Present study was performed to identify the genetic make-up of thirteen mid-season mango verities i.e.Zardalu, Banganpali, Taimuria, Saleh Bhai, Badia Muna Syed, Intikhab, Anmole, Bombay Alphanso, Ghulab Khas, Pan, Lahotia, Kachnaliwala, and Jo villagey by applying seventy SSR markers, data collected on genetic diversity estimation bifurcated the varieties into three clusters. cluster 1, enclosed three varieties namely Bombay Alphonso, Intikhab, and Anmole together in genetic resemblance. Similarly, the cluster 2 assembled seven varieties i.e. Jo Village, Taimuria, Zardalu, Bagan Palli, Saleh Bhai, Badia and Munna Syed, making it the larger cluster. Lastly, the genetic similarity of the remaining four varieties such as Pan, Ghulab Khas, Lahotia, and Kachnalwala was found in close proximity. The current study singled out three unique fingerprints amongst the seventy SSRs applied. Baganpali cultivar contained unique fingerprints against primer MiKVR_a010; Gulab khas cultivar showcased unique characters against primer MiKVR_a052 and another distinguished fingerprint was observed in mango cv Baganpali against primerMiKVR_a109. It was an indication that these varieties possessed specific genotypic behavior against applied markers. It is concluded that these varieties can play a significant role in improving the genetic makeup of mango varieties and can divulge useful features for varietal selection in a breeding program.
Research Article
Molecular Finger Printing of Thirteen Pakistani Mid-season Mango (Mangifera Indica L.) Varieties by Using Ssr Markers
https://doi.org/10.21203/rs.3.rs-1868360/v1
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Mango
Genetic
Markers
DNA
diversity
Mango (Mangifera indica L) belongs to the Anacardiaceae family, well known for enclosing the fruits of special traits such as cashew. Pakistan is a good producer of mango after citrus fruits with total 167.5 thousand hectares cultivated area with 1,732 thousand tons annual production. The country is contributing 8.5% of world mango trade (Nazish et al., 2017). Punjab and Sindh are two top mango producing provinces of Pakistan. The Punjab province’s districts spearheading in mango production are Multan, Rahim Yar khan, Bahawalpur and Muzzafargarh. Some popular and exportable varieties grown in the region are Sindhri, Black Chaunsa, Sammar Bahisht, Sufaid Chaunsa, Chenab Gold and Azeem Chaunsa. Mango germplasm studies are desirable in horticulture to characterize its morphological traits. There is an ambiguity in validating the parentage of genotypes. Therefore, it is important to understand the link between genotypes before starting any breeding program so that appropriate parents can be selected (Krishna & Singh, 2007). Breeding in mango is complicated due to long-term factors such as heterozygosity, low fruit set, polyembryonic behavior, long duration of evaluation, juvenility and out crossing behavior. Moreover, the conventional methods of parent selection are slow and ambivalent (Ravishankar et al., 2015). Nonetheless, the use of molecular markers in identifying parents has high efficiency and authentic predictability. Mango breeding is affected due to lack of genome sequence as there is limited information about the markers present in mango genome and the application of molecular technologies is limited in mango breeding programs(Kuhn et al., 2017). Molecular characterization is considerable and reliable due to advantageous properties such as less time consumption and high accuracy. Similarly, the results obtained by molecular investigation are more authentic. It helps to identify those useful traits which are costly and difficult to observe. Markers base identification of polymorphism is helpful because it targets specific gene and minimize the change of linkage lose during recombination(Dillon et al., 2014).Molecular markers are available is many types depending on their application and ability to identify genomes such as Randomly Amplified Polymorphic (RAPD), Amplified Fragment Length Polymorphism (AFLP), Polymerase Chain Reaction (PCR) based markers and Simple Sequence Repeat (SSR) (Bukhari et al., 2022). PCR markers are used for genetic diversity analysis. Likewise, RAPD, AFLP and SSR are frequently used for investigative purpose. SSR markers are preferred due to their ability to identify co-dominance, reproducibility, multiallelic nature, high relative abundance, auto-mutation, chromosomal location and authentic genomic coverage (Gupta, Kumari, Muthamilarasan, Parida, & Prasad, 2014). In mango genetic variability, SSR markers are frequently used because of complex nature of mango genome, so short sequences are more handy for its characterization (Shamili, Fatahi, & Hormaza, 2012). Therefore, SSRs are useful in mango genome sequencing studies due to their characters, which can be utilized for the fruit’s improvement within short duration and enhancing its genome accessibility for other molecular techniques (Mahato et al., 2016).As stated earlier, morphology is influenced by environmental conditions; thereby, morphological studies do not truly showcase genotypic representation of a specie. Moreover, apart from its arduous nature and uncertain results, morphological identification is a time-consuming process to get desired characters on later ages of the plant. We cannot differentiate limited polymorphism by using morphological and protein markers. However, genetic markers are helpful to identify polymorphism among the species (Bukhari et al., 2022). In this connection, a study was devised to find out genetic diversity among thirteen mid season Pakistani mango varieties to find the unique fingerprints among them that can aid in charting out futuristic breeding programs in the country.
Leaf samples of thirteen mid-season mango (Mangifera indica L) varieties were picked from Mango Research Station, Shujabad, district Multan. Varieties were selected for simple sequence repeat (SSR) based investigation of molecular characteristics and fingerprinting. Fully developed fresh young leaves, were collected for grinding and DNA extraction. Leaves were packed in sealed airtight zipper bags with respective variety tags, kept in ice box and were brought to laboratory where they were stored in -20℃ refrigerator till further used for DNA extraction. The name of the cultivars are given in Table 1.
DNA extraction and SSR marker analysis : DNA was extracted using cetyl trimethyl ammonium bromide (CTAB) protocol proposed by Doyle and Doyle (1990) (Doyle, Doyle, & Brown, 1990) with minor modifications (Oza et al., 2008). Quality of extracted DNA was accessed on 0.8% gel by using gel electrophoresis and visualized on Trans Illuminator. . Spectrophotometer was used for DNA quantification and finally concentration of 25.0 ng/μL of DNA was made for PCR analysis. 70 seventy SSR markers (Table 2) were used for genetics analysis of thirteen mango (Mangifera indica)varieties.
Polymerase chain reaction (PCR): PCR was performed by using simple sequence repeat (SSR) marker as primers, each PCR tube was containing total volume of 20 µl with 5 µl of DNA template, 4 µl of buffer with added MgCl2, 2 µl of forward and reverse primer and 0.25 µl of Taq Polymerase. Gene Amplification system (Applied Biosystems) was used for PCR. The profile was set as initial denaturation of DNA for 5 min at 95°C and followed by 35 cycles of 95℃ for 1 min, 55°C for 30 sec (annealing) and 72°C for 1 mint (extension). DNA bands were separated on PAGE (Chrambach and Rodbard, 1971) and only reproducible and robust bands were scored for further data analysis
Data analysis: DNA bands appeared on PAGE were scored as 0 and1 depicting the absence and presence of the bands respectively. Polymorphism was calculated on the basis of these bands. Software POPGENE (version 1.32) was used for calculation of pairwise genetic distance (Nei 1973).The genetic relatedness between the varieties was reported by calculating the genetic distances and identities according to Nei’s coefficient. The unweighted pair group method with arithmetic averages (UPGMA) was performed for cluster analysis.
The polymorphic information content (PIC) was calculated for individual SSR marker separately by using the formula: PIC= 1-∑P2ijand Pij is the frequency of the jth allele of marker i (Anderson et al., 1993)
SSR Marker Based Polymorphism
70 SSR primers were used to obtain amplified product of genetic DNA of 13 mango varieties (Mid-Season). During the study banding pattern was observed stable for all primers in investigated thirteen mango varieties.This banding pattern helped in effective way to check polymorphism. Total 240 loci were detected among which 162 loci were polymorphic and percentage for polymorphism for each primer is shown in table 3.
Polymorphic Information Content (PIC)
Polymorphic Information Content (PIC) is a fundamental parameter to count degree of informative data for any marker. Different markers for sample genome can give different ranges of polymorphism. In this study, the values of polymorphism were calculated where 0.000 was minimum value and 0.8348 was maximum value (Table 3). These values provided base to understand genetic diversity and genetic relatedness of the mango varieties.
Phylogenetic Tree: To understand the genetic distance among the varieties, phylogenetic tree was constructed with TFPGA for all amplified patterns of primers. A UPGMA (unweighted pair group with arithmetic average). Dendrogram divided the varieties into three clusters. Those varieties which appeared in same clusters were close relative and sharing common genetic formation. Clusters were named as Cluster 1, Cluster 2 and Cluster 3. (Fig. 1). Cluster 1: Cluster 1 consisted of three varieties named as Bomby Alphanso, Anmole and Intikhab. Intikhab and Anmol were more genetically similar (0.7143) than Bomby Alphanso (0.600, 0.571).. This similarity index value showed that Anmole and intikhab were genetically similar with maximum value. Cluster 2: Cluster 2 contained total six varieties and further bifurcated in Cluster 2(A) and Cluster 2(B). Cluster 2(A) contained only one variety Jo village and Cluster 2(B) comprised by five varieties i.e. Taimuria, Zardalu, baganpali, Saleh bhai and Badia muna syed. Genetic similarity values of Jo village with Taimuria, Zardalu baganpali, Saleh bhai and Badia muna syed were 0.4143, 0.3714, 0.3714, 0.4714 and 0.4429, respectively. Genetic similarity showed that Jo village has more genetic distance to all these varieties in Cluster 2. Genetic similarity value between Taimuria and Zardalu was 0.5429. The varieties Taimuria and Baganpali shared genetic similarity value as 0.5143 whereas this value for Zardalu and Baganpali was 0.5857. These similarity values were proximal to each other so these three varieties were placed in same sub cluster. Besides, Saleh bhai and Badia Muna Syed were two cultivars placed into same sub branch and their similarity value was 0.5714.
Cluster 3: Cluster 3 contained total 4 varieties and divided into two sub clusters cluster 3(A) and cluster 3(B). Cluster 3(A) contained two varieties Pan and Gulab khas while cluster 3(B) contained
Lahotia and Kachnaliwala with similarity value 0.67
DNA Fingerprinting
Pattern of DNA Fingerprinting
Thirteen Mid-season varieties of Mango were analyzed by using seventy SSR markers. DNA fingerprints were separated on PAGE. Total 240 loci were counted by using seventy primers. Profile of primer products helped to understand the fingerprints of thirteen cultivars of mango on molecular bases. Most common range of loci or allele for primers was 2-6 loci for each sample. MiKVR_a079, MiKVR_a052, MiKVR_a026 were primers which gave maximum number of alleles (6). Most of primers gave 5 numbers of alleles. MiKVR_a108 was only primer which gave one allele in one locus with monomorphic pattern and 0% polymorphism. However, only three unique fingerprints were obtained as reported below.
Unique Fingerprints
Fingerprints or bands which appeared in different base pair size and unique banding pattern of any sample as compared to all other varieties were called as unique fingerprints. In the thirteen selected cultivars, three fingerprints were showing different product pattern for following SSR primers(Table 6).
Primer MiKVR_a010: MiKVR_a010 primer gave 5 alleles with 100% polymorphism. Sample 2 of baganpali variety gave unique banding pattern. Two alleles were obtained for this primer 2nd allele appear in different form when compared to leader which showed this sample have unique locus which can be associate with any useful trait.
Primer MiKVR_a052: MiKVR_a054 gave multiple alleles for half cultivars while only 1 to 2 alleles for remaining cultivars. However, sample 9 of variety Gulab khas showed different band which was showing more base pair size as compared to rest of varieties which can be associated with useful characteristic. This molecular base locus discovery can be useful for phenotypic comparison for variety selection. Following 3 is showing fingerprints for this primer.
Primer MiKVR_a109: MiKVR_a109 gave unique fingerprinting pattern for sample 2 of baganpali. Sample gave 5 alleles among them allele 5 gave different band location on gel. Locus 5 of sample do not appeared in any other varieties. This band showed locus has lighter product size.
Genetic Diversity
Genetic diversity is the basic knowledge about a genotype’s similarity and difference. It helps to understand pressure of environment during evolutionary process. This study representing diversity of thirteen mid-season mango varieties with the help of 70 SSR markers and was effective in showing genetic diversity among the cultivars. Polymorphism was calculated for every primer against all the varieties. The majority of primers provided polymorphism value in range of 50-83%. Six primers showed no polymorphism; whereas, few primers provided 33.3% value of polymorphism content. Four primers showed 100% polymorphism. Average value for polymorphism of all primers was calculated 67% according to total polymorphic band calculation in all varieties. Hence, all markers showed moderate level of polymorphism. PIC value basically helps to find out genetic diversity of any gene in a specific population. Hence, the evolutionary effects can be found with help of PIC value. PIC value was also calculated for all primers. The Highest calculated value was 0.834 against primer MiKVR_a040. Allelic variation was found for each marker. PIC data for this research showed that seven markers gave 0.00 values so no allele variations were observed for the gene tagged to these markers. Majority of PIC value fall in range of 0.35-0.8. Allelic variation was also moderate for mango. Jaccard’s similarity coefficient is known as common genomic content among sets or group of many genotypes and it was calculated by comparing each variety. If two genotypes are exactly similar, they give maximum value which is 1.00. Whereas if no similarity occur among two genotypes this value become 0.00. Maximum similarity value was 0.7143 among baganpali and Kachnaliwala varieties. Minimum value 0.3143 was calculated among Pan and Baganpali varieties. Frequency based genetic distance was also calculated to find out close relative among given varieties. Maximum value of genetic distance can be 1 for unrelated species while low value of genetic distance shows genetic relatedness. For this study, common range of genetic distance among varieties was 0.35-0.65 approximately.
The results proved that the mango varieties had moderate level of evolutionary effects and the species are related to each other. Similarly, the allelic variations were observed but no unique and unrelated genotype was observed as similarity coefficient was not giving value ‘1’ for any marker against any cultivar. Cluster analysis with help of UPGMA and coordinate analysis by using SSR markers reflected the pattern of pedigree among cultivars and Dendrogram indicated sharing of same allele in closely linked member of same cluster. However, the selected cultivars were divided into three main clusters on the basic of their genetic divergences to each other. First cluster consisted of 3 cultivars, which were further branched into two clusters placing Anmole separately from Bombay Alphonso and Intikhab (Table 4). Second cluster was having maximum number of cultivars. Five varieties-- Taimuria, Zardalu, baganpali, Saleh bhai and Badia munawala—showcased more genetic semblance; thereby, fitted into one sub-branch unlike Jo Village, which was perfected into its own branch because of its varied genetic distance to the others (Table 4). The third cluster was sub divided into two sub branches and each branch was containing two varieties. Pan and Gulab khas were closed relative on basis of genetic relatedness whereas Kachnaliwala and Lahotia were genetically closed to each other. On the basis of polymorphic content, frequency based genetic distance and similarity and phylogenetic tree helped to describe genetic diversity.
Unique Fingerprinting
The results showed that Baganpali cultivar contained unique fingerprint against primer MiKVR_a010 and this type of banding pattern was not observed in any other cultivar. Second unique fingerprint was identified in Gulab khas cultivar against primer MiKVR_a052 which made variety’s specific genotypic behavior. Last variety with unique fingerprint was Baganpali against MiKVR_a109. These varieties can play significant role in important of mango production as their genotype can reveal useful feature for varietal selection in breeding process.
After investigating diverse nature of mango, the differences among the genome of cultivars showed the effect of epigenetic modification, condition in which genes show same sequence but their phenotypic behavior is different, due to environmental influences (Achark et al., 2002) Somatic modification and bud variations are also contributing in polymorphism in mango and other wood plants (Ahmad et al., 2006). Same clones of genome can show notable variation in their visual characteristic such as fruit taste and aroma etc. proving the role of environment. Some superior mango clones can explain somatic mutation effects. Sometimes, somatic mutation do not cause any change in phenotype but can identify at genetic or molecular level and leads to variations among cultivars (Weir, 1990).
This study concluded that SSRs are powerful tools for genetic studies in mango’s improvement as the diversity of genome has been analyzed in 13 mid-season mango varieties by using these markers effectively. As a result, the relativeness, diversity, and similarity among the selected genotypes were found with the help of the data generated using SSR based amplified genomic product. In a similar manner, DNA fingerprinting and its contribution to the molecular based study was highlighted and unique fingerprints were reported in three SSRs. Further studies can employ these unique fingerprints in association with the morphological characters of the varieties to get useful traits and beneficial characteristics in the hybrids. It is also suggested to compare diversity of these mid-season cultivars with other mango cultivars so that genetic relatedness can be reported at larger level. Similarly, PIC (polymorphism information content) should be compared with remaining mango varieties in region so that effect of epigenetic and environment can be understood in more effective manner.To overcome the problems being faced by the current commercial cultivars of mango, it is high time that the molecular markers-based approaches must be adopted over the conventional breeding programs wherein genetic similar parents are being crossed repeatedly without getting any tangible results.
Acknowledgment: This work is an outcome of M.Sc. (Hons.) Dissertation.
Ethical Approval: Not applicable (No human and/ or animal studies has been involved in this study.)
Conflict of Interest: Authors declare no potential conflict of interest.
Funding: The authors did not receive support from any organization for the submitted work.
Authors Contribution:
SR: Conceived the idea, Designed the experiment and wrote the manuscript HS: Helped in Data collection MB: Supervised the research work AG: Helped in Data analysis FS: Helped in data collection, BB: Helped in execution of research, AM: Helped in data collection SR: Helped in data analysis and proof read the manuscript, SK: Helped in data collection, MAQ: Helped in data analysis and write up, MSZ: Helped in data collection, NS: improved the language of paper and proof read the paper, and SIUSB: Helped in data analysis.
Availability of data and materials: Not applicable
- Ahmad, I, Malik, A.U., Tabassam, N., Rahman, M. and Zafar, Y. (2006. Application of DNA fingerprinting technology to estimate genetic divergence among mango cultivars-genotypes. Paper presented at the XXVII International Horticultural Congress-IHC2006: International Symposium on Citrus and Other Tropical and Subtropical 773.
- Anderson, C.L., Carew, E.A., Powell, J.R. 1993. Evolution of the Adh locus in the Drosophila willistoni group: the loss of an intron and shift in codon usage. Mol. Biol. Evol. 10(3): 605–618.
- Bukhari, B., Mehvish, A., Babar, M., Raza, S., Ullah, S. I., Qureshi, M. A., Zafar, M. S., Naz, A., Rauf, S., Shaukat, F., and Rauf, N. 2022. Geneticdiversity estimation among thirteen late maturing mango varieties using SSR markers. Plant Cell Biotechnology and Molecular Biology, 23(21–22), 32–41.
- Chrambach, A., Rodbard, D. 1971 Polyacrylamide gel electrophoresis. Science.; 172:440–451.
- Collard B., Jahufer M, Brouwer J, Pang E. (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica, 142:169–196.
- Dillon, N.L., David, J., Bally, I.S.E., Wright, C.L., Devitt, L.C. and Dietzgen, R.G. 2014. Expressed sequence tag-simple sequence repeat (EST-SSR) marker resources for diversity analysis of mango (Mangifera indica L.). Diversity, 6: 72–87.
- Doyle, J.J., Jane, L. and Brown, A.H.D. 1990. A chloroplast-DNA phylogeny of the wild perennial relatives of soybean (Glycine subgenus Glycine): congruence with morphological and crossing groups. Evolution, 44: 371–389.
- Krishna, H. and Singh, S.K. 2007. Biotechnological advances in mango (Mangifera indica L.) and their future implication in crop improvement—a review. Biotechnology Advances, 25: 223–243.
- Kuhn, D.N., Bally, I.S.E., Dillon, N.L., Innes, D., Groh, A.M., Rahaman, J. Sherman, A. 2017. Genetic map of mango: a tool for mango breeding. Frontiers in plant science, 8: 577.
- Mahato, A.K. Sharma, N., Singh, A., Srivastav, M. Singh, S.K. Singh, A.K. Singh, N.K. 2016. Leaf transcriptome sequencing for identifying genic-SSR markers and SNP heterozygosity in crossbred mango variety ‘Amrapali’(Mangifera indica L.). PloS one, 11(10), e0164325.
- Nazish, T., Shabbir, G., Ali, A., Sami-ul-Allah, S., Naeem, M., Javed, M. and Aslam, K. 2017. Molecular diversity of Pakistani mango (Mangifera indica L.) varieties based on microsatellite markers. Genetics and Molecular Research, 16: 213–128
- Nei, M. and Li, W.H. 1979. Mathematical Model for Studying Genetic Variation in Terms of Restriction Endonucleases. Proceedings of the National Academy of Sciences of the United States of America 76:5269–5273.
- Shamili, M., Fatahi, R., and Hormaza, J.I. 2012. Characterization and evaluation of genetic diversity of Iranian mango (Mangifera indica L., Anacardiaceae) genotypes using microsatellites. Scientia Horticulturae, 148: 230–234.
- Weir, B.S. 1990. Genetic data analysis. Methods for discrete population genetic data: Sinauer Associates, Inc. Publishers.
Tables 2 and 4 are not available with this version.
Table 1: Mid season Mango varieties of Pakistan used in this Study
|
Sr. No. |
Cultivars Name |
Place of collection |
Taxonomic labels |
|
1 |
Zardalu |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
2 |
Baganpali |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
3 |
Taimuria |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
4 |
Saleh bhai |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
5 |
Badia Muna Syed |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
6 |
Intikhab |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
7 |
Anmole |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
8 |
Bombay Alphanso |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
9 |
Ghulab Khas |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
10 |
Pan |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
11 |
Lahotia |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
12 |
Kachnaliwala |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
|
13 |
Jo village |
Mango Research Station Shujabad |
(Mangifer Indica L.) |
Table 3. Allelic frequencies, allelic number, genetic diversity, Polymorphic Iinformation Content and Polymorphism percentage of 70 SSR primers
|
SSR Primers |
Major Allelic Frequency |
Allele No |
Gene Diversity |
PolymorphicInformation Content |
NUMBER OF LOCI
|
POLYMORPHISM PERCENTAGE
|
|
|
Total |
polymorphic percentage |
||||||
|
MiKVR_a002 |
0.5385 |
6.0000 |
0.6627 |
0.6337 |
3 |
3 |
100% |
|
MiKVR_a008 |
1.0000 |
1.0000 |
0.0000 |
0.0000 |
2 |
0 |
0% |
|
MiKVR_a009 |
0.3077 |
8.0000 |
0.8284 |
0.8092 |
6 |
5 |
83% |
|
MiKVR_a010 |
0.3846 |
7.0000 |
0.7811 |
0.7563 |
5 |
5 |
100% |
|
MiKVR_a011 |
0.6154 |
2.0000 |
0.4734 |
0.3613 |
3 |
2 |
66.66% |
|
MiKVR_a013 |
0.9231 |
2.0000 |
0.1420 |
0.1319 |
2 |
1 |
50% |
|
MiKVR_a014 |
0.3846 |
6.0000 |
0.7574 |
0.7239 |
5 |
4 |
80% |
|
MiKVR_a019 |
0.6154 |
4.0000 |
0.5562 |
0.5056 |
5 |
4 |
80% |
|
MiKVR_a026 |
0.5385 |
3.0000 |
0.5562 |
0.4652 |
6 |
5 |
83% |
|
MiKVR_a027 |
0.6154 |
2.0000 |
0.4734 |
0.3613 |
2 |
1 |
50% |
|
MiKVR_a029 |
0.4615 |
4.0000 |
0.6272 |
0.5556 |
5 |
4 |
80% |
|
MiKVR_a032 |
0.3846 |
5.0000 |
0.7219 |
0.6760 |
5 |
3 |
60% |
|
MiKVR_a033 |
0.3077 |
6.0000 |
0.7929 |
0.7630 |
5 |
4 |
80% |
|
MiKVR_a034 |
0.6923 |
2.0000 |
0.4260 |
0.3353 |
2 |
1 |
50% |
|
MiKVR_a039 |
0.4615 |
3.0000 |
0.6154 |
0.5353 |
3 |
2 |
66.66% |
|
MiKVR_a040 |
0.2308 |
8.0000 |
0.8521 |
0.8348 |
5 |
4 |
80% |
|
MiKVR_a045 |
0.7692 |
4.0000 |
0.3905 |
0.3693 |
5 |
4 |
80% |
|
MiKVR_a047 |
0.5385 |
2.0000 |
0.4970 |
0.3735 |
3 |
2 |
66.66% |
|
MiKVR_a048 |
0.6923 |
2.0000 |
0.4260 |
0.3353 |
2 |
1 |
50% |
|
MiKVR_a050 |
0.3077 |
5.0000 |
0.7574 |
0.7176 |
4 |
3 |
75% |
|
MiKVR_a051 |
0.8462 |
2.0000 |
0.2604 |
0.2265 |
3 |
2 |
66.66% |
|
MiKVR_a052 |
0.5385 |
2.0000 |
0.4970 |
0.3735 |
6 |
5 |
83% |
|
MiKVR_a054 |
0.8462 |
2.0000 |
0.2604 |
0.2265 |
2 |
1 |
50% |
|
MiKVR_a055 |
0.3846 |
5.0000 |
0.7456 |
0.7067 |
6 |
4 |
66.66% |
|
MiKVR_a056 |
0.5385 |
7.0000 |
0.6746 |
0.6529 |
6 |
5 |
83% |
|
MiKVR_a058 |
0.6923 |
2.0000 |
0.4260 |
0.3353 |
2 |
1 |
50% |
|
MiKVR_a059 |
0.8462 |
2.0000 |
0.2604 |
0.2265 |
3 |
2 |
66.66% |
|
MiKVR_a064 |
0.7692 |
3.0000 |
0.3787 |
0.3434 |
4 |
3 |
75% |
|
MiKVR_a065 |
1.0000 |
1.0000 |
0.0000 |
0.0000 |
2 |
0 |
0% |
|
MiKVR_a067 |
0.6923 |
2.0000 |
0.4260 |
0.3353 |
3 |
1 |
33.33% |
|
MiKVR_a068 |
0.4615 |
6.0000 |
0.7219 |
0.6912 |
5 |
4 |
80% |
|
MiKVR_a070 |
0.4615 |
5.0000 |
0.6746 |
0.6231 |
5 |
4 |
80% |
|
MiKVR_a071 |
0.7692 |
3.0000 |
0.3787 |
0.3434 |
3 |
2 |
66.66% |
|
MiKVR_a073 |
0.3846 |
5.0000 |
0.7337 |
0.6905 |
4 |
3 |
75% |
|
MiKVR_a075 |
0.6154 |
3.0000 |
0.5207 |
0.4434 |
3 |
2 |
66.6% |
|
MiKVR_a078 |
0.6923 |
2.0000 |
0.4260 |
0.3353 |
3 |
1 |
33.3% |
|
MiKVR_a079 |
0.3077 |
7.0000 |
0.8047 |
0.7791 |
6 |
5 |
83% |
|
MiKVR_a080 |
0.4615 |
5.0000 |
0.6982 |
0.6560 |
4 |
3 |
75% |
|
MiKVR_a083 |
0.9231 |
2.0000 |
0.1420 |
0.1319 |
2 |
1 |
50% |
|
MiKVR_a088 |
0.6923 |
2.0000 |
0.4260 |
0.3353 |
2 |
1 |
50% |
|
MiKVR_a090 |
0.6154 |
3.0000 |
0.5207 |
0.4434 |
3 |
2 |
66.6% |
|
MiKVR_a094 |
0.5385 |
2.0000 |
0.4970 |
0.3735 |
3 |
2 |
66.6% |
|
MiKVR_a097 |
1.0000 |
1.0000 |
0.0000 |
0.0000 |
2 |
0 |
0% |
|
MiKVR_a098 |
1.0000 |
1.0000 |
0.0000 |
0.0000 |
3 |
0 |
0% |
|
MiKVR_a099 |
0.6923 |
2.0000 |
0.4260 |
0.3353 |
3 |
2 |
66.6% |
|
MiKVR_a100 |
0.4615 |
5.0000 |
0.6982 |
0.6560 |
5 |
4 |
80% |
|
MiKVR_a101 |
0.6923 |
4.0000 |
0.4852 |
0.4505 |
4 |
3 |
75% |
|
MiKVR_a102 |
0.4615 |
3.0000 |
0.6154 |
0.5353 |
4 |
2 |
50% |
|
MiKVR_a108 |
1.0000 |
1.0000 |
0.0000 |
0.0000 |
1 |
0 |
0% |
|
MiKVR_a109 |
0.3846 |
4.0000 |
0.6746 |
0.6130 |
4 |
3 |
75% |
|
MiKVR_a112 |
0.4615 |
3.0000 |
0.5680 |
0.4723 |
2 |
2 |
100% |
|
MiKVR_a113 |
0.6154 |
4.0000 |
0.5562 |
0.5056 |
3 |
2 |
66.6% |
|
MiKVR_a114 |
0.4615 |
3.0000 |
0.6391 |
0.5659 |
4 |
3 |
75% |
|
MiKVR_a117 |
0.4615 |
4.0000 |
0.6746 |
0.6197 |
4 |
3 |
75% |
|
MiKVR_a118 |
0.5385 |
2.0000 |
0.4970 |
0.3735 |
3 |
2 |
66.6% |
|
MiKVR_a119 |
0.7692 |
2.0000 |
0.3550 |
0.2920 |
2 |
1 |
50% |
|
MiKVR_a122 |
0.3846 |
5.0000 |
0.7219 |
0.6760 |
3 |
2 |
66.6% |
|
MiKVR_a125 |
0.3846 |
3.0000 |
0.6627 |
0.5888 |
3 |
2 |
66.6% |
|
MiKVR_a126 |
0.3077 |
7.0000 |
0.8166 |
0.7937 |
5 |
4 |
80% |
|
MiKVR_a127 |
0.6154 |
3.0000 |
0.5444 |
0.4836 |
3 |
1 |
33.3% |
|
MiKVR_a128 |
1.0000 |
1.0000 |
0.0000 |
0.0000 |
2 |
0 |
0% |
|
MiKVR_a134 |
1.0000 |
1.0000 |
0.0000 |
0.0000 |
2 |
0 |
0% |
|
MiKVR_a136 |
0.6923 |
3.0000 |
0.4734 |
0.4269 |
2 |
2 |
100% |
|
MiKVR_a137 |
0.4615 |
4.0000 |
0.6272 |
0.5556 |
4 |
3 |
75% |
|
MiKVR_a139 |
0.5385 |
2.0000 |
0.4970 |
0.3735 |
2 |
1 |
50% |
|
MiKVR_a141 |
0.4615 |
4.0000 |
0.6746 |
0.6197 |
4 |
2 |
50% |
|
MiKVR_a143 |
0.6154 |
2.0000 |
0.4734 |
0.3613 |
2 |
1 |
50% |
|
MiKVR_a145 |
0.3846 |
4.0000 |
0.6746 |
0.6130 |
3 |
2 |
66.6% |
|
MiKVR_a146 |
0.5385 |
2.0000 |
0.4970 |
0.3735 |
2 |
1 |
50% |
|
MiKVR_a148 |
0.4615 |
3.0000 |
0.5680 |
0.4723 |
3 |
2 |
66.6% |
|
Mean |
0.5956 |
3.4000 |
0.5033 |
0.4468 |
240 |
162 |
67% |
Table5: genetic similarity based on frequency
Table 6: primer and varieties with unique loci
|
Sr No. |
Primer Name |
Variety with unique fingerprint |
|
1 |
MiKVR_a010 |
Baganpali |
|
2 |
MiKVR_a052 |
Gulab khas |
|
3 |
MiKVR_a109 |
Baganpali |
No competing interests reported.