Lentil (Lens culinaris Medikus.) is a self-pollinating, annual, winter season crop plant with an approximately genome size of 4 Gbp. Lentil belongs to the family Leguminaceae with important dietary source of energy, protein, carbohydrates, fiber, minerals, vitamins and antioxidant compounds as well as diverse non-nutritional components such as protease inhibitors, tannins, α- galactoside oligosaccharides and phytic acid (Urbano et al., 2007). Lentil also have important medicinal value i.e. prevent homocysteine accumulation, improve cholesterol, controls blood pressure level, colon cancer, constipation, ulcers and small pox etc. Lentil also helps in soil fertility because of its leguminous nature by providing high nitrogen fixation resulting it’s uses in crop rotation with cereal crop which promote sustainable agriculture. Genetic variation and selection act as basis for any plant breeding programme. Understanding genetic diversity, genetic relationship among different accessions and association of yield with different attributes play important role in any crop improvement programme. The most direct approach to improve lentil productivity for salt stress is to identify and increase the presence of novel genes and alleles associated with it in commercially relevant lentil germplasm (Singh et al., 2017 and Singh et al., 2020). During past years, huge focus has been given to the implementation of NGS (next generation sequencing) techniques for genetic improvement that include DNA markers, advanced sequencing technologies and bioinformatics tools. Molecular study for salt tolerance has been studied in many crops including cereals, oilseeds and pulses. Quantitative trait loci (QTL), significant single nucleotide polymorphism markers (SNPs) and candidate genes are already identified for salt tolerance in other crops. Various bioinformatics approaches like GBS (genotyping by sequencing), association mapping, annotation and omics approaches like transcriptomics have been studied in other crops but limited information is available on salt tolerance in lentil. To breed varieties with stable grain yield under salt stress conditions, a deep insight into the genetic basis of yield, it’s attributing traits, nutrient content and associated salt tolerance mechanism is necessary. Integration of classical breeding approaches with new generation genomics and phenomics tools with generation acceleration protocols can hasten the progress in the pulse crop improvement programme.
Like other crops, some studies have been conducted in pulses to evaluate genetic diversity and other biotechnological applications but limited information are available in case of lentil. Zaccardelli et al., (2012) found that microsatellite (SSR) molecular markers were highly effective in discerning genetic variations and relationships among different lentil landraces with these polymorphic bands successfully differentiating each accession. Verma et al., (2014) successfully developed 122 new SSR markers for lentil by constructing a GA/CT motif enriched genomic library and clustered into two clusters indicating genetic relationship within and between lentil species. Kushwaha et al., (2015) examined 96 lentil accessions from Nepalese research centers using 33 polymorphic SSR markers for genetic profiling and classified them into four clusters across two main groups, revealing significant genetic diversity, with the largest cluster representing 58.33 per cent of genotypes. Wong et al., (2015) evaluated 60 germplasm collection of lentil using genotype by sequencing technique where different taxa were formed under phylogenetic tree construction followed by identification of four gene pools named as L. culinaris/L. orientalis/L. tomentosus, L. lamottei/L. odemensis, L. ervoides and L. nigricans that represent primary, secondary, tertiary and quaternary gene pools, respectively. Idrissi et al., (2016) examined the diversity analysis of 70 Mediterranean lentil landraces using SSR and amplified length polymorphic markers under drought stress. Yadav et al., (2016) evaluated the genetic diversity of 185 diverse lentil germplasm by using 30 polymorphic SSR markers for molecular and revealed that ten major clusters underlie divergence in lentil accessions, recommending their utility in future lentil breeding programme.
Singh et al., (2017) analyzed fifty lentil accessions utilizing 20 genomic and 54 EST-SSR markers and revealed that diversity among these accessions, with lower polymorphism observed in EST-SSRs compared to genomic SSRs. Pandey et al., (2018) assessed genetic diversity in ten genotypes of the lentil through microsatellite markers. Skliros et al., (2018) utilized omics technologies to study the metabolic responses of lentil (Lens culinaris) to salinity, examining both acclimated as well as non-acclimated plants. Tsanakas et al., (2018) assessed genetic diversity within the 'Eglouvis' lentil landrace with comparison to the 'Samos' and 'Demetra' varieties using both morphological and molecular markers where divergence distance among individuals and PCoA (principal coordinate analysis) indicated that the 'Eglouvis' possess distinct genetic base, significantly differing from both 'Samos' and 'Demetra'. Chowdhury et al., (2020) investigated the genetic diversity among twenty lentil genotypes with formation of three clusters using Ward's method. Singh et al., (2020) assayed the salt stress tolerance and screened 495 SSR markers over population and found 11 polymorphic markers between the parents with genomic region of QTL on LG-1. Dissanayake et al., (2021) evaluated 276 genotypes of lentil using targeted genotyping by sequencing (t-GBS) and transcriptome genotyping by sequencing (GBS-t). Johnson et al., (2021) conducted phenotyping of 143 accessions to evaluate the pre-biotic carbohydrates and also conducted GWAS to detect linked markers. Tomar et al., (2023) used SSR markers to analyze the diversity among 37 lentil genotypes with resulting in formation of two clusters with PIC values (up to 0.77). Among a variety of molecular markers, microsatellite markers, simple sequence repeats (SSRs) are the most commonly and preferably used for genetic diversity studies. SSRs are cost-effective, easy to score, quick, reliable, and require minimal DNA quantities (Gupta and Varshney, 2000). They are highly polymorphic, co-dominant in nature and abundantly distributed across genome so preferentially favoured for genetic diversity analysis (Hoshino et al., 2012; Mondini et al., 2009).
Our present study focuses on the exploration of the pattern of genetic diversity among lentil accessions, population structure and genotypic relationships using microsatellite markers that could facilitate the conservation and utilization of studied germplasm resources.