Climate change is a global threat to food and nutritional security (Leisner, 2020; Shahzad et al., 2021) as predicted by the intergovernmental panel on climate change (IPCC) (Climate.gov, 2022). The expected average global temperature rise between 2°C and 3°C by 2100 is anticipated to severely impact both abiotic and biotic components of the environment (Tito et al., 2018; Juroszek et al., 2020; Skendžić et al., 2021; Pielke et al., 2022), resulting in impacts on soil nutrients and other ecological resources, as well as the growth, abundance, distribution, physiology and phenology of a wide range of species (Shao and Halpin, 1995; Tollefson, 2020). Agriculture is particularly vulnerable to the effects of climate change, with significant yield losses due to heat and drought waves and the emergence of new diseases. The inconsistent precipitation, water deficit, extreme temperatures and sodicity have been among the most devastating stresses that have caused enormous reduction in crop productivity (Rajpal et al., 2019a; Rajpal et al., 2019b; Zeroual et al., 2023). Many modelling studies conducted in multiple countries and agro-climatic zones have predicted large-scale reduction in agricultural productivity, habitat loss, distribution, range shifts and even extinction of species coupled with climate change (Bellard et al., 2012; Iizumi et al., 2018; Gupta and Mishra, 2019; Román-Palacios and Wiens, 2020; Zilli et al., 2020; Kadiyala et al., 2021; Lychuk et al., 2021; Affoh et al., 2022; Ait-El-Mokhtar et al., 2022; Gordeev et al., 2022; Nguyen and Scrimgeour, 2022; Ntiamoah et al., 2022 ) and the risks being exacerbated in species with narrow distribution range and/or genetic base (Dubos et al., 2022; Galushko and Gamtessa, 2022). Besides mitigating commercial cultivars to adapt to the changing climates, there is a pressing need to enhance crop productivity to feed the world’s ever-growing population which is expected to reach 9 billion by the year 2050. This can be achieved by increasing the rate of genetic gains using novel technologies enabling the crop breeding reduction, increasing genetic gains accuracy and using wide genetic diversity. Breeding climate-smart crop varieties that can withstand multiple stresses in field conditions, therefore, is the focus of modern plant breeding research worldwide. The identification and availability of stress-responsive genes and loci, which is a prerequisite for implementing these strategies has also become a thrust area of research.

In this context, the crop wild relatives (CWRs), landraces and exotic germplasm serve as important reservoirs of useful genes for resistance to insect pests, diseases and various abiotic stresses. A plethora of published reports has clearly demonstrated that a variety of traits like increased resistance against late blight, grassy stunt disease, drought and heat tolerance, increased nutritional value and productivity (Brar and Khush, 1997; Bamberg and Hanneman, 2003; Sheehy et al., 2005; Song et al., 2014; Janzen et al., 2019; Wang et al., 2019; Hao et al., 2020; Gramazio et al., 2021; Quezada-Martinez et al., 2021 ) in diverse crops including wheat, potato, soybean, mustard and rice have been achieved by introgressing useful genes from the CWRs gene pools into the commercial cultivars. Introgression breeding has given rise to improved cultivars in many leguminous species also such as peanut, urd bean, common bean mung bean, chick pea, pigeon pea and lentils (Singh et al., 1997; Singh et al., 2013; Tullu et al., 2013; Kahraman et al., 2015; Ogutcen et al., 2018; Kumar et al., 2021; Khan et al., 2022). The importance of CWRs in the breeding of novel cultivars with improved acclimatization ability to various biotic and abiotic stresses, and in broadening the genetic base of modern crops has been very well established. Therefore, efforts have been done globally to characterize and conserve these important genetic treasures for future crop protection and sustenance of agri-food systems (Jarvis et al., 2008; Rajpal et al., 2016a; Coyne et al., 2020; Dissanayake et al., 2020; García-García et al., 2021; Quezada-Martinez et al., 2021; Pratap et al., 2021; Renzi et al., 2022; Rajandran et al., 2022).

The genus Lens (2n=2x=14), an important source of food, fodder and dietary protein is one of the most important members of the family Fabaceae (Schaefer et al., 2012). The genus has undergone many taxonomical revisions and according to the most accepted classification system, it consists of seven taxa, viz. L. culinaris ssp. culinaris; L. culinaris ssp. orientalis; L. culinaris ssp. odemensis; L. ervoides; L. culinaris ssp. tomentosus; L. lamottei and L. nigricans (Ferguson et al., 2000; Ferguson and Erskine, 2001). L. culinaris ssp. culinaris commonly known as lentil is the only cultivated species of the genus with L. culinaris ssp. orientalis and L. nigricans being its most closely related and distant progenitors, respectively (Wong et al., 2015).

Lentil (L. culinaris ssp. culinaris), an annual, herbaceous and self-pollinated old world crop is believed to have been domesticated around 8500 BC in Syria and Turkey (Hansen and Renfrew, 1978; Cubero, 1981; Harlan, 1992; Bahl et al., 1993; Zohary and Hopf, 2000). It originated in the Near East and Asia Minor (Ladizinsky, 1979; Zohary and Hopf, 1988; Ferguson et al., 2000) and has since spread to other regions such as North Africa, South Asia, Central and Southern Europe, North America, and Oceania after its origin from Eastern Fertile Crescent (Duke, 1981; Ahmad et al., 1997). Lentil is now widely cultivated in a range of climates and elevations and is the 3^rd most important grain legume after chickpea and pea. It is a dual-purpose crop with its grains being a source of high dietary protein and straw being a valuable livestock feed. There has been a significant increase in global yield potential for lentil over the past 25 years (FAOSTAT, 2019) leading to an increase in global production from 0.85 to 6.53 metric tonnes (FAOSTAT, 2020). Canada is world’s largest lentil producer (48% of world’s production) and exporter (64%. of global lentil exports), while India is the second largest producer (with 15.7% of world’s production) but the largest importer of lentil due to high consumption and low productivity (Dissanayake et al., 2020; Rajendran et al., 2021; http://www.fao.org/faostat/en/#data/QC; http://www.fao.org/faostat/en/#data/TP, Guerra-García et al., 2021).

The successful breeding and genetic enhancement of crops depend on the availability of genetic diversity in their gene pools, identification and characterization of the novel alleles and detailed crossability data for selecting relevant taxa as parents (Rajpal et al., 2016a; Rajpal et al., 2016b). On the basis of crossability data, the species of genus Lens have been grouped into three gene pools, with the primary gene pool being represented by Lens culinaris ssp. culinaris, L. culinaris ssp. orientalis, and L. odemensis. The secondary, and tertiary gene pools are represented by two species each L. ervoides, L. nigricans and L. lamottei and L. tomentosus, respectively (Ladizinsky, 1999; Muehlbauer and McPhee, 2005; Fratini and Ruiz, 2006). These gene pools are the reservoirs of useful crop traits such as resistance to various pathogens and other phenological and agronomic traits (Gupta and Sharma, 2006; Cristobal et al., 2014) that can be transferred to cultivated lentils.

Traditionally, lentil breeding has been undertaken through extensive germplasm screening which has allowed selection and release of superior cultivars such as varieties BARI M4-M8 (Bangladesh) (Kumar et al., 2021) and ILL 404 (Nepal) (Materne and McNeil, 2007) with improved yield and disease resistance for commercial cultivation. An exotic variety ‘Percoz’ has resulted in many improved Indian cultivars Angoori, Narendra M1, and VL Masoor 507 (Kumar et al., 2013). However, intensive breeding and domestication have led to a narrow genetic base and reduced yield of local lentil cultivars, which limits the prospects of further increasing crop productivity through selections. Based on morphological differences, the cultivated lentil species L. culinaris encompass the small-seeded (microsperma) and large-seeded (macrosperma) groups (Singh et al., 2020). In India, traditionally grown lentil belongs to ‘microsperma’ (pilosae type), which has a narrow genetic base, low seedling vigor, pod set and harvest index and increased rate of flower drop. It is also poor in dry matter accumulation and lacks resistance to abiotic and biotic stresses (Ferguson et al., 1998; Kumar et al., 2004; Khazaei et al., 2016; Zeroual et al., 2023). To achieve enhanced genetic gains in lentil breeding, the identification of new target traits from CWRs and their introgression into cultivated taxa is desired in order to broaden the genetic base of cultivars. This can be accomplished by deploying additional alleles from alien and secondary and tertiary gene pools. Recent advances in large-scale genome analyses, such as next generation sequencing (NGS), high throughput genotyping (HTG) and high throughput phenotyping (HTP) have added to the breadth of genetic diversity, development of genomic resources databases and knowledge on phylogenetics in the genus Lens. This information can be used for precise and efficient molecular genetic improvement and enhancement programs of lentils (Kumar et al., 2021; Pratap et al., 2021; Hussain et al., 2022; Salaria et al., 2022; Salgotra and Stewart, 2022; Singh et al., 2022a; Tiwari et al., 2022; Civantos-Go´ mez et al., 2022; Roy et al., 2023; Zeroual et al., 2023 ) similar to what has been achieved in major crops such as rice, wheat and maize (Yoshino et al., 2019; Mishra et al., 2021).

The present Review has compiled information on the available genetic and genomic resources, genotyping efforts, genetic maps and databases, marker-assisted and genomic selections, identification of QTLs, ESTs, genes associated with desired crop traits and genome assemblies in lentil and its CWRs. This collation will aid in understanding the spectrum of diversity available for introgression and the development of elite lentil germplasm with desired productivity levels for future food and nutritional security and adaptability to changing climates.

Lentil is a self-pollinated, diploid (2n=2x = 14) species with a C DNA value of 4.2 pg (Arumuganathan and Earle, 1991; Singh et al., 2018). The taxonomy of genus Lens at the species and subspecies levels has been quite contentious (Van Oss et al., 1997; Ferguson et al., 2000; Fratini and Ruiz, 2006; Suvorova, 2014; Koul et al., 2017). The most recent classification system (Wong et al., 2015; Koul et al., 2017) recognizes seven taxa in the genus grouped into four genepools: L. culinaris, L. orientalis and L. tomentosus in the primary genepool; L. odemensis, L. lamottei in the secondary genepool; and one species each L. ervoides and L. nigricans in the tertiary and the quaternary gene pools, respectively. Despite these reorganizations at taxonomic level, it is generally agreed that L. culinaris ssp. orientalis is the most closely related wild progenitor of L. culinaris ssp. culinaris, while the most distantly related species L. nigricans has a distinct gene pool (Reddy et al., 2009; Wong et al., 2015; Liber et al., 2021 ). Although viable hybrid formation has been reported between L. culinaris ssp. orientalis and L. odemensis (Ladizinsky et al., 1984; Abbo and Ladizinsky, 1994; Fratini et al., 2004; Fratini and Ruiz, 2006; Muehlbauer et al., 2006), the fertility of the hybrids may be affected by chromosomal rearrangements (Ladizinsky et al., 1984; Ladizinsky, 1979 ). Crosses are also possible between the cultivated lentil, L. culinaris and the species belonging to the other gene pools, but hybrids may be sterile owing to chromosomal rearrangements that aborts the hybrid embryos at a high rate (Abbo and Ladizinsky, 1991; Ladizinsky, 1993; Abbo and Ladizinsky, 1994; Gupta and Sharma, 2005). In vitro embryo rescue methods are used to overcome these barriers (Fratini and Ruiz, 2006; Fratini and Ruiz, 2011; Kumar et al., 2014).

Studies have reported a close relationship between L. odemensis, L. nigricans and L. culinaris ssp. orientalis based on morphological markers (Fratini et al., 2006), however, other studies using morphological features and molecular markers suggest the need for revisions in the taxonomic status of L. culinaris ssp. odemensis and L. tomentosus which have distinct morphological features and karyotypes (Ladizinsky, 1997; Van Oss et al., 1997; Koul et al., 2017 ). These differences in the karyotypes might contribute to the reproductive isolation between Lens species, even though they share the same diploid chromosome number (Muehlbauer and McPhee, 2005).

Further, three major cultivated lentil groups have been identified by Khazaei et al. (2016) based on studies on lentil accessions from 54 countries reflecting the world’s Mediterranean, northern temperate and south Asian (sub-tropical savannah) agro-ecological zones. Four major clusters have also been revealed by Dissanayake et al. (2020) with the taxa grouped as L. culinaris/L. orientalis in cluster 1; cluster 2 with L. odemensis/L. lamottei; and two species L. ervoides and L. nigricans clustered separately. Studies by Pavan et al. (2019) showed correlation between assessment of seed size and early flowering traits, genetic clustering and geography in Mediterranean germplasm. Cultivated and wild lentil accessions showed little correlation in their geographical origins. These reports indicate that present-day lentil diversity has been articulated by both natural and artificial selection (Liber et al., 2021).

Worldwide, gene banks hold a large number of 58,405 Lens accessions spread across 103 countries. The International Centre for Agricultural Research in the Dry Areas (ICARDA) maintains the largest collection of 14,577 accessions, including 11,405 landraces, 2,580 breeding lines and 612 wild accessions from 26 countries (Kumar et al., 2015; Guerra-García et al., 2021). Other large germplasm collections of lentil are maintained by the Australian Grains Gene (AGG) bank (6,218 accessions), the European Cooperative Programme for Plant Genetic Resources (4,598 accessions), the USDA Agricultural Research Service, USA (3,247 accessions), the Seed and Plant Improvement Institute of Iran (3,000 accessions), the Vavilov Institute, Russia (2,598 accessions), and Plant Gene Resources of Canada (1,150 accessions). In India, the ICAR-National Bureau of Plant Genetic Resources (NBPGR) of India maintains 2537 accessions, while, Indian Institute of Pulses Research (IIPR), Kanpur, maintains 71 accessions from wild species and 117 landraces of the cultigen from the Mediterranean region (Kumar et al., 2015; Singh and Chung, 2016; Malhotra et al., 2019). The distribution of lentil world collections is listed in Table 1 .

Keeping in view the global mandate for lentil improvement, accessions of different wild species of lentil are screened at various research institutions such as ICARDA, and IIPR for various biotic and abiotic stresses, as well as agro-morphological traits. Further, hybridization programs involve crossing ‘microsperma’ and ‘macrosperma’ lentils (Erskine et al., 1998) to produce promising germplasm for lentil breeding programs in South Asia (Sarker and Erskine, 2006; Sarker et al., 2010). The introduction of exotic germplasm of macrosperma variety ‘Precoz’ with early flowering trait has led to the development of improved cultivars with large seeds, short duration and rust resistance (Singh et al., 2006; Asghar et al., 2010). There are many cultivars that have been developed and released in India using promising breeding lines developed at ICARDA (Dixit et al., 2009).

A recent initiative, INCREASE (Intelligent Collections of Food Legumes Genetic Resources for European Agrofood Systems) launched in 2020 by the European Union’s Horizon (https://www.pulsesincrease.eu) aims to enhance the phenotypic and genotypic characterization of four food legumes genetic resources including lentil (García-García et al., 2019; Cortinovis et al., 2021; Guerra-García et al., 2021; Kroc et al., 2021).

To manage a large number of accessions, concept of developing ‘core’ and ‘mini core’ collections has been used to represent maximum variability in limited number of accessions (Brown, 1989 ). While a core collection represents 10-20% (Yonezawa et al., 1995) of the total base collection of accessions in a species, a mini core collection includes 1-2% of entire collection (Zhang et al., 2012). Core collections are attractive as they represent a sizeable genetic diversity in a manageable number of accessions and have been developed in many crop species like rice, wheat, maize, and many pulses (Upadhyaya et al., 2006; Mourad et al., 2020; Vilayheuang et al., 2020; Raturi et al., 2022). In the genus Lens, Singh et al. (2014) analysed 405 accessions of all seven taxa with morphological and biotic resistance markers to construct a core set of 96 lentil accessions using the statistical program ‘PowerCore’. The core set was then screened for resistance to rust (Uromyces fabae (Grev.) Fuckel) and Powdery mildew (Erysiphe polygoni DC.) for three seasons under two agro-climatic conditions in India (Singh et al., 2014). Another core set of lentil accessions comprising of 170 accessions (137 Indian and 33 exotic) has been constructed based on the agro-morphological data and geographical distribution (Tripathi et al., 2021). Recently, Heineck et al. (2022) screened a part of the lentil core collection derived from single seed for resistance against Fusarium oxysporum. They found differences in disease severity and biomass traits among lentil accessions. Further, they used genome-wide association study (GWAS) and SNP markers to identify 11 QTLs, two pairs of which were located near putatively orthologous sequences linked to disease resistance.

Conventional breeding has resulted in considerable genetic improvement of lentils, but productivity has become stagnant in the recent years. Utilization of divergent germplasm from crop wild relatives, landraces and exotic germplasm can broaden the genetic base with useful genetic variation and infuse the lost variability which can result in improved productivity and introgression of desirable characters in lentil (Doyle, 1988; Tanksley and McCouch, 1997; Gupta and Singh, 2009; Pratap and Gupta, 2009). Domesticated lentil has revealed very poor genetic variability compared to its related wild species L. culinaris ssp. orientalis in multiple studies (Muench et al., 1991; Mayer and Soltis, 1994; Alvarez et al., 1997; Ford et al., 1997; Alo et al., 2011). Many studies have indicated that wild Lens taxa show resistance to various biotic and abiotic stress conditions (Bayaa et al., 1994; Bayaa et al., 1995; Hamdi et al., 1996; Hamdi and Erskine, 1996; Gupta and Sharma, 2006). These species are a source of useful alleles for traits like resistance to key diseases, parasitic weeds and insect pests. The different sources of important crop traits in lentils are listed in Table 2 .

Lentil breeding has been laid around a systematic breeding scheme where trait specific donors and recipient cultivars can be selected (Kumar et al., 2014). Many studies have shown that alien gene introgression from exotic wild species has substantially demonstrated higher variations for productivity and its associated traits in new segregating F₂ population (Gupta and Sharma, 2007; Singh et al., 2013). Several agronomic and other potential traits like disease resistance and biofortification have been introgressed from L. orientalis and L. ervoides into pre-bred lines from various sources by ICARDA. These improved lines are being tested in different locations and exhibit more than 40% increase in yield compared to the check (Bakaria) along with higher percentage of micronutrients and 80–100 days of short-season cycle (Kumar et al., 2019). Recently, a lot of research interest has shifted to wild Lens relatives for identification of useful traits.

The productivity gains so far achieved in lentils are largely based on the use of traditional breeding approaches. Developing climate-resilient smart crop varieties with broad-spectrum tolerance to withstand multiple simultaneous stresses in a short span of time would not be possible by traditional crop breeding alone. Further, since the economically important crop traits are mostly quantitative in nature and get highly affected by their immediate environment, such GxE interactions add another level of complexity to breeding programs. The deployment of a multitude of advanced genomics tools in integration with traditional breeding pipelines, however, has made this task achievable in many important crop species (Maghuly et al., 2022). These new genomic tools and technologies including molecular DNA markers, cutting-edge sequencing technologies, high-density genotyping and phenotyping platforms, genome mapping, genome dissection, genomic selection, predictions and editing methods have expedited the breeding of improved varieties (Sihag et al., 2021; Kumar et al., 2021; Dhakate et al., 2022). The availability of high quality reference genomes is constantly growing due to the access to newer methods to sequence large whole genomes with affordability. The advancement in allied disciplines of bioinformatics, statistics, data science and modelling strategies coupled with traditional breeding are assisting in realizing enormous sustainable agricultural productivity gains much faster than before. The integration of traditional breeding methods with a new era of molecular breeding can tackle the challenges of changing global climate and sustain the crop productivity for future food and nutritional security (Huang et al., 2022; Yaqoob et al., 2023). Although, limited efforts have gone into the genomics-assisted breeding of lentil so far (Tiwari et al., 2022; Zeroual et al., 2023), an accelerated development of genomic resources during the last decade raises many hopes (Kumar et al., 2015; Kumar et al., 2021 ).

Lentil CWRs have been extensively studied for useful traits that can serve as important genomic resources for future breeding programs. Various molecular marker systems such as restriction fragment length polymorphisms (RFLPs), inter simple sequence repeats (ISSRs), simple sequence repeats (SSRs), randomly amplified polymorphic DNAs (RAPDs), and amplified fragment length polymorphisms (AFLPs) have been used to study the genetic diversity and phylogenetic relationships within the genus Lens (Havey and Muehlbauer, 1989; Abo-elwafa et al., 1995; Fratini et al., 2004; Ferguson et al., 2000; Sharma et al., 1995; Sharma et al., 1996; Fikiru et al., 2007; Babayeva et al., 2009; Hamwieh et al., 2009; Toklu et al., 2009; Gupta et al., 2012a; Gupta et al., 2012b; Kumar et al., 2014; Idrissi et al., 2015; Kushwaha et al., 2015; Mekonnen et al., 2015; Wong et al., 2015; Dissanayake et al., 2020; Hussain et al., 2022 ).

Many new marker systems like (DAMD- directed amplification of minisatellite), (iPBS-transcriptase primer binding site), sequence-related amplified polymorphism (SRAP) have also been used in assessing genetic diversity and characterization of Lens species (Bermejo et al., 2014). Based on all these marker systems, Lens species can be readily distinguished from each other and support the earlier reports that L. culinaris ssp. orientalis is the progenitor species of the cultivated one (Alo et al., 2011; Liber et al., 2021). Among all the above-mentioned DNA molecular markers, simple sequence repeats (SSRs), have been most extensively utilized for the construction of lentil linkage maps (Hamwieh et al., 2005; Verma et al., 2014) and have been coupled with transcriptomic analysis as well (Kaur et al., 2011; Kant et al., 2017).

More recently, the availability of large transcriptomic and genomic data of lentils generated using cutting-edge sequencing have facilitated the generation of high throughput marker systems like expressed sequence tags (ESTs) and single nucleotide polymorphisms (SNPs) that have been extensively used singly or coupled with SSRs for lentil genotyping, genetic diversity, phylogenetics and linkage mapping. (Cheung et al., 2006; Bouck and Vision, 2007). Besides molecular markers, the access to suitable mapping populations are a prerequisite for executing efficient molecular breeding programs. To identify the genomic regions associated with desired crop traits, many RIL mapping populations have been developed in lentil (Tullu et al., 2008; Aldemir et al., 2017; Ma et al., 2020; Gela et al., 2021a). Further, the availability of a reference genome is a prerequisite for modern breeding programs as it allows comparison and identification of allelic variants in different populations, their mapping followed by establishing their connection with phenotypic variation, if any.

Genome sequencing of Lens species is challenging as they possess large (approx. 4 Gbp; Arumuganathan and Earle, 1991 ) and complex genomes. A draft genome of lentil, an exome capture array based on the ‘CDC Redberry’ lentil cultivar was developed using short read transcript resources (Ramsay et al., 2016). The probes were designed to target both cultivated lentil and wild species, and the phylogenetic analyses corroborated previous conclusions of existence of 4 distinct gene pools (Ogutcen et al., 2018). In the cultivar ‘CDC Redberry’ genome assembly was generated covering 3.8 Gbp from genome size of 3.92-Gbp (Ramsay et al., 2021; https://knowpulse.usask.ca/genome-assembly/Lcu.2RBY). A long-read assembly of the lentil cultivar ‘PBA Blitz’ is also completed (Guerra-García et al., 2021). A complete genome assembly is also generated from the related species L. ervoides accession ‘IG 72815’ with estimated genome size of 3.4-Gbp (Ramsay et al., 2021, https://knowpulse.usask.ca/genome-assembly/Ler.1DRT) (Guerra-García et al., 2021). Recently, efforts to develop genome assemblies have also been extended to lentil CWRs. Genome assembly (Ramsay et al., 2021) and complete chloroplast genome sequencing of wild L. ervoides (Tayşi et al., 2022) and transcriptome assemblies of cultivated lentil and its CWRs (Gutierrez-Gonzalez et al., 2022) are quite encouraging.

The genome and transcriptome assemblies in cultivated lentil and its CWRs will help in going beyond simple genetic maps for dwelling upon the structural rearrangements that have shaped the evolution of genus Lens and comparison across legume species to earmark the genetic control of traits of common interest. With all these developments, the genus lentil is picking pace with the omics technologies gradually and steady growth is anticipated in the coming years towards the molecular breeding of this important pulse crop.

The construction of detailed genetic linkage maps is essential for localization of genes and/or QTLs linked to desirable traits, map-based cloning and MAS ( Semagn et al., 2006 ). The first lentil genetic linkage map was constructed by Zamir and Ladizinsky (1984) using isozymes and one morphological marker. Subsequently DNA markers based genetic linkage maps have been constructed by many workers ( Table 3 ) using RFLPs, ISSRs, SSRs RAPDs, AFLPs and SNPs. These maps have been used for localization of genes and QTLs linked to desirable traits, map-based coning and MAS. The first lentil linkage map was constructed using morphological markers and isozymes (Zamir and Ladizinsky, 1984; Havey and Muehlbauer, 1989; Vaillancourt and Slinkard, 1993; Tahir and Muehlbauer, F., 1994 ) followed by the usage of PCR markers (Eujayl et al., 1998; Rubeena and Taylor, 2003; Hamwieh et al., 2005; Phan et al., 2007; Tullu et al., 2008; Saha et al., 2010a; Verma et al., 2015 ) and SNPs (Fedoruk et al., 2013;.Gujaria-Verma et al., 2014; Ates et al., 2018; Polanco et al., 2019) The length of these maps varies from 333 centimorgans (cM) to 1868 cM with an average density of 8.9 cM. These maps have been constructed using interspecific crosses involving cultivated lentil and wild species L. ervoides, L. odomensis and L. orientalis) and RIL populations (Eujayl et al., 1998; Gujaria-Verma et al., 2014; Polanco et al., 2019 ) and have revealed a direct macro-syntenic relationship between L. culinaris ssp. culinaris and Medicago truncatula genetic maps.

The first extensive genetic linkage map of lentil with molecular markers was constructed by Eujayl et al. (1998) saturated with total 177 markers comprised of morphological and molecular (RAPD, RFLP, and AFLP) markers using 86 RILs generated from an interspecific cross. Rubeena and Taylor, (2003) generated a lentil genetic map with 9 linkage groups (length 784.1cM) saturated with 3 RGA, 100 RAPD and 11 ISSR markers using a F₂ population developed from a cross of cultivars differing in resistance to Ascochyta blight. Likewise, Hamwieh et al. (2005) constructed a map using 283 markers linked to Fusarium wilt disease.

An F₅ population of L. culinaris ssp. culinaris was used to construct a gene-based genetic linkage map (928.4 cM long) with 7 linkage groups utilising 18 SSR and a high number of intron-targeted amplified polymorphic (79 ITAP) markers (Phan et al., 2007). The linkage groups detected in the above study comprised of 5–25 markers with 80.2 to 274.6 cM length variations. A direct macro-syntenic relationship between L. culinaris ssp. culinaris and Medicago truncatula genetic maps was revealed by analysing mapped markers previously assigned to the M. truncatula genetic and physical maps. Tullu et al. (2008) developed a lentil map (1868 cM long) for earliness and plant height traits using 207 markers (AFLPs, RAPDs and SSRs), and revealed 12 linkage groups with an average marker density of 8.9 cM. A molecular linkage map of 1396.3 cM length with 11 linkage groups was constructed using 166 markers (morphological, RAPDs, ISSRs and AFLPs) in an RIL population (Tanyolac et al., 2010). A subset (420) of SNPs were also selected for amplification and mapping in the F₇ RIL population (Precoz × WA8649041) along with 15 SSR, and 29 ISSR markers.

Interspecific populations were raised using wild and cultivated taxa (L. culinaris and L. orientalis, L. odemensis and L. ervoides) for the purpose of constructing genetic maps (Eujayl et al., 1998; Durán et al., 2004; Gujaria-verma et al., 2014; Polanco et al., 2019). An F₂ segregating intersubspecific population (L. culinaris ssp. culinaris and L. culinaris ssp. orientalis), using 235 markers (SSR, ISSR and RAPD) was mapped covering 3843.4 cM into 11 linkage groups (LGs), with an average marker distance of 19.3 cM (Gupta et al., 2012a). A previous Lens genetic map representing L. culinaris ssp. culinaris × L. culinaris ssp. orientalis was improved by adding 31 new markers, reaching upto 190 markers that formed eight linkage groups covering 2234.4 cM (de la Puente et al., 2013). Andeden et al. (2013) constructed a linkage map using F₂ population of the cross between Karcadağ x Silvan cultivars using 47 SSR markers with 43 loci assigned to six linkage groups. A consensus linkage map (977.47 cM long), has been made using diversity arrays technology (DArT) markers with 3 RIL mapping population including ‘ILL8006’ x ‘CDC Milestone’, ‘PI320937’ x ‘Eston’ and ‘CDC Redberry’ x ‘ILL7502’ (Ates et al., 2018). It covered a total of 9,793 markers with an average distance of 0.10 cM in between the markers. With seven linkage groups the length of the map was comparable with that of Sharpe et al. (2013).

Many lentil mapping populations have been raised using intra- and interspecific crosses between such as drought sensitive ‘JL-3’ and drought resistant ‘PDL-1’ and ‘FLIP-96-51’ cultivars, in order to study the inheritance mechanism of drought tolerance and identify the linked polymorphic markers. Bulk segregant analysis results have shown the association of seven out of 51 SSR markers with drought tolerance detected at the seedling stage (Singh et al., 2016). These seven markers were screened and mapped (133.2 cM distance) in F₂ mapping population (JL-3×PDL-1) of 101 individuals. As evident, lentil linkage map studies have benefitted a lot by application of SSR markers.

SNP markers have also been extensively utilized in lentil and have contributed enormously to linkage mapping, genetic diversity and trait association studies (Kaur et al., 2011 ; Gujaria-Verma et al., 2014; García-García et al., 2019; Pavan et al., 2019; Wang et al., 2020). Many studies have used SNP markers to identify genetic markers associated with drought tolerance and devlop high-resolution maps. About 377 SNPs were identified from TOG sequences in L. ervoides and used to generate a map with seven linkage groups (Gujaria-Verma et al., 2014). In another study, Gupta et al. (2012b) used among other markers a set of 15 M. truncatula EST-SSRs in an RIL population of ‘Northfield (ILL5588) × cv. Digger (ILL5722)’ which clustered across 1156.4 cM map length into 11 linkage groups. A genetic linkage map of 697 cM was developed in Lens using 563 SNPs, 10 SSRs, and four loci of seed color (Fedoruk et al., 2013). Another recent technique, genotyping by sequencing (GBS) approach was used in the genus Lens to generate a total of 266,356 SNPs across whole genome for use in phylogenetic and population structure analysis (Wong et al., 2015). A comprehensive characterization of SNPs has been achieved in L. culinaris and wild L. ervoides genotypes (Khazaei et al., 2016). Recently, GBS-based Diversity array technology (DArT) markers were used in lentil for the identification of SNPs and development of high-resolution genetic maps ( Pavan et al., 2019; Dadu et al., 2021 ). However, despite above efforts, MAS has not been widely used in lentil breeding due to poor association of markers with the desired genes and the poor resolution issues associated with genetic maps.

Nevertheless, the availability of these genetic linkage maps, along with the draft genome assemblies and high-throughput marker systems, has greatly facilitated the genomics-assisted breeding of lentil for the development of climate-resilient smart crop varieties with broad-spectrum tolerance to withstand multiple simultaneous stresses.

The rapid development of an array of molecular markers in the past few decades has enabled the identification of many useful QTLs linked to agronomic traits in many crops. QTL mapping is based on linkage mapping and genotypic data and has been utilized for marker-trait association or marker-assisted breeding in many crops including lentil.

Genetic mapping studies have helped in identifying many genes and QTLs controlling abiotic and biotic stress tolerance, growth, development and nutritional parameters have been mapped in lentil (Eujayl et al., 1998; Tullu et al., 2003; Tullu et al., 2006b; Durán et al., 2004; Kahraman et al., 2004; Hamwieh et al., 2005; Gupta et al., 2012a; Saha et al., 2013; Kaur et al., 2014; Ates et al., 2016; Idrissi et al., 2016; Sudheesh et al., 2016; Rodda et al., 2017; Ates et al., 2018; Polanco et al., 2019; Ma et al., 2020; Mane et al., 2020; Gela et al., 2021a, b). The details about genetic linkage maps constructed with QTLs governing the traits of interest have been listed in Table 3 . Lately, mapping of quantitative traits like mineral concentration in seeds, days to flower, desirable seed characters and Aphanomyces root rot has been carried out by association mapping ( Khazaei et al., 2017; Khazaei et al., 2018; Neupane, 2019; Ma et al., 2020 ).

The flowering time and seed characteristics are important productivity-related crop traits. In this regard, five QTLs each for the height of first ramification and flowering time, seven for pod dehiscence, three for plant height, and one each for number of shoot and seed diameter were detected in inter-subspecific genetic map in Lens (Durán et al., 2004). Many QTLs for plant height and earliness were identified from RILs using cross between ‘Eston × PI320937’ (Tullu et al., 2008). RILs derived from a cross between genotypes ‘WA 8649090 × Precoz’ were used to detect QTLs for winter survival and injury (Kahraman et al., 2004). For seed diameter and weight, three and five QTLs respectively were identified (Saha et al., 2013). Further, in 78 RIL populations derived from a cross between a cultivar ‘Alpo’ of L. culinaris and L. odemensis accession ‘ILWL235’, three QTLs for seed size and one each QTL for stem pigmentation, spotting on the seed coat, the color of flower and timing of flowering were identified. QTLs for the seed weight and seed size traits were identified in an RIL derived from cross between L. culinaris cultivars ‘Precoz x L830’ which generated one QTL each for the traits (seed weight and size) present on the same linkage group (Verma et al., 2014).

Among the biotic stresses, Ascochyta blight, Stemphylium blight, anthracnose and rust diseases represent the most potent pathogens that limit lentil productivity worldwide. Many QTLs associated with these pathogens have been identified. These genomic resources can be extremely helpful in lentil breeding for biotic resistance and productivity gains. RIL population developed from a cross between L. culinaris ‘Eston’ and ‘PI 320937’ was used to identify markers associated with Ascochyta blight resistance, using a QTL analysis (Tullu et al., 2003; Tullu et al., 2006b). Further, three more QTLs were detected for Ascochyta blight resistance at seedling and pod maturity stages against Ascochyta lentis (Gupta et al., 2012a). Similarly, Sudheesh et al. (2016) identified multiple QTLs associated with A. lentis in 112 and 117 RILs obtained between crosses ‘IH (Indian Head) x DIG (Digger)’ and ‘IH x NF (Northfield)’, respectively. In yet another F₂ population derived from ‘ILL7537 × ILL6002’, three QTLs accounting for 47% (QTL-1 and QTL-2) and 10% (QTL-3) of Ascochyta blight resistance variation were mapped. Further, QTLs conferring resistance to Stemphylium blight and rust diseases (caused by Uromyces vicia-fabae) using RIL populations were also identified (Saha et al., 2010a; Saha et al., 2010b). The RIL population for Stemphylium blight resistance (‘ILL5888 × ILL-6002’), showing contrasting agro-morphological traits, were used to detect three QTLs related to days to 50% flowering. Composite interval mapping from an RIL population (F₉) between two L. ervoides accessions, revealed 11 QTLs with associated resistance to Colletotrichum lentis resistance at different stages against anthracnose, and three QTLs for Stemphylium botryosum resistance against blight disease (Bhadauria et al., 2017). LAB C01 resistance at BC2F3:4 generation was screened for the race 0 of anthracnose (C. lentis) and Stemphylium blight (S. botryosum) and identified QTLs on chromosomes 3 and 7 (Gela et al., 2021b). 15 putative genes associated with resistance to Aphanomyces root rot (Ma et al., 2020) have been identified on seven QTL clusters using QTL and association mapping. Differential expression of three of these genes at the early stages of infection was correlated with ARR resistance (Ma et al., 2020).

Climate change-inflicted abiotic stresses have affected yield and lentil productivity substantially, hence, identification of genomic resources can be really helpful in developing stress-tolerant varieties. In an RIL population of a cross between lentil accessions ‘ILL6002 and ILLL5888’, Idrissi et al. (2016) identified eighteen QTLs with different root and shoot traits under drought stress. Sodicity represents one of the most important abiotic stresses responsible for reduction in crop yields. By crossing lentil salt-sensitive ‘L-4076 and L-4147’ and salt-tolerant genotypes ‘PDL-1 and PSL-9’, Singh et al. (2020) identified a QTL linked to seedling survival under salinity conditions. Further, efforts to link a QTL to cold hardiness have resulted in the identification of a stable QTL, that expressed uniformly in different cold conditions. This QTL can be pipelined for MAS ( Kahraman et al., 2004 ). Although the above reports highlight the usage of lentil genotypes harboring the stress-tolerant QTLs, efforts must be extended to CWRs to explore more useful genomic resources which can be used in appropriate breeding strategies to improve lentil productivity.

Plant growth depends on many factors and alterations in minerals and/or micronutrient uptake plays a key role in determining plant growth in changing climate scenarios. Studies on mineral ion uptake in lentils identified a few QTLs linked to boron, selenium, manganese and other ions uptake (Kaur et al., 2014; Ates et al., 2016; Khazaei et al., 2017; Ates et al., 2018; Khazaei et al., 2018 ). Further studies in this direction can lead to breeding of biofortified micronutrients rich lentil.

For realizing the full potential and applications of identification of QTLs and other genomic resources in the lentil improvement, association and mapping studies are extremely important so that these resources can be effectively utilized in MAS. Some useful attempts have been made in this direction. For instance, Kaur et al. (2014) identified QTLs in ‘Cassab × ILL2024’ mapping population related to boron tolerance. The authors used transcriptome sequencing generated SNPs and EST-SSRs for simple interval mapping (SIM) and composite interval mapping (CIM). A comparison of the flanking markers to genome sequences with model species like M. truncatula could identify many candidate genes associated with micronutrient (Boron) tolerance that might become useful in marker assisted breeding. Similarly, Fedoruk et al. (2013) used SNPs, SSRs and seed coat color markers in RIL population of lentil to identify QTLs for seed dimension. Significant QTLs on 6 linkage groups were identified like linkage group 2 with seed coat color pattern and linkage group 1 with cotyledon color locus (Fedoruk et al., 2013). Polanco et al. (2019) analysed F₇ RILs (L. culinaris x L. odemensis) and identified a single QTL controlling ‘time to flowering’ and three QTLs for ‘seed size regulation’. QTLs were also mapped in lentil for Ascochyta blight resistance in chromosome 6. Further, Neupane (2019) observed 4 QTLs for ‘days to flowering’ after evaluating 324 lentil accessions in multiple locations in different parts of the world. The mapping population was a cross between accessions ‘IPL 220 and ILWL 118’ of wild species L. orientalis (Kumar et al., 2019). A QTL hotspot was observed consisting of six QTLs for lengths of root, shoot and seedling within a map distances of 56.61-86.81 cM range on LG1 using F₁₀ RIL population of cross ‘WA8649090 x Precoz’ (Mane et al., 2020). Likewise, a total of 143 accessions were analysed by GWAS to establish associations between prebiotic carbohydrates and candidate genes (Johnson et al., 2021). The study identified many SNPs and associated genes controlling useful traits. This study can further guide the molecular breeding programs based on prebiotic carbohydrates in lentil.

In summary, many studies have used transcriptome profiling and QTL mapping to identify genes and genic regions associated with abiotic and biotic stress tolerance, growth, development and nutritional parameters in lentils. The studies have involved use of RIL populations and various methods such as transcriptome sequencing, SNPs, EST-SSRs, SSRs, seed coat color markers, GWAS and more. The studies have identified a wide range of QTLs associated with boron tolerance, proline metabolism, membrane proteins, defense-related functions, and phytohormones, as well as QTLs for traits such as plant height, flowering time, seed characteristics, time to flowering, cold hardiness, Ascochyta and Stemphylium blight resistance, rust resistance, salinity and drought tolerance. These findings have important implications for marker-assisted breeding and the development of more stress-tolerant lentil cultivars.

Transcriptomic studies provide information about functionality and regulation of genes and show how reprogramming at transcriptional level can modulate innate physiological parameters in plants to withstand external stresses. Transcriptomic studies in lentil have resulted in identification of many candidate genes/loci linked to useful agronomic traits ( Kaur et al., 2011; Sudheesh et al., 2016; Cao et al., 2019; García-García et al., 2019; Morgil et al., 2019; Singh et al., 2019; Wang et al., 2020; Dadu et al., 2021; Kumar et al., 2021; Tiwari et al., 2022 ). ESTs-based methods coupled with NGS are widely used for transcriptome studies. In lentil, 33,371 ESTs are currently publicly available (Kumar et al., 2021). A high quality of 847,824 sequence reads and 84,074 unigenes transcriptome assemblies were generated as a result of massive transcriptome sequencing in lentil (Sharpe et al., 2013; Verma et al., 2013). Further, an EST library was developed using lentil cultivars with varying seed phenotypes by Vijayan et al. (2009), while Kaur et al. (2011) revealed 2,393 loci for EST-SST markers upon cDNA sequencing of six lentil genotypes. Interestingly, 47.5% polymorphism was revealed among 13 different lentil genotypes screened with 192 out of these markers. Immediately after, a large number of ESTs were generated using tissues of leaves infected with C. truncatum in lentil (Bhadauria et al., 2011; Kumar et al., 2014).

Many studies have tried to unravel the mode of action of various biotic and abiotic stresses with the help of transcriptome profiling in lentil. To study the transcriptome profiling during cold stress, Barrios et al. (2017), performed a Deep Super-SAGE transcriptome analysis on RIL populations of a cross between ‘cold tolerant WA8649041 and susceptible genotype Precoz’ to identify around 300 differentially expressed tags mainly associated with expressing proline rich, dormancy related membrane proteins.

Similarly, to understand the functionality of drought stress response, Singh et al. (2017b) revealed that 11,435 transcripts were up- and 6,934 were down-regulated to study the effect of drought stress in a resistant (PDL-2) and sensitive (JL-3) cultivar in comparison with the control. Further, DEG (Differentially expressed gene) analysis showed upregulation of genes involved in electron transport chain, glucose metabolism, TCA cycle and down regulation of photosynthetic functions and photorespiration in the tolerant cultivar ( Singh et al., 2017a; Morgil et al., 2019 ). The latter study further showed that the number of DEGs in roots of L. culinaris cultivar ‘Sultan’ increased from 2,915 to 18,237 in short-term and long-term drought conditions, respectively ( Morgil et al., 2019 ). A similar transcriptomic profiling has been done by Singh et al. (2019) to study the mechanism of heat stress tolerance. Heat stress is one of the major abiotic challenges for reduced crop production under changing climate scenarios. By comparing the heat tolerant lentil cultivar ‘PDL-2’ with heat sensitive ‘JL-3’ cultivar, Singh et al. (2019) could identify as many as 16,817 heat responsive DEGs, with their number being higher in heat tolerant cultivar. Functionally, the observed DEGS were mostly correlated with secondary metabolism, wax deposition, cell wall deposition enzymes and many transcription factors (Singh et al., 2019 ). A transcriptome annotation with 26,449 EST-SSR markers in six lentil genotypes followed by a selection of 276 screened markers to circumscribe 94 accessions showed 125 markers to be polymorphic among the analysed accessions (Wang et al., 2020 )

The biotic stresses in the form of Ascochyta and Stemphylium blights, anthracnose and rust contribute to major losses ranging upto 70% in lentil production across the world (Singh et al., 2017a; Cao et al., 2019). The transcriptomic studies (Cao et al., 2019; Singh et al., 2019; Mishra et al., 2021; Tiwari et al., 2022) have largely focussed on foliar diseases caused by the two most potent lentil pathogens A. lentils and S. botryosum. The transcriptome profile was studied in two L. ervoides cultivars ‘LR-66-637’ (resistant) and ‘LR-66-577’ (susceptible) to S. botryosum. A total of 8,810 disease responsive genes along with 1,284 DEGs were identified and as many as 712 genes were upregulated in resistant cultivar as compared to 572 in the susceptible one (Cao et al., 2019). Similarly, Khorramdelazad et al. (2019), studied the transcriptome profiling of ‘ILL7537’ (resistant) and ‘ILL6002’ (susceptible) lentil cultivars infected with A. lenti, after 2, 6 and 24 hours after the infection to reveal upregulation of two genes involved in defense-related functions namely calmodulin domain protein kinase-like (CDPK) genes, and LRR-receptor like kinase (LRR-RLKs) (Khorramdelazad et al., 2019 ). Interestingly, some common DEGs expressed during infection with both the above pathogens correlated with genes associated with phytohormones, E3 ubiquitin protein, LRR-RLKs, CDPK indicate the prevalence of a common defence mechanism against both these lentil pathogens (Tiwari et al., 2022).

In nutshell, transcriptomic studies in lentils have been widely used to understand the mechanisms of biotic and abiotic stress tolerance and have resulted in identification of many candidate genes and loci linked to useful agronomic traits. The studies have revealed the up- and down regulation of genes involved in different processes such as proline rich dormancy-related and membrane proteins, electron transport chain, glucose metabolism, TCA cycle, photosynthetic functions, photorespiration, and secondary metabolism during cold, drought and heat stress in lentils. Many studies have identified DEGs associated with stress tolerance responses. In addition, transcriptomic studies have been conducted to understand the resistance mechanism to foliar diseases caused by pathogens such as Ascochyta and Stemiphylium and have revealed the upregulation of defense-related genes such as calmodulin domain protein kinase-like (CDPK) and LRR-receptor like kinase (LRR-RLK) in resistant cultivars. Overall, these studies have provided valuable insights into the molecular mechanisms of stress tolerance and resistance in lentils and have potential applications in breeding programs aimed at improving the crop’s stress tolerance and disease resistance.

The large-scale genomics datasets can result in practical applications once they are correlated with the phenotypes or the phenome (Mir et al., 2019). The conventional manual phenotypic approaches are lately getting replaced by through-put sensor-based phenotypic methods that use ‘artificial intelligence’ and ‘machine learning’ approaches to increase precision and speed of phenotyping (Singh et al., 2016; Tiwari et al., 2022). For example, a comparison of conventional phenotyping with high throughput (HTP) digital red-green-blue (RGB) imaging followed by fluorescence scanning revealed that the latter method had better precision and consistency (Dissanayake et al., 2020). Proteomics studies involving translational and post-translational studies on peptides and proteins, once the candidate genes and loci get identified by genomics studies are important parts of the larger process of crop trait improvement. Likewise, metabolomics signifies the culmination of all the aforementioned genomics technologies and shows a direct correlation with the phenotypes. Researchers have begun to look into the drought and salinity stress management by analysing contrasting lentil genotypes ( Scippa et al., 2008; Caprioli et al., 2010; Scippa et al., 2010; Muscolo et al., 2015; Skliros et al., 2018; Shaheen et al., 2022 ), although more research is needed in this area.

Recent efforts have tried to identify genomic regions that are associated with markers and traits in lentils. For instance, Tiwari et al. (2022) found 19 common metabolites in lentils that belong to phenolic and organic acids, saccharides, and flavan/flavanol and flavaone derivatives. This study suggests that there is a dynamic cross-talk during stress management in plant systems, and it highlights the need for comprehensive integrated future investigations in lentil and other crop species. It is important to identify pan-stress-ameliorating genes and/or loci and common stress mitigation pathways, if any, as in the natural field conditions crops are exposed to multiple simultaneous biotic and abiotic stresses. This will be very useful for external stress management and will help to ease the pressure off the agricultural productivity issues. Additionally, the identification of signature peptides and metabolites as markers associated with useful agronomic traits will be helpful in lentil breeding.

Understanding the evolutionary and domestication processes in crop species requires knowledge about the genetic and phenotypic characteristics of available genetic resources such as accessions, landraces and genotypes as well as understanding the genetic basis of divergence. The documented variability serves as the foundation of all crop improvement programs aimed at increasing productivity, disease resistance, stress mitigation and climatic adaptations. The genetic and genomic analysis of crop wild resources (CWRs) across cereals, legumes, oils and other diverse groups of plants has demonstrated that the CWRs possess high heterozygosity and many useful crop traits that can be used in crop breeding programs. The availability of enormous CWRs and land races offers interesting opportunities for wild gene introgression into the cultivated gene pools of legumes and other crop species.

The last few decades have seen an unprecedented growth in the development of methods for genetic research and breeding in plants. Plant breeding exercises have advanced greatly from the usage of a plethora of molecular markers to next generation sequencing to genotyping-by-sequencing. At the same time, assembly of large and complex genomes, development of high-density genetic maps for high resolution QTL mapping, genome-wide association studies, development of genomic resources in the form of mini and/or core populations, trait-specific mapping populations, multi-parent advanced generation inter-cross (MAGIC) and nested association mapping (NAM) populations, and the development of pan or super-pan genomes of cultivated species and CWRs through whole genome sequencing (WGS) have substantially modernized the crop breeding programs. These technologies have enabled the identification and characterization of genes associated with important agronomic traits such as disease resistance, drought tolerance and yield, which can be used to develop new cultivars with improved traits.

The legume agricultural production system including lentils has inherently been constrained by cultivation in limited geographical habitats, poorly defined breeding histories, genetic bottleneck and erosion, intensive agricultural systems and novel pathogens under global climatic changes. Since the genetic diversity locked in CWRs is considered to offer viable solutions to food productivity problems, intensive efforts should be undertaken to collect, characterize and protect CWRs of grain legumes. Recently, a shift has been noted during the germplasm characterization exercises towards cataloguing diversity at the desirable gene level rather than the phenotype level. Furthermore, since the characterization of genetic diversity and dissection of complex traits are pivotal to the idea of genetic improvement, a centralized data base management system should be put in place to host the collated information about the wild alleles controlling specific traits.

Overall, in the small genus Lens, an important plant-based protein source, which was once considered an orphan species, significant wild germplasm characterization efforts have taken place. These efforts have led to advancements in understanding the genomic relationships between the wild and cultivated lentil genomes, identification of genes, QTLs and traits associated with desired crop traits and stress management, and the development of genetic maps and databases, global genotyping, the use of marker assisted and genomic selection techniques, draft genomes’ assemblies, complete chloroplast genome sequencing and transcriptome assemblies of cultivated lentil and its CWRs (Gutierrez-Gonzalez et al., 2022). These developments have assisted in unravelling the intricacies of genome architecture and the landscape of variability available in the gene pools of cultivated lentil and its wild relatives and evolutionary and domestication history of the species. However, there is still a need for better management of various biotic and abiotic stresses associated with the global climatic changes and to maintain the desired productivity levels for the future food security. One key area of focus is to characterize lentil germplasm resources in their centres of origin, where they are most diverse, in order to identify genes and traits that can help mitigate the effects of climate change and maintain productivity levels for future food security (Chen et al., 2017; Singh et al., 2018). Additionally, it is important to characterize the genetic and phenotypic diversity at individual accession level rather than just at the genotype level that represents a pool of accessions. To achieve genetically enhanced and biofortified lentil, data should be integrated from multiple omics technologies, such as robust marker association studies, machine and AI-assisted phenomics studies, advanced proteomics and metabolomics and biofortification studies carried out in CWRs and the cultivated lentil (Tiwari et al., 2022). All these findings should be represented in a centralized curated data base repository for information sharing to aid future breeding efforts.

The development and use of MAGIC populations has been quite beneficial for gene mapping and function analysis, detection of QTLs, dissection of stress and yield related traits and genetic resource development in the form of elite breeding near isogenic lines (NILs) and recombinant inbred lines (RILs) in legumes such as chickpea, faba bean, pigeonpea, cowpea, soybean and groundnut. These important genomic resources’ development needs attention of lentil breeders.

Most genomic and transcriptomic studies in the genus Lens have involved commercial accessions of L. culinaris. Recent efforts to develop genome assemblies of L. culinaris and wild L.ervoides (Ramsay et al., 2021), complete chloroplast genome sequencing of wild L. ervoides (Tayşi et al., 2022) and transcriptome assemblies of cultivated lentil and its CWRs (Gutierrez-Gonzalez et al., 2022) are quite encouraging and will help researchers better understand the genomic relationships between wild and cultivated lentil genomes to tap into the unexploited variability lying hidden in CWRs. Many specific legume databases such as Pulse crop data base (https://www.pulsedb.org/), Legume information system (LIS; https://legumeinfo.org; Dash et al., 2016) and KnowPulse (https://knowpulse.usask.ca) are really helpful for accessing useful genetic data for lentil breeding. The future efforts should aim at comprehensive linking of genetic datasets to phenotypes and also connecting these data pipelines under the umbrella of a centralized curated database management system. The implementation of dedicated large scale global legume improvement projects like EVOLVES (https://knowpulse.usask.ca/study/2691111) and European Union’s Horizon 2020 research and innovation program INCREASE (https://www.pulsesincrease.eu/crops/lentil) (Guerra-García et al., 2021) are important strategic policy decisions that will help in the conservation and sustainable use of crop agro-biodiversity in pulse crop species including lentil. These projects provide a way forward to consolidate global efforts in addressing the challenges of climate change.

Conceptualization VR and AS, Literature survey and Original Draft writing: AS and VR. Tables AS. Review and Editing: VR, AS, RK, RT, MK, AP, MH, SR. All authors have read and approved the MS in the present form. All authors contributed to the article and approved the submitted version.