Analysing Solanum tuberosum L. Genetic Divergence using Molecular Marker Data

Genetic polymorphism has important implications for the conservation and evolutionary studies among species as well as within genomes. Hence an enhanced understanding of intra-specific heterogeneity is anticipated which is and based on accurate database or unruffled by environmental conditions. In this context, molecular markers due to their simplicity and ubiquity have been used for genetic divergence studies of tetraploid potato. In the present study genetic diversity, marker attributes and population structure of 48 potato genotypes based on 20 SSR markers data were analysed which were able to successfully generate significant levels of DNA polymorphism to discriminate the experimental material. A total of 33 different loci were amplified that exhibited an average of 90 per cent polymorphism. The PIC value ranged from 0.11 to 0.70. PCR amplification exhibited genetic diversity was analyzed using program NTSYS-PC 2.21. Similarity coefficient or Jaccard coefficient were calculated using SIMQUAL program which varied from 0.32 to 0.92 and dendogram constructed using UPGMA cluster analysis ordered the populations of 48 genotypes into ten clusters. The maximum genetic similarity (0.92) was found between Pant Sel-09 and Pant Sel-09-04 and lowest (0.32) between Pant Sel-09-20 and Pant Sel-09-01. Most diverse groups found were cluster X and cluster II thus, can be utilized as diverse parents in potato breeding programmes.


INTRODUCTION
Improving skills is a prerequisite in today's technology driven world which needs researchers to stay abreast of the latest advancements in crop research, especially staple crops like potato (Solanum tuberosum L.). Potato is the most important non-cereal crop and a key component to address poverty and hunger sustaining food security especially in developing countries (Tillault and Yevtushenko, 2019). Moreover, potato is considered as the fourth most important food crop in the world having potential to deal with the challenges of combating malnutrition and reassuring nutritional food security to meet the demands of ever increasing population in developing countries (Ma et al., 2017). Being an important cash crop, it has potential to address farmer's distress by enabling them to increase their income, thus, depleting poverty by providing more nutrition and yield per unit area of land compared to major crops (Zaheer and Akhtar, 2016). According to Zaheer and Akhtar (2016), on an average potato tuber contain 77% water, 20% carbohydrate, 3% protein, dietary fiber, vitamins and minerals. Potato covers major economic share in global agricultural market being a short duration crop with wide climate adaptability enabling its cultivation in diverse geographical borders. The worldwide demand for potato production requires constant development of new potato varieties, with improved yield, disease resistance and varied climatic resilience (Tillault and Yevtushenko, 2019). Potato production must be assured qualitatively and quantitatively at grower, processor and most importantly consumer level.
In this context, crop improvement strategy is of the utmost importance, can prove a valuable aid in both quantitative and qualitative breeding program employed for trait improvement prompting superior variety production in potato, which in turn demands wide germplasm collection, germplasm diversity know-about and their genetic relationships (Hameed et al., 2018). Many cultivated potato cultivars are autotetraploid (2n=4x=48) with highly heterozygous genome having enormous genetic diversity. Potato has its origin centre in Andes of South America where diploid potato cultivars are also cultivated though they suffer from severe inbreeding depression and self-incompatibility (Xiaoyan et al., 2016). The evolutionary diversity of potato germplasm makes them excellent material for improving the narrow genetic base especially of cultivated potato providing enormous opportunity for breeders to choose best parents for proper breeding scheme and strategies (Anoumaa et al., 2017;Carputo et al., 2013). Genetic diversity among germplasm helps not only in choosing better performing say high yielding and resistant germplasm, but prompting them to be directly incorporated not only into breeding programmes (as a rule in conventional method) (Halterman et al., 2016;Dar et al., 2017), but also in molecular aided breeding (Carrasco et al., 2009). Where on one hand using conventional method during diversity analysis researcher is likely to misinterpretate the germplasm performance based on field data as it is directly affected by the environmental conditions, molecular marker on other hand are fully deprived of such limitation.
Molecular markers owing to their high resolution and accuracy in differentiating germplasm have become important tool in genetic diversity studies of agronomic and horticultural crops (Bered et Wu et al., 2019). These SSRs or microsatellites are found throughout the nuclear genomes ranging from mono to hexa nucleotide in length among which di-, tri-and tetranucleotide repeats are most common choice for molecular genetic studies (Selkoe and Toonen, 2006). Different types of SSRs have been classified by source of development (Genomic SSRs, Genic SSSRs and Organellar (chloroplast and mitochondrial SSRs)), types of repeat sequence (Simple and compound with perfect and imperfect SSRs) and length of repeat motifs (Class I and II microsatellites) (Al-Samrai and Al-Kazaz, 2015). Microsatellites with tandem DNA repeats along with random genome distribution (throughout coding and non-coding regions), codominant nature, high polymorphism, high specificity with better reproducibility are promising for germplasm evaluation aiding diversity analysis and molecular assisted breeding (Qiu et al., 2006;Tabkhkar et al., 2012;Singh et al., 2013). As reported by various researchers a low quality DNA is enough for SSR markers for evaluating genetic diversity, moreover, if these markers could be associated with the resistance conferring trait (Barone, 2004;Gavrilenko et al., 2010), may furthermore assist in germplasm fingerprinting (Yang et al., 2015), genetic linkage mapping (Jian et al., 2017) and phylogenetic studies (Duan et al., 2018). Thus, SSRs markers have pivotal role in diversity analysis even for tetraploid species like potato offering new opportunities for selection of superior genotypes backing a sustained potato breeding program with main goal to obtain new cultivar exhibiting better yield and quality traits, along with biotic and abiotic stress resistance. The present study aimed at executing primary step of breeding program i.e. analyzing diversity of 48 potato genotypes based on SSR markers desired to provide the researchers with more options for designing breeding programs for producing superior potato cultivars.

Experimental material
The molecular analysis was performed at

Genomic DNA isolation
The fresh and green leaves of 48 potato genotypes were collected and the genomic DNA was extracted by using the CTAB (cetyl trimethyl ammonium bromide) method of Doyle and Doyle (1990) with slight modifications (Deshmukh et al. 2007). Approximately, 2 g of leaf tissues was collected to extract the genomic DNA using the CTAB method. Genomic DNA was quantified using a NanoDrop spectrophotometer and quality of the genomic DNA was checked using electrophoresis on 1% agarose gel and later the samples stored at ─80 °C. DNA concentration was quantified by using UV spectrophotometer and the OD (optical density) was measured at 260 nm for estimating the DNA concentration. The concentration relates to the OD and calculated by equation (DNA concentration (µg/µl) = OD 260 x 50 x dilution factor/ 1000). Here, OD recorded at 260/280 nm to calculate the ratio OD206 /OD280 where, a ratio of 1.8 is best for DNA preparation. DNA was diluted to50 ng/µl and stored at 4º C for use in PCR, and concentrated stocks were stored at -80ºC for future use.

PCR amplification & Gel electrophoresis
The molecular divergence study was performed using 20 SSR primers pairs obtained from various sources evenly  Table 2). The Polymerase Chain Reaction (PCR) was performed in eppendorf thermocycler. Master Mix containing dNTP mix (1.5 µL), Taq DNA polymerase (0.1 µL), forward and reverse primer 1.5 µL (50 ng/ µL), reaction buffer A 2µL (10X) and deionized water (6.6 µL) was prepared. The master mix was then distributed in each tube (11.5 µL each) and finally 1 µL of different template DNA was added in each tube. The mixture was gently mixed and centrifuged for ten seconds. The PCR amplification was achieved in thermo cycler (eppendorf thermocycler). The amplification cycles used were initial denaturation at 94 ºC for 3 minutes, followed by 35 cycles of denaturation at 94º C for 1 minute, annealing at 60-65 ºC for 45-50 sec and synthesis at 72 ºC for 1 minute culminating into final extension step of 5-7 minutes at 72ºC. Later gel electrophoresis was done where the amplified DNA product along with molecular marker was run on 2.5 % agarose gel electrophoresis and visualized under U.V. transilluminator using gel documentation system.

SSR data analysis
Amplified SSR profile of all the genotypes with each primer were documented using gel documentation system. DNA for each fragment profiles was scored in a binary fashion with 0 indicating absence and 1 indicating presence of a band for each SSR locus. Primers with null allele where an amplification product could not be detected were not considered in the analysis. Principal Component analysis was done using the software NTSYSpc version 2.2 whereas marker attributes like allele frequency (FA), allele number, polymorphic information content (PIC), Gene diversity, Effective multiplex ratio (EMR) and marker index (MI) were estimated by using the Power Marker statistics software version 3.25 (Liu and Muse 2005). Allele frequency was calculated as nu/N, where nu is number of alleles present and N is total number of genotypes (Dar et al., 2017). The PIC detects an allelic variability and was calculated as according to Botstein et al. (1980). Marker index was calculated as product of EMR and PIC (Varshney et al., 2007). Further the binary data were used to calculate genetic similarities based on Jaccard coefficients among the isolates using SIMQUAL program (Jaccard, 1908) and on the basis of these coefficients, dendogram was constructed using UPGMA (Unweighted Pair Group Mean Average) method to determine the genetic relationship of potato genotypes.

SSR polymorphism
A total of 20 SSR primers used for distinguishing potato genotypes were selected based on the quality criteria, genome coverage, and locus-specific information content as studied by Ghislain et al., (2009). Out of twenty SSR primers fourteen primers were polymorphic and six primers were found monomorphic (STI0003, STM0040, STM1031, STM1058, STM1045 and STM0019). A total of 33 different loci were amplified that exhibited 90 per cent polymorphism. The PIC value ranged from 0.11 to 0.70. Analysis for polymorphism in SSR markers has been provided in Table 3. All the loci amplified by the primer which were found to be polymorphic varied in size from <100bp to >300bp. Maximum number of four polymorphic bands were amplified using primer STM2005 where primers STG0016, STI0023, STI0014 and STM5127 amplified three bands each. The PCR profile of primer STM2005 and STG0016 provided in Fig. 1

Fig.2. Amplification pattern of primer STG0016
According to Demeke et al. (1993), identification across database becomes easy once a fixed set of primer combination were taken in consideration. Present study in which SSR amplified a total of 33 different loci that exhibited 90 per cent polymorphism gave a better insight to which genotype are genetically more diverse.

Genetic diversity analysis
Based on the SSR marker data the Jaccard's similarity coefficients were estimated between pair of genotypes. The similarity coefficient was found to vary from 0.32 to 0.92. The highest value for genetic similarity (0.92) was found between Pant Sel-09 and Pant Sel-09-04 followed by both

Cluster analysis
UPGMA based on Jaccard's similarity matrix of SSR markers ordered the populations of 48 genotypes into a single big group further dividing into ten clusters (Fig. 3). The biggest clusters were cluster IV and cluster II with maximum genotypes. Cluster II consisted of ten genotypes    Kufri Jyoti and K. Jawahar shared the same cluster IV which is likely because K. Jyoti is included in the parentage of K. Jawahar. However, K. Chipsona -3 having K. Chipsona-2 in parentage were found in different groups. This observation can explain the poor correlation among coancestries and performance of the progeny. Kufri Jawahar, Kufri Chipsona-1, Kufri Chipsona-3 (all late blight resistant varieties) along with Kufri Jyoti, Kufri Pushkar, Kufri Giriraj, Kufri Himalini (moderately susceptible to late blight resistant variety) belonged to cluster IV. It is likely that other genotypes viz. Pant Sel-08-07-01(CT), Pant Sel-09, Pant Sel-09-04, Pant Sel-09-08 and Pant Sel-09-21, belonging to the same cluster could confer resistance to late blight disease. However, late blight resistant varieties namely Kufri Badshah, K. Chipsona-2 and K. Khyati (field resistant) shared common cluster VI along with a late blight susceptible variety K. Ashoka. Although, they all were high yielding types and shown field resistant to blight disease which is similar to findings of Rocha et al. Therefore, geographical diversity of the material alone would not help in selection of genetically divergent parents. For example during field trial, genotypes namely Pant Sel-09-38, Kufri Frysona, Kufri Himalini, Pant Sel-09-04, Pant Sel-08-11, Kufri Pushkar, Pant Sel-09-50 and Pant Sel-09-43 were the best yielding genotypes but during molecular analysis, they all belonged to different clusters along with low yielding genotypes. Moreover, germplasm namely Pant Sel-08-02, Pant Sel-09-04, Pant Sel-09-43, Pant Sel-09-20, Pant Sel-09-11, Kufri Badshah, Kufri Ashoka, Kufri Chipsona-1 and Kufri Chipsona-2 showed high to moderate field resistance to late blight disease but no clear cut grouping was observed in resistant and susceptible genotypes by SSR primers as compared to field data indicating limited or low kinship relationship between morphological and molecular data among forty eight potato genotypes. This observation confirms that divergence is at intron and exon level both, making markers important for new hybrid development programme via combining distantly related genotypes. Molecular marker led cluster analysis provided an insight to marker's potential to carry out more comprehensive diversity analysis (Barandella et al.

IV. CONCLUSION
Evaluation of the genetic diversity of 48 potato genotypes based on 20 SSR markers gave clear idea about the genetic relationship among genotypes which resulted into grouping on the basis of the genetic distance among them aiding to deep knowledge about genetic makeup of genotypes. On the basis of PCR amplification various distantly related genotypes were identified. From this study, it may be concluded that significant diversity and variability was present among the genotypes and divergence analysis using SSR markers was proved to be better than morphological data for discrimination among genotypes. It is clear that microsatellites offer an effective means of analysing genetic distance between potato varieties which are especially useful for potato breeding program.