G25, and G34 together remarked as subgroup \u0026lsquo;c\u0026rsquo; although G34 was taken a position in a unique subgroup by NJ method. Our findings are validated by Wu et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) when studied 33 plum varieties using ISSR primers. Additionally, the findings of NNet phylogenetic analysis are motivated to territoriality compared to NJ phylogenetic analysis, as the NNet analysis based on split networks enhances extra topology associated factors during the phylogenetic study (Huson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Usually, genetically nearness accessions are often clustered in an identical group whereas the accessions that were not grouped may be due to geographic blockade resulting in genetic divergence which is also supported in the case of the study by Schic \u003cem\u003eet al.\u003c/em\u003e (2015). For adaptation with genetic variation, the constant and natural selection stress could be a vital aspect within the same region. However, the association among geographical factors and genetic variation within the accessions was also elucidated via molecular tools, isozymes, as well as morphological analysis (Wu et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Factorial analysis\u003c/h2\u003e \u003cp\u003ePrincipal coordinates analysis (PCoA) employs eigen analysis to determine the principal axes and computes a sequence of eigenvalues and eigenvectors. Eigenvalues are commonly ordered from largest to lowest. The PCoA analysis in our study revealed that PC1 (15.05%) and PC2 (5.81%) recorded the most variance, which is corroborated by Rungnoi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) who reported 90.3% variance headed by the top three PCs using PCoA analysis in Bambara groundnut. Using SSR markers, PCoA accounted for 84.3% covered by the first three principal components (Odongo et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), whereas the first two principal components showed 37.30% (DArT) and 19.50% (SSR) when PCoA analysis was applied to Bambara groundnut accessions (Molosiwa et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We discovered considerable variance in accession by region-based partitioning using factorial analysis, and this inference is supported by the earlier results obtained by Odongo et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) used SSR markers to discover a similar trend of divergence in 105 Bambara groundnut populations from various regions. The principal co-ordinate analysis is a data minimization approach that groups and separates correlated variables from those that have little or no association (Khan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021d\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Population genetic structure analysis\u003c/h2\u003e \u003cp\u003eThe admixture model-based analysis was executed to illustrate the genetic structure of 44 Bambara groundnut accessions sampled from 11 populations of different agro-ecological zones. Based on the likelihood of accession sampled from a certain population our result suggested that accessions are composed of three distinct genetic components. Therefore, in the case of accessions assortment, there was little inconsistency was observed between the output of structure analysis (best delta K\u0026thinsp;=\u0026thinsp;3) and grouping of accessions by phylogenetic tree (NJ and NNet) analysis. Determination of delta K is an \u003cem\u003ead hoc\u003c/em\u003e quantity related to the second-order rate of change of the log-likelihood of data related to the number of clusters (Zimisuhara et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Bar plot depicts those 35 accessions were comparatively unique or genetically pure based on Q\u0026thinsp;\u0026gt;\u0026thinsp;0.60 relations to that of unique standard (Wu et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), indicating that these accessions might have an inadequate genetic drift to other accessions. On the other hand, accessions G33 of population Giiwa and G31, G32 from Katwa had Q\u0026thinsp;\u0026lt;\u0026thinsp;0.60 saying that they are either genetically admixed or govern by genes of other accessions. This finding is consistent with the similar trend of results reported by Olukolu et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported ΔK\u0026thinsp;=\u0026thinsp;4 using DArT assay of 40 Bambara groundnut genotypes whereas Rungnoi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) estimated ΔK\u0026thinsp;=\u0026thinsp;2 using ISSR and RAPD in 363 Bambara groundnut genotypes. There are some other related researches such as Wu et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found ΔK\u0026thinsp;=\u0026thinsp;3 using ISSR, Nilkanta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found ΔK\u0026thinsp;=\u0026thinsp;3 using ISSR, Zarei and Erfani-Moghadam \u003cem\u003eet al.\u003c/em\u003e (2021) found ΔK\u0026thinsp;=\u0026thinsp;3 using SCoT, Barbosa et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found ΔK\u0026thinsp;=\u0026thinsp;3 using ISSR, Zimisuhara et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found ΔK\u0026thinsp;=\u0026thinsp;2 using ISSR, Li and Zhang, (2020) found ΔK\u0026thinsp;=\u0026thinsp;2 using ISSR strongly advocated our current investigation using \u003cem\u003eV. subterranea\u003c/em\u003e accessions. The population belong to the yellow color cluster was more complex compared to that of the purple color cluster, the fact is due to the incorporation of genetic material from the population of the red color cluster (G41, G43, and G44) and purple color cluster (G36, G38, and G42).\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThe present research is the first initiative on the valuation of genetic structure and differentiation in \u003cem\u003eV. subterranea\u003c/em\u003e accessions using molecular markers in Malaysia. ISSR primers are widely used to reveal the genetic variation, structure, and relatedness of a crop species. The result of our investigation proved the efficiency of ISSR primers revealing the significant level of differentiation among the accessions of \u003cem\u003eV. subterranea\u003c/em\u003e. The detection of polymorphism and banding patterns in our study is considerably accountable. Based on data generated from ISSR markers we found a broad-spectrum differentiation among the population sampled from the Gombe region. Accessions from the Jatau population had a maximum variation with a range from 30.98\u0026ndash;41.57%. Neighbor-joining and split clustering result validated by Bayesian clustering based on structure analysis revealed three genetic clusters in which accessions belong to cluster 1 (red) were highly pure one at standard likelihood value Q\u0026thinsp;\u0026gt;\u0026thinsp;0.60 though, cluster 2 (yellow) and cluster three (purple) composed of both pure and admixture accessions. In our study low genetic variation among the population compared to within-population couples with significant diversity at species level suggested that the emphasis should be needed to conserve and protect the existing accessions of \u003cem\u003eV. subterranea\u003c/em\u003e. However, for gaining efficient, reliable, and precise inference dominant marker couple with codominant marker is strongly recommended. To broaden the genetic variation for breeding new cultivars of this crop mutation and inter- genotypic hybridization are also suggested. Furthermore, these current findings will provide a sharp knowledge of the accessions evaluated, enrich the gene pool, and strengthen the future breeding program for this crop improvement.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDNA: Deoxy ribonucleic acid; RNA: Ribonucleic acid; ISSR = Inter simple sequence repeat; NJ: Neighbour joining; NNet: Neighbour network; PCR: Polymerase chain reaction; RAPD: Random Amplified Polymorphic DNA; RFLP: Restriction fragment length polymorphism; AFLP: Amplified fragment length polymorphism; GPS: Global positioning system; CATB: Cetyl trimethylammonium bromide; EDTA: Ethylenediaminetetraacetic acid; PCoA: Principal coordinate analysis; MVSP: Multivariate statistical packages; UPMGA: Unweighted Pair Group Arithmetic Mean, MEGA: Molecular Evolutionary Genetic Analysis; AMOVA: analysis of molecular variance; MCMC: Markov Chain Monte Carlo; CLUMPAK: Cluster Markov Packager Across K\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this article and full length of gel or blots are represented in supplementary file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict interests in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBangladesh Agricultural Research Council (BARC- Project of NATP Phase-II), The People\u0026rsquo;s Republic of Bangladesh, World Bank, IFAD, and Universiti Putra Malaysia (research grant: vote number 6282518).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.M.H.K. and M.Y.R. created the paper\u0026apos;s concept, design, and methodology. M.M.H.K. collected the data. M.M.H.K. performed statistical analysis, used software, and interpreted the results. M.M.H.K. wrote the first draft and prepared the text. M.Y.R. is in charge of supervision. S.I.R. and M.J. investigate the situation. M.M.H.K., B.C.K., and M.A.M. wrote the article; they also reviewed and edited it. The final, published version of the paper has been reviewed and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Bangladesh Agricultural Research Council (BARC- Project of NATP Phase-II) and Bangladesh Agricultural Research Institute (BARI) of the People\u0026apos;s Republic of Bangladesh are gratefully acknowledged by the writers. Another deserving of praise is Malaysia\u0026apos;s University Putra Malaysia (UPM).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlansi, S., Tarroum, M., Al-Qurainy, F., Khan, S., \u0026amp; Nadeem, M. (2016). 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Structure and principal components analyses reveal an intervarietal fusion in Malaysian mistletoe fig (Ficus deltoidea Jack) populations\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Vigna subterranea L., Banding pattern, Population structure, Phylogenetic linkage, and Genetic diversity","lastPublishedDoi":"10.21203/rs.3.rs-2678771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2678771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA set of 44 selected Bambara groundnut (\u003cem\u003eVigna subterranea\u003c/em\u003e L. Verdc.) accessions was sampled from 11 distinct populations of four geographical zones to assess the genetic drift, population structure, phylogenetic relationship, and genetic differentiation linked with ISSR primers. In Malaysia, this is an exotic legume introduced from Africa and having tremendous nutritional values and diverse usages.\u003c/p\u003e\u003ch2\u003eMethods and Results\u003c/h2\u003e \u003cp\u003eThe amplification of genomic DNA with 32 ISSR markers detected an average of 97.64% polymorphism while 35.15% and 51.08% polymorphism per population and geographical zone, respectively. Genetic diversity estimated by Shannon\u0026rsquo;s information index (\u003cem\u003eI\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;0.177 (average) and populations under Gombe showed maximum diversity (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.271) with 90.98% polymorphism. Analysis of molecular variance revealed significant variation within population 75% and between population 25% whereas within region 84% and between region 16%. The study also divulged total genetic variation \u003cem\u003eHt\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1781 closer to within population diversity (\u003cem\u003eHs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1155). Among the population, Cancaraki revealed 40.39% polymorphism while the average polymorphism was 35.15%. The Bidillali exposed greater number of locally common band i.e., NLCB (\u0026le;\u0026thinsp;25%)\u0026thinsp;=\u0026thinsp;25 and NLCB (\u0026le;\u0026thinsp;50%)\u0026thinsp;=\u0026thinsp;115 were shown by Cancaraki while the lowest was recorded as NLCB (\u0026le;\u0026thinsp;25%)\u0026thinsp;=\u0026thinsp;6 and NLCB (\u0026le;\u0026thinsp;50%)\u0026thinsp;=\u0026thinsp;72 for Roko and Maibergo, accordingly. The highest PhiPT value was noted between Roko and Katawa (0.405*) whereas Nei\u0026rsquo;s genetic distance was maximum between Roko and Karu (0.124). The genetic differentiation among population Gst\u0026thinsp;=\u0026thinsp;0.3514 (35.14%) leaving 65.86% of genetic variation leads to within-population with gene flow of \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9229. Based on Nei\u0026rsquo;s genetic distance, a radial phylogenetic tree was constructed that assembled the entire accessions into 3 major clusters for further confirmation unrooted NJ vs NNet split tree analysis based on uncorrected P distance exposed the similar result. Principal coordinate analysis showed variation as PC1 (15.04%)\u0026thinsp;\u0026gt;\u0026thinsp;PC2 (5.81%). Mantel test exposed a significant correlation among genetic and geographic distance of accessions. STRUCTURE analysis (Bayesian) grouped the accessions into 3 major genetic components based on best ΔK\u0026thinsp;=\u0026thinsp;3 and admixture population.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe current study leads to prompting the genetic improvement and future breeding program by maximum utilization and better conservation of existing \u003cem\u003eV. subterranea\u003c/em\u003e accessions in this subtropical environment.\u003c/p\u003e","manuscriptTitle":"Molecular Insight into the Genetic structure and Banding pattern Analysis of Bambara groundnut (Vigna subterranea L.) with Random Amplified Microsatellites (RAMs)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-24 14:27:10","doi":"10.21203/rs.3.rs-2678771/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2023-04-20T07:31:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-03-22T06:12:39+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-14T17:36:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2023-03-12T12:23:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d2f129a2-987a-4f9b-ab20-24150b9048c7","owner":[],"postedDate":"March 24th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T22:10:55+00:00","versionOfRecord":{"articleIdentity":"rs-2678771","link":"https://doi.org/10.1007/s11033-023-08693-x","journal":{"identity":"molecular-biology-reports","isVorOnly":false,"title":"Molecular Biology Reports"},"publishedOn":"2023-08-02 21:51:13","publishedOnDateReadable":"August 2nd, 2023"},"versionCreatedAt":"2023-03-24 14:27:10","video":"","vorDoi":"10.1007/s11033-023-08693-x","vorDoiUrl":"https://doi.org/10.1007/s11033-023-08693-x","workflowStages":[]},"version":"v1","identity":"rs-2678771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2678771","identity":"rs-2678771","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}