Genetic Relationship and Diversity among Pea (Pisum sativum L.) Genotypes Assessed using Morphological and Molecular Markers

Pea (Pisum sativum L., 2n=14) is commonly known as matar and garden pea. It is an annual herbaceous legume vegetable belonging to the family Fabaceae and native to Ethiopia, Israel, Syria and Turkey, where it is valued for its many uses as pulses and livestock feed (Choudhury et al., 2007). In India, it is mainly grown in Uttar Pradesh, Madhya Pradesh, Punjab, Jharkhand, Himachal Pradesh, West Bengal, Chhattisgarh and Haryana, having a 549,000-hectare area, 5,680,000 metric tonnes of production and productivity of 10.34 metric tonnes per hectare (Anonymous, 2022). In Karnataka, it is grown in districts like Bangalore Urban, Bangalore Rural, Chikka ballapur, Ramanagar, Kolar, Chitradurga, Davanagere, Belagavi, Dharwad, Bidar and Chikkamagalur with a 1612-hectare area, 25732 metric tonnes of produce and productivity of 15.97 metric tonnes per hectare (Anonymous, 2020). It is cultivated for its tender green pods, dried seeds, canned, frozen, or dehydrated form (Santalla et al., 2001).

Widespread crop cultivation, replacing landraces and traditional populations, notably those resistant to biotic and abiotic stress, narrowed down the genetic base and led to the loss of genetic variability. New breeding challenges imposed by global climate change and to meet the global demand, pea breeders have to undertake more efficient methods of selection and better take advantage of the extensive genetic diversity present in the gene pool using advanced methods. Morphological traits represent the action of many genes, but they can be unreliable due to significant environmental influences. In contrast, molecular markers are useful to complement the morphological traits because they are independent of environmental effects and allow identification of the cultivar in the early stages of the crop. Among the various DNA markers available for molecular profiling of genotypes, SSRs or microsatellites are highly reliable, accurate, co-dominant, highly polymorphic and cost-effective (Jain et al., 2014).

In peas, various genetic diversity studies were done using phenotypic traits (Priyanka and Lal, 2021; Singh et al., 2021a). Loridon et al., (2005) initially developed a set of SSRs for genetic diversity and linkage mapping of a pea. Later, Ram et al. (2021) and Singh et al. (2021b) reported using some polymorphic SSR markers for genetic diversity studies in peas.

Hence, the present study was undertaken to assess the level of genetic diversity among twenty-two pea genotypes using both morphological traits and SSR markers. This would aid the long-term objective of identifying diverse parental lines to generate segregating populations for tagging essential traits with molecular markers.
 

Genetic diversity assessment using morphological traits
 
Based on phenotypic data, analysis of variance showed a significantly higher variation among the genotypes for all the characters studied (Table 3). On the basis of Mahalanobis  values, all pea genotypes were classified into five clusters (Priyanka and Lal, 2021). A comparison of cluster mean values revealed that for improving specific characters, genotypes should be chosen from the cluster with the highest mean value for that character. This comparison shows that clusters IV and V had better cluster means for most characters; thus, these clusters may be better for selecting genotypes as divergent parents. (Table 4). Similar results are exhibited in the findings of Bijalwan et al., (2018).




 
Genetic diversity assessment using molecular markers

SSR marker analysis of pea germplasm
 
Out of the 8 SSR primer pairs, three primers that generated clear and distinct polymorphic alleles used in this study produced a total of 6 alleles. The average number of bands per locus was 1, with a range of 1 to 2 effective alleles produced per locus. Similar reports were presented by Ram et al. (2021) and Haliloglu et al., (2022). The PCR amplification profile of SSR markers in twenty-two garden pea genotypes for primers PSMPA5, PSMPA6 and PSMPA9 are shown in Fig 1, Fig 2 and Fig 3, respectively.


 
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Polymorphic Information Content (PIC) and genetic diversity estimates of SSR primers.
  
Further, the most informative locus under this study was PSMPA5, with a PIC value of 0.75. A marker with a PIC value greater than 0.5 is considered to be very informative (Prakash et al., 2016). Few studies also suggested that a more significant number of genotypes and primer pairs need for improving allelic richness (Negisho et al., 2017). The highest (0.50) and lowest (0.34) expected heterozygosity (He) values were obtained with PSMPA9 and PSMPA6, respectively. In contrast, the highest unbiased expected heterozygosity and Shannon’s information index were obtained with PSMPA9. It formed the average Shannon information index (I) value of 0.60, which was lower compared to the studies of Ram et al. (2021). Expected heterozygosity (He) ranged from 0.34 to 0.50 in primers PSMPA6 and PSMPA9, respectively. Unbiased expected heterozygosity (uHe) ranged from 0.34 to 0.51 in primers PSMPA6 and PSMPA9 (Table 5). Low allelic richness in the analyzed germplasm is indicated by lower estimates of all diversity parameters, suggesting that more germplasm must be added to enhance the allelic richness.


 
Levels of genetic dissimilarity
 
Jaccard’s dissimilarity index ranged from 9.32 between dwarf type genotypes ‘Arka Pramodh, Arka Uttam, Arkel’ and tall type genotypes ‘IIHR-688’ (distant) to 1.78 between the pairs of tall type genotypes ‘Magadi local, IIHR-758, IIHR-775, GS-10, Arka Priya, Arka Apoorva, Arka Chaitra and Arka Ajit.’ and ‘IIHR-688’ (closest).
 
Dissimilarity and cluster analysis using Agglomeration method: Ward’s method
 
Cluster analysis based on Agglomeration method: Ward’s method, dendrogram (Fig 4) outlined by SSR primer pairs classified the 22 pea genotypes into two major groups based on tall and dwarf cultivars, with the major groups further subdivided into five subgroups. The highest dissimilarity was found between subgroups II and V, indicating that the genotypes in these subgroups had high genetic divergence and could be used as parents in a hybridization program to produce superior segregants. These results are in agreement with Choudhury et al., (2007), Suman et al., (2019) and Singh et al., (2021) and also showed that there is no correspondence of genotypic matrix with the geographical location. A similar type of finding where reported by Jain et al., (2014), Rana et al., (2017), Tahir et al., (2018) and Singh et al., (2021b). Thus, hybridization between genotypes across clusters may yield combinations with a high heterotic response and superior recombinants.

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The garden pea germplasm was separated into tall and dwarf plant types using molecular marker data, demonstrating that molecular markers were more reliable for determining genetic diversity. The SSR-based cluster analysis showed that the grouping did not correspond to pedigree data or breeding centers. Furthermore, as indicated by molecular markers, the relationship between the distinct genotypes was not significantly correlated with those based on morphological attributes, suggesting that the estimates of the genetic relationships between the genotypes provided by the two systems are not the same. The genotypes of clusters with the highest dissimilarity should be crossed to produce a broad spectrum of variability and isolate desirable recombinants in subsequent generations. The high discriminating power of the SSR loci suggested that a subset of the most robust SSR markers could be used to distinguish between genotypes. SSRs were found to be an efficient and reliable marker system for the genetic analysis of pea germplasm.

The authors are thankful to the ICAR-Krishi Vigyan Kendra, Babbur farm, Hiriyur, Karnataka, India for its facilities throughout the study and there is no conflict of interest exists among authors.