INTRODUCTION
⌅Pea (Pisum sativum L., 2n=2x=14) is an Old World legume that was first cultivated 10,000 years ago (Zohary et al., 2012) and is the most important domesticated crop in the legume group. The cultivated varieties of Pisum are comprised of several morphologically distinct intraspecific forms, each developed for a specific end-use, such as human consumption as pulse and vegetable, livestock feed or ornamental gardening. According to FAO (http://www.fao.org/es/faodef/fdef04e.htm), it holds a status of primary pulse that serves as a valuable source of protein (17.58 to 28.67%; Weihai et al., 2017) for both man and animals. Furthermore, it is quite inexpensive and a readily available source of starch, fibers and sucrose. It is also enriched with various minerals, phytochemicals, antioxidants, flavonoids, tannins, and other phenolic compounds, all of which have significant health benefits (Kumari & Deka, 2021). Besides, peas are excellent plant species for biological nitrogen fixation and green manuring with its ability to enhance the productivity of its successor crop (Uhlarik et al., 2022).
The greater diversity in the Pisum gene pool has led its adaptation to a vast geographical area that includes Asia, Europe, Africa, the Americas and Oceania (Kreplak et al., 2019). World production of green peas in 2019 was 19.73 million tonnes and the major producers were China (11.38 million tonnes), India (5.56 million tonnes), France (0.28 million tonnes) and USA (0.23 million tonnes) that accounted for >85% of the total world production. Further, insight into data showed that although the area and production under green peas has become almost double from 1999 (1.5 million hectares and 11.39 million tonnes, respectively) to 2019 (2.51 million hectares and 19.73 million tonnes), a slight change has been observed in its productivity globally (7.6 to 7.8 tonnes/ha; http://www.fao.org/es/faodef/fdef04e.htm). It is widely recognized that during the domestication and crop improvement process, market-driven intense breeding for higher yield and uniformity has resulted into unwanted loss of genetic diversity of many crops, thus making them vulnerable to stresses. Further, self-pollinating crops such as pea end up with increased homozygosity and increasing loss of genetic variation (Cieslarová et al., 2011). Vegetable pea farming in India is presently dominated by few selected varieties: ‘Arkel’, ‘Azad Pea-3’ (AP-3), ‘Kashi Udai’, ‘Kashi Nandini’, ‘Punjab-89’, ‘Arka Ajit’ and ‘Arka Karthik’. The large-scale seed multiplication of ‘AP-3’, ‘Kashi Nandini’ and ‘Punjab-89’ by the local farmers has again narrowed down the cultivation of local land races, resulting into considerable genetic loss.
While confronted with new challenges of crop genetic erosion, various biotic and abiotic stresses, productivity stagnation and issues of global climate change, pea breeding greatly relies on the exploitation of its genetic resources that could be part of cultivated gene pool or its wild relatives (Burstin et al., 2015). Moreover, understanding the detailed relationships between the genomes of modern cultivars, old cultivated landraces, ecotypes and wild relatives is critical because it may help breeders to develop climate resilient varieties with desirable alleles (Uhlarik et al., 2022). Additionally, genotypic characterization is also necessary for successful conservation, protection and utilization of naturally occurring variations in gene pool (Sharma et al., 2022). The current rate of genetic gain in peas could be doubled only through expansion of genetic diversity (Bari et al., 2021).
Time to time, breeders have made efforts to study the diversity and evolution of pea, and a large number of polymorphic data points have been produced for each collection using morphological, cytological, biochemical markers and more recently through DNA-based technologies (Jing et al., 2010; Sanwal et al., 2021). A rich phenotypic diversity has been reported for various yield traits in peas (Kwon et al., 2012; Gixhari et al., 2014; Sanwal et al., 2021). Morphological traits represent the action of numerous genes and thus contain high information value but can be unreliable owing to a strong influence of the environment (Smykal et al., 2008b). In comparison, molecular markers can accurately score the underlying genetic variation and a wide range of markers based on polymorphism of DNA sequences such as RAPD and ISSR (Baranger et al., 2004; Tar’an et al., 2005), AFLP (Dyachenko et al., 2014), RBIP (Jing et al., 2010), SSR markers (Nisar et al., 2017; Sharma et al., 2022) and SNP (Bari et al., 2021; Uhlarik et al., 2022) have been used to dissect the variation present in Pisum. The SSRs have several advantages over other markers, including high polymorphism and abundance, codominant nature, relative ease of transfer and low cost of development (Izzah et al., 2013). Furthermore, it is important to determine how former as well as novel variations can be used to aid in the development of new varieties capable of responding to new environmental challenges. Thus, the current study aims to (i) assess the genetic diversity and relatedness of pea breeding material using agro-morphological traits at phenotypic and SSR markers at molecular level, and (ii) identify potential donors in order to target trait-specific pea breeding.
MATERIAL AND METHODS
⌅Location of study
⌅The present investigation was undertaken at the experimental farm of ICAR–Indian Institute of Vegetable Research, Varanasi, India, which is located at 82°52'37" E and 25°18'21" N at an elevation of 83 m above mean sea level, during the winter season (Nov-March) of 2019-20. The site is agro-climatically representative of India’s Middle Gangetic Plain Region, that has a humid subtropical climate with an annual average rainfall of 998 mm.
Plant material
⌅To assess diversity, 45 pea genotypes were selected from 10 different centres across India (Table S1 [suppl]): 21 cultivars, 12 advanced breeding lines and 12 germplasm lines including exotic collections. Each genotype, in general, has its own breeding potential that, depending upon the need of hour can be a valuable resource for future breeding programs. As demand for trait-specific breeding lines has been raised, we summarized the special characteristics of these lines that have been previously reported (Table S1). These lines were diverse for one or more character such as maturity groups, flowering, pod or seed characters. All the accessions were grown under normal field conditions for their phenotypic evaluation. Each genotype was planted in Randomized Block Design with three replications. The genotypes were raised in plot consisting of 3 rows of 3-m length with spacing of 30 cm between rows and 10 cm between plants. The recommended package of practices was adopted to grow healthy crops. The mean meteorological data for the growth period is presented in the Fig. S1 [suppl].
Recording of observations
⌅The 45 genotypes of peas were characterized for 17 agro-morphological traits. The molecular characterization of these accessions was also done by using SSR markers. Horticultural traits data were recorded on 10 competitive plants in each replication for nine traits: days to 50% flowering (DTF; No); plant height (PH; cm); pod length (PL; cm); pod width (PW; cm); pods per plant (PPP; No); 10-pod weight (10-PW; g); seeds per pod (SPP; No); 100-green seed weight (100-GSW; g) and yield per plant (YPP; g). Characterization for flower colour, maturity groups, PH, pod number/axil and plant anthocyanin pigmentation followed DUS descriptors of PPV & FRA (2007); seed shape as Tzitzikas et al. (2006); and mature seed colour was compared with RHS Colour Chart (1986).
DNA extraction, PCR, gel electrophoresis, allele calling and sizing
⌅DNA was extracted from all the 45 pea genotypes individually by selecting young leaves at the 8-10 leaf stage. A standard CTAB method of DNA extraction with minor modification was used to isolate DNA from each pea accession (Doyle & Doyle, 1990). The isolated DNA was treated with RNase A (by adding 10 µL of RNase A to each DNA sample) for 60 min at 37℃. The DNA quality was checked by electrophoresis in 0.8% agarose gels using lambda DNA (50 ng) as a standard. A total of 52 SSR markers distributed across the genome were used to genotype the 45 pea accessions. All these markers were chosen from the mapping and diversity studies conducted by previous researchers (Ek et al., 2006; Gong et al., 2010; Ahmad et al., 2015). PCR amplifications were conducted in total volume of 25 μL comprising 1 μL of template DNA (25-35 ng), 0.4 μmol/L of each forward and reverse primers (Table S2) and 5 μL of 5× CTaq-& LOAD Mastermix (MP Biomedicals; 1.5 μmol/L dNTP final concentration). Amplifications were performed on a Thermocycler (Bio-Rad, Missisauga, ON, Canada) with the following profile: 95°C initial denaturation for 2 min, followed by 36 cycles of 30 s at 95°C, appropriate annealing temperature for 45 s and 1 min extension at 72°C. PCR products were resolved on 2.5% metaphor agarose gel stained with ethidium bromide in TBE buffer and analysed under UV light (Bio-Rad, gel analyser). To determine the size of each amplified product a 100 bp DNA ladder (Invitrogen, USA) was used. The amplified and not amplified products were scored ‘1’ and ‘0’, respectively, and allelic sizes were assigned to each product.
Diversity and population structure analysis
⌅Phenotypic diversity analysis and principal component analysis (PCA) was constructed using the DARwin 6.0 program. For cluster analysis neighbor-joining approach was used. Based on SSR allelic data, the molecular-genetic relationship of 45 pea genotypes were determined using Nei’s genetic distance (Nei, 1972). PowerMarker 3.51 (Liu & Spencer, 2004) was used to determine the major allele frequency, the number of alleles per locus, polymorphic information content (PIC), gene diversity (expected heterozygosity), and observed heterozygosity. A cladogram was constructed with MEGA 11 using N-J method. Robustness of the node of the cladogram was assessed from 1000 bootstrap replicates. Population structure was analysed using Structure 2.3.4 (Pritchard et al., 2000). The admixture model was used to investigate the structure of 45 individuals, and population numbers (k=2 to k=10) were accessed with a burn-in-period of 10,000 steps, followed by 100,000 Markov-chain Monte Carlo (MCMC) replicates with 10 iterations. The output was obtained from the online Structure Harvester program (Earl & VonHoldt, 2012) which showed the highest peak at k=2.