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Simple Sequence Repeats-Based DNA Fingerprinting and Varietal Identification of Mango Cultivars

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25 September 2023

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26 September 2023

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Abstract
Assessment of the genetic distinctiveness of a cultivar through morphological descriptors is an important tool for both the registration and the protection. New mango genotypes have been improved using valuable diverse germplasm resources to ensure food security. DNA fingerprinting based simple sequence repeats (SSR)-markers have been the most broadly used, effective and accurate in evaluation of genetic characterization of a cultivar. Molecular breeding is an effective source of genetic gain after improvement of fruit trees using marker assisted genomic selection. Mango (Mangifera indica L.) is an allotetraploid (2n = 4X= 40) drupe fruit and has high nutritional value belongs to genus Mangifera and family Anacardiaceae. Mango cultivars are used with worldwide acceptance to pharmacological, ethnomedical, and phytochemical industries. This study investigated the molecular evaluation of a new mango cultivar ‘Azeem Chaunsa’ using a set of the most effective 50 hyper-variable polymorphic SSR markers. Highly specific DNA fingerprints were identified in the genome of this mango cultivar, ‘Azeem Chaunsa’ compared with three standard cultivars such as Sindhri, Samar Bahisht (S.B) Chaunsa and Sufaid Chaunsa. Our results showed that SSR markers could efficiently assess genetic diversity in mango. An agglomerative hierarchical clustering method was used to construct dendrogram based on the ‘Unweighted Pair- Group Method with Arithmetic Mean’ (UPGMA). The genetic similarity coefficients were recorded between the mangos cultivars ranged from 0.49 to 0.67. Cultivar identification (CID) evaluates association among standard cultivars and Azeem Chaunsa and further concludes significant variations. CID results concluded that cultivar ‘Azeem Chaunsa’ varied significantly from the check cultivar, Sindhri (46.2%), S.B Chaunsa (45%) and Sufaid Chaunsa (46.7%). The results obtained in this study will orient cultivar identification strategies for a successful future mango breeding programmes in the context of climate change.
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1. Introduction

The mango (Mangifera indica L.) is the most popular cultivated commercial fruit with great economic value and important long-lived evergreen tree [1]. Mango has been ranked third in production and second major fruit in Pakistan, participating to food and nutritional security to rural economy. Mango is known as “king of fruits” originated from subcontinent belonging to an earliest cultivated fruits. Mango production is concentrated in Pakistan in Sindh province as tropical climate while in Punjab have the subtropical climate [2]. Pakistan has commercial exportable mango cultivars as Sindhri (Early), SB Chaunsa (Mid) and Sufaid Chaunsa (Late) fruit availability and have approximately 3.5 months window for export of mangoes to other countries [3]. Mango fruit trees have been widely grown in tropical and subtropical areas. China and Pakistan are among the top mango producer countries. Mangoes are one of the major horticultural fruit crops in Punjab with 76% and Sindh provinces 24% share in mango production in Pakistan. In China, tropical provinces such as Hainan, Guangxi, Guangdong, Yunnan, Fujian, and Sichuan are major mango producers. Selection among chance seedling with superior traits of fruit quality as well as planned breeding with marker assisted selection considered a quick and precise technique for the development of mango varieties. Mango is often cross-pollinated [4], an allotetraploid (2n = 4X= 40) [5], highly heterozygous tree fruit, mono- and polyembryonic seed and having small imputed genome size of approximately 440-480 MB[6,7]. The genes controlling the traits can be mapped using high-throughput sequencing [8,9]. New mango varieties are generated by hybridization, introduction, selection and breeding of novel transgenes or genotypes [10,11].
Commercial mango varieties have largely been grown under varying planting geometries and were maintained by using clonal propagation by grafting of a specific variety or mutated branches [12]. Mango trees are heteroecious and cross-breeding has become dominant with high world popularity. DNA fingerprinting is widely used to identify and evaluate genetic diversity of mango plant varieties and hybrids [11,13,14,15,16,17]. Simple sequence repeats (SSR) or microsatellites are categorized as genetic loci and are tandem repeats, highly abundant and broadly distributed across both the prokaryotic and eukaryotic genomes. Compared with RFLP and RAPD, the advantages of SSR molecular markers include multi-allelic, clear loci, highly polymorphic, good repeatability, high resolution, codominate, reliable detection, high abundance, simple experimental design, easy operation and high distribution in plant genomes [18,19,20,21]. The SSR DNA markers are widely used for genotype DNA identification, variety or hybrid certification, parent detection, diversity analysis in diverse forest fruit species including mango [22,23,24], date palm [25], peach [26], sweet cherry[27], wild kiwifruit [28], papaya [29] and plum [30]. More than 1000 named mango commercial cultivars have been reported to exist around the worldwide. More than 100 SSR markers have been used to identify, characterize and evaluate various mango germplasm [31,32,33]. Germplasm evaluation and genetic diversity in mango using SSR markers gain significant advancement for evaluation of hybrids or cultivars, determination of genetic variations and conservation of germplasm [7,34]. SSR markers have widely used for identification of the domestication and movement of germplasm [35]. Genetic diversity evaluation in candidate cultivar using SSR molecular technology is based on SSR-PCR amplifications. The polyacrylamide gel electrophoresis and silver staining procedures were used to visualize and analyze the amplified segments as DNA fingerprints [36,37].
In Pakistan, mango is an exportable fresh fruit commodity. Several breeding lines are created to develop competitive cultivars with excellent production [38]. In mango, variety evaluation is important for utilization of the valuable genetic resource. The present study was performed to estimate the genetic variability created in mango cultivar ‘Azeem Chaunsa’ recommended for cultivation in Punjab using SSR markers. The genetic diversity of Azeem Chaunsa’ cultivar was also compared with other improved standard mango cultivars cultivated in Punjab. Mango reciprocal cross breeding method is frequently used for breeding new mango cultivars in hybridization program [39]. The progeny have the probability of both superior and inferior traits, tested after passing long juvenility. It is difficult to differentiate the authenticity of the offspring of hybrid. To evaluate cultivar identification and diversity of Pakistani mango genetic resources, 50 standard polymorphic SSR markers were selected for rapid genetic purity assessment in mango. Several commercially grown mango cultivars or hybrids were assessed accurate parentages. The present study was performed to determine genetic diversity among candidate mango cultivar and standard mango cultivars using hyper-variable polymorphic SSR markers. In the current study, unique and rare alleles were also identified and reconfirmed that would be useful for determination of genetic purity of cultivars in mango.

2. Materials and Methods

2.1. Experimental Material

The mango cultivars classified according to fruit availability such as Sindhri (Early season), Samar Bahisht (S.B) Chaunsa (Mid-season) and Sufaid Chaunsa (Late season), and the candidate cultivars ‘Azeem Chaunsa’ were grown in separate block and follow the planting geometry of 27 feet distance in between the rows and 22 feet distance in plants which accommodated 72 plants per acre in Mango Research Station, Shujabad, Multan. Freshly emerged tender leaves were collected for extraction of DNA samples. A set of perfect mango polymorphic SSR markers was selected for testing based on high polymorphism, stable amplification and clear banding patterns. The SSR primers were obtained from different genomic databases based on wide genome coverage. The fully grown mature and uniform mango trees having uniform age and size in the experimental orchard of “Mango Research Station” Shujabad, Punjab (Pakistan), located at latitude 29.8717° N and 71.3231° E, belonging to the Sub-Tropical Arid Climate. The standard cultivars are commercially grown in all the provinces of Pakistan and differ in geographical region. The standard cultivars such as Sindhri, Samar Bahisht (S.B) Chaunsa and Sufaid Chaunsa, have been approved by Punjab Seed Council, Lahore, Punjab, Pakistan. Experimental materials were collected in compliance with the institutional, national, and international guidelines and legislation.

2.2. Genomic DNA (gDNA) Extraction and Analysis

Total genomic DNA (gDNA) was generated from 4-5 young fully expanding leaves of each cultivar. The gDNA extraction was performed using the dried ground leaves of seedlings using the cetyltrimethylammonium bromide (CTAB) protocol with minor modification. The quality of gDNA was evaluated by loading 15 ng DNA of each genotype on 0.8% agarose gel prepared in IX TBE buffer and stained with ethidium bromide (10 ng/100 ml). Samples showing intact bands were selected to use for further study. The DNA concentration and purity of each cultivar was determined using a Nano Drop® ND-1000 spectrophotometer by estimating absorbance (OD260/280). Intact gDNA bands were marked for further SSR-PCR. The gDNA was stored at − 20 °C.

2.3. Search for Mango Simple Sequence Repeat Markers and Choice of PCR Primers

SSR markers were selected from the reference databases [33,40,41,42]. A total of 50 pairs of highly polymorphic SSR primers with different amplification bands among ‘Sufaid Chaunsa’, ‘Sindhri’, and S.B. Chaunsa ’and‘ Azeem Chaunsa′ were selected for cultivar identification (Table 1). The amplification efficiency of the selected SSR markers was evaluated using SSR-PCR.

2.4. PCR Amplification and SSR Fragment Analysis

PCR was completed with all 50 SSR primers pairs and 200 samples of variety used in this study. PCR was performed 25μL reaction volumes containing 12μL of 2x Green PCR master mix, 0.6 uM forward and reverse primers (approximately 25 ng of gDNA), and 50ng of gDNA as a template. Amplification was performed in a Thermal cycler (eppendorf Mastercycler gradient). The Mastercycler was programmed to pre-denaturation step of 94°C for 5 min followed by 35 cycles of denaturation 94°C for 30 sec, approximately annealing 55-60°C for 1 min (varied with Tm of different primers) and 72°C for 1 min followed by a final synthesis at 72 °C for 5 min. The reactions were then held at 4 °C. Amplifications were performed for twice and only reproducible products were considered for further data analysis.

2.5. Denaturing Polyacrylamide Gel Electrophoresis (PAGE)

In order to explore genetic polymorphism, 3μL of denatured SSR-PCR mixture was resolved on 6% Polyacrylamide (19:1 acrylamide: bis-acrylamide) Gel Electrophoreses (PAGE) (for high resolution). The 50bp DNA ladder (Fermentas, USA) was used as a molecular size marker. The amplified bands were visualized by silver nitrate staining in an ethidium bromide solution as described [37]. The gel profile was photographed under UV light as digital images using a gel documentation and analysis system.

2.6. Band Recording DATA Analysis for DNA Fingerprinting

The binary data matrices obtained from SSR markers were processed at DN fingerprinting level. The presence of band will be scored as 1, whereas the absence will be scored as 0. The binary data matrix will be used for dendrogram construction. Cluster analysis was performed on the similarity coefficient matrix. The Jaccard similarity matrix was used for cluster analysis using Unweighted Pair Group Method of Arithmetic average (UPGMA)[43] into Numerical Taxonomy System of Multivariate Programs (NTSyspc) (version 2.10e) software package [44]. Exact size of DNA fragment was recorded for each variety and primer. The distinct bands are identified and labeled as DNA fingerprints.

2.6. Staistical Analysis

The amplified SSR bands resulting from the SSR-PCR were summarized as graphical representations using R-language (version 3.1.1, software version 3.5.1)[45].

3. Results

3.1. Genetic Amplification of Mango Cultivars using SSR Markers

A total of 50 pairs of polymorphic SSR markers were selected to process two hundred leaf samples of DNA from each standard cultivar and candidate mango line ‘Azeem Chaunsa’ as templates (Table 1). The polymorphic SSR marker primers were selected to amplify and distinguish bands for screening. Out of 50 polymorphic SSR primers, 47 primers pairs amplified 82 SSR fragments from the candidate Azeem Chaunsa. Similarly 49 primers set amplified a total of 105 SSR fragments in Sufaid Chaunsa genome. The 41 primers amplified 62 polymorphic bands in Sindhri, and each primer pair amplified an average of 1.5 polymorphic fragments. The 41 primers amplified 70 polymorphic bands in S.B Chaunsa, and each primer pair amplified an average of 1.7 polymorphic fragments. A total of 319 DNA fragments were obtained across all genotypes using the 50 SSR primers. A set of 34 SSR primers showed amplification in all cultivars. Similarly, 40 common fragments were amplified in all cultivars (Figure 1 and Figure 2 and Table 2)
Out of fifty primers of polymorphic SSR markers, only 47 primers showed amplification in Azeem Chaunsa, 41 primers showed amplification in Sindhri and SB Chaunsa. The highest efficiency of primers observed in Sufaid Chaunsa (Figure 1).
Seven SSR marker showed highest amplification of fragments in all cultivars; MiSHRS-48, MGDSSR11, MGDSSR34, MillHR21, mMiCIR016, mMiCIR030, and MIAC251-1. Some SSR marker primers showed very low amplification of fragments: MillHR20a and AJ635178 (Table 2).

3.2. Distribution of Unique SSRs with Polymorphism

The 50 SSR primer pairs generated a total of 154 alleles with an average of 3.08 alleles per primer pair in all cultivars of Mango. Out of 154 alleles, 130 were found polymorphic alleles. The highest allele size range was observed in following SSR markers: mMiCIRO18, MGDSSR11, MillHR21, MITGg62, MICA231-1, MICA235, MIGA2O3, MIAC251-1, and AJ635164. The highest number of alleles was generated by SSR markers in mango cultivars: mMiCIRO18, MiSHRS-48, MGDSSR5, MGDSSR34, mMiCIR030, and MICA231-1. The highest number of polymorphic alleles was mMiCIRO18, MiSHRS-48, MGDSSR5, mMiCIR030, and MICA231-1 (Table 3). The highest rate of polymorphism generated by following SSR markers: mMiCIRO18, MGDSSR5, mMiCIR030, and MICA231-1 (Table 3).

3.3. SSR Fingerprinting/ Allelic Diversity

Four mango cultivars were DNA fingerprinted. The SSR profiling of highly diverse candidate line Azeem Chaunsa cultivar exhibited polymorphism using 50 SSR markers. Out of 50 SSR, 47 SSR primer pairs yielded strong amplification in candidate cultivar. The allele size varied from 90 bp in MIAC-4 to 700 bp in MIAC251-1 in Azeem Chaunsa cultivar. The number of alleles per marker varied from 1 (LMMA1F) to 4 (MGDSSR34, MillHR21).
In total, 82 SSR alleles were amplified in the candidate line Azeem Chaunsa using 50 SSR markers. Out of 82 SSR alleles, 60 SSR alleles were detected as polymorphic. The SSR alleles of 160 bp, 200 bp (marker name mMiCIRO14), 280 bp (mMiCIRO18), 210 bp (MiSHRS-1), 280 bp (MiSHRS-48), 100 bp (MIAC-4), 390 bp (MIAC-6), 100 bp, 175 bp, 180 bp (MGDSSR34), 230 bp (mMiCIR005), 100 bp (MITGI75), 170 bp of (MITGg62), 320 bp (MICA231-1), 380 bp (MIGA2O3), 190 bp (MillHR20a) were amplified only in Azeem Chaunsa genome. The analysis revealed a total of 60 polymorphic alleles ranging from 1 to 3 per locus, with an average of 1.2 alleles per locus in candidate line (Table 4). However, there are four SSRs i.e. mMiCIRO14, MGDSSR34, MillHR21 and MITGg62 which yielded 3 alleles per locus.

3.5. DNA Fingerprinting Analysis

The genetic relation at DNA fingerprint level among the standard cultivars and candidate line was evaluated using cluster analysis. The cultivar identification diagram (CID) was constructed using UPGMA algorithm for the evaluation of genetic diversity and relatedness among the mango cultivars. CID presenting association among standard cultivars (Sufaid Chaunsa, Sindhri and S.B Chaunsa) based on the phylogenetic relationship using coefficients by NTSYS cluster analysis (Figure 3). Dice similarity coefficients were calculated for the 50 SSR markers, and a UPGMA tree was generated (Figure 3). Cluster I consists of Azeem Chaunsa and further divided into two cultivars, S.B Chaunsa and Sindhri. Cluster II consists of Sufaid Chaunsa. Cluster III consisted of Chenab-Gold. X-axis represents similarity coefficient between genotypes with ranged from 0.49-0.67. CID results concluded that candidate cultivar ‘Azeem Chaunsa’ varied significantly from the standard cultivar Sufaid Chaunsa (46.7% dissimilarity), Sindhri (46.2% dissimilarity) and SB Chaunsa (45% dissimilarity)

4. Discussion

The standard cultivars included in the present study probably represent a major component of the mango gene pool in Pakistan. Further, SSR markers have been broadly used in mango genetic research to differentiate cultivars, hybrids and to evaluate new varieties [23,24,46,47]. The improvement in genetic and agronomic traits for high yield potential in fruit plants is highly based on the proper assessment of diversity analysis. Traditionally used plant morphological, phyto-chemical as well as physio-chemical parameters have not been found to be effective in discrimination of varieties. It needs to warrant more precise techniques. Several methods and molecular techniques have been developed to detect genetic diversity within and among cultivars [48,49,50]. The discovery of molecular marker such as SSRs has improved the productivity and accuracy in classical plant breeding, playing a vital role in molecular diversity studies [51,52,53,54,55]. Systematic mango breeding is laborious, time-consuming and is a long-term endeavor (up to 25 years) due to a highly heterozygous genome as well as long juvenility. Promising selection, introduction, evaluation of cross-breeding progenies and mutational breeding have been widely used to develop mango cultivars, varieties and hybrids [39,56,57,58]. In current study, a mango candidate line was developed and further molecular diversity was analyzed with three standard cultivars using SSR markers. Highly unique, diverase cultivars of mango have been grown in Punjab and Sindh provinces of Pakistan as they have a long history of breeding. In mango, variety identification has been greatly challenging. The SSR markers are very sensitive to evaluate hybrid mango lines to identify genetic contamination. Several microsatellite as molecular markers such as SSR have been developed for the Mango [33,40,42]. These markers have shown to be reliable, consistent and reasonably discriminative for use by several laboratories as a mango genotyping tool. SSR markers developed for Mangifera indica have been used recently to evaluate mango varieties in India [33,59,60], China [24,61], Indonesia [62], Pakistan [3,63], Maxico [64] and Japan [23].
In this study, candidate mango ‘Azeem Chaunsa, cultivar in Pakistan was clearly distinguished from standard mango cultivars. In this study, 50 pairs of SSR primers were used for PCR amplifications of different bands in all four mango cultivars (Table 1 and Table 2). Among these primers, 45 pair of primers showed amplification in the candidate line ‘Azeem Chaunsa’ as shown (Figure 1 and Table 2). The highest polymorphic ratio of the SSR primers are associated with the hyper variable nature of the SSR markers[65]. The maximum number of polymorphic bands was obtained by the mMiCIRO18, MiSHRS-48, MGDSSR5, mMiCIR030, mMiCIR030, and MICA231-1 SSR primers (Table 3). MillHR21 and mMiCIR030 primers recorded the highest (twelve) amplified bands. While the lowest (one) numbers of amplified bands was recorded in MillHR20a and AJ635178 (Table 2). The polymorphism ratio of amplified alleles was observed very high, and several unique alleles were identified in the candidate line which provides important basis for subsequent use these primers. These results are in agreement with previous studies on mango [23,24]. The 45 primer pairs generated clear single-locus polymorphic bands and 5 primers pairs yielded monomorphic bands in the candidate line. The 50 SSR primer pairs generated a total of 319 fragments in 4 mango cultivars with an average of 6.38 fragments per primer. Notably, primers MGDSSR1, mMiCIR013, MillHR07a, MillHR20a, AY942826, AJ635178 and AB190349 did not present any polymorphic bands (Table 2 and Table 3).
Generally for UPGMA based cultivar identification and the construction of dendrogram, more than 10 markers were used[66]. Therefore, these highly polymorphic SSR primer pairs can be applied as core primer pairs for variety identification. In this study, DNA fingerprints of the 4 mango cultivars were constructed according to the original data matrix of amplification results (Figure 3). The number of bands produced across 4 mango varieties by different SSR primers is consistent with published reports on microsatellite frequency in the mango genome. Hence, the exportable mango varieties of Pakistan are mostly large >400g fruit weight but not have the small size mango fruits to fetch the high-end market of UK. The fruit of mango cv. Azeem Chaunsa is approximately 250g and grab the market efficiently. Further, the mango fruit has extended shelf life and good on-tree storability for more than four weeks make this cultivar on increasing trend in grabbing more area under its cultivation.

5. Patents

The candidate mango line ‘Azeem Chaunsa’ has been approved by Punjab Seed Council, Lahore as a new variety of mango for commercial cultivation throughout the Punjab.

Author Contributions

Conceptualization, J.I., M.A.A., and N.Y.; methodology, J.I., S.K., I.H., M.A.A., Q.C.; software, A.R., Y.L., Y.K., I.S.; validation, A.G.G., J.I., M.A.A., N.Y., A.R. and Y.K.; formal analysis, J.I.; investigation, S.R., J.I., M.A.A., I.S and Q.C.; resources, N.Y., J.I., Y.L.; data curation, J.I., M.A.A., Q.C.; writing—original draft preparation, M.A.A., J.I.,. Q.C., and I.S; writing—review and editing, N.Y., M.A.A., J.I., Q.C., Y.L., I.S., and Y.Y; visualization, Q.C., J.I., and M.A.A.; supervision, J.I., N.Y., and M.A.A.; project administration, J.I., and N.Y.; funding acquisition, J.I., N.Y., and Y.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Open Project of Guangxi Key Laboratory of Biology for Mango (GKLBM02203), the Central Public-interest Scientific Institution Basal Research Fund (1630052023003), Guangxi First-class Disciplines (Agricultural Resources and Environment). The APC was funded by N.Y.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are highly thankful to Chief Scientist, Agriculture Biotechnology Research Institute, AARI, Faisalabad, Pakistan for DNA fingerprinting analysis.

Conflicts of Interest

The authors declare that they no competing interest.

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Figure 1. Venn Diagram of SSR marker primer pairs showing amplifiction in mango cultivars. SSR primers amplified different fragments in four cultivars: Sufaid Chunsa, Sindhri, S.B Chunsa and Azeem Chunsa. The degree of ovelap between mango cultivars was obserevd at primer level. The intersetcion of four cultivars showed 34 common SSR primers pairs.
Figure 1. Venn Diagram of SSR marker primer pairs showing amplifiction in mango cultivars. SSR primers amplified different fragments in four cultivars: Sufaid Chunsa, Sindhri, S.B Chunsa and Azeem Chunsa. The degree of ovelap between mango cultivars was obserevd at primer level. The intersetcion of four cultivars showed 34 common SSR primers pairs.
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Figure 2. Venn Diagram showing common and exclusive fragments obtained from mango cultivars genomes. SSR primers amplified different fragments in four cultivars: Sufaid Chunsa, Sindhri, S.B Chunsa and Azeem Chunsa. The degree ovelap between mango cultivars was obserevd at fragment level. The intersetcion of four cultivars showed 40 common fragments amplified by SSR primer pairs.
Figure 2. Venn Diagram showing common and exclusive fragments obtained from mango cultivars genomes. SSR primers amplified different fragments in four cultivars: Sufaid Chunsa, Sindhri, S.B Chunsa and Azeem Chunsa. The degree ovelap between mango cultivars was obserevd at fragment level. The intersetcion of four cultivars showed 40 common fragments amplified by SSR primer pairs.
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Figure 3. Cultivar identification diagram (CID) based on hierarchical NTSYS cluster estimating Jaccard's similarity coefficient. The UPGMA based CID shows the clustering and association of mango cultivars based on SSR marker data.
Figure 3. Cultivar identification diagram (CID) based on hierarchical NTSYS cluster estimating Jaccard's similarity coefficient. The UPGMA based CID shows the clustering and association of mango cultivars based on SSR marker data.
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Table 1. List of microsatellite markers used in mango DNA fingerprinting study.
Table 1. List of microsatellite markers used in mango DNA fingerprinting study.
SSR Primer Pair ID Forward Primer Reverse Primer
LMMA1F ATGGAGACTAGAATGTACAGAG ATTAAATCTCGTCCACAAGT
LMMA7F ATTTAACTCTTCAACTTTCAAC AGATTTAGTTTTGATTATGGAG
LMMA9F TTGCAACTGATAACAAATATAG TTCACATGACAGATATACACTT
mMiCIRO14 GAGGA CATAAAGATGGTG GACAAGATAAACAAC TGGAA
mMiCIRO18 CCTCAATCTCACTCAACA ACCCCACAATCAAACTAC
mMiCIRO32 TCATTGCTGTCCCTTTTC ATCGCTCAAACAATCC
MiSHRS-1 TAACAGCTTTGCTTGCCTCC TCCGCCGATAAACATCAGACA
MiSHRS-48 TTTACCAAGCTAGGGTCA CACTCTTAAACTATTCAACCA
MIAC-4 CGTCATCCTTTACAGCGAACT CATCTTTGATCATCCGAAAC
MIAC-6 CGCTCTGTGAGAATCAAATGGT GGACTCTTATTAGCCAATGGGAG
MGDSSR1 CGAAATGAGACACCTGCAAA TTTCCTCCATTGCTTTTTCG
MGDSSR2 GGGAATGGTAGAGACGGACA ATCCAAGCAGTCACCATCAA
MGDSSR5 CGATAGTGCCAATCTGGTGA TCATCTCACACACTCTCTCTCTCTC
MGDSSR11 GGGAATGGTAGAGACGGACA TTCATCATAGGTCCCACACG
MGDSSR14 AATGCTGAGCCTGGTAAGGA CAACATCCTCTTTCTTCCCTGT
MGDSSR34 GAAAGTGAGACCTTCGGTTCC AAGGCCCCTTCTTCACATTT
MillHR21 TTTGGCTGGGTGATTTTAGC TTAATTGCAGGACTGGAGCA
mMiCIR005 GCCCTTGCATAAGTTG TAAGTGATGCTGCTGGT
mMiCIR009 AAAGATAAGATTGGGAAGAG CGTAAGAAGAGCAAAGGT
mMiCIR013 GCGTAAAGCTGTTGACTA TCATCTCCCTCAGAACA
mMiCIR016 TAGCTGTTTTGGCCTT ATGTGGTTTGTTGCTTC
mMiCIR030 GCTCTTTCCTTGACCTT TCAAAATCGTGTCATTTC
MiSHRS-37 CTCGCATTTCTCGCAGTC TCCCTCCATTTAACCCTCC
MIAC-11 GTGCGAGGAGATATCTGT CTGGTTCTTCATTGTTGAGATG
MITGI75 TGCGTCTTGTGTGTGTGTGT GGAATGCTGTGTGTGTGTG
MITGg62 TGTTCGATTTGCAAACTTTTT GGCCTAATGTGTGTGTGTG
MICA231-1 TGGAAGGACCATGCTTGAAT GGTCACACACACACACACA
MICA235 TGTCACACACACACACACA AATGGAAGGACCATGCTTGA
MIGA2O3 TGAAGGATAGGTGTGGTG CATGAGAGAGAGAGAGAGA
MIGA224 CACGAGAGAGAGAGAGAGA GGGTCTCAGAGGGAGGATTT
MIAC251-1 CCTTGGGTTCATTCGCTAAA GGACGCCACACACACACAC
MIAC251-2 TGGCGCTACACACACACAC CACACACACACACACACACG
LMMA8 CATGGAGTTGTGATACCTAC CAGAGTTAGCCATATAGAGTG
MillHR04c CGTTTTTGACCCTCTTGAGC CCGCATACTTCCCTTCACAT
MillHR06 CGCCGAGCCTATAACCTCTA ATCATGCCCTAAACGACGAC
MillHR07a GCCACTCAGCTAAATAGCCTCT TGCAGTCGGTAAAGTGATGG
MillHR11a CAGTGAAACCACCAGGTCAA TGGCCAGCTGATACCTTCTT
MillHR20a CCTAACGCGCAAGAAACATA ACCCACCTTCCCAATCTTTT
AJ635164 AAACAAAGAATGGAGCA TGGACTGAATGTGGATAG
AY942826 TGTGAAATGGAAGGTTGAG ACAGCAATCGTTGCATTC
AJ635178 GTATAAATCGCGTGCAT AGTTTCCCTCCTTGTATCT
AJ635187 ATCCCCAGTAGCTTTGT TGAGAGTTGGCAGTGTT
AY942817 TAACAGCTTTGCTTGCCTCC TCCGCCGATAAACATCAGAC
AY942825 CGAGGAAGAGGAAGATTATGAC CGAATACCATCCAGCAAAATAC
AJ635166 CTTGAAAGAGATTGAGATTG AGAAGGCAGAAGGTTTAG
AJ635184 TGTCTACCATCAAGTTCG GCTGTTGTTGCTTTACTG
AY942820 AGGTCTTTTATCTTCGGCCC AAACGAAAAAGCAGCCCA
AB190349 AATTATCCTATCCCTCGTATC AGAAACATGATGTGAACC
AY942828 CTCGCATTTCTCGCAGTC TCCCTCCATTTAACCCTCC
AJ635189 ACGGTTTGAAGGTTTTAC ATCCAAGTTTCCTACTCCT
Table 2. SSR-PCR amplification profile of 50 SSR mango markers on gDNA of four mango cultivars resulted different fragments obtained in this study. SSR loci that distinguish Azeem Chaunsa and standard mango cultivars.
Table 2. SSR-PCR amplification profile of 50 SSR mango markers on gDNA of four mango cultivars resulted different fragments obtained in this study. SSR loci that distinguish Azeem Chaunsa and standard mango cultivars.
SSR Marker ID Sufaid Chaunsa Sindhri SB Chaunsa Azeem Chaunsa
LMMA1F 290, 295 310 290, 295, 310 295
LMMA7F 260 205, 220, 260, 340 220, 340 220, 340
LMMA9F 205 0 200, 205 200
mMiCIRO14 210 205 0 160, 200, 210
mMiCIRO18 195, 240, 250, 350 380 250 250, 280
mMiCIRO32 190, 200 200 190, 200 190, 200
MiSHRS-1 180 175, 180 175, 240 210
MiSHRS-48 180, 190, 200 180 180, 210, 220 180, 200, 280
MIAC-4 90, 125 90, 125 90 90, 100, 125
MIAC-6 250 0 250 390
MGDSSR1 205 205 205 205
MGDSSR2 260, 270 260 150, 260 0
MGDSSR5 155, 295 155, 190, 300 160, 300 300
MGDSSR11 190, 240, 390 190, 240, 390 200, 240 190, 240
MGDSSR14 150, 200, 225, 250 150 150, 225 200
MGDSSR34 150, 190 150, 190 150, 190 100, 150, 175, 180
MillHR21 140, 310, 400, 425 400 140, 400, 425 140, 310, 400, 425
mMiCIR005 210, 240, 250 210, 240 250 230
mMiCIR009 175, 240 175, 220, 240 220 220
mMiCIR013 160, 220 160, 220 160, 220 160, 220
mMiCIR016 250, 260, 280, 360 260 250, 260, 280 250
mMiCIR030 230, 245, 250 245, 250, 295 180, 245, 290, 295 180, 290
MiSHRS-37 200, 220 200 140, 245 140, 200
MIAC-11 145, 150 145, 150 145, 150 145
MITGI75 110, 150, 175 175 0 100, 110
MITGg62 450 175, 200 200 170, 200, 450
MICA231-1 300 600 195, 300, 620 320, 600
MICA235 120, 200, 400 0 400 200
MIGA2O3 155, 275 155 155 155, 275, 380
MIGA224 250, 300 250 300 250, 300
MIAC251-1 350, 600, 700 350, 600 350, 600 350, 600, 700
MIAC251-2 200 175, 200 175, 200 200
LMMA8 480 430 0 430
MillHR04c 160, 250 160 0 160
MillHR06 105 0 120 105
MillHR07a 160 0 160 160
MillHR11a 190, 220, 290 220 220 190, 220
MillHR20a 0 0 0 190
AJ635164 240, 380 240 240 240, 380
AY942826 225 0 0 225
AJ635178 240 0 0 0
AJ635187 240, 250 290 290 240
AY942817 200, 210, 250 200 190, 200 210, 250
AY942825 230, 260, 280 260 0 260
AJ635166 225, 250, 290 225 225 225, 290
AJ635184 160, 165, 190 175 165 165, 175
AY942820 200, 205, 250 205, 250 200, 250 205, 250
AB190349 130 130 130 0
AY942828 130, 135, 160 0 0 135
AJ635189 145, 155 145 145 145, 155
Table 3. Allele distribution, polymorphism and diversity in four mango cultivars.
Table 3. Allele distribution, polymorphism and diversity in four mango cultivars.
SSR Primer Pair ID Tm °C Allele Size (bp) No. of Loci No. of Polymorphic loci Polymorphic loci %
LMMA1F 59 290-310 3 3 100
LMMA7F 55 205-340 4 4 100
LMMA9F 56 200-205 2 2 100
mMiCIRO14 57 160-210 4 4 100
mMiCIRO18 59 195-380 6 6 100
mMiCIRO32 57 190-200 2 1 50
MiSHRS-1 65 175-240 4 4 100
MiSHRS-48 57 180-280 6 5 83.33
MIAC-4 59 90-125 3 2 66.66
MIAC-6 65 250-390 2 2 100
MGDSSR1 62 205 1 0 -
MGDSSR2 65 150-270 3 3 100
MGDSSR5 65 155-300 5 5 100
MGDSSR11 65 190-390 4 3 75
MGDSSR14 65 150-250 4 4 100
MGDSSR34 65 100-190 5 4 80
MillHR21 64 140-425 4 4 100
mMiCIR005 58 210-250 4 4 100
mMiCIR009 57 175-240 3 3 100
mMiCIR013 60 160-220 2 0 -
mMiCIR016 58 250-360 4 4 100
mMiCIR030 55 180-295 6 6 100
MiSHRS-37 65 140-245 4 4 100
MIAC-11 61 145-150 2 1 50
MITGI75 65 100-175 4 4 100
MITGg62 59 170-450 4 4 100
MICA231-1 65 195-600 5 5 100
MICA235 65 120-400 3 3 100
MIGA2O3 60 155-380 3 2 66.66
MIGA224 63 250-300 2 2 100
MIAC251-1 64 350-700 3 1 33.33
MIAC251-2 65 175-200 2 1 50
LMMA8 60 430-480 2 2 100
MillHR04c 65 160-250 2 2 100
MillHR06 65 105-120 2 2 100
MillHR07a 65 160 1 0 0
MillHR11a 65 190-290 3 2 66.66
MillHR20a 64 190 1 0 0
AJ635164 56 240-380 2 1 50
AY942826 60 225 1 0 0
AJ635178 57 240 1 0 0
AJ635187 61 240-290 3 3 100
AY942817 65 190-250 4 4 100
AY942825 62 230-280 3 2 66.66
AJ635166 56 225-290 3 2 66.66
AJ635184 59 160-190 4 4 100
AY942820 64 200-250 3 2 66.66
AB190349 57 130 1 0 0
AY942828 65 130-160 3 3 100
AJ635189 58 145-155 2 1 50
Table 4. Allele distribution and polymorphism was estimated in Azeem Chaunsa cultivar.
Table 4. Allele distribution and polymorphism was estimated in Azeem Chaunsa cultivar.
SSR Loci ID Nature Polymorphic type Polymorphic alleles (N.) Allele size (bp)
LMMA1F polymorphic co-dominate 1 295
LMMA7F polymorphic co-dominate 2 220, 340
LMMA9F polymorphic co-dominate 1 200
mMiCIRO14 polymorphic co-dominate 3 160, 200, 210
mMiCIRO18 polymorphic co-dominate 2 250, 280
mMiCIRO32 polymorphic co-dominate 1 190, 200
MiSHRS-1 polymorphic co-dominate 1 210
MiSHRS-48 polymorphic co-dominate 2 180, 200, 280
MIAC-4 polymorphic co-dominate 2 90, 100, 125
MIAC-6 polymorphic co-dominate 1 390
MGDSSR1 monomorphic dominant 0 205
MGDSSR2 polymorphic co-dominate 0 0
MGDSSR5 polymorphic co-dominate 1 300
MGDSSR11 polymorphic co-dominate 1 190, 240
MGDSSR14 polymorphic co-dominate 1 200
MGDSSR34 polymorphic co-dominate 3 100, 150, 175, 180
MillHR21 polymorphic co-dominate 3 140, 310, 400, 425
mMiCIR005 polymorphic co-dominate 1 230
mMiCIR009 polymorphic co-dominate 1 220
mMiCIR013 polymorphic co-dominate 0 160, 220
mMiCIR016 polymorphic co-dominate 1 250
mMiCIR030 polymorphic co-dominate 2 180, 290
MiSHRS-37 polymorphic co-dominate 2 140, 200
MIAC-11 polymorphic co-dominate 0 145
MITGI75 polymorphic co-dominate 2 100, 110
MITGg62 polymorphic co-dominate 3 170, 200, 450
MICA231-1 polymorphic co-dominate 2 320, 600
MICA235 polymorphic co-dominate 1 200
MIGA2O3 polymorphic co-dominate 2 155, 275, 380
MIGA224 polymorphic co-dominate 2 250, 300
MIAC251-1 polymorphic co-dominate 1 350, 600, 700
MIAC251-2 polymorphic co-dominate 0 200
LMMA8 polymorphic co-dominate 1 430
MillHR04c polymorphic co-dominate 1 160
MillHR06 polymorphic co-dominate 1 105
MillHR07a monomorphic dominant 0 160
MillHR11a polymorphic co-dominate 1 190, 220
MillHR20a monomorphic dominant 0 190
AJ635164 polymorphic co-dominate 1 240, 380
AY942826 monomorphic dominant 0 225
AJ635178 monomorphic dominant 0 0
AJ635187 polymorphic co-dominate 1 240
AY942817 polymorphic co-dominate 2 210, 250
AY942825 polymorphic co-dominate 1 260
AJ635166 polymorphic co-dominate 1 225, 290
AJ635184 polymorphic co-dominate 2 165, 175
AY942820 polymorphic co-dominate 1 205, 250
AB190349 monomorphic dominant 0 -
AY942828 polymorphic co-dominate 1 135
AJ635189 polymorphic co-dominate 1 145, 155
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