Genetic divergence studies through microsatellite markers in pigeonpea [Cajanus cajan (L) Millsp.]

Pigeonpea [Cajanus cajan (L) Millspaugh] is an important grain legume crop of the world and it is currently being grown on 5.4 million ha area with a total production of 4.48 million tonnes in subtropical and tropical regions of the world. India is the major pigeonpea growing country with 3.88 million hectare area and 2.84 million tonnes production during the year 2016 (FAOSTAT, 2016). India is the largest producer of pigeonpea (63.4% of the global production) followed by Myanmar, Malawi and Tanzania. Pigeonpea is a valuable crop for small and marginal farmers due to its hardiness and ability to grow in water deficient areas with low inputs. It can be used for human food as dhal, feed for animals and fuel wood for rural kitchens (Kumar et al., 2016). Its deep and extensive root system helps to improve the physical and structural properties of soil. Pigeonpea productivity was low and stagnant for the last 3-4 decades so there is a need to break the stagnation level. To tackle this problem, knowledge of genetic diversity present in pigeonpea germplasm collections will be useful for the hybrid breeding programs. With the advancement in technology, the pigeonpea breeding programs can be strengthened by using modern molecular tools.
       
Various types of morphological markers have been used to assess genetic diversity in pigeonpea but these markers have drawback as they are influenced by environment and fail to give true and accurate results. Through the development of DNA marker technology, now it is easy to assess genetic diversity by molecular markers by bypassing the problems related to environment effects. A range of molecular markers has been used for genetic diversity analysis by various workers, e.g., RAPD (Ratnaparkhe et al., 1995), RFLP (Nadimpalli et al., 1993), AFLP (Panguluri et al., 2006), DArT (Yang et al., 2006), SSR (Saxena et al., 2010) and SNP (Saxena et al., 2012). Among these markers, SSRs are mostly preferred due to their multi-allelic, abundance and co-dominant nature. The SSR markers consist of short tandem repeats (1-6 bp) and these work well and consistently even with degraded samples of DNA. These properties make SSRs as the genetic marker of choice for the genetic diversity analysis, linkage mapping and QTL analysis.
       
Molecular studies revealed very low amount of polymorphism among cultivated pigeonpea, so to exploit heterosis and to generate new genetic variation, diverse parents should be used for hybridization. Genetic diversity analysis through SSR markers will be useful for the breeders to identify diverse parents to be used in the breeding programs. Hence, the present study focused on the assessment of genetic diversity among a set of male sterile, maintainer and germplasm lines being used for the development of new breeding materials.

The experimental material comprised of 96 accessions including 15 male sterile, 13 maintainer and 68 germplasm lines, which are being maintained in Pulses Section, Department of Plant Breeding & Genetics, Punjab Agricultural University, Ludhiana. The names and origin of the plant material used in the present investigation is given in Table 1.
 

 
Isolation of genomic DNA
 
Total genomic DNA of 96 pigeonpea genotypes were isolated from leaf samples from one month old plants by using CTAB method as described by Murray and Thompson (1980).In the extraction buffer, 1% polyvinylpyrrolidone and 0.2% β-mercaptoethanol were also added. Isolated genomic DNA samples were evaluated both quantitatively and qualitatively by using electrophoresis on 0.8% agarose gel.
 
Selection of primers and SSR analysis
 
In the present study, 44 SSR primer pairs (Table 2) belonging to CcM series were selected from the published source (Bohra et al., 2012). These SSR primers were synthesised through Integrated DNA Technologies (IDT). The PCR amplifications were carried out in a 20 μl reaction mix consisting 2μl DNA at a concentration of 25 ng/μl DNA template, 2 μl of 10× PCR buffer, 4 μl of 1 mM dNTPs, 1.2 μl of 25 mM MgCl₂, 2 μl of 5 μM of each forward and reverse primer, 0.2 μl Taq DNA polymerase and final volume (20 μl) was made by adding deionized double distilled water. Amplification was performed using the following conditions: denaturation at 94°C for 2 minutes; 35 cycles of 1 minute denaturation at 94°C, 1 minute annealing at temperatures adjusted depending upon the SSR primer sequence (48°C - 55°C), 1 minute extension at 72°C, a final extension at 72°C for 7 minutes and hold at 4°C until the tubes were removed. The SSR amplification products were separated in 2.5% agarose gel in 1X TBE buffer at 180V. The gels were visualized under UV light and photographed using photo gel documentation system (Alphaimager HP, Alpha Innotech, USA).
 

 
Statistical analysis
 
For diversity analysis, marker profiles obtained on 2.5% agarose gels were scored manually. Total number of alleles was recorded for each microsatellite marker in 96 pigeonpea genotypes by assigning allele number as 1, 2, 3 and so on. The amplified alleles were recorded as 1 (band present) or 0 (band absent) to construct binary matrix used for diversity analysis by using DARwin-Computer software program (Perrier and Jacquemond-Collet, 2006). Genetic distances between genotypes were calculated by using a dissimilarity matrix. Dissimilarly matrix for SSR primers was constructed using Dice coefficient of associations to find out genetic relationships. The data were subjected to unweighted pair groups method with arithmetic mean (UPGMA) analysis to generate dendrogram. The genetic divergence of each microsatellite locus was obtained by calculating the frequency of the microsatellite allele based on polymorphism information content (PIC) using the equation:

            

 
Where P_ij is the frequency of j^th allele in i^th primer and summation extends over ‘n’ patterns.

A total of 112 alleles were detected by these 44 primers in the 96 genotypes with an average of 2.54 alleles per primer. Out of total SSR primer pairs used in this study, 33 were polymorphic and 11 primers were found to be monomorphic. This showed 75% polymorphism among the used markers. Data for number of alleles detected per primer pair and the Polymorphic Information Content (PIC) values for each of the 44 SSR primers are presented in Table 3. The total number of alleles amplified for each primer ranged from 2 to 4. The maximum number of alleles (4) was amplified by four SSR primer pairs (CcM 0183, CcM 1524, CcM 2707 and CcM 2904), three alleles were observed for 19 primer pairs and two alleles were observed for 10 primer pairs. The results of PCR amplification of four alleles amplified by four SSR primer pairs i.e., CcM 0183, CcM 1524, CcM 2707 and CcM 2904 were in contrast to the results reported by Bohra et al., (2011) who recorded 7 to 10 alleles for each primer CcM 1524 (7), CcM 0183 (8), CcM 2707 (8) and CcM 2904 (10). The main causes of this genetic divergence in this study can be deletions, insertions and chromosomal inversion at DNA level which generate polymorphism or genetic allelic diversity. Similar results were reported by Manju et al., (2017) by using 80 SSR primers, out of which 65 primers showed polymorphism. They observed 2-7 amplified alleles by each primer with an average of 3.4 alleles and PIC ranging from 0.24 to 0.86. They also reported low level of dissimilarity among the genotypes. However, Pushpavalli et al., (2016) found higher level of diversity by observing the number of alleles per primer pair ranging from 2 to 5 with an average of 4.18 alleles. They also concluded that markers with PIC values of 0.5 or higher are best for revealing genetic divergence. Sheikh et al., (2015) used 123 SSR primer pairs to assess the genetic diversity in pigeonpea and found 54.47% polymorphism, while Singh et al., (2013) reported that 59 alleles were amplified by 24 polymorphic SSR primers with an average of 2.46 alleles per primer and the number of alleles per primer ranged from 2 to 4.
 

       
The PIC values provide an estimate of the discriminating power of a marker by taking into account not only the number of alleles at a locus but also relative frequencies of these alleles. These values depend upon the genetic diversity among the genotypes being studied. The PIC values obtained in the present study varied from 0.26 (CcM 0183) to 0.78 (CcM 0402, CcM 0721) with an average value of 0.55 for 33 polymorphic primers. The amplification pattern of primers on some genotypes is shown in Fig 1 and 2, respectively. Seven primers viz., CcM 0402, CcM 0721, CcM 0974, CcM 1991, CcM 2176, CcM 2004 and CcM 2697 recorded PIC values of more than 0.70. The primer CcM 0183 registered the minimum PIC value of 0.26. Similar kind of results were reported by Petchiammal et al., (2015), where they found PIC values for SSR markers ranging from 0.14 to 0.78 and number of alleles produced ranged from 2 to 6. In pigeonpea, a range of PIC values have been reported for SSR markers, e.g., 0.23-0.94 (Datta et al., 2010), 0.30 to 0.76 (Singh et al., 2013), 0.60 to 0.92 (Oinam et al., 2015), 0.03-0.89 (Njunge et al., 2016), 0.21 to 0.68 (Jaggal et al., 2016), 0.01 to 0.38 (Sarkar et al., 2017), 0.34 to 0.79 (Lokesha et al., 2018).
 

 

               
The UPGMA cluster analysis showed that all the 96 pigeonpea genotypes were grouped into eight main clusters (Table 4). The dendrogram showing genetic relationships among 96 genotypes based on 44 microsatellite markers is presented in Fig 3. Cluster I and cluster VII were the largest comprising of 22 genotypes each whereas cluster III and cluster IV were the smallest with two genotypes each. The cluster II, cluster V, cluster VI and cluster VIII consisted of 10, 15, 14 and 9 genotypes, respectively. Cluster I and cluster II represented a distinct pattern with all cytoplasmic male sterile lines and their respective maintainer lines. The grouping of genotypes into different clusters did not follow any specific pattern. For example, genotypes originated from Coimbatore, Hisar, Ludhiana, New Delhi were included in cluster V. All the male sterile lines and maintainer lines were grouped together in the cluster I and cluster II due to presence of similarity in genetic backgrounds. In cluster VII, out of the 22 genotypes, eight genotypes namely Pant A151, Pant A252, Pant A169, Pant A37, Pant A163, Pant A251, Pant A174 and PA 342 were developed at Pantnagar; five genotypes namely ICPL 88031, ICP 3977, IC 245186, ICPL 93081 and ICPL 85035 were developed at ICRISAT, Hyderabad; four genotypes namely P 954, P 971, Pusa 991 and P 226 were developed at IARI, New Delhi; The genotypes namely AL 1599 and T-21 were developed at PAU, Ludhiana and CSAU (U.P), respectively and three genotypes namely MTH 103, GAYT 110 and SP 2-2 with unknown origin. Cluster I included 13 male sterile and 8 maintainer lines developed at PAU, Ludhiana and one genotype ULA 11 with unknown origin. Cluster II consisted of 10 genotypes, out of which seven (2 male sterile and 5 maintainer lines) were developed at PAU, Ludhiana and P 2012, AH 06-1, TAT 108 were originated from New Delhi, Hisar and Maharashtra, respectively. Cluster III consisted of 2 genotypes, AH 06-8 originated from Hisar and AL 209 originated from Ludhiana. Cluster IV also included 2 genotypes, ICP 8947 from ICRISAT, Hyderabad and AL 301 from Ludhiana. Cluster VI included 14 genotypes out of which 6 genotypes (IC 245183, IC 245139, ICPL 92045, IC 245132, ICPL 20340, IC 245219) originated from ICRISAT, Hyderabad, three genotypes (H 93-32, H 94-6, H 93-2) originated from CCSHAU, Hisar, two genotypes (P 2002, P 951) originated from IARI, New Delhi and the genotypes VRG 62, AL 15, CORG 169 were originated respectively from Vamban, Ludhiana and Coimbatore. Cluster VIII consisted of nine genotypes out of which four (H 93-22, MANAK, AH 06-3, PARAS) were originated from CCSHAU, Hisar, two genotypes (IC 245273, ICPL 9008) from ICRISAT, Hyderabad, whereas MN 1, 1371 A, PUSA B 4 were originated from Minnesota (USA), Vamban and IARI, New Delhi respectively. In the present investigation, the dissimilarity coefficients ranged from 0.02 to 0.32 signifying dissimilarity among the pigeonpea genotypes under study. Genotypic pairs having highest genetic dissimilarity of 32% were Pusa 991 and ULA 11, while the minimum genetic dissimilarity (2%) was observed between the lines AL 112A and AL 113A. Earlier studies conducted to assess genetic diversity by using AFLP (Panguluri et al., 2006), DArT (Yang et al., 2006) and SSRs (Odeny et al., 2007) reported low level of genetic diversity in pigeonpea gene pool. Narrow genetic diversity has been reported in pigeonpea due to use of genotypes with high degree of relatedness in breeding programs for the development of new cultivars. In pigeonpea, good amount of diversity exists with respect to morphological traits but with the use of molecular marker analysis, it showed low amount of diversity at the molecular level (Yang et al., 2006). So, the present study approves that there is need for broadening genetic bases of cultivated pigeonpea crop. It can be attained by attempting crosses between genotypes with high molecular diversity and wild species. Following genotypic pairs viz., AL 1476 and Pusa 991, AL 1476 and ICPL 93081, AF 352 and Pusa 991, AF 352 and P 951-1, CORG 105 and ICPL 92045, H 93-13 and P 951-1, P 2002 and Pant A-251 were having more than 28% dissimilarity coefficients. So, these genotypic combinations can be used for broadening the genetic base of pigeonpea gene pool.