INTRODUCTION

Cowpea [Vigna unguiculata (L.) Walp.] is an important leguminous crop with chromosome number (2n=2x=22) with genome size of about 620 million base pairs (Boukar et al., 2019). It belongs to order Rosales, family Fabaceae, genus Vigna and is native to Central Africa (Darlington and Wylie, 1955). The crop is autogamous, but up to 5% outcrossing has been reported in the cultivated varieties, probably due to insect activities (Badiane et al., 2014). Cowpea is mainly cultivated in tropical and subtropical countries such as Africa, Asia, Central and South America. Because of its high protein content, cowpea is referred to as “vegetable meat” and have biological value on a dry weight basis (Ram, 2014). Cowpea is economically grown throughout Indian subcontinent for variety of purposes such as seeds as pulses, long green pods as vegetables, foliage as fodder for cattle, green manuring and cover crop (Meena et al., 2015). The young leaves are used as spinach in eastern and southern Africa (Boukar et al., 2015).

 Cowpea grains have a nutritional profile of 23.4% protein, 60.3% carbohydrates and 1.8% fat, as well as a good source of vitamins (folic acid and vitamin B) and phosphorus necessary for prevention of congenital malformations (Venkatesan et al., 2003, Srivastava et al., 2016). The crop is well known in India and Southeast Asia for its immature tender pods and dry seeds, which serve as a cost-effective source of protein (Khandait et al., 2016).

Cowpea is also known for producing high-quality forage. It produces dense vegetative growth and effectively covers the land preventing soil erosion. It fixes nitrogen as it is a leguminous crop and improves fertility of the soil (Hall et al., 2003, Kumar et al., 2015). Because of its, drought tolerance, soil-restoring properties, smothering nature and multi-purpose uses, it is considered as a versatile legume crop and fits well into most cropping systems as a pulse crop (Das et al., 2020).

The worldwide annual average cowpea production from 2014−2018, was 6.57 mt on a harvested area of 12.4 mha with an average yield of 0.53 t ha^-1 (Anonymous, 2020). Worldwide, Nigeria is the largest producer of cowpea followed by Niger, Burkina Faso, Cameroon, and Mali.

Crop genetic diversity is crucial for both food security and sustainable development, as it serves as a source of genes needed in the development of better performing and well adapted varieties (Dossa et al., 2016). Food production and security depend on the conservation and wise use of agricultural biodiversity.

Several approaches have been utilized to enhance our knowledge of the nature and extent of variability among cowpea accessions stored in different genetic resource centers. To evaluate the genetic diversity of a given crop, morphological (phenotype) and molecular (genotype) markers have been used. However, most cowpea accessions are characterised primarily based on morphological data, which are fraught with environmental fluctuations (Nkongolo, 2003). Molecular markers, particularly Simple Sequence Repeats (SSR), are playing an increasingly important role in assessment, characterization and conservation of plant genetic resources (Kanavi et al., 2019). The sequences are abundant and randomly distributed throughout the genome, highly polymorphic, inherited co-dominantly and are not influenced by environmental variations and have shown great potential for various genetic studies (Tautz, 1989, Pradeepkumar et al., 2017). They have been used to assess the diversity of various cowpea germplasm from different countries (Li et al., 2001, Uma et al., 2009, Asare et al., 2010, Gupta and Gopalakrishna, 2010, Adesoye et al., 2016, Desalegne et al., 2016, Lal et al., 2016). Therefore, an attempt was made to investigate molecular diversity in the germplasm considered for present study.

MATERIALS AND METHODS

2.1.  Plant materials and DNA Isolation

In the present study, 32 cowpea genotypes were used for molecular diversity analysis (Table 1). The experimental material was grown during rabi (October−February, 2020−21) at C-block of Regional Agricultural Research Station (RARS), Warangal, Telengana state, India. It falls on 17.58° N latitude and 79.40° E longitudes. Molecular analysis was performed at Biotechnology Laboratory at RARS, Warangal. For isolation of genomic DNA, young leaves of 21−28 days were selected. The standard protocol as described by Doyle and Doyle (1987) was followed with few modifications and was then quantified spectrophotometrically on a nano spectrophotometer (Implen, Germany).

Table 1: List of 32 cowpea varieties used for molecular diversity analysis

2.2.  SSR-PCR amplification

25 SSR primers were used to screen cowpea germplasm lines presented in Table 2. The concentration of 30 ng µl^-1 of genomic DNA was used for SSR-PCR (Eppendorf) amplification. PCR amplification was performed with reaction conditions programmed as Initial denaturation at 94°C for 4 m, followed by 35 cycles of denaturation for 1 m at 94°C. Annealing temperature 55°−60°c for 1 m and extension at 72°C for 1 m. A final extension was performed at 72°C for 7 m and storage at 4°C. The PCR amplified products obtained were loaded on 4% agarose gel which was prepared with 1X TAE buffer as well as ethidium bromide (10 mg ml^-1). SYNGENE system was used for documentation of the gel.

Table 2: List of primers used in the present study

Table 2: Continue...

2.3.  Data analysis

DNA bands generated from PCR amplification were subjected to binary system where 25 SSR markers were scored for presence (1) or absence (0) of band. To avoid poor reproducibility, the faint and diffused bands were excluded from scoring. Only distinct and unambiguous bands were utilized. The band sizes were estimated by comparing the amplified products to a 100 bp DNA ladder. Such data was used to calculate Jackard’s similarity matrix using NTSYS-pc version 2.2 (Rohlf, 1988). Similarity matrix were organized for all pairs of accessions with the help of Jackard’s similarity coefficient and dendrogram for genetic diversity was constructed using UPGMA (unweighted pair-group method with arithmetic mean analysis). 

RESULTS AND DISCUSSION

3.1. SSR polymorphism

32 genotypes of cowpea were subjected to SSR assay to analyze molecular diversity by using 25 SSR markers. Out of 25 SSR markers, only eight markers showed polymorphic pattern (VM-8, VM-9, VM-10, VM-16, VM-18, VM-28, VM-36, VM-39) and 15 markers exhibited monomorphic banding pattern and remaining 2 markers showed no banding pattern. The observed % polymorphism was 32% for all the markers studied indicating the presence of less variable SSR loci for these markers in the germplasm studied. The level of polymorphism detected in our study was low to moderate which is in agreement with previous series reported by several cowpea researchers which may be due to the hindrance induced by a single domestication event in addition to its inherent mechanism of self-pollination. (Li et al., 2001, Badiane et al., 2004, Diouf and Hilu, 2005, Sarr et al., 2021).

Eight SSRs generated a total of 27 alleles. The number of alleles ranged between 2−5 locus^-1 with an average of 3.37 alleles. A maximum number of alleles (5) were generated by VM-28 and a minimum of two alleles was produced by marker VM-10. Gel profiling of cowpea genotypes for marker VM-28 was given in Figure 1. The Polymorphic Information Content (PIC) was determined for each marker to assess the informativeness of the marker to detect polymorphism. PIC value refers to the value of a marker’s ability to detect polymorphism within a population, depending upon the number of alleles detected and their distribution frequency. Thus, it gives an assessment of the marker’s discriminating power. (Nagy et al., 2012). In the present study, the PIC values (Figure 2) among eight polymorphic SSR markers ranged from a minimum of 0.702 (VM-10) to maximum of 0.945 (VM-28) with an average value of 0.86, indicating that these markers are highly informative in detection of polymorphism. However, there are various findings that had reported more number of alleles in cowpea collected from Senegal in which alleles ranged from 2 to 15 (Sarr et al., 2021). In contrast Devi and Jayamani (2019) reported 1 to 3 alleles in local cowpea accessions, while Sonker et al. (2019) reported 1 to 10 alleles in cowpea accessions obtained from Scientific and Applied Research Centre (SARC), Meerut (UP). These findings were in agreement with recent reports on the number of alleles detected using SSR makers in other legumes, such as, 4 to 12 in mungbean (Suman et al., 2019), 2 to 5 in yardlong bean (Saha et al., 2020), 1 to 7 in chickpea (Vashist et al., 2019) and 2 to 6 in pea (Ram et al., 2021).

Figure 1: PCR amplification of 32 cowpea genotypes with SSR marker VM-28. (The lane numbers written on the gel corresponds to the list of cowpea genotypes as given in Table 1)

Figure 2: Graphical representation of PIC values of markers

3.2.  Dendrogram analysis

A dendrogram (Figure 3) was constructed based on Jaccard’s similarity coefficients using UPGMA (Unweighted Pair Group Method with Arithmetic mean) and SAHN (Sequential, Agglomerative, Hierarchical and Nested) clustering algorithm of NTSYS-pc version 2.1 software. 32 genotypes of cowpea were grouped into three major clusters (Table 3) i.e., cluster I, II and III at a cut-off Jaccard’s similarity coefficient of 0.23.

Figure 3: Dendrogram of 32 cowpea genotypes using SSR markers

Table 3: Grouping of 32 cowpea genotypes based on dendrogram analysis

Cluster I was formed at a similarity coefficient of 0.26 with 14 genotypes which was further grouped into two minor sub-clusters IA and IB at similarity coefficients 0.26 and 0.45 respectively. Cluster II appeared at similarity coefficient of 0.30 while cluster III was formed at 0.28 similarity coefficient. The genotypes in each cluster were clearly distinguished from one other. Cluster analysis revealed presence of low magnitude of diversity among the genotypes studied. The values for similarity coefficient ranged from 0.13−1.00. The genotypes grouped close to 0.13 showed more dissimilarity whereas the genotypes grouped close to 1.00 showed high similarity. The average of similarity coefficient of all 32 genotypes was found to be 0.57. High similarity of 100% was observed between the genotypes WCP-4 and WCP-6, GC-1712 and PCP-1124, PGCP-69 and CPD-313, WCP-16 and WCP-34. The genotypes namely TPTC-29 and KBC-9 were found to be more diverse with 26% similarity. The maximum number of genotypes i.e., 12 were formed under cluster IB (DC-15, PCP-0306, KBC-12, CPD-311, KBC-13, CPD-313, PGCP-69, PCP-1124, GC-1712, PGCP-70, WCP-29, WCP-28), followed by cluster II (WCP-26, WCP-18, WCP-23, WCP-17, WCP-21, SKAU-WCP-149, CPD-331, TC-1901, WCP-1) and cluster III (WCP-26, WCP-18, WCP-23, WCP-17, WCP-21, SKAU-WCP-149, CPD-331, TC-1901, WCP-1) with 9 genotypes while a minimum of two genotypes accumulated under cluster IA (TPTC-29, KBC-9). The genetic background of 32 cowpea accessions was moderate according to the molecular dendogram and this information will reduce the overall time required in screening large populations of potential parents in identifying breeding stock. The similar results were obtained by Li et al. (2001), Devi and Jayamani (2019), though, contradictory results were observed in Malawian landraces by Nkongolo (2003). In general, the dendogram of accessions and level of polymorphism detected in this study supported the established view that genetic diversity in cowpea is mode rate (Zannouou et al., 2008 and Sarr et al., 2021).

CONCLUSION

The existence of moderate to low genetic similarity between genotypes indicated the presence of less variable SSR loci for these markers among the genotypes studied. Highest similarity was observed between WCP-4 and WCP-6, GC-1712 and PCP-1124, PGCP-69 and CPD-313. The genotypes namely TPTC-29 and KBC-9 appeared more divergent than remaining genotypes. The marker data generated in present study is of great significance and can be utilized in marker assisted breeding programmes.

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