Legume Research

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Prioritization of Microsatellite Markers Linked with Drought Tolerance Associated Traits in Chickpea (Cicer arietinum L.)

Prakash N. Tiwari1, Sharad Tiwari1,*, Swapnil Sapre1, Anita Babbar2, Niraj Tripathi3, Sushma Tiwari4, Manoj Kumar Tripathi4,*
1Biotechnology Centre, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur-482 004, Madhya Pradesh, India.
2Department of Genetics and Plant Breeding, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur-482 004, Madhya Pradesh, India.
3Directorate of Research Services, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur-482004, Madhya Pradesh, India.
4Department of Plant Molecular Biology and Biotechnology, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior-474 002, Madhya Pradesh, India.
  • Submitted13-06-2023|

  • Accepted23-08-2023|

  • First Online 12-09-2023|

  • doi 10.18805/LR-5191

Background: Being a vital source of high-quality dietary protein, chickpea is an unavoidable legume. The present investigation was performed to study the applicability  of the microsatellite markers linked with drought tolerance in Indian chickpea genotypes collected from different genetic background.

Methods: In Rabi 2021-22, forty chickpea genotypes including national check for drought tolerance, elite cultivars, released varieties and advanced breeding lines were screened employing forty microsatellite markers linked with drought tolerance associated traits.

Results: Among forty drought tolerance related microsatellite markers, twenty-six were found to be polymorphic and produced a total of 66 alleles, with a mean of 2.5 alleles per locus. Genotype NCPGR127 had highest numbers of alleles, gene diversity and polymorphism information content tracked by genotype TR19. These genotypes may be used for selecting drought tolerant genotypes through marker assistant selection (MAS). Model-based population structure analysis clearly distinguished the drought tolerant genotypes including ICC4958, JG74, JAKI9218, JG16, JG6, JG14 and JG11. Principle coordinate analysis (PCoA) and analysis of molecular variance (AMOVA) further confirmed these results. Findings of the present investigation have a greater potential for further utilization in breeding of drought specific chickpea cultivar(s).

Legumes are very essential in human diet as they are not only complementing nutrients in the cereal diet but also improve the taste and texture of staple dish (Varol et al., 2020; Yadav et al., 2023). Chickpea (Cicer arietinum L., 2n = 16) is a nutrition-rich economical source of high-quality protein comprised of globulin and albumins as compared to animal protein and is hence vital for nutritional security in developing nations, especially in India (Gupta et al., 2021; Ningwal et al., 2023). “Desi” and “Kabuli” are two main types (Sahu et al., 2020) of cultivated chickpeas in the world. In which “Desi” chickpea covers about 80-85% of the total chickpea cultivation area in the world, primarily grown in South Asia, East Africa and Australia. In India, chickpea has contributed to the ‘Pulse Revolution’, making the country near self-sufficient in pulses from 2014-15 to 2020-21 with a remarkable increase in chickpea production and productivity. Chickpea production rose to an all-time high of 12.61 mt during 2020-21 from a level of 7.59 mt in 2014-15, with an increase of nearly 66% in production and >26% in productivity during six years (Dixit, 2021).
Abiotic stresses, especially drought and heat reduce yield of different crops (Mishra et al., 2021; Sharma et al., 2021; Asati et al., 2022) including chickpea over 70% (Varshney et al., 2019). The application of molecular markers may shorten the crop breeding cycle and ultimately leads to crop improvement particularly in complex traits controlled by polygenes (Mishra et al., 2022; Solanki et al., 2022; Yadav et al., 2023). Several quantitative trait loci (QTLs) for drought-tolerance associated traits have been reported in chickpea. The QTL-hotspot genomic region present on CaLG04 explained >50% phenotypic variation for drought tolerance (Varshney et al., 2014). Additionally, genome-wide microsatellite markers associated with drought tolerance related traits were also reported (Li et al., 2018; Varshney et al., 2019).
Marker-assisted backcross breeding has been successfully employed for developing superior chickpea lines with improved yield and drought tolerance. Drought tolerant variety Pusa Chickpea 10216 has also been released for commercial cultivation in India (Bharadwaj et al., 2021). In the light of above facts, employing drought tolerance linked microsatellite markers, the present study was aimed to assess the pattern and level of genetic diversity and developing population structure among national check for drought tolerance, elite cultivars, released varieties and advanced breeding lines.
Plant materials
 Forty chickpea genotypes were obtained from the All India Coordinated Research Project (AICRP) on Chickpea, Department of Plant Breeding and Genetics, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur (Table 1). The genotypes were grown in the Net house of Biotechnology Centre, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur during Rabi 2021-22.

Table 1: Details of the forty chickpea genotypes used for present study with their parentage and source.

DNA extraction and quantification
Genomic DNA from young leaves of grown chickpea genotypes was isolated using the cetyl triethyl ammonium bromide (CTAB) method (Doyle and Doyle 1987). Two grams of leaf samples were ground in liquid nitrogen into a fine powder using sterilized pestle and mortar. About 100 mg of leaf powder was transferred to 2 ml micro-centrifuge tubes containing 1 ml of extraction buffer (0.1M Tris–HCl (pH 7.6), 0.005M EDTA and 0.2M Tris-HCl, 0.05M EDTA, 2M NaCl and 2% CTAB). Sample tubes were incubated at 65°C for one hour then centrifuged at 10000 rpm for 15 minutes with chloroform-isoamyl alcohol (24:1) mixture for protein separation. Supernatant was transferred to fresh tube and chilled isopropanol was used for the precipitation of DNA. Precipitated DNA was pelleted down through centrifugation and the DNA pellet was dried and dissolved in 50 μl of Milli-Q (MQ) water. The quantity and quality of all DNA samples were checked using agarose gel (0.8%). The working DNA sample was obtained by diluting to a final DNA concentration of 40-50 ng per microlitre (μL).
Polymerase chain reaction (PCR) and gel electrophoresis
Forty genome wide distributed drought tolerance linked microsatellite markers were selected based on polymorphic information content (PIC), allelic richness and heterozygosity from previous studies (Table 2). Primers were synthesized from Integrated DNA Technologies (Asia Pacific, Singapore). The PCR reactions were performed with a total volume of 10ìl containing 40-50 ng of DNA, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.4 mM each of the forward and reverse primers and 1U Taq DNA polymerase in a Sure Cycler 8800 (Agilent Technologies) after optimizing the annealing conditions specific for each primer pair. The PCR was performed with an initial denaturation step of 5 min at 94°C, tracked by 35 cycles of 30s denaturation at 94°C, annealing at 55-60°C (depending on the primer) for 50s, initial extension at 72°C for 50s. The final extension was accomplished at 72°C for seven min. The PCR products were resolved on 2.8% agarose gel in 1 × TAE buffer with a 6 × DNA loading dye. Electrophoresis was carried out on horizontal electrophoresis with 65V for two and half hours using a standard 100 bp DNA ladder (Promega, Madison, USA). Gel pictures were snapped on a Vilber Laurmet, QUANTUM-ST5 gel electrophoresis system.

Table 2: Details of the drought tolerance related markers used in the study.

Analysis of genotypic data through a model-based approach
Scoring each polymorphic SSR marker was done with the help of Gel Analyzer V19.1 software. The allelic data scores were used for computing locus-based diversity indices with the help of GenAlEx V6.51b2 software and Power Marker V3.25 software. The Population structure for 40 genotypes was constructed using STRUCTURE version 2.3.4 software. An analysis of molecular variance (AMOVA) and Principal Coordinates Analysis (PCoA) within and among populations were performed using GenAlEx V6.51b2.
Modern chickpea improvement program largely necessitates the identification of highly diverse chickpea germplasm, highly polymorphic trait-specific molecular markers, which can be effectively utilized for the development of improved varieties. Thus, the characterization of chickpea germplasm has the great potential for playing a vital role in future international breeding programs (Mir et al., 2022), especially in India (Asati et al., 2022; Yadav et al., 2023), which is the largest chickpea-producing nation (Dixit, 2021). Consequently, for identification of new sources of germplasm having valuable genes/QTLs to improve yield and enhance resistance/tolerance to various abiotic and biotic stresses, investigation of structure and nature of genetic diversity and phylogenetic relationship within and among cultivated chickpea varieties, breeding lines and its wild relatives is obviously needed (Mir et al., 2022). For Indian chickpea genotypes, molecular diversity and population structure studies using drought tolerance related traits linked microsatellite markers are very limited, hence this study was initiated to analyze the nature and structure of genetic diversity and phylogenetic relatedness among 40 chickpea genotypes.
Polymorphism among chickpea genotypes
The SSRs have been widely utilized for genetic diversity analysis, germplasm grouping and population structure analysis in numerous crops, including chickpea (Choudhary et al., 2012; Mishra et al., 2020; Rathore et al., 2022). The present investigation determined the suitability of selected trait linked microsatellite markers for genetic diversity analysis of chickpea genotypes. Out of 40 applied markers, only 26 showed consistent polymorphic banding patterns with genotypes, indicating their suitability for genetic diversity analysis. The remaining fourteen that produced monomorphic bands among genotypes revealing one allele at each locus were not considered for diversity analysis.
Overall, 66 alleles were identified by 26 polymorphic markers from the 40 tested genotypes with a mean of 2.5 alleles per locus (Table 3). The allelic richness (Na) per locus ranged between two (most of the markers) to four (TR 19 and NCPGR 127). The numbers of effective alleles (Ne) ranged from 1.22 (ICCeM058) to 3.57 (NCPGR 127), with an overall mean of 1.92 effective alleles per locus. The most frequent major allele frequency (0.90) was found for the marker ICCeM0058 and the lowest (0.40) for marker NCPGR127, with an average of 0.66. The highest gene diversity (0.72) and PIC value (0.67) were found for the marker NCPGR127, whilst the lowest gene diversity (0.18) and PIC value (0.16) was investigated for the marker ICCeM0058, with an average of 0.44 and 0.37, respectively. The microsatellite marker analysis result indicated moderate allelic richness per locus and relatively moderate to high PIC, Ho and He values. A high level of genetic diversity indicated the existence of higher molecular variation among the 40 tested chickpea genotypes, in agreement with the previous studies (Mir et al., 2022). During present study, the most of the loci (20) exhibited a low level of observed heterozygosity in comparison to the expected heterozygosity and high fixation index (Choudhary et al., 2012). It indicated high levels of expected inbreeding among tested chickpea genotypes because chickpea is a self-pollinated crop and only 0 to 1.58% of outcrossing is reported (Ghaffari et al., 2014). The rest of the six loci had a high level of observed heterozygosity compared to the expected heterozygosity with a low associated fixation index. It implied that higher mutation rates or inbreeding depression could be associated with these loci (Choudhary et al., 2012).

Table 3: Estimated genetic diversity parameters of 26 polymorphic microsatellite loci across 40 chickpea genotypes.

Population structure
Model-based clustering method of the STRUCTURE software is valuable in showing the presence of population structure in tested genotypes, identifying different genetic populations, assigning individuals to populations and identifying pure and admixed individuals (Varshney et al., 2019). A population structure model without admixture was investigated using all 40 genotypes and 26 polymorphic markers for maximum likelihood by correlating allele frequency with a 5000-burn period with a run length of 50000 to 10 Markov Chain Monte Carlo (MCMC) with varying K from 1 to 15 with five iterations. The highest peak was observed at K = 4 with 30.78 Delta K value in Delta K analysis, indicating the presence of four populations in tested genotypes (Fig 1). At K=4, all genotypes were stratified into four populations viz., P1, P2, P3 and P4, representing 22., 22.5, 37.5 and 17.5% of genotypes used in structure analysis, respectively (Fig 2A, presents the inferred population structure). Population group 2 had clearly distingued the drought tolerant genotypes viz., ICC4958, JG74, JAKI9218, JG16, JG6, JG14 and JG11 from other studied chickpea genotypes. These distinguished drought tolerant chickpea genotypes had been already established as drought tolerant chickpea genotypes. The ICC4958 is a national check for drought tolerance in India, JG74 is an early maturing chickpea cultivar and established as most suitable chickpea cultivar for rainfed conditions of central region of India (Mannur et al., 2019), whilst JAKI9218 and JG16 are established as drought tolerant chickpea varieties. JG14 is identified as a heat tolerant chickpea variety (Dixit et al., 2019) and JG11 is established as a drought tolerant chickpea variety (Dixit, 2021).

Fig 1: Delta K analysis for assuming structured populations.


Fig 2: (A); Population structure. (B); Principal coordinate analysis of 40 chickpea genotypes.

In principal coordinate analysis (PCA) the first two Eigen vectors classified the studied genotypes into four major groups and distinguished the drought tolerant chickpea genotypes from investigated genotypes, which were comparable to observation obtained from STRUCTURE analysis (Fig 2B). The genotypes were divided into four structured populations, revealing that tested chickpea genotypes evolved from four population types demonstrating varying degrees of introgression from four types of ancestors into tested genotypes. It might be due to the resulting genotypes from independent evolutionary mechanisms including genetic drift, migration, mutation, selection and germplasm exchange that split them into discrete gene pools.
Analysis of molecular variance (AMOVA)
The four population groups generated from the structural analysis were subjected to AMOVA to estimate the percentage of genetic variation within as well as among individuals and populations (Table 4). The total genetic variance of genotypes was contributed by 7% variation within individuals, 77% among individuals and 16% among populations (Fig 3). Pairwise inbreeding coefficient within subpopulations (Fst) value (0.155) demonstrated significant variation among all the four pairs of populations, suggesting that all four groups were different from each other. The results acquired from AMOVA analysis agreed with the observations found through the basic summary statistics, structure analysis and principal coordinate analysis, confirming both the presence of a statistically moderate genetic diversity among genotypes and a high level of population structure among genotypes.

Table 4: Summary AMOVA table of populations assigned from structure.


Fig 3: AMOVA of structured populations.

The AMOVA results demonstrated that most variation among genotypes might be credited to variation among individual genotypes compared to variation among and within the population. It suggested that individual variation of genotypes might be more valuable for chickpea improvement programs. The PCoA result depicted that the chickpea genotypes were equally spread in all four quadrants regardless of their population structure. Population one was exclusively grouped in quadrant I, showing their distinctness from the rest of the populations evenly spread in all four quadrants because of their highly diverse nature. Similar results have also been reported in various earlier studies using microsatellite markers (Mir et al., 2022).
Trait linked microsatellite markers based on the molecular characterization of 40 chickpea genotypes showed presence of substantial genetic variation and population structure among genotypes. Summary statistics concluded that a moderate genetic variation was present among the genotypes. Marker NCPGR127 having the highest Na, Ne, He, Ht, gene diversity and PIC values followed by TR19. These markers might be used for selecting drought tolerant genotypes through marker assisted selection (MAS) approach. The population structure analysis identified four major population groups among studied genotypes clearly distinguished the drought tolerant genotypes viz., ICC4958, JG74, JAKI9218, JG16, JG6, JG14 and JG11 from other studied chickpea genotypes. The results obtained from the present investigation could thus be efficiently employed in the MAS programme to breed drought tolerant cultivar (s).
The first author acknowledges the Indian Council of Agricultural Research (ICAR) for the award of the ICAR Junior/Senior Research Fellowship (F. No. EDN/1/25/2015-Exam Cell) for PhD degree programme.

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