Characterization and population structure assessment of Indian Common bean using Microsatellite Markers

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Jugran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1726098/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Common bean ( Phaseolus vulgaris L. ) is an important crop of family Fabaceae which is used as a potential source of proteins, fibres and minerals across the globe. The Indian Himalayan region harbours plentiful varieties of common bean, but it is nearly unexplored till date. The present study was attempted to assess genetic diversity and population structure of 119 common bean samples belonging to 20 diverse accessions gathered from Uttarakhand, India using newly designed chloroplast microsatellite (SSR) markers. A total of 218 polymorphic alleles were identified for 8 SSR loci. Mean number of alleles per locus (Na = 1.55), effective allele number (Ne = 1.370), Shannon information index (I = 0.313), expected heterozygosity (He = 0.213) and average polymorphic loci (10.9) were estimated based on Cp-SSR data. Maximum genetic diversity (He) was recorded in the Jhalla accession where it was found minimum in the accession collected from Supi. Bayesian-based STRUCTURE evaluation using SSR based information showed that 20 bean accessions were genetically separated into two main clusters. Also, the genetic distance-based cluster analysis separated 20 common bean accessions using these markers into 2 major clusters corresponding Mesoamerican and Andean. These Cp-SSR markers also demonstrated transferability among Fabaceae members like Vigna radiata , Macrotyloma uniflorum , Glycine max, Vigna mungo in this study. The findings from the study can be used to select better accessions of the bean for production, conservation, and future breeding programs. Likewise, markers displaying transferability can be used for monitoring the genetic heterogeneity of other members of the family Fabaceae. Common bean Accessions Genetic diversity Polymorphic Information Content Transferability Indian Himalayan Region Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Phaseolus is a large genus of the family Fabaceae comprises about eighty species in the world (Ulloa Ulloa et al. 2018 ; Chacón-Sánchez et al. 2021 ). Five species namely, Phaseolus vulgaris L. (Common bean), Phaseolus lunatus L. (Lima bean), Phaseolus acutifolius A. Gray (Tepary bean), Phaseolus coccineus L. (Runner bean) and Phaseolus dumosus or Phaseolus polyanthus Greenman (Year bean) are the dominant species of this family (Mina-vargas et al. 2016 ; Nadeem et al. 2018 ). The common bean is most important species of this genus believed to be domesticated approximately 8,000 years ago in Central America as its geographical origin (Chacón-Sánchez et al. 2021 ). After domestication, the species was introduced in other parts of the world by the Portuguese traders. Two major diverse gene pools of common bean have been reported- Mesoamerican and Andean (Angioi et al. 2010 ). The Mesoamerican gene pool has been distributed from Mexico throughout the Central America, into Columbia and Venezuela, while the Andean gene pool has its distribution throughout the southern Peru, Chile, Bolivia and Argentina (Rana et al. 2015 ; Chaudhary et al. 2017). In India, common bean was dispersed mainly by Portuguese, English, Dutch, and French traders in the beginning of the 16th century via the Red and Arabian Seas and Chinese through the Hindustan Silk Route (Rana et al. 2015 ). Since then, the species has undergone an adaptive evolutionary process for approximately 400 years in these areas (Westphal 1974 ; Chaudhary et al. 2017). The morphological, biochemical, and molecular evaluation of common bean revealed that the Mesoamerican originated wild community differs from the Andean (Desiderio et al. 2013 ). These two original gene pools are morphologically diverse from each other, in seed size and some other variations (Angioi et al. 2010 ). Another differentiating factor is the S or T type of phaseolin protein found in the seeds of common bean (Chaudhary et al. 2017). Common bean (also known as kidney bean or Rajmash) is one of the most ancient legumes cultivated over a 90% growing area globally for its edible seeds as dry and green beans (Celmeli et al. 2018 ). The global annual production of bean grain is about 31 million tons and Brazil is the leading producer of common bean, followed by India, Myanmar, China, United States and Mexico (FAOSTAT 2020; Delfini et al. 2021 ). In India, Uttarakhand produces 40.22 tonnes of common bean and acquires 16th position at national level [National Horticulture Board (NHB), 2017–2018]. Immature pods of this species, known as snap beans, are eaten as vegetable while the straw from the plant is utilised as fodder. Common bean is consumed worldwide as a major reservoir of protein, dietary fibres and microelements (Broughton et al. 2003 ; Głowacka et al. 2019 ). It is highly proteinaceous (14–33%) and consumed preferentially as a vegetarian protein reserve (Venkidasamy et al. 2019 ; Flores-sosa et al. 2020 ). In addition, common bean is a prosperous resource of iron, zinc, folic acid, potassium, phosphorous, magnesium, manganese, selenium etc (Broughton et al. 2003 ; Petry et al. 2015 ). Besides this, diverse essential amino acids like phenylalanine + tyrosine (53–105 mg/g), lysine (10–104 mg/g) and leucine (14–92 mg/g) have been recorded from common bean. Moreover, the species is deficient in the sulphur containing amino acids: methionine + cysteine (Moraes et al. 1971; Flores-sosa et al. 2020 ). Generally, common bean is a self-fertilizing crop grown in tropical, semi-tropical, and temperate regions of the world (Wang et al. 2012 ; Gupta et al. 2019 ). In India, common bean has been cultivated in the plains of Uttar Pradesh, Andhra Pradesh and Maharashtra during autumn, and in hilly areas such as Uttarakhand, it is grown as a kharif crop during summer and winter (Sharma et al. 2014). Many well-known landraces named Auli, Harshil and Munsiyari have been cultivated in the high mountain regions of Uttarakhand which are morphologically distinct and possess remarkable adaptability to local environmental settings (Rana et al. 2015 ). Although the genetic characterization of a few local accessions/varieties of common bean gathered from Pantanagar (Uttarakhand) have been investigated using microsatellite markers (Kumar et al. 2013 ), local accessions of the species from the entire Uttarakhand have not been evaluated systematically and scientifically till date. Therefore, the present study is attempted (i) to design and validate new chloroplast markers, (ii) to measure the magnitude of genetic polymorphism, diversity, and population composition among common bean accessions, and (iii) to investigate the cross transferability among the selected members of the genus. Material And Methods Plant materials Altogether, 119 genotypes of twenty diverse common bean accessions were gathered from local farmers' fields in Uttarakhand, India and subjected to further study (Table 1; Fig. 1). Local pulses such as Vigna radiata (Mung bean), Macrotyloma uniflorum (Gahat), Glycine max (Soybean), and Vigna mungo (Black gram) were also collected from the farmers of Jakholi, Rudraprayag, Uttarakhand (Longitude- 78°53’33.57” °E; Latitude- 30°23’33.02 °N; Altitude 1792) and used for further study. Common bean seeds collected from various locations were brought into the laboratory and sown in a seedling tray. After 33 days of seed sowing [seed sowed in off season (December], the fresh young juvenile leaves were harvested, washed, and used for further experimentation. Chloroplast microsatellites (cpSSR) marker designing The complete common bean chloroplast genome (Guo et al. 2007) was retrieved from NCBI (National Center for Biotechnology Information) and the MISA (Microsatellite Search Module) a web based server was implemented for the identification of SSRs (Thiel et al. 2003). The minimum length threshold condition applied to investigate SSRs was dinucleotide (6 bp repeats), trinucleotide (4 bp repeats), and tetranucleotide (3 bp repeats), while mononucleotides (5 or 6 nucleotide repeats) were debarred from the evaluation. Ideal and compound SSRs were determined by means of the MISA pipeline. The repeat sequences were interrupted by non-repeat sequences (100 bp) in the case of compound SSRs. The Batch Primer-3 version 1.0 programme developed by You et al. (2008) was accustomed to design primers flanking repeated regions of chloroplast SSR loci in the chloroplast genome (You et al. 2008). The cpSSR primers were developed using parameters as follows: (i) primer length ranged from 18 to 23 bp; (ii) product size ranged from 100–300 bp; and (iii) an optimum GC content of 50% with a range of 40%–70% (Table 2). The identified chloroplast-based markers were designated as PV_cpSSR ( Phaseolus vulgaris chloroplast simple sequence repeats) primers. DNA isolation and cpSSR marker amplification The fresh, young, juvenile leaves at the apex of the plant were used for genomic DNA extraction using the CTAB (Cetyl tri-methyl ammonium bromide) buffer assay with slight alterations (Jugran et al. 2013a & b). The quality of extracted DNA samples was examined by electrophoresing on an agarose gel (1%) prepared in 0.5 X TBE (Tris-Cl, Boric acid, EDTA) buffer. DNA fragments were visualised under the gel documentation system (I Gene Labserve, India) using a standard DNA ladder. DNA of common bean was amplified through PCR reaction using 20 μL of reaction mixture having 1μL template DNA, 10mM DNTPs, 10 picomole of each forward and reverse primer, 1U Taq polymerase, 10X PCR buffer and 25mM MgCl 2 by following Sharma et al. (2019). All reactions were conducted in a thermocycler (Biometra, Germany) using the following reaction conditions: early denaturation for 2 min at 94 °C, 35 cycles of denaturation for 30 seconds at 94 °C, 1 min annealing at 55 °C, 2 min extension at 72 °C, and final extension at 72 °C for 5 min. PCR products (amplicon) were qualitatively analysed on a 3% agarose gel (Bio Rad, USA) and fragment size was estimated using the 100 bp DNA ladder (O’ Gene Ruler, Hi Media) as a reference (Figure S1a). A total of fifteen newly designed cpSSR markers were utilised for DNA amplification. Of which only eight Cp-SSR markers showed polymorphism after initial screening and these eight primer pairs were used for final amplification of common bean samples. Likewise, the cross-transferability of cp-SSR markers among other Fabaceae members namely Vigna radiata (Mung bean), Macrotyloma uniflorum (Gahat), Glycine max (Soybean), and Vigna mungo (Black gram) were also evaluated using the same method (Fig. S1b). Data analysis Out of 15 primer pairs screened, only 8 primer pairs exhibited polymorphism and produced clear and reproducible fragments, and therefore used for final study. A total of 119 genotypes of 20 common bean accessions from Uttarakhand were subjected to detect polymorphism at genetic level using eight primer pairs. Markers that produced the same sized fragments throughout the common bean were designated as mono-morphic, while cpSSRs producing varied fragments were considered polymorphic. A genotype that does not cause any type of amplification under standard circumstances is considered a null allele. The polymorphic information content (PIC) of each marker was measured using the following formula mentioned in Anderson et al. (1993): PIC= 1- Ʃ p 2 ij , where p ij is the frequency of the patterns ( j ) for each cpSSR marker ( i ) Markers showing polymorphism were utilised to assess various attributes related to variability in common bean. The SSR fragments were scored on the basis of the occurrence (1) or non-occurrence (0) in all the samples examined. The binary data of each sample from diverse accessions was investigated using POPGENE version 32 (Yeh et al. 1999). The matrix was used to determine polymorphic loci number (Np), polymorphic loci percent (Pp%), number of observed alleles (Na), effective alleles per locus (Ne), heterozygosity (He= Nei’s gene diversity), Shannon’s information index (I), gene flow estimation (Nm), and genetic differentiation (Gst) using the POPGENE program. The PHYLIP version 3.68 software was employed to create a dendrogram of studied accessions by first transforming binary data into PHYLIP format, followed by generating matrices using the GENEDIST program. Ultimately, the NEIGHBOR program, followed by the CONSENSE programme from PHYLIP was employed to create an unrooted phylogenetic tree (Felsenstein 1995). A principal coordinate analysis (PCoA) was executed to evaluate the genetic relationship between accessions based on Nei genetic distance using a covariance standardised PCoA method in GenAlEx 6.5 (Peakall and Smouse 2006, 2012). An analysis of molecular variance (AMOVA) was performed to partition hereditary differences among accessions using the above-mentioned program. Cross-transferability and fragment size produced with cpSSR markers were recorded for all studied species. The degree of mixing and population composition was estimated based on the Bayesian clustering algorithm by using STRUCTURE software version 2.3.4 (Pritchard et al. 2000). Subpopulations within common bean samples were measured by 3 independent interactions as K values from 1 to 10 initially with different interactions and a burning length period to remove additional load on the computer. Final analysis was performed at 100,000 interactions with a 300,000-burn period using 20 interactions of K1 to 5 by employing an admixture model throughout the previously allocated population as a sampling location and with the frequency of linked alleles among populations. The numbers of best possible K groups were determined by estimating ΔK parameter using Structure Harvester program (Earl and von Holdt 2012) as projected by Evanno’s method (Evanno et al. 2005). Results Data mining and cp-SSR marker development Common bean chloroplast genome (NCBI Reference Sequence: NC_009259.1) was loaded from National Center for Biotechnology Information (NCBI) and used for mining of SSRs. Fifteen primers were developed which qualified different parameters during the process, and all the selected primers were synthesised and used for validation of amplification in 119 individuals from 20 common bean accessions. With the availability of complete genomic sequences of common bean and co-dominant property, ease of use, repeatability, and multi-allelic nature establish SSRs as the preferred marker of choice to measure genetic inconsistency (Kumar et al. 2006; Matondo et al. 2017). Genetic variability of common bean Eight Cp-SSR primer pairs were utilized for DNA amplification of 119 individuals of common bean. Average number of polymorphic loci was recorded 10.9 from 119 individuals of common bean. The study showed that marker PV_cpSSR3 was reasonably informative (0.25<PIC<0.5) while the remaining seven markers were highly informative in nature (0.50.75). The PIC value of these primers ranged from 0.428 (PV_cpSSR3) to 0.817 (PV_cpSSR12), with a mean of 0.615 (Table 2). Further, the allele size varies from 112 to 368 bp (Table 2). Highest (80%) polymorphic loci percent was detected in Harsil accession and lowest (40%) in Khati & Natwar accessions using Cp-SSR markers. The Na ranged from 1.40 (Khati & Natwar accessions) to 1.80 (Harsil accession) with a mean of 1.55, Ne ranged from 1.229 (Supi accession) to 1.505 (Harsil accession) with a mean value of 1.370, I varied from 0.223 (Supi accession) to 0.431 (Harsil accession). Heterozygosity (He) was recorded at its maximum (0.292) in the Jhalla accession while it was observed at its lowest (0.145) in the Supi accession (Table 3). Genetic differentiation and gene flow Total genetic variation was partitioned by subjecting the common bean samples to evaluate the molecular variance (AMOVA) by estimating the variance among populations and within populations. The high within population variation (99%) was recorded as compared to the among (1%) population variation (Table 4). These findings were somewhat complemented by the G ST (0.264), which also demonstrated high within population diversity (73.6%) in the studied accessions. The gene flow level was observed to be 1.396 among common bean accessions under the study. Genetic relationships and population composition The pair-wise genetic dissimilarity between accessions of common bean was evaluated to measure the relationship between accessions using Nei’s approach (Nei’s 1978). A maximum (0.1977) genetic distance was recorded between Jatoli and Harsil accessions, while it was minimum (0.0173) between Lata and Jumma accessions (Table S2). The dendrogram construction through the neighbor joining method separated all studied samples into two main groups: group A and B. Group A encompasses the sample gathered from Jhalla, while Group B contains the remaining 19 accessions. Group B was further separated into two sub-clusters, BI and BII (Fig.3). To estimate the genetic structure, PCoA analysis showed distribution of 20 accessions in a three dimensions space constructed on genetic distances. The genetic variance percentage defined by all three PCoA coordinates in the study was 8.57%, 16.73%, and 24.44% (Fig. 4). In order to culminate the possible genetic populations, 119 genotypes were assessed by structure software using an admixture model. Evano’s ΔK statistics were utilised to select the ideal K value based on the increase in possibility ratios between runs. The ideal sub-populations were found to be K = 2 (Fig.2). Based on K = 2 groups, population structure investigation exhibited that all common bean samples were distributed into 2 major groups. The relationship among populations in the two components is similar to the clustering pattern obtained in the study. Population structure of common bean relies on binary data obtained using cpSSR markers, was investigated by Pritchard et al. (2000) method and demonstrated that the log likelihood approximations increased regularly as K increased and started to decrease when K = 2 (Figure 2 a). A mean log likelihood plot is prepared by placing values over 10 runs for K values ranging from 1 to 5. The optimum K value was 2 as assessed by the ΔK statistic STRUCTURE based on employed markers (Figure 2 b). It was observed that the best possible subgroup number was reasonably low as compared to the total studied accessions, exhibited extensive gene flow levels, either presently or historically. At probability threshold (Q) of 0.60 using cpSSR marker’s structure analysis, generally the samples were visibly isolated to a definite cluster. Of which, 39 individuals (32.773%) were dispersed among cluster-1 and 76 individuals (63.866%) were found in cluster-2. No clear pattern of allocation of the individuals of the studied accessions was detected in Cluster-1 and Cluster-2, depending on the threshold of 60% in structure analysis. Taxon analysis and marker transferability Four commonly growing pulse species namely, Glycine max , Macrotyloma uniflorum , Vigna mungo, and Vigna radiata along with common bean samples were evaluated for genetic variability and transferability using cpSSR markers designed for the aforementioned study. In sum, eight markers displayed polymorphism in the samples and the existence of transferability in the studied samples (Fig. S1). Discussion Identification of genotypic variations and their underlying structure is imperative for future breeding programs and conservation of plant genetic resources (Savic et al. 2020). Therefore, the development of molecular tools for characterizing genetic variability is indispensable as it significantly enhanced the effectiveness of utilization of accessible genetic resources. Despite of the diverse classes of currently available markers, knowledge of their genetic informative potential is critical to exploit the germplasm diversity. SSRs are used to examine the genetic diversity and linkage mapping, among other aspects, has been widely explained in the literature (Angioi et al 2008 ; Vidak et al. 2017 ). For the common bean, the present work is the first to assess the genetic diversity and structure of common bean from Uttarakhand (India) using SSR markers. They were found to be reasonably effective in assessing the genetic diversity of common bean genotypes. In Indian Himalayan Region common bean is one of the extensively cultivated legume crops. Prerequisite information about the genetic multiplicity of common bean is essential to achieve effective breeding programs. Among molecular markers, SSR marker is considered accurate and reliable tool to characterize genetic variability among legume crops. The cp-SSR markers used to assess 119 individuals from 20 accessions in this study amplified a total of 218 polymorphic alleles ranging from 8 (Khati and Natwar) to 16 (Harsil) polymorphic alleles per loci with a mean of 10.9. These findings are in agreement with the previous study in which 138 genotypes from Jammu and Kashmir and one variety from VPKAS, Almora, India was investigated (Mahajan et al. 2016 ). The study of Valentini et al. ( 2018 ), reported a mean of 4 alleles per SSR locus when studying the 109 accessions from Brazil, whereas genetic assessment of 102 genotypes from Jammu, Kashmir and Ladakh, India, Bashir et al. ( 2020 ) demonstrated the incidence of 30 alleles per locus of SSR. The reason for a smaller number of alleles as compared to Bashir et al. ( 2020 ) might be the due to the use of chloroplast SSRs in our study, as genomic SSRs can resolve within gene pool variation. In present study, the percentage of polymorphic loci ranged from 40 to 80% for different SSR primers with a mean of 54.5% which is approximately parallel to (66.7%) the study of Hegay et al. 2012 . Percentage of polymorphic loci may vary from 0 (Hegay et al. 2012 ) to 100% (Asfaw et al. 2009 ). Furthermore, Pp% was found to be 83.33% in an ISSR (Inter Simple Sequence Repeats) marker-based study involving 28 accessions from Jammu and Kashmir, India (Dar et al. 2016 ). Current study showed average gene diversity (expected heterozygosity) as 0.213, which is nearly similar to other studies on common bean (Mercati et al. 2013 ; Pratap et al. 2016 ; Mahajan et al. 2016 ; Matondo et al. 2018). However, expected heterozygosity was observed lower as compared to the investigation of Bilir et al. ( 2019 ) in 102 genotypes from Turkey and study of Mir et al. ( 2021 ) in 96 genotypes from Jammu and Kashmir (India). Higher heterozygosity in Bilir et al. ( 2019 ) and Mir et al. ( 2021 ) is because of the abundant sampling of common bean genotypes. PIC is the distinguishing ability of a particular marker, predominantly based on alleles per locus and allele frequency in studied germplasm (Mercati et al. 2013 ; Suvan et al. 2019 ). As stated by Bashir et al. ( 2020 ), SSR-based PIC values can be exploited to detect the capability of the marker to detect genetic multiplicity. The PIC value was found to be 0.615 which was in order with the value (0.634) reported by Mahajan et al.2016 in Indian germplasm and Matondo et al. ( 2017 ) in DR- Congo germplasm. It was found lesser than the earlier studies of researchers from Mizoram, India (Dutta et al. 2015) and Jammu & Kashmir and Ladakh, India (Bashir et al. 2020 ). The lower PIC values are recorded from closely associated genotypes and higher values for genetically distant genotypes. On the other hand, the PIC in the present study was found to be higher as compared to six traditional common bean varieties studied in Himachal Pradesh, India (Sharma et al. 2014) and 135 genotypes studied from northern India (Gupta et al. 2020 ). Based on the studies of Bashir et al. ( 2020 ), the high level of polymorphism is due to huge diversity among genotypes and selection of highly polymorphic markers. In addition to support this, Sharma et al. (2014) stated that a marker with a PIC ranging from 0.3 to 0.8 is considered functional for the measurement of genetic differences in the population. The present research on common bean displayed a PIC range from 0.428 to 0.817, indicating that microsatellite markers are considerably useful and possess good discrimination capacity. Also, discussing about four pulse species assessed in recent study, it was (0.409) lesser than Suvan et al. ( 2019 ), who reported a PIC value of 0.60 for the SSR marker in black gram, 0.60 in mung bean (Pratap et al. 2015), higher than 0.199 in Soybean (Bisen et al. 2015 ) and more or less comparable to 0.50 in Gahat (Chahota et al. 2017 ). Data evidence from Pratap et al. ( 2016 ) also stated that a high fraction of SSR markers were transferred from common bean to mung bean that indicates the higher possibility of SSR transfer to other legumes. Previously, it was demonstrated that primer pairs devised for one species can be used for other species of the same genus along with different genera of the same family (Oliveira et al. 2006 ). This microsatellite attribute is known as transferability or cross-species amplification, which can be explored as a tool to measure the genetic variability of related species or genera. Subsequently, distinction observed in alleles per loci, heterozygosity and PIC may be attributed to the germplasm size and versatility, ecogeographical locations of gathered germplasm and number of polymorphic markers used in the study. The amount of molecular variance in this study showed that within population holds large level of genetic variations (99%) as compared to among population (1%). These findings are relatively comparable to the study from Mexico, Gill langarica et al. (2011), where 93.8% variance was discovered within populations and 0.87% and 5.32% variance among populations within groups and among groups respectively. Likewise, the study from Ethiopia and Kenya displayed 66% variance within gene pool and 34% among gene pools (Asfaw et al. 2009 ). Somewhat, Analogous outcomes were also recorded from Jammu and Kashmir (India), where high (75%) and low (25%) diversity detected among accessions (Dar et al. ( 2016 ). The comparative analysis indicates that topographical structure can be the accountable factor for constraining genetic discrimination among populations, which may lead to much superior genetic variation within populations (Gill langarica et al. 2011). Likewise, in this study, average genetic differentiation (Gst) was found 0.264 which was less than 0.41 reported by Zhang et al. ( 2008 ). Similarly, gene flow (Nm) was measured to be 1.396 which was less than 2.6 and 3.927 in Rajmash accessions described by Zhang et al. ( 2008 ) and Asfaw et al. ( 2009 ), respectively. To estimate the evolution of a particular species, it is significant to divide populations according to the geographical location. The unweighted neighbor joining method categorized gathered germplasm into two groups. Further, the PCoA coordinates displayed clear division of genotypes by accessions in the present study. Likewise, population architecture assessment based on Bayesian method exhibited the formation of two subpopulations (K = 2) in our study. The outcomes of the present study support the earlier report published by Chaudhary et al. (2017) which states that the Indian Himalayan region (north-western) common bean germplasm made up of two gene pools - Mesoamerican and Andean. Their phaseolin analysis showed two main types of phaseolin - S and T - type in the bean samples from Indian Himalayan region with the prominence of T-type (Andean) phaseolin in local bean landraces from Jammu and Kashmir. However, other previous studies have separated common bean population into 2–7 subpopulations based on population structure assessment Asfaw et al. 2009 ; Blair et al. 2012 ; Nemli et al. 2014 ; Valentini et al. 2018 ; Bashir et al. 2020 ). Maternal inheritance characteristics of cp-SSR markers make them able to observe changes in population composition in most angiosperms (Angioi et al. 2010 ). Therefore, they are broadly used in the analysis of population genetics, genetic diversity, and evolutionary studies of different plants (Pan et al. 2014 ). The cpSSR markers may perhaps contribute to distinctiveness, uniformity, and stability (DUS) characterization and plant varietal registration, linkage studies etc. (Cabral et al. 2011 ; Matondo et al. 2017 ). The genetic variations revealed by cpSSR markers in this study established their usefulness for studying other legume crops as well. Genetic diversity demonstrated by these markers might be employed in advanced breeding programmes in India for better-quality germplasm selection of common bean. Conclusion Information on the distribution of wild and cultivated germplasm of a species is essentially needed to estimate its genetic multiplicity. In the present study, designed SSR markers from the chloroplast portion of the species have established a considerable number of variations among common bean accessions from Uttarakhand. The significant amount of polymorphism recorded among studied samples could be further extended for the identification and conservation studies of local accessions. The present investigation revealed that chloroplast SSRs with higher polymorphis can be employed for genome wide association mapping in near future. The marker set provides a useful resource for accession detection, evolutionary studies, and assists in molecular breeding of significant common bean accessions. Thus, genetic resources developed through this study could be utilised by breeders for the large-scale screening and DNA fingerprinting of common bean accessions and other Fabaceae members for conservation and genotypic improvement. Declarations Acknowledgements The authors are thankful to Er. Kireet Kumar, Director In charge, GB Pant National Institute of Himalayan Environment (GBPNIHE) and co-ordinator National Mission on Himalayan Studies (NMHS). Also, thanks to Late Dr. R. S. Rawal, Ex Director GBPNIHE for his encouragement. Help Received from members of Biodiversity Genomics Lab, Srinagar Uttarakhand is acknowledged. Declarations All authors have shown that there are no competing financial gains that may appear to persuade the work reported in this MS. Funding This work is supported from National Mission on Himalayan Studies (NMHS) funded project (NMHS/2019-20/MG_60) under medium grant scheme. Conflicts of interest All authors declare no conflicts of interest in publishing this MS. Availability of data and material All data linked with this manuscript is available within this Manuscript. Code availability NA Author contribution AKJ conceptualized the study. YB, HS and AKJ standardized methodology and conducted the experiments. YB and AKJ performed data analysis. AKJ contributed in management of resources and funds. All authors contributed in Writing - Review & Editing of the manuscript. 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No. Accession Number Collection Site District Latitude (N) Longitude (E) Altitude (m) No of samples 1 AKJ/KK/PS/Harshil/18 Harsil Uttarkashi 31°02'18" 78°44'14" 2498 8 2 AKJ/KR/Jatoli/38 Jatoli Bageshwar 30°06'56'' 79°55'42" 2056 7 3 AKJ/AR/KK/Dargi/15 Dargi Tehri 30°19'06" 78°24'39" 1700 5 4 AKJ/YB/PS/Parvada/47 Parvada Nainital 29°25'29'' 79°39'01" 2046 5 5 AKJ/KR/Jaikuni/36 Jakuni Bageshwar 30°08'35'' 79°55'22" 2402 6 6 AKJ/RA/Bheeda/31 Bheeda Pauri 30°01'20" 79°02'52" 1800 7 7 AKJ/KK/AR/Jakhol/26 Jakhol Uttarkashi 31°06'52" 78°15'01" 2200 7 8 AKJ/KK/AR/Sankari/25 Sankari Uttarkashi 31°04'40" 78°11'03" 1850 6 9 AKJ/YB/Khati/37 Khati Bageshwar 30°06'44" 79°56'24" 2245 5 10 AKJ/KK/PD/Jhalla/23 Jhalla Uttarkashi 31°01'34" 78°42'52" 1158 6 11 AKJ/YB/PS/Chanpatta/41 Chanpatta Pithoragarh 29°48'46'' 80°14'50" 1727 6 12 AKJ/AR/PD/Tolma/04 Tolma Chamoli 30°31'24'' 79°45'02'' 2655 6 13 AKJ/YB/KR/Bona/39 Bona Pithoragarh 30°03'57'' 80°22'40" 2134 6 14 AKJ/KK/AR/Natwar/024 Natwar Uttarkashi 31°03'53" 78°06'19" 1158 5 15 AKJ/AR/KK/Raithal/021 Raithal Uttarkashi 30°49'01" 79°36'13" 2140 6 16 AKJ/KK/PS/Haltari/42 Haltari Uttarkashi 31°03'31'' 78°09'00" 2416 6 17 AKJ/YB/PS/Supi/43 Supi Nainital 29°26'45'' 79°39'01" 520 5 18 AKJ/YB/PS/Umagarh/45 Umagarh Nainital 29°25'50'' 79°33'08" 1895 5 19 AKJ/RA/PD/Lata/06 Lata Chamoli 30°29'40'' 79°42'50'' 2370 6 20 AKJ/RA/PD/Jumma/01 Jumma Chamoli 30°36'18'' 79°48'23" 2451 6 Table 2 : Chloroplast Single Sequence Repeat (cp-SSR) markers applied for genetic diversity evaluation of various accessions of Common beans Locus Seq ID Primer Sequences (5’- 3’) Position Repeat Motif Size Range (bp) Length/Tm PIC 1 PV_cpSSR3F ATGGTGATTGCACAGTCC psbM-petN IGS (AT)6 112-208 18 Tm 48 0.428 PV_cpSSR3R GAAGAAATGGATTCCTACTCC 21 Tm 50 2 PV_cpSSR5F CCACATATCTATTGTGGC CA atpI-atpH IGS (ATCT)3 249-368 21 Tm 50 0.590 PV_cpSSR5R CCCATATGGATACAATCAAGG 21 Tm 50 3 PV_cpSSR7F AGTTCCGCCTATTTATCAAC petA-psbJ IGS (AT)6 150-256 20 Tm 48 0.524 PV_cpSSR7R GGACTCTAGGAAAGGACAAAG 21 Tm 52 4 PV_cpSSR8F CGAACTGAACTAAGACCGTTT trnW-CCA IGS (AT)9 150-200 21 Tm 50 0.665 PV_cpSSR8R CCGTATTCTATGAGATGAGCA 21 Tm 50 5 PV_cpSSR11F AATCCCCTTTTCTTACCAAG ycf1-ndhF (TATT)3 120-235 20 Tm 48 0.803 PV_cpSSR11R GGGCGAATATCTTCGTATATC 21 Tm 50 6 PV_cpSSR12F CTCGGTGCATAGAATTTCAC ndhG-ndhI IGS (TA)7 195-235 20 Tm 50 0.817 PV_cpSSR12R GGGTCGTTTACCAGTATCAGT 21 Tm 52 7 PV_cpSSR13F GGGAAAAACAACCACTTCTA ndhA (TA)6 300 20 Tm 48 0.567 PV_cpSSR13R TTTGCTATACGGTTCTCCTT 20 Tm 48 8 PV_cpSSR14F CGCAAACATGATTCAAATGG psaC IGS-ndhE (TTGA)3 285 20 Tm 48 0.529 PV_cpSSR14R ACCGGCTATTGTTTCCTCAAT 21 Tm 50 Mean 0.615 T m = Melting temperature; IGS = intergenic spacer; PIC= polymorphism information content Table 3 : Characteristics of 15 chloroplast microsatellite markers in 20 accessions of common beans S. No. Accession Number No of polymorphic loci Polymo rphic loci (%) Na Ne He I 1 AKJ/KK/PS/Harshil/18 16 80 1.80 1.505 0.290 0.431 2 AKJ/KR/Jatoli/38 13 65 1.65 1.454 0.261 0.382 3 AKJ/AR/KK/Dargi/15 11 55 1.55 1.413 0.229 0.332 4 AKJ/YB/PS/Parvada/47 13 65 1.65 1.430 0.246 0.364 5 AKJ/KR/Jaikuni/36 9 45 1.45 1.287 0.171 0.254 6 AKJ/RA/Bheeda/31 11 55 1.55 1.422 0.234 0.338 7 AKJ/KK/AR/Jakhol/26 10 50 1.50 1.363 0.203 0.295 8 AKJ/KK/AR/Sankari/25 11 55 1.55 1.367 0.214 0.316 9 AKJ/YB/Khati/37 8 40 1.40 1.283 0.161 0.235 10 AKJ/KK/PD/Jhalla/23 15 75 1.75 1.510 0.292 0.429 11 AKJ/YB/PS/Chanpatta/41 11 55 1.55 1.384 0.222 0.325 12 AKJ/AR/PD/Tolma/04 11 55 1.55 1.344 0.200 0.298 13 AKJ/YB/KR/Bona/39 10 50 1.50 1.332 0.193 0.285 14 AKJ/KK/AR/Natwar/024 8 40 1.40 1.303 0.170 0.246 15 AKJ/AR/KK/Raithal/021 12 60 1.60 1.446 0.252 0.365 16 AKJ/KK/PS/Haltari/42 12 60 1.60 1.391 0.231 0.342 17 AKJ/YB/PS/Supi/43 9 45 1.45 1.229 0.145 0.223 18 AKJ/YB/PS/Umagarh/45 10 50 1.50 1.335 0.198 0.292 19 AKJ/RA/PD/Lata/06 9 45 1.45 1.298 0.174 0.258 20 AKJ/RA/PD/Jumma/01 9 45 1.45 1.301 0.177 0.261 Mean 10.9 54.5 1.55 1.370 0.213 0.313 Na= observed number of alleles, Ne = effective number of alleles;He = gene diversity; I = Shannon’s information index Table 4: Molecular variance analysis (AMOVA) using Gene Alex 6.5 software Source df Sum of Square Estimated Variance % Variance Among Pops 19 98.045 0.031 1% Within Pops 99 492.644 4.976 99% Total 118 590.689 5.007 100% Supplementary Files SupplementaryInformationFigS1ab.pptx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-1726098","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":114583414,"identity":"1346725e-8922-432b-9f22-e5870e05b75b","order_by":0,"name":"Yogita Bisht","email":"","orcid":"","institution":"Govind Ballabh Pant National Institute of Himalayan Environment and Sustainable Development: GB Pant National Institute of Himalayan Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yogita","middleName":"","lastName":"Bisht","suffix":""},{"id":114583415,"identity":"732d51c8-a2ef-4bcb-805f-3eb23ea644bd","order_by":1,"name":"Himanshu Sharma","email":"","orcid":"","institution":"NABI: National Agri-Food Biotechnology Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Himanshu","middleName":"","lastName":"Sharma","suffix":""},{"id":114583416,"identity":"97498c86-2b91-4fa7-a939-d110d86dc54c","order_by":2,"name":"Arun K. Jugran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYLACCTDJfADElCFFC1sCiMlDil08BmCSoDr+2WfMHli21ebzz+75/OpGjQUPA/vhoxvwuulcjrmBZNtxyxl3zm6zzjkGdBhPWtoNvNac4TGTkGw7ZsBwI3ebcQ4bUIsEjxleLfIwLfI3cp4Z5/wjQosBREuNgcGNHObHuW1EaDE8w1YmIXHugIHhjTQz5tw+CR42Qn6RO8O8TVqirM5A7kby48853+rk+NkPH8PvfSBglmA4DKLZwFHKRkg5CDB+YKgDa/1AjOpRMApGwSgYeQAA4+RCnP0ZqB8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3294-2847","institution":"Govind Ballabh Pant Institute of Himalayan Environment and Development: Govind Ballabh Pant National Institute of Himalayan Environment and Sustainable Development","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Arun","middleName":"K.","lastName":"Jugran","suffix":""}],"badges":[],"createdAt":"2022-06-04 19:21:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1726098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1726098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23596100,"identity":"53a477cc-d8df-45b9-9e97-18727226d418","added_by":"auto","created_at":"2022-07-07 16:42:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1479358,"visible":true,"origin":"","legend":"\u003cp\u003eSeeds of twenty different accessions of common been collected from Uttarakhand to study genetic variation\u003c/p\u003e","description":"","filename":"ScreenShot20220707at12.24.11PM.png","url":"https://assets-eu.researchsquare.com/files/rs-1726098/v1/5faa80eab2f7b961c2db122a.png"},{"id":23596101,"identity":"7b2b9afc-90b2-4aa3-a4f2-d280fafe5855","added_by":"auto","created_at":"2022-07-07 16:42:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291527,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Estimation of most possible number of subpopulations based on the delta K value determined using the program Structure Harvester; \u003cstrong\u003e(b)\u003c/strong\u003e Population structure of 119 individuals of 20 common bean accessions concluded from Cp-SSR markers. Each individual is represented by a vertically color-coded segment indicating the ideal fraction to the K value= 2\u003c/p\u003e","description":"","filename":"ScreenShot20220707at12.24.38PM.png","url":"https://assets-eu.researchsquare.com/files/rs-1726098/v1/54fc1a3f7eb24e23c27a11b2.png"},{"id":23595510,"identity":"2fb7bff0-1ecc-4f3a-9352-4659733c6afe","added_by":"auto","created_at":"2022-07-07 16:37:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68832,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of 20 common bean accessions from Uttarakhand based on selected SSR loci recorded by the neighbor joining method using PHYLIP Program\u003c/p\u003e","description":"","filename":"ScreenShot20220707at12.24.46PM.png","url":"https://assets-eu.researchsquare.com/files/rs-1726098/v1/55ea04503c0c851650f5311d.png"},{"id":23595512,"identity":"18cfc9ed-8bdc-47bc-bd73-eaeef7ee42e8","added_by":"auto","created_at":"2022-07-07 16:37:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150529,"visible":true,"origin":"","legend":"\u003cp\u003ePCoA plot of common bean accessions based on cp-SSR marker data. Coordinate 1 and coordinate 2 comprised about 8.57 and 16.73% variations respectively\u003c/p\u003e","description":"","filename":"ScreenShot20220707at12.25.18PM.png","url":"https://assets-eu.researchsquare.com/files/rs-1726098/v1/97aef765366c4c05930a4bf6.png"},{"id":23596104,"identity":"e8bc9468-fe94-4d50-8dd2-3a25ead8a2fe","added_by":"auto","created_at":"2022-07-07 16:42:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":658782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1726098/v1/18b1e3ee-459b-463c-8378-fc690b0223ff.pdf"},{"id":23595513,"identity":"1e8219f2-a590-4e2d-aeb2-8a6263163336","added_by":"auto","created_at":"2022-07-07 16:37:21","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":86814,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationFigS1ab.pptx","url":"https://assets-eu.researchsquare.com/files/rs-1726098/v1/bdeeeba6c6d7fe4c96b44bcf.pptx"}],"financialInterests":"","formattedTitle":"Characterization and population structure assessment of Indian Common bean using Microsatellite Markers","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003ePhaseolus\u003c/em\u003e is a large genus of the family Fabaceae comprises about eighty species in the world (Ulloa Ulloa et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chac\u0026oacute;n-S\u0026aacute;nchez et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Five species namely, \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L. (Common bean), \u003cem\u003ePhaseolus lunatus\u003c/em\u003e L. (Lima bean), \u003cem\u003ePhaseolus acutifolius\u003c/em\u003e A. Gray (Tepary bean), \u003cem\u003ePhaseolus coccineus\u003c/em\u003e L. (Runner bean) and \u003cem\u003ePhaseolus dumosus or Phaseolus polyanthus Greenman\u003c/em\u003e (Year bean) are the dominant species of this family (Mina-vargas et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nadeem et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The common bean is most important species of this genus believed to be domesticated approximately 8,000 years ago in Central America as its geographical origin (Chac\u0026oacute;n-S\u0026aacute;nchez et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). After domestication, the species was introduced in other parts of the world by the Portuguese traders. Two major diverse gene pools of common bean have been reported- Mesoamerican and Andean (Angioi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Mesoamerican gene pool has been distributed from Mexico throughout the Central America, into Columbia and Venezuela, while the Andean gene pool has its distribution throughout the southern Peru, Chile, Bolivia and Argentina (Rana et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chaudhary et al. 2017). In India, common bean was dispersed mainly by Portuguese, English, Dutch, and French traders in the beginning of the 16th century via the Red and Arabian Seas and Chinese through the Hindustan Silk Route (Rana et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Since then, the species has undergone an adaptive evolutionary process for approximately 400 years in these areas (Westphal \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Chaudhary et al. 2017). The morphological, biochemical, and molecular evaluation of common bean revealed that the Mesoamerican originated wild community differs from the Andean (Desiderio et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These two original gene pools are morphologically diverse from each other, in seed size and some other variations (Angioi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Another differentiating factor is the S or T type of phaseolin protein found in the seeds of common bean (Chaudhary et al. 2017).\u003c/p\u003e \u003cp\u003eCommon bean (also known as kidney bean or Rajmash) is one of the most ancient legumes cultivated over a 90% growing area globally for its edible seeds as dry and green beans (Celmeli et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The global annual production of bean grain is about 31\u0026nbsp;million tons and Brazil is the leading producer of common bean, followed by India, Myanmar, China, United States and Mexico (FAOSTAT 2020; Delfini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In India, Uttarakhand produces 40.22 tonnes of common bean and acquires 16th position at national level [National Horticulture Board (NHB), 2017\u0026ndash;2018]. Immature pods of this species, known as snap beans, are eaten as vegetable while the straw from the plant is utilised as fodder. Common bean is consumed worldwide as a major reservoir of protein, dietary fibres and microelements (Broughton et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Głowacka et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is highly proteinaceous (14\u0026ndash;33%) and consumed preferentially as a vegetarian protein reserve (Venkidasamy et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Flores-sosa et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, common bean is a prosperous resource of iron, zinc, folic acid, potassium, phosphorous, magnesium, manganese, selenium etc (Broughton et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Petry et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Besides this, diverse essential amino acids like phenylalanine\u0026thinsp;+\u0026thinsp;tyrosine (53\u0026ndash;105 mg/g), lysine (10\u0026ndash;104 mg/g) and leucine (14\u0026ndash;92 mg/g) have been recorded from common bean. Moreover, the species is deficient in the sulphur containing amino acids: methionine\u0026thinsp;+\u0026thinsp;cysteine (Moraes et al. 1971; Flores-sosa et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, common bean is a self-fertilizing crop grown in tropical, semi-tropical, and temperate regions of the world (Wang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gupta et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In India, common bean has been cultivated in the plains of Uttar Pradesh, Andhra Pradesh and Maharashtra during autumn, and in hilly areas such as Uttarakhand, it is grown as a kharif crop during summer and winter (Sharma et al. 2014). Many well-known landraces named Auli, Harshil and Munsiyari have been cultivated in the high mountain regions of Uttarakhand which are morphologically distinct and possess remarkable adaptability to local environmental settings (Rana et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although the genetic characterization of a few local accessions/varieties of common bean gathered from Pantanagar (Uttarakhand) have been investigated using microsatellite markers (Kumar et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), local accessions of the species from the entire Uttarakhand have not been evaluated systematically and scientifically till date. Therefore, the present study is attempted (i) to design and validate new chloroplast markers, (ii) to measure the magnitude of genetic polymorphism, diversity, and population composition among common bean accessions, and (iii) to investigate the cross transferability among the selected members of the genus.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cp\u003e\u003cstrong\u003ePlant materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAltogether, 119 genotypes of twenty diverse common bean accessions were gathered from local farmers\u0026apos; fields in Uttarakhand, India and subjected to further study (Table 1; Fig. 1). Local pulses such as \u003cem\u003eVigna radiata\u0026nbsp;\u003c/em\u003e(Mung bean), \u003cem\u003eMacrotyloma uniflorum\u0026nbsp;\u003c/em\u003e(Gahat), \u003cem\u003eGlycine max\u0026nbsp;\u003c/em\u003e(Soybean), and \u003cem\u003eVigna mungo\u0026nbsp;\u003c/em\u003e(Black gram) were also collected from the farmers of Jakholi, Rudraprayag, Uttarakhand (Longitude- 78\u0026deg;53\u0026rsquo;33.57\u0026rdquo; \u0026deg;E; Latitude- 30\u0026deg;23\u0026rsquo;33.02 \u0026deg;N; Altitude 1792) and used for further study. \u0026nbsp; Common bean seeds collected from various locations were brought into the laboratory and sown in a seedling tray. After 33 days of seed sowing [seed sowed in off season (December], the fresh young juvenile leaves were harvested, washed, and used for further experimentation. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChloroplast microsatellites (cpSSR) marker designing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete common bean chloroplast genome (Guo et al. 2007) was retrieved from NCBI (National Center for Biotechnology Information) and the MISA (Microsatellite Search Module) a web based server was implemented for the identification of SSRs (Thiel et al. 2003). The minimum length threshold condition applied to investigate SSRs was dinucleotide (6 bp repeats), trinucleotide (4 bp repeats), and tetranucleotide (3 bp repeats), while mononucleotides (5 or 6 nucleotide repeats) were debarred from the evaluation. Ideal and compound SSRs were determined by means of the MISA pipeline. The repeat sequences were interrupted by non-repeat sequences (100 bp) in the case of compound SSRs. The Batch Primer-3 version 1.0 programme developed by You et al. (2008) was accustomed to design primers flanking repeated regions of chloroplast SSR loci in the chloroplast genome (You et al. 2008). The cpSSR primers were developed using parameters as follows: (i) primer length ranged from 18 to 23 bp; (ii) product size ranged from 100\u0026ndash;300 bp; and (iii) an optimum GC content of 50% with a range of 40%\u0026ndash;70% (Table 2). The identified chloroplast-based markers were designated as PV_cpSSR (\u003cem\u003ePhaseolus vulgaris\u0026nbsp;\u003c/em\u003echloroplast simple sequence repeats) primers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA isolation and cpSSR marker amplification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fresh, young, juvenile leaves at the apex of the plant were used for genomic DNA extraction using the CTAB (Cetyl tri-methyl ammonium bromide) buffer assay with slight alterations (Jugran et al. 2013a \u0026amp; b). The quality of extracted DNA samples was examined by electrophoresing on an agarose gel (1%) prepared in 0.5 X TBE (Tris-Cl, Boric acid, EDTA) buffer. DNA fragments were visualised under the gel documentation system (I Gene Labserve, India) using a standard DNA ladder. DNA of common bean was amplified through PCR reaction using 20 \u0026mu;L of reaction mixture having 1\u0026mu;L template DNA, 10mM DNTPs, 10 picomole of each forward and reverse primer, 1U \u003cem\u003eTaq\u003c/em\u003e polymerase, 10X PCR buffer and 25mM MgCl\u003csub\u003e2\u003c/sub\u003e by following Sharma et al. (2019). All reactions were conducted in a thermocycler (Biometra, Germany) using the following reaction conditions: early denaturation for 2 min at 94 \u0026deg;C, 35 cycles of denaturation for 30 seconds at 94 \u0026deg;C, 1 min annealing at 55 \u0026deg;C, 2 min extension at 72 \u0026deg;C, and final extension at 72 \u0026deg;C for 5 min. PCR products (amplicon) were qualitatively analysed on a 3% agarose gel (Bio Rad, USA) and fragment size was estimated using the 100 bp DNA ladder (O\u0026rsquo; Gene Ruler, Hi Media) as a reference (Figure S1a). A total of fifteen newly designed cpSSR markers were utilised for DNA amplification. Of which only eight Cp-SSR markers showed polymorphism after initial screening and these eight primer pairs were used for final amplification of common bean samples. Likewise, the cross-transferability of cp-SSR markers among other Fabaceae members namely \u003cem\u003eVigna radiata\u0026nbsp;\u003c/em\u003e(Mung bean), \u003cem\u003eMacrotyloma uniflorum\u0026nbsp;\u003c/em\u003e(Gahat), \u003cem\u003eGlycine max\u0026nbsp;\u003c/em\u003e(Soybean), and \u003cem\u003eVigna mungo\u0026nbsp;\u003c/em\u003e(Black gram) were also evaluated using the same method (Fig. S1b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Out of 15 primer pairs screened, only 8 primer pairs exhibited polymorphism and produced clear and reproducible fragments, and therefore used for final study. A total of 119 genotypes of 20 common bean accessions from Uttarakhand were subjected to detect polymorphism at genetic level using eight primer pairs. Markers that produced the same sized fragments throughout the common bean were designated as mono-morphic, while cpSSRs producing varied fragments were considered polymorphic. A genotype that does not cause any type of amplification under standard circumstances is considered a null allele. The polymorphic information content (PIC) of each marker was measured using the following formula mentioned in Anderson et al. (1993): PIC= 1- Ʃ\u003cem\u003ep\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e, where p \u003cem\u003e\u003csub\u003eij\u0026nbsp;\u003c/sub\u003e\u003c/em\u003eis the frequency of the patterns (\u003cem\u003ej\u003c/em\u003e) for each cpSSR marker (\u003cem\u003ei\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eMarkers showing polymorphism were utilised to assess various attributes related to variability in common bean. The SSR fragments were scored on the basis of the occurrence (1) or non-occurrence (0) in all the samples examined. The binary data of each sample from diverse accessions was investigated using POPGENE version 32 (Yeh et al. 1999). The matrix was used to determine polymorphic loci number (Np), polymorphic loci percent (Pp%), number of observed alleles (Na), effective alleles per locus (Ne), heterozygosity (He= Nei\u0026rsquo;s gene diversity), Shannon\u0026rsquo;s information index (I), gene flow estimation (Nm), and genetic differentiation (Gst) using the POPGENE program. The PHYLIP version 3.68 software was employed to create a dendrogram of studied accessions by first transforming binary data into PHYLIP format, followed by generating matrices using the GENEDIST program. Ultimately, the NEIGHBOR program, followed by the CONSENSE programme from PHYLIP was employed to create an unrooted phylogenetic tree (Felsenstein 1995). A principal coordinate analysis (PCoA) was executed to evaluate the genetic relationship between accessions based on Nei genetic distance using a covariance standardised PCoA method in GenAlEx 6.5 (Peakall and Smouse 2006, 2012). An analysis of molecular variance (AMOVA) was performed to partition hereditary differences among accessions using the above-mentioned program. Cross-transferability and fragment size produced with cpSSR markers were recorded for all studied species. The degree of mixing and population composition was estimated based on the Bayesian clustering algorithm by using STRUCTURE software version 2.3.4 (Pritchard et al. 2000). Subpopulations within common bean samples were measured by 3 independent interactions as K values from 1 to 10 initially with different interactions and a burning length period to remove additional load on the computer. Final analysis was performed at 100,000 interactions with a 300,000-burn period using 20 interactions of K1 to 5 by employing an admixture model throughout the previously allocated population as a sampling location and with the frequency of linked alleles among populations. The numbers of best possible K groups were determined by estimating \u0026Delta;K parameter using Structure Harvester program (Earl and von Holdt 2012) as projected by Evanno\u0026rsquo;s method (Evanno et al. 2005).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eData mining and cp-SSR marker development\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommon bean chloroplast genome (NCBI Reference Sequence: NC_009259.1) was loaded from National Center for Biotechnology Information (NCBI) and used for mining of SSRs.\u0026nbsp;Fifteen primers were developed which qualified different parameters during the process, and all the selected primers were synthesised and used for validation of amplification in 119 individuals from 20 common bean accessions. With the availability of complete genomic sequences of common bean and co-dominant property, ease of use, repeatability, and multi-allelic nature establish SSRs as the preferred marker of choice to measure genetic inconsistency (Kumar et al. 2006; Matondo et al. 2017).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic variability of common bean\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEight Cp-SSR primer pairs were utilized for DNA amplification of 119 individuals of common bean. Average number of polymorphic loci was recorded 10.9 from 119 individuals of common bean. The study showed that marker PV_cpSSR3 was reasonably informative (0.25\u0026lt;PIC\u0026lt;0.5) while the remaining seven markers were highly informative in nature (0.5\u0026lt;PIC\u0026gt;0.75). The PIC value of these primers ranged from 0.428 (PV_cpSSR3) to 0.817 (PV_cpSSR12), with a mean of 0.615 (Table 2). Further, the allele size varies from 112 to 368 bp (Table 2). Highest (80%) polymorphic loci percent was detected in Harsil accession and lowest (40%) in Khati \u0026amp; Natwar accessions using Cp-SSR markers. The Na ranged from 1.40 (Khati \u0026amp; Natwar accessions) to 1.80 (Harsil accession) with a mean of 1.55, Ne ranged from 1.229 (Supi accession) to 1.505 (Harsil accession) with a mean value of 1.370, I varied from 0.223 (Supi accession) to 0.431 (Harsil accession). Heterozygosity (He) was recorded at its maximum (0.292) in the Jhalla accession while it was observed at its lowest (0.145) in the Supi accession (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic differentiation and gene flow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal genetic variation was partitioned by subjecting the common bean samples to evaluate the molecular variance (AMOVA) by estimating the variance among populations and within populations. The high within population variation (99%) was recorded as compared to the among (1%) population \u0026nbsp; variation (Table 4). These findings were somewhat complemented by the G\u003csub\u003eST\u003c/sub\u003e (0.264), which also demonstrated high within population diversity (73.6%) in the studied accessions. The gene flow level was observed to be 1.396 among common bean accessions under the study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic relationships and population composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pair-wise genetic dissimilarity between accessions of common bean was evaluated to measure the relationship between accessions using Nei\u0026rsquo;s approach (Nei\u0026rsquo;s 1978). A maximum (0.1977) genetic distance was recorded between Jatoli and Harsil accessions, while it was minimum (0.0173) between Lata and Jumma accessions (Table S2). The dendrogram construction through the neighbor joining method separated all studied samples into two main groups: group A and B. Group A encompasses the sample gathered from Jhalla, while Group B contains the remaining 19 accessions. Group B was further separated into two sub-clusters, BI and BII (Fig.3). To estimate the genetic structure, PCoA analysis showed distribution of 20 accessions in a three dimensions space constructed on genetic distances.\u0026nbsp;The genetic variance percentage defined by all three PCoA coordinates in the study was 8.57%, 16.73%, and 24.44% (Fig. 4). In order to culminate the possible genetic populations, 119 genotypes were assessed by structure software using an admixture model. Evano\u0026rsquo;s \u0026Delta;K statistics were utilised to select the ideal K value based on the increase in possibility ratios between runs. The ideal sub-populations were found to be K = 2 (Fig.2). Based on K = 2 groups, population structure investigation exhibited that all common bean samples were distributed into 2 major groups. The relationship among populations in the two components is similar to the clustering pattern obtained in the study. Population structure of common bean relies on binary data obtained using cpSSR markers, was investigated by Pritchard et al. (2000) method and demonstrated that the log likelihood approximations increased regularly as K increased and started to decrease when K = 2 (Figure 2 a).\u0026nbsp;A mean log likelihood plot is prepared by placing values over 10 runs for K values ranging from 1 to 5. The optimum K value was 2 as assessed by the \u0026Delta;K statistic STRUCTURE based on employed markers (Figure 2 b). It was observed that the best possible subgroup number was reasonably low as compared to the total studied accessions,\u0026nbsp;exhibited extensive gene flow levels, either presently or historically. At\u0026nbsp;probability threshold (Q) of 0.60 using cpSSR marker\u0026rsquo;s structure analysis, generally the samples were visibly isolated to a definite cluster. Of which, 39 individuals (32.773%) were dispersed among cluster-1 and 76 individuals (63.866%) were found in cluster-2. No clear pattern of allocation of the individuals of the studied accessions was detected in Cluster-1 and Cluster-2, depending on the threshold of 60% in structure analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTaxon analysis and marker transferability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour commonly growing pulse species namely, \u003cem\u003eGlycine max\u003c/em\u003e, \u003cem\u003eMacrotyloma uniflorum\u003c/em\u003e, \u003cem\u003eVigna mungo,\u003c/em\u003e and \u003cem\u003eVigna radiata\u0026nbsp;\u003c/em\u003ealong with common bean samples were evaluated for genetic variability and transferability using cpSSR markers designed for the aforementioned study. In sum, eight markers displayed polymorphism in the samples and the existence of transferability in the studied samples (Fig. S1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIdentification of genotypic variations and their underlying structure is imperative for future breeding programs and conservation of plant genetic resources (Savic et al. 2020). Therefore, the development of molecular tools for characterizing genetic variability is indispensable as it significantly enhanced the effectiveness of utilization of accessible genetic resources. Despite of the diverse classes of currently available markers, knowledge of their genetic informative potential is critical to exploit the germplasm diversity. SSRs are used to examine the genetic diversity and linkage mapping, among other aspects, has been widely explained in the literature (Angioi et al \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vidak et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For the common bean, the present work is the first to assess the genetic diversity and structure of common bean from Uttarakhand (India) using SSR markers. They were found to be reasonably effective in assessing the genetic diversity of common bean genotypes. In Indian Himalayan Region common bean is one of the extensively cultivated legume crops. Prerequisite information about the genetic multiplicity of common bean is essential to achieve effective breeding programs. Among molecular markers, SSR marker is considered accurate and reliable tool to characterize genetic variability among legume crops. The cp-SSR markers used to assess 119 individuals from 20 accessions in this study amplified a total of 218 polymorphic alleles ranging from 8 (Khati and Natwar) to 16 (Harsil) polymorphic alleles per loci with a mean of 10.9. These findings are in agreement with the previous study in which 138 genotypes from Jammu and Kashmir and one variety from VPKAS, Almora, India was investigated (Mahajan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The study of Valentini et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), reported a mean of 4 alleles per SSR locus when studying the 109 accessions from Brazil, whereas genetic assessment of 102 genotypes from Jammu, Kashmir and Ladakh, India, Bashir et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated the incidence of 30 alleles per locus of SSR. The reason for a smaller number of alleles as compared to Bashir et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) might be the due to the use of chloroplast SSRs in our study, as genomic SSRs can resolve within gene pool variation.\u003c/p\u003e \u003cp\u003eIn present study, the percentage of polymorphic loci ranged from 40 to 80% for different SSR primers with a mean of 54.5% which is approximately parallel to (66.7%) the study of Hegay et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e. Percentage of polymorphic loci may vary from 0 (Hegay et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to 100% (Asfaw et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, Pp% was found to be 83.33% in an ISSR (Inter Simple Sequence Repeats) marker-based study involving 28 accessions from Jammu and Kashmir, India (Dar et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Current study showed average gene diversity (expected heterozygosity) as 0.213, which is nearly similar to other studies on common bean (Mercati et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pratap et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mahajan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Matondo et al. 2018). However, expected heterozygosity was observed lower as compared to the investigation of Bilir et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in 102 genotypes from Turkey and study of Mir et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in 96 genotypes from Jammu and Kashmir (India). Higher heterozygosity in Bilir et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Mir et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is because of the abundant sampling of common bean genotypes. PIC is the distinguishing ability of a particular marker, predominantly based on alleles per locus and allele frequency in studied germplasm (Mercati et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Suvan et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As stated by Bashir et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), SSR-based PIC values can be exploited to detect the capability of the marker to detect genetic multiplicity. The PIC value was found to be 0.615 which was in order with the value (0.634) reported by Mahajan et al.2016 in Indian germplasm and Matondo et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in DR- Congo germplasm. It was found lesser than the earlier studies of researchers from Mizoram, India (Dutta et al. 2015) and Jammu \u0026amp; Kashmir and Ladakh, India (Bashir et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The lower PIC values are recorded from closely associated genotypes and higher values for genetically distant genotypes. On the other hand, the PIC in the present study was found to be higher as compared to six traditional common bean varieties studied in Himachal Pradesh, India (Sharma et al. 2014) and 135 genotypes studied from northern India (Gupta et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on the studies of Bashir et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the high level of polymorphism is due to huge diversity among genotypes and selection of highly polymorphic markers. In addition to support this, Sharma et al. (2014) stated that a marker with a PIC ranging from 0.3 to 0.8 is considered functional for the measurement of genetic differences in the population. The present research on common bean displayed a PIC range from 0.428 to 0.817, indicating that microsatellite markers are considerably useful and possess good discrimination capacity. Also, discussing about four pulse species assessed in recent study, it was (0.409) lesser than Suvan et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who reported a PIC value of 0.60 for the SSR marker in black gram, 0.60 in mung bean (Pratap et al. 2015), higher than 0.199 in Soybean (Bisen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and more or less comparable to 0.50 in Gahat (Chahota et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Data evidence from Pratap et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also stated that a high fraction of SSR markers were transferred from common bean to mung bean that indicates the higher possibility of SSR transfer to other legumes. Previously, it was demonstrated that primer pairs devised for one species can be used for other species of the same genus along with different genera of the same family (Oliveira et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This microsatellite attribute is known as transferability or cross-species amplification, which can be explored as a tool to measure the genetic variability of related species or genera. Subsequently, distinction observed in alleles per loci, heterozygosity and PIC may be attributed to the germplasm size and versatility, ecogeographical locations of gathered germplasm and number of polymorphic markers used in the study.\u003c/p\u003e \u003cp\u003eThe amount of molecular variance in this study showed that within population holds large level of genetic variations (99%) as compared to among population (1%). These findings are relatively comparable to the study from Mexico, Gill langarica et al. (2011), where 93.8% variance was discovered within populations and 0.87% and 5.32% variance among populations within groups and among groups respectively. Likewise, the study from Ethiopia and Kenya displayed 66% variance within gene pool and 34% among gene pools (Asfaw et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Somewhat, Analogous outcomes were also recorded from Jammu and Kashmir (India), where high (75%) and low (25%) diversity detected among accessions (Dar et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The comparative analysis indicates that topographical structure can be the accountable factor for constraining genetic discrimination among populations, which may lead to much superior genetic variation within populations (Gill langarica et al. 2011). Likewise, in this study, average genetic differentiation (Gst) was found 0.264 which was less than 0.41 reported by Zhang et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Similarly, gene flow (Nm) was measured to be 1.396 which was less than 2.6 and 3.927 in Rajmash accessions described by Zhang et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Asfaw et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), respectively. To estimate the evolution of a particular species, it is significant to divide populations according to the geographical location. The unweighted neighbor joining method categorized gathered germplasm into two groups. Further, the PCoA coordinates displayed clear division of genotypes by accessions in the present study. Likewise, population architecture assessment based on Bayesian method exhibited the formation of two subpopulations (K\u0026thinsp;=\u0026thinsp;2) in our study. The outcomes of the present study support the earlier report published by Chaudhary et al. (2017) which states that the Indian Himalayan region (north-western) common bean germplasm made up of two gene pools - Mesoamerican and Andean. Their phaseolin analysis showed two main types of phaseolin - S and T - type in the bean samples from Indian Himalayan region with the prominence of T-type (Andean) phaseolin in local bean landraces from Jammu and Kashmir. However, other previous studies have separated common bean population into 2\u0026ndash;7 subpopulations based on population structure assessment Asfaw et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Blair et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nemli et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Valentini et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bashir et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Maternal inheritance characteristics of cp-SSR markers make them able to observe changes in population composition in most angiosperms (Angioi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, they are broadly used in the analysis of population genetics, genetic diversity, and evolutionary studies of different plants (Pan et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The cpSSR markers may perhaps contribute to distinctiveness, uniformity, and stability (DUS) characterization and plant varietal registration, linkage studies etc. (Cabral et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Matondo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The genetic variations revealed by cpSSR markers in this study established their usefulness for studying other legume crops as well. Genetic diversity demonstrated by these markers might be employed in advanced breeding programmes in India for better-quality germplasm selection of common bean.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eInformation on the distribution of wild and cultivated germplasm of a species is essentially needed to estimate its genetic multiplicity. In the present study, designed SSR markers from the chloroplast portion of the species have established a considerable number of variations among common bean accessions from Uttarakhand. The significant amount of polymorphism recorded among studied samples could be further extended for the identification and conservation studies of local accessions. The present investigation revealed that chloroplast SSRs with higher polymorphis can be employed for genome wide association mapping in near future. The marker set provides a useful resource for accession detection, evolutionary studies, and assists in molecular breeding of significant common bean accessions. Thus, genetic resources developed through this study could be utilised by breeders for the large-scale screening and DNA fingerprinting of common bean accessions and other Fabaceae members for conservation and genotypic improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to Er. Kireet Kumar, Director In charge, GB Pant National Institute of Himalayan Environment (GBPNIHE) and co-ordinator National Mission on Himalayan Studies (NMHS). Also, thanks to Late Dr. R. S. Rawal, Ex Director GBPNIHE for his encouragement. Help Received from members of Biodiversity Genomics Lab, Srinagar Uttarakhand is acknowledged. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have shown that there are no competing financial gains that may appear to persuade the work reported in this MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported from National Mission on Himalayan Studies (NMHS) funded project (NMHS/2019-20/MG_60) under medium grant scheme.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest in publishing this MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data linked with this manuscript is available within this Manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability \u0026nbsp; NA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAKJ conceptualized the study. YB, HS and AKJ standardized methodology and conducted the experiments. YB and AKJ performed data analysis. AKJ contributed in management of resources and funds.\u0026nbsp;All authors contributed in Writing - Review \u0026amp; Editing of the\u0026nbsp;manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis MS comprises the original work of the authors\u0026apos; which has not been previously published elsewhere. This article is not currently considered in other publications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors are informed and agreed to submit this manuscript to the GRCE Journal for publication. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson JA (1993) Optimizing parental selection for genetic linkage map. Genome 36:181\u0026ndash;186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngioi SA, Rau D, Attene G, Nanni L, Bellucci E, Logozzo G, Negri V, Zeuli SPL, Papa R (2010) Beans in Europe: origin and structure of the European landraces of \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L. 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University of Alberta, Edmonton\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou FM, Huo N, Gu YQ, Luo MC, Ma Y, Hane D, Lazo GR et al (2008) BatchPrimer3: A high throughput web application for PCR and sequencing primer design. BMC Bioinform 9(1):253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Blair MW, Wang S (2008) Genetic diversity of Chinese common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) landraces assessed with simple sequence repeat markers. Theor Appl Genet 117:629\u0026ndash;640. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00122-008-0807-2\u003c/span\u003e\u003cspan address=\"10.1007/s00122-008-0807-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Common bean\u0026nbsp;accessions collected from diverse locations of Uttarakhand\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccession Number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollection Site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistrict\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude (E)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltitude (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KK/PS/Harshil/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eHarsil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUttarkashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e31\u0026deg;02\u0026apos;18\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e78\u0026deg;44\u0026apos;14\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KR/Jatoli/38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eJatoli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eBageshwar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;06\u0026apos;56\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;55\u0026apos;42\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/AR/KK/Dargi/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eDargi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eTehri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;19\u0026apos;06\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e78\u0026deg;24\u0026apos;39\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Parvada/47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eParvada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eNainital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e29\u0026deg;25\u0026apos;29\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;39\u0026apos;01\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KR/Jaikuni/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eJakuni\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eBageshwar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;08\u0026apos;35\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;55\u0026apos;22\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/RA/Bheeda/31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eBheeda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003ePauri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;01\u0026apos;20\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;02\u0026apos;52\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KK/AR/Jakhol/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eJakhol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUttarkashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e31\u0026deg;06\u0026apos;52\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e78\u0026deg;15\u0026apos;01\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KK/AR/Sankari/25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eSankari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUttarkashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e31\u0026deg;04\u0026apos;40\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e78\u0026deg;11\u0026apos;03\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/YB/Khati/37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eKhati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eBageshwar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;06\u0026apos;44\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;56\u0026apos;24\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KK/PD/Jhalla/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eJhalla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUttarkashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e31\u0026deg;01\u0026apos;34\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e78\u0026deg;42\u0026apos;52\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Chanpatta/41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eChanpatta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003ePithoragarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e29\u0026deg;48\u0026apos;46\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e80\u0026deg;14\u0026apos;50\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/AR/PD/Tolma/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eTolma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eChamoli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;31\u0026apos;24\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;45\u0026apos;02\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/YB/KR/Bona/39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eBona\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003ePithoragarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;03\u0026apos;57\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e80\u0026deg;22\u0026apos;40\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KK/AR/Natwar/024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eNatwar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUttarkashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e31\u0026deg;03\u0026apos;53\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e78\u0026deg;06\u0026apos;19\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/AR/KK/Raithal/021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eRaithal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUttarkashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;49\u0026apos;01\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;36\u0026apos;13\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/KK/PS/Haltari/42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eHaltari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUttarkashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e31\u0026deg;03\u0026apos;31\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e78\u0026deg;09\u0026apos;00\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Supi/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eSupi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eNainital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e29\u0026deg;26\u0026apos;45\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;39\u0026apos;01\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Umagarh/45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eUmagarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eNainital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e29\u0026deg;25\u0026apos;50\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;33\u0026apos;08\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/RA/PD/Lata/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eLata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eChamoli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;29\u0026apos;40\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;42\u0026apos;50\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.804123711340207%\"\u003e\n \u003cp\u003eAKJ/RA/PD/Jumma/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eJumma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eChamoli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e30\u0026deg;36\u0026apos;18\u0026apos;\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003e79\u0026deg;48\u0026apos;23\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Chloroplast Single Sequence Repeat (cp-SSR) markers applied for genetic diversity evaluation of various accessions of Common beans\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeq ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer Sequences (5\u0026rsquo;- 3\u0026rsquo;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepeat Motif\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize Range (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength/Tm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR3F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eATGGTGATTGCACAGTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003epsbM-petN IGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(AT)6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e112-208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e18\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e48\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.753086419753085%\"\u003e\n \u003cp\u003ePV_cpSSR3R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.03703703703704%\"\u003e\n \u003cp\u003eGAAGAAATGGATTCCTACTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.345679012345679%\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR5F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eCCACATATCTATTGTGGC CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003eatpI-atpH IGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(ATCT)3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e249-368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.753086419753085%\"\u003e\n \u003cp\u003ePV_cpSSR5R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.03703703703704%\"\u003e\n \u003cp\u003eCCCATATGGATACAATCAAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.345679012345679%\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR7F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eAGTTCCGCCTATTTATCAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003epetA-psbJ IGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(AT)6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e150-256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e20\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e48\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.753086419753085%\"\u003e\n \u003cp\u003ePV_cpSSR7R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.03703703703704%\"\u003e\n \u003cp\u003eGGACTCTAGGAAAGGACAAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.345679012345679%\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e52\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR8F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eCGAACTGAACTAAGACCGTTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003etrnW-CCA IGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(AT)9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e150-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.753086419753085%\"\u003e\n \u003cp\u003ePV_cpSSR8R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.03703703703704%\"\u003e\n \u003cp\u003eCCGTATTCTATGAGATGAGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.345679012345679%\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR11F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eAATCCCCTTTTCTTACCAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003eycf1-ndhF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(TATT)3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e120-235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e20\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e48\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.641975308641975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.753086419753085%\"\u003e\n \u003cp\u003ePV_cpSSR11R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.03703703703704%\"\u003e\n \u003cp\u003eGGGCGAATATCTTCGTATATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.345679012345679%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Tm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR12F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eCTCGGTGCATAGAATTTCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003endhG-ndhI IGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(TA)7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e195-235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Tm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.181818181818183%\"\u003e\n \u003cp\u003ePV_cpSSR12R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.09090909090909%\"\u003e\n \u003cp\u003eGGGTCGTTTACCAGTATCAGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e52\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR13F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eGGGAAAAACAACCACTTCTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003endhA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(TA)6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e20\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e48\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.181818181818183%\"\u003e\n \u003cp\u003ePV_cpSSR13R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.09090909090909%\"\u003e\n \u003cp\u003eTTTGCTATACGGTTCTCCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e48\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003ePV_cpSSR14F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003eCGCAAACATGATTCAAATGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003epsaC IGS-ndhE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e(TTGA)3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e20\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e48\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.181818181818183%\"\u003e\n \u003cp\u003ePV_cpSSR14R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"34.09090909090909%\"\u003e\n \u003cp\u003eACCGGCTATTGTTTCCTCAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.363636363636363%\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTm\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"31.25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.615\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eT\u003csub\u003em\u003c/sub\u003e= Melting temperature;\u0026nbsp;IGS = intergenic spacer;\u0026nbsp;PIC= polymorphism information content\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eCharacteristics of 15 chloroplast microsatellite markers in 20 accessions of common beans\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccession Number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of polymorphic loci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003erphic loci (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KK/PS/Harshil/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KR/Jatoli/38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/AR/KK/Dargi/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Parvada/47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KR/Jaikuni/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/RA/Bheeda/31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KK/AR/Jakhol/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KK/AR/Sankari/25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/YB/Khati/37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KK/PD/Jhalla/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Chanpatta/41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/AR/PD/Tolma/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/YB/KR/Bona/39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KK/AR/Natwar/024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/AR/KK/Raithal/021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/KK/PS/Haltari/42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Supi/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/YB/PS/Umagarh/45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/RA/PD/Lata/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003eAKJ/RA/PD/Jumma/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.896907216494846%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e54.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.370\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.213\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.313\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNa= observed number of alleles, Ne = effective number of alleles;He = gene diversity; I = Shannon\u0026rsquo;s information index\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Molecular variance analysis (AMOVA) using Gene Alex 6.5 software\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmong Pops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithin Pops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e492.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e590.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Common bean, Accessions, Genetic diversity, Polymorphic Information Content, Transferability, Indian Himalayan Region","lastPublishedDoi":"10.21203/rs.3.rs-1726098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1726098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.\u003cem\u003e)\u003c/em\u003e is an important crop of family Fabaceae which is used as a potential source of proteins, fibres and minerals across the globe. The Indian Himalayan region harbours plentiful varieties of common bean, but it is nearly unexplored till date. The present study was attempted to assess genetic diversity and population structure of 119 common bean samples belonging to 20 diverse accessions gathered from Uttarakhand, India using newly designed chloroplast microsatellite (SSR) markers. A total of 218 polymorphic alleles were identified for 8 SSR loci. Mean number of alleles per locus (Na\u0026thinsp;=\u0026thinsp;1.55), effective allele number (Ne\u0026thinsp;=\u0026thinsp;1.370), Shannon information index (I\u0026thinsp;=\u0026thinsp;0.313), expected heterozygosity (He\u0026thinsp;=\u0026thinsp;0.213) and average polymorphic loci (10.9) were estimated based on Cp-SSR data. Maximum genetic diversity (He) was recorded in the Jhalla accession where it was found minimum in the accession collected from Supi. Bayesian-based STRUCTURE evaluation using SSR based information showed that 20 bean accessions were genetically separated into two main clusters. Also, the genetic distance-based cluster analysis separated 20 common bean accessions using these markers into 2 major clusters corresponding Mesoamerican and Andean. These Cp-SSR markers also demonstrated transferability among Fabaceae members like \u003cem\u003eVigna radiata\u003c/em\u003e, \u003cem\u003eMacrotyloma uniflorum\u003c/em\u003e, \u003cem\u003eGlycine max, Vigna mungo\u003c/em\u003e in this study. The findings from the study can be used to select better accessions of the bean for production, conservation, and future breeding programs. Likewise, markers displaying transferability can be used for monitoring the genetic heterogeneity of other members of the family Fabaceae.\u003c/p\u003e","manuscriptTitle":"Characterization and population structure assessment of Indian Common bean using Microsatellite Markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-07 16:37:19","doi":"10.21203/rs.3.rs-1726098/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c02364a3-eb39-473f-aee7-83d7ab2bb4f2","owner":[],"postedDate":"July 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-07T16:37:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-07 16:37:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1726098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1726098","identity":"rs-1726098","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}