genetic diversity thesis

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• Published: 01 May 2020

Exploiting the diversity of tomato: the development of a phenotypically and genetically detailed germplasm collection

• Estefanía Mata-Nicolás 1 ,
• Javier Montero-Pau   ORCID: orcid.org/0000-0002-0864-8157 2 ,
• Esther Gimeno-Paez 1 ,
• Víctor Garcia-Carpintero 1 ,
• Peio Ziarsolo 1 ,
• Naama Menda 3 ,
• Lukas A. Mueller 3 ,
• José Blanca 1 ,
• Joaquín Cañizares 1 ,
• Esther van der Knaap 4 , 5 &
• María José Díez 1  

Horticulture Research volume  7 , Article number:  66 ( 2020 ) Cite this article

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• Agricultural genetics
• Plant breeding

A collection of 163 accessions, including Solanum pimpinellifolium , Solanum lycopersicum var. cerasiforme and Solanum lycopersicum var. lycopersicum , was selected to represent the genetic and morphological variability of tomato at its centers of origin and domestication: Andean regions of Peru and Ecuador and Mesoamerica. The collection is enriched with S. lycopersicum var. cerasiforme from the Amazonian region that has not been analyzed previously nor used extensively. The collection has been morphologically characterized showing diversity for fruit, flower and vegetative traits. Their genomes were sequenced in the Varitome project and are publicly available (solgenomics.net/projects/varitome). The identified SNPs have been annotated with respect to their impact and a total number of 37,974 out of 19,364,146 SNPs have been described as high impact by the SnpEeff analysis. GWAS has shown associations for different traits, demonstrating the potential of this collection for this kind of analysis. We have not only identified known QTLs and genes, but also new regions associated with traits such as fruit color, number of flowers per inflorescence or inflorescence architecture. To speed up and facilitate the use of this information, F2 populations were constructed by crossing the whole collection with three different parents. This F2 collection is useful for testing SNPs identified by GWAs, selection sweeps or any other candidate gene. All data is available on Solanaceae Genomics Network and the accession and F2 seeds are freely available at COMAV and at TGRC genebanks. All these resources together make this collection a good candidate for genetic studies.

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Super-pangenome analyses highlight genomic diversity and structural variation across wild and cultivated tomato species

Introduction.

Tomato, Solanum lycopersicum var. lycopersicum L. (SLL), is one of the most consumed vegetables all over the world with a production that exceeds 180 million tonnes (FAO, 2017). Its cultivation has become highly efficient thanks to the introduction of technological advances and the development of modern varieties. These modern varieties are the result of intensive plant breeding programs since the beginning of the 20th century, and the natural biodiversity of tomato wild species has been key in this success.

The cultivated tomato and its wild relatives came from the Peruvian and Ecuadorian regions of South America. According to allozyme variation, Rick and Fobes 1 proposed that SLL evolved from S. lycopersicum var. cerasiforme (Dunal) Spooner, G.J. Anderson & R.K. Jansen (SLC). Recently, Blanca et al. 2 , 3 proposed a two-step domestication process from SLC to SLL based on molecular and morphological evidence. The first step involves the pre-domestication of SLC in the Amazonian region of Southern Ecuador and Northern Peru. Subsequently, SLC would have migrated to Mesoamerica where it would be domesticated to SLL. Razifard et al. 4 proposed that many traits considered typical of cultivated tomatoes arose in South America. However, these domestication traits were lost or diminished once these partially domesticated forms spread to Mesoamerica, where it was finally morphed into the SLL 5 , 6 . This domestication and diffusion process was accompanied by a selection of alleles related to fruit color, size and shape and also changes in plant architecture 7 , 8 , 9 , 10 . This process also included various genetic bottlenecks that progressively narrowed the genetic diversity of modern tomato, compared to its wild species 3 , 11 . The main loss of variability occurred during the migration to Mesoamerica from the Peruvian and Ecuadorian Amazon region. Most of the allelic variants present in european vintage tomato are already present in these Amazonian SLC populations 3 .

Solanum pimpinellifolium L. (SP) is the closest wild relative to SLC and SLL. It is also a red-fruited species and native to coastal areas from Ecuador to Southern Peru. According to its distribution, this species presents varying degrees of genetic variation 12 , 13 , 14 , 15 and morphological differences such as flower and inflorescence size, style exertion, or fruit color 12 . This fact and its capacity to hybridize with tomato, make this species a valuable source of desired traits in tomato breeding. For instance, SP has been used as a genetic source for quality improvement related to solid content, firmness, fruit color 16 , 17 , volatile compounds 18 , 19 , or resistance against fungi or viruses such as Tomato leaf curl virus 20 , Alternaria solani , Fusarium oxysporum , and Phytophthora infestans 21 or Cladosporium fulvum 22 . SLC has a worldwide distribution in tropical regions, but it is native to the Andean region of Ecuador and North of Peru 1 . This species is found over a vast range of environmental conditions such as tropical or arid regions, sea level or high altitudes 23 , and it has also been collected at native markets 24 . It usually bears red and small fruits, but Rick and Holle 25 described a remarkable morphological variability in fruits, plant habit, or leaf size and shape. A higher genetic variability has been described in Ecuadorian and Peruvian accessions 1 , 2 due to the development of morphological diversity during a pre-domestication phase. In fact, tomatoes collected in local markets of Ecuador were morphologically classified as vintage tomato; but they have been genetically classified as SLC 3 . These studies and data show that SLC from Northern Peru are very close to Mexican and vintage tomatoes. Despite that, Amazonian SLC has not been used frequently for tomato improvement as opposed to SP. Furthermore, SLC has been characterized as a valuable genetic source for abiotic and biotic stresses, such as moisture-tolerance 26 or resistance to root rot caused by Phytophthora 27 ; traits related with the global climate change and sustainability challenges currently facing agriculture.

Most modern breeding programs have usually focused on resistance, yield and quality traits, such as firmness, color, or texture 28 , plant habit and adaptation to machine harvesting in processing cultivars or traits related to fruit appearance for fresh market 28 . However, nowadays, the new objectives of tomato breeding focus on sustainable production or adaptation to unfavorable environmental conditions due to climate change and nutritional quality. The genetic variation of exotic germplasm collections has been used in tomato breeding to bypass the limited genetic diversity of SLL. These germplasm collections have mainly included S. pimpinellifolium, S. chilense (Dunal) Reiche , S. peruvianum L. s. str. , S. habrochaites S. Knapp & D.M. Spooner and S. pennellii Correl. Thus, the maintenance and characterization of germplasm collections are essential in order to achieve these breeding goals. Germplasm is a good source of natural allelic variants, useful for genetic analyses and subsequent breeding applications. Consequently, the creation of genebank collections characterized at genetic and phenotypic level is a primary objective for a sustainable breeding. In addition, it is crucial that these data and genetic resources are easily available to the scientific community to exploit this extensive amount of information.

The advent of NGS technologies has created a huge amount of available genetic information about germplasm held in genebanks 29 that can be useful for improving breeding cultivars 30 . For instance, the availability of its genomes in association with its characterization at phenotypic and molecular level allows the development of genome-wide association studies (GWAS). GWAS studies have already identified regions of the genome related to morphological and metabolic diversity 31 , 32 . For example, Bauchet et al. 31 , 33 detected associations for traits such as fruit weight, flowering time, early fruit development, malate, and phenalyacetaldehyde/phenylethanol content. Finally, the first meta-analysis of GWAS has revealed numerous candidate genes involved in tomato flavor 34 . Full genome sequences have been published in several studies and more than 725 genome sequences of tomato accessions are available 35 , 36 , 37 , 38 , 39 . A pan-genome analysis of tomato including SLL, SLC, and SP has discovered 4873 genes that are not present in the reference genome 37 thus increasing the interest of these populations for tomato breeding. Once a candidate region of the genome, gene or SNP has been characterized as significantly associated with a trait, it is necessary to validate its role in the control of the trait by using segregating families or mutants. However, this latter step sometimes becomes limiting as the development of such populations is time consuming and costly.

In the present study, we have morphologically characterized the variability of fruit, flower, and vegetative characters from a collection of 163 tomato accessions of the Varitome project, for which the full genome is available 37 . These accessions include SP, SLC, and SLL and represent the diversity at the center of origin and domestication of tomato. We have annotated the identified SNPs within our collection using SnpEff. We have performed GWAS analysis for all of our morphological descriptors with the aim of detecting candidate regions. In addition, a collection of segregating families has been developed by crossing the complete set of accessions with a representative accession for each of the three species. These populations could help to speed up the validation of candidate genes and SNPs. The combination of passport, phenotypic, genetic information, and germplasm with easy accessibility converts this collection into a powerful instrument for genetic studies and breeding.

Morphological analysis

A germplasm collection of 163 accessions was selected with the aim of representing the geographical, morphological, and genetic diversity of tomato and its closest wild relatives at their region of origin (Supplemental Fig. S1 and Supplemental Table S1 ). These materials consisted of 15 accessions of SLL from Mexico; 121 accessions of SLC coming from Ecuador, Peru, Mexico, and different countries of Mesoamerica and 27 accessions of SP from Ecuador and Peru. The accessions have been grouped based on their geographical origin and on previous genetic studies 2 , 3 . Plants were evaluated for a total of 54 morphological traits (Supplemental Table S2 ) describing the variability of this collection for plant architecture, leaves, inflorescences, flowers, and fruits (Figs. 1 , 2 , Supplementary Table S3 ). The lowest morphological variability was found in quantitative traits related to plant architecture such as height until first or last inflorescence (Fig. 1a ) or stem width (Fig. 1b ). However, SP can be differentiated from the rest of species by this last trait. Qualitative traits related to plant architecture showed that most accessions had an indeterminate growth habit and that a wide range of variation related to the way that leaves were held naturally exists (Fig. 1c ).

Morphological variation. Distribution for eight quantitative and four qualitative morphological traits related to vegetative ( a – c ), leaf ( d – f ), flower ( g – i ), and fruit ( j – l ) descriptors for each geographical group. p Values (in brackets) of the differences between species are shown. Morphological traits were measured as follows: a Plant height until last inflorescence, measured in cm. b Stem width between second and third inflorescence, measured in mm. c The way that leaves are held naturally (1: semi-erect, 3: semi-horizontal, 5: horizontal, 7: horizontal-drooping, 9: drooping, 10: accessions that exhibited variability for their measures). d Number of small leaflets. e Leaf length, measured in cm. f Leave dissection (0: low, 1: intermediate, 2: high, 10: accessions that exhibited variability for their measures). g Number of flowers in the second inflorescence. h Distance from the stem to the last flower of the inflorescence. i Position of the style in relation to stamens (1: inserted, 2: same level as stamen, 3: slightly exerted, 4: highly exerted, 10: accessions that exhibited variability for their measures). j Fruit weight, measured in grams. k Number of locules in the transversal section of the fruit. l Presence and color of green shoulder (0: uniform, 3: light green, 5: medium green, 7: dark green, 10: accessions that exhibited variability for their measures)

a Tomato fruit size, shape, and color. b Variability for flower complexity, related to the number of petals and sepals and their sizes. c Differences between exerted and inserted styles. d Diversity in leaf size, number of small leaflets and border or dissection of small leaflets. e Uniparous inflorescence. f Forked inflorescence. g Irregular inflorescence. h Differences between presence and absent of green shoulder

Quantitative traits related to leaves were the leaf size, the number of primary leaflets and small leaflets; whereas the qualitative ones described the leaf morphology, complexity and leaflet dissection, and shape. The collection exhibited a low variability for number of primary leaflets, while differences were considerably greater for leaf size and the number of small leaflets, as it is shown in Fig. 2d . Figure 1d, e show that SP group was characterized by smaller leaves whereas the maximum values were found in SLC group. Observations related to the type of leaf revealed that SP group was generally characterized by pimpinellifolium type leaf, SLC group exhibited all types but generally leaves were classified as standard ones and SLL exhibited standard and double feathered types. SP leaf was generally characterized by a lack of dissection (Fig. 1f ) and entire or undulating borders. However, SLC and SLL groups exhibited more variability in leaflet dissection (Fig. 1f ) and border.

Traits related to inflorescences included inflorescence length, number of flowers per inflorescence or type of inflorescence, whereas flowers were evaluated for number of petals and sepals and their length, width, and style exertion, among others. The values observed for inflorescence length and the number of flowers per inflorescence demonstrated a wide variability (Fig. 1g, h ). In addition, the complexity of the inflorescence exhibited a considerably diversity (Fig. 2e–g ). For flower traits, most accessions had between 5 and 6 petals and sepals per flower but several accessions were much more complex (Fig. 2b ). SP Ecuador and Peru and SLC Mesoamerica exhibited the simplest flowers and low variability, as oppossed to the complexity observed in SLC Ecuador, SLC Peru, SLC Mexico, and SLL. Finally, the observed variability related to the position of style is represented in Figs. 2 c and 1i .

The high variability for fruit weight and locule number is shown in Fig. 1j, i , respectively. SP was characterized by the smallest fruits whereas SLL group presented the biggest. However, the highest variability appeared in SLC group which produced smaller values than SP or bigger than SLL. Finally, qualitative traits related to fruit appearance revealed that most accessions produced red fruits, although other colors were also present. Some accessions belonging to SP species presented an intense red fruit, and others belonging to SP Peru and SLC Mexico groups exhibited colors ranging from yellow to orange. Other qualitative fruit traits presented high variability, such as the presence and intensity of green shoulders (Figs. 1 l and 2h ). This variability in fruit size, color and shape is shown in Fig. 2a .

Genome-wide association analysis

GWAS analysis revealed significant associations with a total of 15 traits. We found SNPs associated with eight quantitative traits (Fig. 3 and in Table S5 ). For the total number of inflorescences and petal length traits, each was associated with a single SNP located on chromosomes 1 and 9, respectively (Table 1 ). The number of flowers in the second inflorescence revealed associations with two SNPs located in chromosome 7 and 11. The result of leaf length analysis revealed two associated regions on chromosome 2 and 8. Associations with locule number were detected on chromosome 1, 2, and 11 and associations with fruit weight were detected on chromosomes 2, 7, 9, and 12. The most remarkable associations occurred on chromosome 2, since associated SNP were located in the genomic region where locule number and fw2.2 QTLs have been described. On chromosome 11, the association with the trait number of locules is located on the fas gene. On chromosome 9, the previous QTL fw9.2 was detected for fruit weight. Interestingly, there is not a close described QTL for chromosomes 1 and 12 related to locule number or weight, respectively. Several associations for fruit color have been detected, listed in Table 1 and Table S5 . For instance, GWAS for LAB color space’s b value revealed associations on chromosome 1 that were located in a genomic region with an annotated gene as carotenoid cleavage dioxygenase 1B. Regions on chromosome 3 and 10 were close to annotated genes involved in yellow and orange fruit flesh. Finally, the analysis detected also a region on chromosome 5 that has not been previously described for this trait. For LAB color space’s L value, the association detected on chromosome 3 lacks of annotated genes. GWAS analysis also showed associations between SNPs and qualitative traits, as it is shown in Fig. 3 and Table 1 . The genomic region on chromosome 9 associated to dark-green leaves lacked annotated genes and only one significant SNP for low petal curvature was detected on chromosome 7. For the type of inflorescences, one genomic region on chromosome 9 could be involved in forked inflorescence and chromosome 11 could carry another region that could be involved in uniparous inflorescence. For fruit traits, associations with the presence of longitudinal stripes, fasciated fruit, ribbing at calix end, and fruit scar were detected. The most remarkable result was the association for irregular pistil scar, covering a region of 355 kb on chromosome 11 that included several genes and three of them were also associated to ribbing at calix end. Finally, two genomic regions on chromosome 1 were associated with pink fruits (175 kb) and fasciated fruits (200 kb).

Genome-wide association results for some traits that showed significant association

Annotation and prediction of the SNP effects

The SNPs identified in this collection are available at Solanaceae Genomics Network ( https://solgenomics.net/ ). The SNPs were annotated and their putative impacts were predicted by using SnpEff. The number of effects were classified by the impact of these variants, type of effect, and region. The lowest number of variants was detected in our SLL group. SLC groups had a variation between SLL and SP groups with lower levels of variants in SLC Mexico. A total of 37,974 out of 19,364,146 SNPs detected in this collection have been designated as high impact in the SnpEff analysis. The number of variants per type and the number of effects by impact for each group are summarized in Table 2 . However, it is important to take into account that the number of SNPs is influenced by the different number of accessions in each of the groups. Among other mutations, the generation or the loss of stop codon could be one of the most interesting changes because the synthesis of an essential protein could be affected and its function would change. As shown in Fig. S2 , the same pattern of this SNP distribution was observed for the number of these mutations. Finally, genomic regions of candidate genes from GWAS analysis were used to find out allelic variants in the collection labeled as high impact. These 37,974 SNPs with a high putative impact were related to 12 candidate genes (Table 1 ) and are summarized in Supplemental Table S6 .

Development of segregating families

The whole collection was crossed with BGV007109 (SP), LA2278 (SLC), and Money Maker (SLL). The 163 accessions were used as female parents to obtain the F1 generations, except for some accessions, mainly SP, where flowers were too difficult to emasculate due to their small size. F1 plants were self pollinated to obtain the F2 generations. A collection of 485 F1 populations and 457 F2 population were achieved (Supplemental Table S4 ). Considering that most of the cross collections from each accession can have various independent F2 populations, created from different F1s, the total number of different F1 and F2 populations are 1430 and 672, respectively. The seeds of these segregating families are available at COMAV.

Morphological variability

The results of our study revealed a wide range of diversity in our collection for most of the evaluated traits, mainly related to leaves, fruit shape and size, and color or flower morphology. Regarding SLC group, it generally exhibited the highest grade of morphological diversity since the group comprises a wide range of geographical origins. For traits related to leaf shape and size, this group displayed much higher diversity in comparison to the simpler leaves of SP 24 , 25 .

The high variation related to fruit color and shape was an interesting source of variation for breeding and genetic purposes. Some SP and SLC accessions, collected in Peru and Mexico respectively, exhibited colors ranging from yellow to orange. In fact, yellow fruits have been previously described in these sites 12 , 24 , 25 . The fruit shape of SLC group exhibited a considerable variation ranging from round to flattened, fasciated, or elongated fruits. The transition from small and uniform fruits of SP to diversity in fruit size, shape, and locule number was a consequence of variations in flower complexity and an increase in ovary size. For example, the appearance of fasciated phenotype ( fas ) has been suggested to have arrived in Europe from Mexico in the 16th century 9 . These changes in fruit size and shape have been described to be a consequence of derived alleles of fas , sun , ovate , and lc genes 9 . According to the study of Blanca et al. 3 , some accessions from our SLC group carry fas and ovat e and some of our SLL also carries derived alleles. These results support our observations and these derived alleles could be an explanation for the diversity that we have detected.

Changes in flower complexity and style exertion could be other interesting changes related to domestication and further selection processes. For example, the number of petals and sepals tended to increase in SLC and SLL groups, although not in SLC Mesoamerica. The style position was also altered from highly exerted in SP Peru to slightly exerted or even inserted in SP Ecuador. In SLC, the degree of style exertion tended to decrease from Amazonian SLC to SLC Mexico, whereas in the SLL group it tended to be inserted. This correlation between stigma exertion and SP geographical origin had been previously described 12 , 40 , as well as the variation observed in SLC from South America 24 , 25 . This insertion process is related with the migration from the center of origin and resulted in the increasing of the autogamy levels. Interestingly, two different subgroups can be discerned in SLC Mexico, accessions collected as wild that exhibited inserted styles, and accessions with fruit size similar to cultivated tomato that were characterized as exerted ones. This presence of exertion is also detected in SLL and probably related to fasciated and big sized fruits.

Genetic variability

As expected, the highest level of diversity was found in Peru and Ecuador for both SP and SLC groups 2 , 3 . Rick and Fobes 1 described that variation in SLC depended on its geographical origin, being SLC from other countries less variable than SLC from these regions. The analysis within the SLC group also revealed a considerable decrease in the number of SNP variants detected in SLC Mexico. This is in agreement with the loss of variability that took place during the migration to Mesoamérica 2 , 3 , 41 . The detected 37,974 SNPs labeled as high impact show that this collection may be an interesting source of new alleles. This is supported by the SNPs with a high putative effect that were detected for some of the candidate genes from GWAS analysis (Supplementary Table S6 ). For example, candidate genes related to yellow fruit color had a total of 18 allelic variants with high effect. Two of them correspond to a carotenoid cleavage dioxygenase 1B ( Solyc01g087260) , 14 of them correspond to a protease-like protein ( Solyc03g081260 ), and the last 2 correspond to a homeobox leucine-zipper protein ( Solyc05g015030) . Also, a high impact SNP was detected in the genomic region where lc gene is located. Besides, the genomic region associated to fw2.2 presented two allelic variants with high effect. These variants are related with a nodulin MtN21 family protein (Solyc02g087050 ) and with an uncharacterized protein ( Solyc02g091330 ).

GWAS analysis

GWAS analysis revealed a total number of 107 SNPs associated to eight quantitative traits and 30 SNPs associated to seven qualitative traits. This analysis has allowed the identification of known and novel genes for these traits. In addition of the QTLs for flowers per inflorescence previously described on chromosomes 2, 3, and 5 42 , our analysis identified a possible novel genomic region on chromosome 11 which carries genes encoding for Agenet and cellulose synthase proteins. Agenet has been described to be involved in flower development 43 and the expression of cellulose synthase has also been detected in flowers of Arabidopsis thaliana 44 . The association identified for forked inflorescence on chromosome 9 corresponds to a SNP in ARGONAUTE 1 gene ( Solyc09g082830 ). This gene is a member of AGO gene family, which is known to regulate vegetative and reproductive development and stress response 45 . The expression of these genes have been detected in flower and fruit of tomato 46 . A significant association with uniparous inflorescence was identified on chromosome 11 and was located approximately 5 Mb away from a mapped region which is considered to be involved in branched inflorescences of fin mutants 47 . Six associations for dark green leaves were detected on chromosome 9. An annotated gene as chloroplast FLU-like protein was located 2 kb away from this region. FLU is a nuclear-encoded plastid protein that interacts with enzymes involved in chlorophyll synthesis 48 . Besides that, some new identified SNPs lacked in functional annotation, for example SNP associated to the total number of inflorescences. Finally, another several traits were associated with SNPs located on genes that were not apparently related to the trait they are associated with, such as the association between leaf length and a RING-finger protein-like which could regulate ubiquitination processes 49 or the association between fruit longitudinal stripes and heat shock proteins. All these novel detected regions would require further experiments for validation and identification of candidate genes suitable for tomato breeding.

As expected, our analysis has identified several SNPs located close to genes or candidate regions previously characterized. GWAS analysis has allowed the identification of previously described loci associated to fruit size such as fw2.2 50 , fw9.2 51 , locule number (lc) 9 , and fas 9 . For instance, we detected an association between fruit weight and SNPs close to fw2.2 and also close to SNPs that have already been identified in other GWAS analysis 31 , 52 . In case of lc and fas genes, our study revealed associations between the trait number of locules and SNPs located genetically close to both QTLs, which are located on chromosome 2 and 11, respectively. Sacco et al. 52 detected lc gene and also one of our annotated candidate genes on chromosome 11, Solyc11g071840 . This detected region on chromosome 11 was located in a region previously described as fas and really close to the annotated fw11.3 . The fw11.3 is a QTL controlled by cell size regulator, which regulates weight by the control of cell size in the pericarp 53 . Strikingly, no association has been found between the qualitative trait fruit fasciation and chromosome 11 (where fas gene is located). However, two regions previously undescribed that could be associated to fasciated phenotype on chromosomes 1 and 4 were revealed. Despite that fas gene is not located in these detected regions, this result suggests the involvement of new genome regions. In fact, the SNP located on chromosome 1 at 3,717,866 bp was also associated with the number of locules in our analysis.

An association signal for fruit color was identified on chromosome 1 and located in a region with a candidate gene described as carotenoid cleavage dioygenase 1B. Carotenoids are important factors implied in fruit color and modifications or absence of their syntheses are the reason for the orange color of mutants such as tangerine ( t ), delta ( Del ) and beta ( B ) or the yellow flesh ( r ) mutant 54 . The genome region involved in this r mutant is located on chromosome 3 at 9 Mbs from our associated region. For the pink color, associations were located in a genomic region with an annotated gene as colorless fruit epidermis (y gene). This association between the pink fruit color and this gene had already been detected by GWAS analysis and a deletion in this region has been hypothesized to control this trait 52 .

The utility of this germplasm collection

The present work has revealed a wide range of variability in our collection. The novelty of our study is the inclusion of a wide range of geographical origins of SLC accessions and SP from North Ecuador, which has not been widely studied. Moreover, the potential of Andean SLC is still not widely explored and it could be a novel source of interesting agronomic traits for tomato breeding. The genetic variability present in SLC from the Amazonian region is huge in comparison with the variability of the traditional tomato, although it has notably increased recently due to introgressions from wild species. The close phylogenetic relatedness of SLC makes this species specially useful for being exploited in tomato breeding, much more than other more phylogenetically distant species.

The high morphological variability found in our study may be an evidence of the potential variability in other traits not evaluated in this work. This collection is being analyzed for biochemical composition of fruit and deeper approaches for specific morphology analyses of fruit in the context of the Varitome project. The genome sequences of all these accessions are published and available, together with the identified annotated SNPs. The number of allelic variants present in this collection is huge and many of them may have interesting effects, such as lost or gained stop codons, frameshift variants or splice variants. A pan-genome analyses that includes the set of accessions we have used in our work, has described 4873 genes not present in the reference genome 38 . Part of this gene variability is present in our collection and easily accessible. This increases its usefulness, and makes our collection in one of the most characterized of tomato and related species.

The GWAS study has shown that the size and population structure of this collection make it feasible for this type of analysis. Further studies with other traits probably will increase the identification of candidate genes and alleles. Seeds from these sequenced accessions are available from two genebanks, one in Europe (COMAV) and the other in America (TGRC). The characterized and sequenced plants came from a double round of self-pollination of a single plant, so they are quite homozygous and it is possible to use the genotype data to do other GWAS analyses with other traits.

Different segregating families have been developed and have led to the creation of a powerful tool to speed up genetic studies based on this collection. The use of three different parents in crosses with all accessions, allows the testing of the same alleles in different genetic backgrounds. The F2 populations will facilitate the analysis of the segregation of any variant in this collection. Studies can be conducted starting from SNP alleles, presence or absence of a determinate gene or phenotypic variants. By choosing an accession carrying a selected allele and one of the three parental accession which carries the alternative allele, the F1 and F2 segregation families are available for the genetic study of this variant. The availability of these segregating families allows to speed up research to confirm possible candidate genes. Besides sparing the effort of developing segregating families, researchers could analyze different natural mutants of the same gene or study the mutation effect in different genetic backgrounds.

The usefulness of this collection is based on the fact that all these resources are freely and easily available. Seeds of the original and self-pollinating accessions and F2 families are available at COMAV and TGRC genebanks. Passport and characterization data, pedigree information, genome sequences, SNPs and GWAs results are available and integrated at Solanaceae Genomics Network (solgenomics.net). All these resources build up a powerful platform for tomato genetics and breeding that could be reinforced with new studies performed on it.

Material and methods

Plant material.

A germplasm collection of 163 accessions was selected with the aim of representing a broad range of geographical, morphological, and genetic diversity. These plant materials consisted of 15 accessions of SLL from Mexico; 121 accessions of SLC coming from Ecuador, Peru, Mexico, and different countries of Mesoamerica and 27 accessions of Solanum pimpinelifollium (SP), from Ecuador and Peru. Accessions were grouped according to their geographical origin and previous genetic results 2 , 3 , as is shown in Fig. S1 and Table S1 . These accessions were provided by different germplasm banks such as Tomato Genetics Resource Center (TGRC), United States Department of Agriculture (USDA), and Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana (COMAV) of Universitat Politècnica de València. Passport data of these accessions are available in Supplementary Table S1 and COMAV, Solanaceae Genomics Network web pages. For each accession, seeds obtained after a double round of self-pollination from a single plant of each original accession were collected and they were used either for the morphological and genetic characterization and for the creation of segregating populations.

Morphological characterization

Plants used for the morphological characterization were cultivated in a greenhouse at Universitat Politècnica de València (Valencia, Spain) during the spring-summer seasons of 2016. Plants were grown in 12-l pots with coconut fiber and fertirrigated under standard dosages for tomato in our area. A completely randomized experimental design was conducted with two plants per accession, each replicate in a different greenhouse.

Twenty-six quantitative and 27 qualitative traits based on the descriptors developed by IPGRI 55 , mainly related to plant architecture, inflorescences and flowers, leaves and fruit size were evaluated. Some descriptors were modified for a better representation of the morphological variability exhibited in the collection. The descriptors and their definitions are listed in Supplementary Table S2 . All traits were added to the Solanacea Phenotype Ontology, available at SGN ( https://solgenomics.net/search/traits ).

Prior to any analysis, all traits were manually curated to detect possible errors. Differences due to a greenhouse effect were assessed using Student’s t test and Mann–Withney-Wilcoxon test for quantitative traits depending on whether the data was normally distributed. Fisher’s exact test was conducted on qualitative data. As no differences between the two greenhouses were found, data from both greenhouses were joined, and the mean value was calculated for quantitative traits. For qualitative data, a new level for each qualitative trait (named as 10) was created to include the accessions which presented different scale values for this qualitative trait. Robust ANOVA and Fisher test were conducted to detect significance differences between species, depending on whether the data was quantitative or qualitative. A Bonferroni correction of p values was conducted.

Genetic analysis

Genome sequences of the accessions of this collection have been published previously 35 and the SNPs identified in these accessions are publicly available in Solanaceae Genomics Network ( https://solgenomics.net/projects/varitome ). Using these data, the collection of SNPs has been annotated to detect the localization and possible impact of changes using SnpEff 56 , and statistics were calculated for each geographical group.

GWAS between genotypes and phenotypes were calculated for all quantitative and qualitative traits using R package GENESIS v.2.14.1 57 . A total number of 1,479,141 high quality SNPs were used for the GWAS analysis. A PCoA has been done to visualizate genetic structure (Supplemental Fig. S3 ). To test the association, a generalized linear mixed model using the genetic relationship matrix (GRM) as random effects was used in order to account for population stratification. GRM was computed using GCTA v.1.92.1 58 . For count data, a Poisson distribution of residuals was assumed, while for the rest of the quantitative data a Gaussian distribution was applied. Normality was checked using a Shapiro–Wilk normality test and a Box–Cox power transformation was used when necessary. For qualitative traits, a binomial distribution was assumed. For multinomial qualitative traits, each category level was treated as a dummy binary variable. Quantile–quantile plots were used to assess the GWAS model (Supplemental Fig. S4 ). Significant level of association was estimated using GEC (Genetic type 1 Error Calculator) v.0.2 59 .

Development of breeding population

In order to help to exploit the variability detected in our collection and facilitate its use to the research community, F1 and F2 generations were constructed for the 163 accessions by crossing each accession with one accession representative of each species (SP, SLC, and SLL). The accessions, BGV007109 of SP, LA2278 of SLC and Money Maker of SLL were selected as parents. Each fruit from each individual cross was maintained separately in order to facilitate the detection of possible mistakes. Two different F1 plants of each combination were self pollinated to obtain the set of two independent F2 breeding populations. The culture for the F2 family generation was done during the years 2017–2019 in the Centro de experiencias Cajamar de Paiporta (Valencia, Spain). Plants were grown in greenhouses in soil and fertirrigated under standard dosages for tomato in our area.

A complete list of these available materials is recorded in Supplementary Table S4 .

Data availability

Sequences, SNPs, passport data, characterization data, and images of the original collection are available in Solanaceae Genomics Network ( https://solgenomics.net/ ). Seeds of the original germplasm collection are available by request to COMAV genebank ([email protected]) and to the TGRC ( https://tgrc.ucdavis.edu/ ). Requests for the available seeds of F1 and F2 families should be addressed to COMAV genebank.

Rick, C. M. & Fobes, J. F. Allozyme variation in the cultivated tomato and closely related species. Bull. Torre Bot. Club 102 , 376–384 (1975).

Article   Google Scholar  

Blanca, J. Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato. PLoS ONE 7 , e48198, https://doi.org/10.1371/journal.pone.0048198 (2012).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Blanca, J. et al. Genomic variation in tomato, from wild ancestors to contemporary breeding accessions. BMC Genomics . https://doi.org/10.1186/s12864-015-1444-1 (2015).

Razifard, H. et al. Genomic evidence for complex domestication history of the cultivated tomato in Latin America. Mol. Biol. Evol . https://doi.org/10.1093/molbev/msz297 (2020).

Bai, Y. & Lindhout, P. Domestication and breeding of tomatoes: what have we gained and what can we gain in the future? https://doi.org/10.1093/aob/mcm150 (2007).

Peralta, I. E., Spooner, D. M. & Knapp, S. Taxonomy of wild tomatoes and their relatives (Solanum sect. Lycopersicoides, sect. Juglandifolia, sect. Lycopersicon; Solanaceae). Syst. Bot. Monogr. 84 , 1–186 (2008).

Google Scholar  

Ichihashi, Y. & Sinha, N. R. From genome to phenome and back in tomato. Curr. Opin. Plant Biol. 18 , 9–15 (2014).

Article   CAS   PubMed   Google Scholar  

Monforte, A. J., Diaz, A., Caño-Delgado, A. & Van Der Knaap, E. The genetic basis of fruit morphology in horticultural crops: lessons from tomato and melon. J. Exp. Bot. 65 , 4625–4637 (2014).

Rodríguez, G. R. et al. Distribution of SUN, OVATE, LC, and FAS in the tomato germplasm and the relationship to fruit shape diversity. Plant Physiol. 156 , 275–285 (2011).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Paran, I. & Van Der Knaap, E. Genetic and molecular regulation of fruit and plant domestication traits in tomato and pepper. J. Exp. Bot. 58 , 3841–3852 (2007).

García-Martínez, S., Andreani, L., Garcia-Gusano, M., Geuna, F. & Ruiz, J. J. Evaluation of amplified fragment length polymorphism and simple sequence repeats for tomato germplasm fingerprinting: utility for grouping closely related traditional cultivars. Genome 49 , 648–656 (2006).

Article   PubMed   Google Scholar  

Rick, C. M., Fobes, J. F. & Holle, M. Genetic Variation in Lycopersicon pimpinellifolium : evidence of evolutionary change in mating systems*. Plant Syst. Evol. 127 , 139–170 (1977).

Caicedo, A. L. & Schaal, B. A. Population structure and phylogeography of Solanum pimpinellifolium inferred from a nuclear gene. Mol. Ecol. 13 , 1871–1882 (2004).

Sifres, A., Picó, B., Blanca, J. M., De Frutos, R. & Nuez, F. Genetic structure of Lycopersicon pimpinellifolium (Solanaceae) populations collected after the ENSO event of 1997-1998. Genet. Resour. Crop Evol. 54 , 359–377 (2007).

Zuriaga, E. et al. Genetic and bioclimatic variation in Solanum pimpinellifolium . Genet. Resour. Crop Evol. 56 , 39–51 (2009).

Ricks, C. M. Potential improvement of tomatoes by controlled introgression of genes from wild species. in Proc. Conference Broadening the Genetic Base of Crops 167–176 (1978).

Stevens, M. A. & Ricks, C. M. Genetics and breeding. in The Tomato Crop: A Scientific Basis for Improvement (eds Atherton, J. & Rudich, J.) 661 (Chapman and Hall Ltd., 1986). https://doi.org/10.1007/978-94-009-3137-4 .

Capel, C. et al. Wide-genome QTL mapping of fruit quality traits in a tomato RIL population derived from the wild-relative species Solanum pimpinellifolium L. Theor. Appl. Genet. 128 , 2019–2035 (2015).

Rambla, J. L. et al. Identification, introgression, and validation of fruit volatile QTLs from a red-fruited wild tomato species. J. Exp. Bot. 68 , 429–442 (2017).

CAS   PubMed   Google Scholar  

Banerjee, M. K. & Kalloo, M. K. Sources and inheritance of resistance to leaf curl virus in Lycopersicon . Theor. Appl. Genet. 73 , 707–710 (1987).

Alexander, L. & Hoover, M. Disease resistance in wild species of tomato: Report of the national screening Committee. Agric. Exp. Stn. Res. Bull . 752 , 1–76 (1955).

Walter, J. M. Hereditary resistance to disease in tomato. Annu. Rev. Phytopathol. 5 , 131–160 (1967).

Warnock, S. J. Natural habitats of Lycopersicon Species. HortScience 26 , 466–471 (1991).

Rick, C. M. The role of natural hybridization in the derivation of cultivated tomatoes of western South America. Econ. Bot. 12 , 346–367 (1958).

Rick, C. M. & Holle, M. Andean Lycopersicon esculentum var. cerasiforme : genetic variation and its evolutionary significance. Econ. Bot. 44 , 69–78 (1990).

Nuez, F. & Díez, M. J. Tomato. in Vegetables II. Handbook of Plant Breeding (eds Prohens, J. & Nuez, F.) 249–323 (Springer, 2008). https://doi.org/10.1007/978-0-387-74110-9_7 .

Arellano Rodríguez, L. J. et al. Evaluation of the resistance against Phytophthora infestans of wild populations of Solanum lycopersicum var. cerasiforme . Rev. Mex. Cienc. Agr. 4 , 753–766 (2013).

Foolad, M. R. Genome mapping and molecular breeding of tomato. Int. J. Plant Genomics 2007 , 64358, https://doi.org/10.1155/2007/64358 (2007).

Robertson, L. & Labate, J. Genetic Improvement of Solanaceous Crops Volume 2: Tomato . (Science Publishers, 2014).

Rothan, C., Diouf, I. & Causse, M. Trait discovery and editing in tomato. Plant J. 97 , 73–90 (2019).

Bauchet, G. et al. Use of modern tomato breeding germplasm for deciphering the genetic control of agronomical traits by Genome Wide Association study. Theor. Appl. Genet. 130 , 875–889 (2017).

Tieman, D. et al. A chemical genetic roadmap to improved tomato flavor. Science 355 , 391–394 (2017).

Bauchet, G. et al. Identification of major loci and genomic regions controlling acid and volatile content in tomato fruit: implications for flavor improvement. N. Phytol. 215 , 624–641 (2017).

Article   CAS   Google Scholar  

Zhao, J. et al. Meta-analysis of genome-wide association studies provides insights into genetic control of tomato flavor. Nat. Commun. 10 , 1–12 (2019).

Aflitos, S. et al. Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing. Plant J. 80 , 136–148 (2014).

Lin, T. et al. Genomic analyses provide insights into the history of tomato breeding. Nat. Genet. 46 , 1220–1226 (2014).

Gao, L. et al. The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. Nat. Genet. 51 , 1044–1051 (2019).

Soyk, S. et al. Bypassing negative epistasis on yield in tomato imposed by a domestication gene. Cell 169 , 1142–1155 (2017).

Zhu, G. et al. Rewiring of the fruit metabolome in tomato breeding. Cell 172 , 249–261 (2018).

Widrlechner, M. P. Variation in breeding system of lycopersicon pimpinellifolium: implications for germplasm maintenance. Plant Genet. Resour. Newsl. 70 , 38–43 (1987).

Williams, C. E., ST & Clair, D. A. Phenetic relationships and levels of variability detected by restriction fragment length polymorphism and random amplified polymorphic DNA analysis of cultivated and wild accessions of Lycopersicon esculentum . Genome 36 , 619–630 (1993).

Georgiady, M. S., Whitkus, R. W. & Lord, E. M. Genetic analysis of traits distinguishing outcrossing and self-pollinating forms of currant tomato, Lycopersicon pimpinellifolium (Jusl.) Mill. Genetics 161 , 333–344 (2002).

CAS   PubMed   PubMed Central   Google Scholar  

Brasil, J. N. et al. AIP1 is a novel Agenet/Tudor domain protein from Arabidopsis that interacts with regulators of DNA replication, transcription and chromatin remodeling. BMC Plant Biol. 15 , 270 (2015).

Holland, N. et al. A comparative analysis of the plant cellulose synthase (CesA) gene family. Plant Physiol. 123 , 1313–1323 (2000).

Zhang, H., Xia, R., Meyers, B. C. & Walbot, V. Evolution, functions, and mysteries of plant ARGONAUTE proteins. Curr. Opin. Plant Biol. 27 , 84–90 (2015).

Bai, M. et al. Genome-wide identification of Dicer-like, Argonaute and RNA-dependent RNA polymerase gene families and their expression analyses in response to viral infection and abiotic stresses in Solanum lycopersicum . Gene 501 , 52–62 (2012).

Xu, C. et al. A cascade of arabinosyltransferases controls shoot meristem size in tomato. Nat. Genet. 47 , 784–792 (2015).

Meskauskiene, R. et al. FLU: a negative regulator of chlorophyll biosynthesis in arabidopsis thaliana. Proc. Natl Acad. Sci. USA 98 , 12826–12831 (2001).

Joazeiro, C. A. P. & Weissman, A. M. RING finger proteins: mediators of ubiquitin ligase activity. Cell 102 , 549–552 (2000).

Frary, A. et al. fw2. 2: a quantitative trait locus key evolution tomato fruit size. Science 289 , 85–88 (2000).

Grandillo, S., Ku, H. M. & Tanksley, S. D. Identifying the loci responsible for natural variation in fruit size and shape in tomato. Theor. Appl. Genet. 99 , 978–987 (1999).

Sacco, A. Exploring a tomato landraces collection for fruit-related traits by the aid of a high-throughput genomic platform. PLoS ONE 10 , e0137139, https://doi.org/10.1371/journal.pone.0137139 (2015).

Mu, Q. et al. Fruit weight is controlled by Cell Size Regulator encoding a novel protein that is expressed in maturing tomato fruits. PLoS Genet. 13 , 1–26 (2017).

Yoo, H. J. et al. Inferring the genetic determinants of fruit colors in tomato by carotenoid profiling. Molecules 22 , 1–14 (2017).

IPGRI. Descriptors for Tomato Lycopersicum spp. (International Plant Genetic Resources Institute, Rome, Italy, 1996).

Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6 , 80–92 (2012).

Gogarten, S. M. et al. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics https://doi.org/10.1093/bioinformatics/btz567 (2019).

Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88 , 76–82 (2011).

Li, M. X., Yeung, J. M. Y., Cherny, S. S. & Sham, P. C. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum. Genet. 131 , 747–756 (2012).

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Acknowledgements

This research was supported by the National Natural Science Foundation of USA Varitome project (NSF IOS 1564366). We would like to thank the Centro de Experiencias Cajamar de Paiporta (Valencia, Spain) for its excellent work done in growing the tomato plants in their greenhouses. We thank TGRC, ARS-GRIN, and COMAV genebanks for providing seeds and to all genebanks for their titan effort to preserve biodiversity.

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Estefanía Mata-Nicolás, Esther Gimeno-Paez, Víctor Garcia-Carpintero, Peio Ziarsolo, José Blanca, Joaquín Cañizares & María José Díez

Department of Biochemistry and Molecular Biology, Universitat de València, Valencia, Spain

Javier Montero-Pau

Boyce Thompson Institute, Ithaca, NY, USA

Naama Menda & Lukas A. Mueller

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Esther van der Knaap

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J.C., M.J.D., J.B. and E.V.K. conceived the experiment. E.M., E.G. and V.G.C. performed the research. J.B., P.Z., N.M. and L.M. did the data management. E.M., J.M.P., P.Z., V.G.C., J.B. and J.C. analyzed the data. J.C., E.M., M.J.D. and E.V.K. wrote the paper. All authors reviewed and approved this submission.

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Correspondence to Joaquín Cañizares .

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Mata-Nicolás, E., Montero-Pau, J., Gimeno-Paez, E. et al. Exploiting the diversity of tomato: the development of a phenotypically and genetically detailed germplasm collection. Hortic Res 7 , 66 (2020). https://doi.org/10.1038/s41438-020-0291-7

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DOI : https://doi.org/10.1038/s41438-020-0291-7

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Analysis of Genetic Diversity and Population Structure of Cowpea ( Vigna unguiculata (L.) Walp) Genotypes Using Single Nucleotide Polymorphism Markers

Mbali thembi gumede.

1 Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, College of Agriculture, Engineering and Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa

2 Agricultural Research Council—Vegetables, Industrial and Medicinal Plant Institute, Private Bag X293, Pretoria 0001, South Africa

Abe Shegro Gerrano

3 Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT 59717-3150, USA

Assefa Beyene Amelework

Albert thembinkosi modi, associated data.

Not available.

Cowpea ( Vigna unguiculata (L.) Walp) is an important legume crop with immense potential for nutritional and food security, income generation, and livestock feed in Sub-Saharan Africa. The crop is highly tolerant to heat and drought stresses which makes it an extremely important crop for improving resilience in crop production in the face of climate change. This study was carried out to assess the genetic diversity and population structure of 90 cowpea accessions using single nucleotide polymorphism (SNP) markers. Out of 11,940 SNPs used, 5864 SNPs were polymorphic and maintained for genome diversity analysis. Polymorphic information content (PIC) values ranged from 0.22 to 0.32 with a mean value of 0.27. The model-based Bayesian STRUCTURE analysis classified 90 cowpea accessions into four subpopulations at K = 4, while the distance-based cluster analysis grouped the accessions into three distinct clusters. The analysis of molecular variance (AMOVA) revealed that 59% and 69% of the total molecular variation was attributed to among individual variation for model-based and distance-based populations, respectively, and 18% was attributed to within individual variations. Furthermore, the low heterozygosity among cowpea accessions and the high inbreeding coefficient observed in this study suggests that the accessions reached an acceptable level of homozygosity. This study would serve as a reference for future selection and breeding programs of cowpea with desirable traits and systematic conservation of these plant genetic resources.

1. Introduction

Cowpea ( Vigna unguiculata (L.) Walp, 2n = 2x = 22) is an important legume crop that belongs to the genus Vigna, family Fabaceae, and order Fabales, with a genome size of 620 million base pairs [ 1 ]. Cowpea is a herbaceous annual plant widely grown in tropical and subtropical regions of Sub-Saharan Africa, East Asia, and other developing countries [ 2 , 3 ]. The crop plays a major role in both human and animal nutrition and food as well as food security and income generation for farmers and agro-traders [ 3 ]. In addition to the crop’s importance in sustaining food security, cowpea possesses good resilience to extreme heat and drought conditions which is extremely important in improving the resilience of the crop to the current climate change [ 4 , 5 ].

Moreover, cowpea has significant importance to cropping systems on account of its ability to grow in low fertility soils, as a complementary crop in rotation with cereals to break the life cycle of pathogens of cereals infested in the soil and consequently improve the fertility of the soil [ 6 ]. Globally, the cowpea average yield ranges from 0.1 t ha −1 to 0.59 t ha −1 which is lower than its expected potential yield of 1.5 t ha −1 to 3 t ha −1 under suitable environmental conditions [ 7 ]. This is caused by the narrow genetic base of improved varieties and their susceptibility to abiotic and biotic stresses. It is of high importance to develop improved varieties of cowpea to increase productivity in order to help alleviate poverty in Sub-Saharan Africa and also to meet the market demand. The production of improved cowpea varieties for traits such as high yield and nutritional status will largely benefit both subsistence and commercial farmers. Therefore, characterizing genetic diversity in any crop species is important for optimal germplasm utilization, conservation, and crop improvement programs. In support, Kondwakwenda [ 8 ] also stated that the availability of appropriate genetic diversity is imperative for the sustenance and success of any crop breeding program.

The assessment of the genetic diversity of a particular crop is achieved using morphological, biochemical, and molecular markers [ 3 , 9 ]. Although biochemical markers are more reliable than morphological markers, they are both reported to be influenced by environmental factors. These markers provide genetic diversity information on the basis of genotype performances using agronomic traits and may differ at different stages of growth and development as well as in the growing environment. This may hinder the real genetic variation among genotypes [ 10 , 11 ] and reduce the accuracy of the results obtained. Hence, there was an evolution of the development of DNA molecular markers [ 12 , 13 , 14 , 15 ]. Molecular markers are neutral to environmental effects and the genetic diversity is reviewed at the genomic DNA level; therefore, it is helpful to envision the precise genetic diversity among genotypes [ 3 , 16 ]. However, they may not be associated with any agronomic traits and needs to be supplemented with morphological markers in order to infer a meaningful conclusion.

There are several DNA markers that have been developed to determine the genetic diversity of cowpea. These include restriction fragment length polymorphism (RFLP) [ 16 , 17 ], simple sequence repeats (SSR) [ 18 ], single nucleotide polymorphism (SNP) [ 19 ], amplified fragment length polymorphic (AFLP) [ 20 ], and random amplified polymorphic DNA (RAPD) [ 21 ]. In recent advances in molecular genetics and molecular biology, the use of SNP markers has emerged to be the most preferred molecular marker because of its high genomic abundance, cost-effectiveness, reliability, and ease of application in comparison to other polymerase chain reaction (PCR)-based molecular markers [ 22 , 23 ]. Hence, the SNP markers were used in the current study. The study by Desalegne [ 24 ] compared the efficiency of SNP and SSR marker-based analysis of genetic diversity in 95 cowpea accessions collected from East Africa and the International Institute of Tropical Agriculture (IITA) inbred lines. Their study revealed that SNP markers were found to be more effective than SSR markers to determine the association between cowpea varieties; hence, the study suggested the utilization of SNP markers in the future analysis of genetic diversity and population structure in cowpea. Similarly, the recent study by Nkhoma [ 19 ] evaluated the genetic diversity in 90 cowpea genotypes using SNP markers and phenotypic traits and the study showed that SNP markers were more efficient in differentiating the diversity among and within the cowpea genotypes evaluated.

There are sequencing technology-based tools, which is next-generation sequencing (NGS), that have emerged to discover SNP markers, using genotyping-by-sequencing (GBS), which has been reported to be efficient, inexpensive, and fast developing in sequencing plant genomes [ 25 , 26 ]. In addition, a new type of marker known as diversity arrays technology (DArT) has been recently established for genotyping and genome sequencing needlessly of sequence information [ 3 ]. In cowpea, the DArT marker has been recently used by Gbedevi [ 3 ] to study the genetic diversity and population structure of 498 cowpea accessions collected from the Republic of Togo and their study revealed the presence of four major clusters among the accessions studied and the accessions were not clustered according to the regions where they were collected suggesting that the clustering did not closely resemble the geographical areas of the collections. Classic DArT markers have been substituted by DArTseq markers based on GBS. DArTseq and SNP markers based on GBS technology have been successfully applied in different crops including legumes such as cowpea [ 3 , 27 ], chickpeas [ 28 ], common beans [ 29 ], and Bambara groundnut [ 30 ]. Hence, the objective of this study was to assess the magnitude of the genetic diversity and population structure among cowpea genotypes using single nucleotide polymorphisms (SNP) markers.

2.1. Allele Polymorphism

SNP distribution per chromosome and the gene diversity parameters measured from 90 cowpea accessions are presented in Table 1 . The genetic diversity parameters analysis was conducted using 5864 (49%) SNPs that remained after filtering out monomorphic and minor allele frequencies of less than 2%. The number of polymorphic SNPs per chromosome ranged from 345 on chromosome 1 to 668 on chromosome 3 with an overall mean of 488 per chromosome. The proportion of polymorphic SNPs per chromosome varied from 31.82% on SNPs of unknown chromosome origin to 57.35% SNPs on chromosome 9, with an overall mean of value of 49.11% per chromosome. The mean number of effective allele (Ne) per chromosome was the highest on chromosome 7 (1.48 ± 0.013), followed by chromosome 10 (1.47 ± 0.013) and chromosome 4 (1.47 ± 0.013) whilst the lowest values were observed on chromosome 9 (1.34 ± 0.014) and chromosome 8 (1.37 ± 0.013). The observed heterozygosity ranged from 7.6% to 9.6% with a mean value of 8.4%. The unbiased gene diversity (uHe) values ranged from 0.221 to 0.291 per chromosome with an overall mean of 0.267. The fixation index (FIS) values ranged from 64% on chromosome 11 to 72% on chromosome 7 with a mean value of 67%. The mean polymorphic information content (PIC) value was 0.27, in which the PIC values per chromosome ranged from 0.22 to 0.32.

Genetic diversity within and among 90 cowpea accessions genotypes based on 5864 SNPs markers.

Note: %P = Polymorphism, NPS = number of polymorphic SNPs, NSU = number of SNPs used, N e = number of effective alleles per locus, H o = observed heterozygosity, uH e = unbiased gene diversity, F IS = inbreeding coefficient, PIC = polymorphic information content, UN = unknown, the values within the brackets are standard error.

2.2. Population Structure and Clustering

The population structure of the 90 accessions was examined using model- and distance-based structure analyses. At K = 2, the first cluster consisted of 82 genotypes, of which 29% were admixtures while the second cluster contained 8 genotypes in which majority of them (62%) were admixture. At K = 3, the first cluster contained 62% of the cowpea genotypes and 68% were admixtures. The second and the third clusters consisted of 6 and 28 genotypes with 67% admixtures each, respectively ( Figure 1 ). However, the STRUCTURE analysis estimated that the most suitable number of subpopulations was at K  =  4 ( Supplementary Figure S1 ), indicating that the 90 accessions could be grouped into four subpopulations (SP1–SP4) based on differences in their genetic makeup ( Figure 1 ). SP4 (red) contained only five accessions with admixtures from SP2 and SP3. SP2 (yellow) contained 40 accessions that share more admixture membership with the SP1, SP3 and SP4. P3 (green) contained 27 accessions, which share admixture membership with the other three subpopulations. SP4 (blue) contained 18 accessions with admixture from the other subpopulations. The admixture level in the four subpopulations ranged from 70% to 80%, which indicated that these subpopulations shared more admixture memberships. Individuals with a probability score of above 90% for a given cluster were considered as ‘pure’, whereas those with less than 90% were labeled as ‘admixture’.

Population structure analysis using a Bayesian-based approach. Population structure analysis of 90 cowpea accession from K = 2 to K = 4 based on inferred ancestry (Q matrix).

However, the distance-based cluster analysis generated using Nei’s genetic distance using a neighbor-joining algorithm revealed the presence of three distinct clusters in the population represented by the 90 accessions ( Figure 2 ). The clustering patterns of the two approaches were similar and constituted similar sets of accessions. For example, C1 (red) contained 41 accessions, 5 accessions from SP1, 27 from SP4, and 9 from SP3 of the STRUCTURE-generated clusters. C2 (black) consisted of 24 accessions, of which 18 accessions from SP2 and 5 were from SP3. C3 (blue) contained 26 accessions all from SP3. The discrepancy between the two clustering approaches could result from admixtures since only 43% of the tested genotypes were considered pure. The clustering patters did not match the geographic origins of the accessions. C1 consisted of 95% South African, 2% Nigerian, and 2% Tanzanian accessions. The majority of the accession (92%)) clustered in C3 were collected from South Africa and 4% from Nigeria and Tanzania each. However, C2 was dominated with accessions from South Africa (52%), Nigeria (39%), and Kenya (4%).

Neighbor joining analysis showing the genetic relationships among 90 cowpea accession tested using 5864 SNP markers. The different colors indicate the clustering generated by STRUCTURE analysis: SP1 = red, SP2 = orange, SP3 = green, and SP4 = blue. ** represents the pure varieties identified in STRUCTUR analysis.

2.3. Genetic Diversity among Subpopulations

The population genetic diversity estimates on 90 accessions were analyzed based on the four subpopulations generated by STRUCTURE and three populations generated by DARwin ( Table 2 ). SP3 revealed the highest values for most of the genetic parameters and displayed the highest level of genetic diversity (He = 0.247 and I = 0.381). SP1 displayed the lowest level of genetic diversity (He = 0.162 and I = 0.189) but 68.4% of the loci were fixed ( Table 2 ). SP3 had the highest number of private alleles (508) and the highest percentage of polymorphic loci (91%). Based on the three subpopulations generated by cluster analysis, C2 revealed the highest genetic diversity for most of the genetic parameters except for the fixation index. C1, on the other hand, revealed the lowest genetic diversity for all the studied genetic parameters. In C1, 68% of the alleles were fixed and 87% of loci were polymorphic.

Genetic diversity within and among the 90-cowpea accession classified by growth habit.

Note: Na = average number of observed alleles per locus per subpopulation, Ne = average number of effective alleles per locus per subpopulation, I = Shannon information index, Ho = observed heterozygosity per subpopulation, He = expected heterozygosity per subpopulation, FIS = inbreeding coefficient, %P = percentage of polymorphic loci, PA = private alleles, the values within the brackets are standard error.

2.4. Analysis of Molecular Variance (AMOVA)

AMOVA was performed among subpopulations estimated from both model-based and distance-based populations ( Table 3 ). The results of AMOVA in model-based populations indicated that the majority of the variance occurred among individuals within populations and accounted for 59.4% of the total variation. However, 18.4% and 22.1% of the total variation was attributed to differences within individuals and among populations, respectively. Similarly, in the distance-based population, the majority of the variance was observed among individuals within the population and accounted for 68.9% of the total genetic variance. The mean fixation index within individuals was significantly high and positive in all classes of populations suggesting that outcrossing among the tested cowpea populations was low. The variation existed among populations was positive and significant suggesting that these populations were highly differentiated. Similarly, the relatively low level of variation observed within individuals was attributed to the high fixation index value.

Analysis of molecular variance (AMOVA) among 90-cowpea accessions classified based on SNP markers.

Note: DF = degrees of freedom, SS = sum of squares, MS = mean sum of squares, Est. Var = estimated variance, Per. Var = percentage variation.

Genetic differentiation (F ST ) estimates among the subpopulations ranged from 0.103 between SP3 and SP4 to 0.239 between SP1 and SP2. The gene flow ranged from 0.8 (between SP1 and SP2) to 2.2 (between SP3 and SP4). The genetic distance among populations ranged from 0.089 between SP3 and SP4 to 0.214 between SP1 and SP2. The genetic identity (GI) ranged from 0.81 between SP1 and SP2 to 0.92 between SP3 and SP4 ( Table 4 ). According to Wright [ 31 ] standard guidelines for the interpretation of genetic differentiation, all pairs of subpopulations showed a moderate level of population differentiation. However, SP1 showed a relatively higher degree of differentiation (0.239) from the rest of the subpopulations. Gene flow among the subpopulation was relatively high between SP2, SP3, and SP4 ( Table 4 ). The observed high genetic differentiation among the subpopulations could be explained by the low gene flow among subpopulations.

Pairwise estimates of gene flow (above diagonal, within the brackets), genetic differentiation (FST) (above diagonal off brackets), genetic distance (GD) (lower diagonal off brackets), and genetic identity (GI) (lower diagonal within the brackets).

3. Discussion

The analysis of genetic diversity in crops is a prerequisite for the success of any plant breeding program [ 32 ]. Therefore, assessing the population structure and genetic diversity of crops is fundamental to implementing efficient genetic resource management and conservation strategies. The application of high-throughput molecular markers provides a better understanding of genomic diversity and the population structure of germplasm and can speed up the identification of superior groups for further hybrid development [ 33 ]. The current study used 5864 SNP markers to assess the pattern and level of genetic variation and genetic structure among 90 cowpea accessions collected from four geographic origins.

The quality and the discriminatory power of a given marker system are assessed by its PIC values [ 34 ]. It is important to note that SNP markers are bi-allelic in nature, hence their PIC values are restricted to 0.5, which is considered to be low or moderately informative as compared to SSR markers [ 35 ]. The mean PIC value of 0.27 reported in this study agreed with Gbedevi [ 3 ] who reported a PIC value of 0.25 but relatively higher than the one reported by Sodedji [ 36 ] (PIC = 0.22). The results suggest that the SNP markers that were used in this study showed a moderate level of polymorphism and revealed the existence of genetic diversity among the tested genotypes. The number of SNP markers used and the number of accessions studied might explain the observed differences among the reported PIC values and allelic polymorphism in this study and other previously reported studies. Kondwakwenda [ 8 ] also indicated that the observed variation in the quality and performance of SNP markers in different studies depends on the number of accessions studied, the type of markers used and the type of germplasm studied. Nonetheless, the SNP markers used in this study were relatively informative and reliable in assessing the diversity of the cowpea accessions studied.

The mean number of effective alleles per locus reported in this study was 1.43, which was comparable to the 1.41 reported by Fatokun [ 37 ]. The gene diversity was further expressed using the probability of observed (Ho) and expected (He) heterozygosity, which was the true indicator for the degree of genetic variation within and among the populations assessed. The average Ho and He in the present study were 0.075 and 0.202, respectively, for the 90 accessions which were comparable to the Ho of 0.05 and He of 0.31 reported by Gbedevi [ 3 ] for 70 cowpea accessions. Xiong [ 38 ], on the other hand, assessed 768 worldwide cowpea germplasm collections maintained at USDA GRIN and reported a Ho value of 0.06 and a He value of 0.35. Nonetheless, the He was moderately low in this study but generally higher than the Ho for all subpopulations. Fatokun [ 37 ] also reported a similar trend in cowpea. Govindaraj [ 39 ] alluded that the low observed heterozygosity suggests a high level of inbreeding within the subpopulations.

A moderate fixation Index (F IS = 67%) was observed in this study indicating 67% of the SNP loci used were fixed in the studied accessions. However, a relatively high (F IS = 0.83) was reported by Gbedevi [ 3 ]. The low observed heterozygosity and the relatively high rate of fixation index exhibited by the populations were explained by the fact that cowpea is a self-pollinated crop possessing a low out-crossing rate and low within-accession variability. Although the outcrossing rate in cowpea is low and ranges from less than 0.15 up to 1.58% depending on the genotypes involved and the environment, where it is grown [ 40 ], further purification (self-pollination) of the accession is needed. The self-pollination nature and low outcrossing rate of cowpea have been reported to be the major contributor to the observed low genetic variation among cowpea accessions [ 6 , 41 ].

Population structure analysis is the key to assessing the genetic structure of a given population and the basis for complex marker-trait association analysis [ 42 ]. The model-based population structure analysis grouped the 90 accessions into four subpopulations based on the peak of delta K (∆K) at K = 4. The admixture level ranges from 50% in SP4 to 60% in SP1. The high proportion of admixture detected indicates either these subpopulations share the same ancestral progenitor or there was gene flow between the subpopulations. A similar trend has been noted on the clustering based on the geographic origins of the accessions whereby accessions were not clustered together as per the origin. The majority of the accessions in C1 and C3 were from South Africa then Nigeria and Tanzania while C2 was dominated with accessions from South Africa, Nigeria, and Kenya. This could be due to formal or informal seed exchange from among farmer and traders. The distance-based cluster analysis using the neighbor-joining method showed the presence of three distinct clusters, which was not consistent with the results of the structure analysis. However, the pattern and the number of accessions maintained in each clustering approach were similar. The differences observed between the model based on STRUCTURE analysis and distance-based cluster analysis in the size and number of subgroups can be explained by the presence of admixtures within the subpopulations. In both model-based and distance-based clustering approaches, the grouping patterns were inconsistent with the growth habit and geographic origins of the studied accessions. Similar phenomena whereby subpopulations were grouped irrespective of the grouping criteria have also been reported by other researchers studying the genetic diversity in cowpea accessions [ 3 , 36 , 43 ]. In contrast, Ravelombola [ 44 ] reported clustering of genotypes based on growth habit resulted in two highly differentiated subpopulations.

The seed size and seed coat color preference can highly influence the genetic diversity in cowpeas [ 5 ]. Classification based on growth habits and other agronomic traits such as seed shape and seed coat color is significantly influenced by the breeding programs because these traits have been used to classify genotypes. Qualitative traits such as growth habits, seed size, seed shape, and seed coat color are also important traits for farmers and consumers preferences [ 45 ]. Therefore, it is important to incorporate farmers’ and consumers’ preferred traits in future selections to enhance varietal adoption among farmers. In the UPGM clustering, C1 (red) was the highest group comprising of 41 (46%) genotypes with brown (22%), red (12%) and cream (24%) seed coat color. Similarly, 23 accessions were grouped in C2 (black) comprises of 35% of brown seed coat color and 26 accessions grouped in C3 (blue) comprised of 35% black seed coat and 19% brown seed coat. In addition, the majority of accessions clustered in C1 and C2 were kidney-shaped while C3 was dominated with rhomboid-shaped accessions. In terms of phenotypical variation, the erect and prostrate type were not different with respect to seed shape. The study by Hellens [ 46 ] in peas reported the lighter seed coat color as a human preference during domestication.

Similarly, C1 was dominated by erect (32%), while C2 was dominated by accessions with unknown growth habits (44%). C3 was mainly dominated by the prostate (54%) growth habit type. Regarding the wide distribution of cowpea, accession was studied based on growth habit, the prostrate and erect types were more dominant than the semi-erect and climbing types. Growth habit is a morphologically important qualitative trait in cowpea production that highly affects crop yield and tillage method and further defines the shape of the plants and dictates how the plant should be harvested [ 47 ]. Plant growth habit has been a major breeding target for crop improvement. Therefore, determining the genetic mechanisms that control the plant type will assist in cowpea growth development improvements. However, the results revealed that growth habits could not be used as an index for evaluating genetic diversity and for genotype classifications. The study by Khan [ 48 ] on Bambara groundnut has also reported the same findings.

The AMOVA results revealed that the majority of the total molecular variation (59% and 69%) was due to differences among individuals within a population, 22% and 12% of the variation were attributed to the difference among the population, and 18% and 19% was due to variation within individuals. In the model-based approach, the among-population variation was higher than the within individuals variation. The magnitude of variations among and within populations was further quantified by genetic differentiation observed among the populations (F ST = 0.221). The results indicate high genetic differentiation between four subpopulations based on the standard guideline of Wright [ 31 ]. The studies by Gbedevi [ 3 ] also reported a high genetic differentiation (F ST ) value of 0.423 between two major reported subpopulations of 498 cowpea accessions. However, Sarr [ 43 ] also reported a low genetic differentiation ranging from 0.018 to 0.100. The differences reported in genetic differentiations could be attributed to the diversity and number of accessions used and the number of markers involved to assess the genetic diversity.

The reported level of genetic differentiation reported in this study could be explained by the gene flow among subpopulations [ 49 ]. The gene flow among the studied populations ranged from 0.796 to 2.172 and according to Wright [ 50 ], where gene flow < 1 indicates limited gene exchange among population. This result suggested that a moderate gene flow occurred in this study and led to high genetic differentiation between the populations. Furthermore, genetic distance was used to measure the relatedness between individuals in a population. In this study, the low pairwise genetic distance was observed ranging from 0.089 to 0.214 revealing wide genetic variations among the tested cowpea accessions. This result suggests that the accessions studied are unique and have greater potential to contribute to new varieties for breeding programs in South Africa. The understanding of genetic diversity among cowpea populations studied in this study will improve the subsequent planning in future cowpea breeding and contribute useful information in conservation and managing genetic diversity required for the vigorous and successful breeding program.

4. Materials and Methods

4.1. plant materials.

The study evaluated the genetic diversity of 90 cowpea accessions sourced from the Agricultural Research Council—Vegetables, Industrial and Medicinal Plants (ARC-VIMP) gene bank, Pretoria, South Africa. These accessions were collected from different parts of Africa including South Africa, Tanzania, Kenya, and Nigeria. The geographic origin and growth habits of the accessions are presented in Supplementary Table S1 .

4.2. DNA Extraction and Sequencing

The cowpea genotypes were grown in a seed germination chamber at the Biosciences eastern and central Africa International Livestock Research Institute (BecA-ILRI) hub in Nairobi, Kenya for genotyping. Ten-day-old leaf materials were sampled from the seedlings and the leaf samples were frozen in liquid nitrogen and stored at −80 °C for genotyping. DNA extraction was done using a NucleoMag Plant DNA extraction kit from Takara Bio USA, Inc. The genomic DNA extracted was in the range of 50–100 ng/μL. DNA quality and quantity were checked on 0.8% agarose gel.

Libraries were constructed according to Killian [ 51 ] using the DArTSeq complexity reduction method through digestion of genomic DNA using a combination of PstI and MseI enzymes and ligation of barcoded adapters and common adapters followed by PCR amplification of adapter-ligated fragments at the Biosciences Eastern and Central Africa hub of the International Livestock Research Institute (BecA-ILRI) in Nairobi. Libraries were sequenced using Single Read sequencing runs for 77 bases. Next-generation sequencing was carried out using Hiseq2500. DArTseq markers scoring was achieved using DArTsoft14, which is an in-house marker scoring pipeline based on algorithms. Two types of DArTseq markers were scored, SilicoDArT markers and SNP markers, which were both scored as 1 for presence, 0 for absence, and 2 for heterozygotes of the restriction fragment with the marker sequence in genomic representation of the sample. The DArTseq markers were scored using 11,940 SNP markers, which were set to 11 chromosomes of cowpea. The SNP markers were aligned to the cowpea reference genome, Vunguiculata_469_v1.0 to identify chromosome positions.

4.3. Data Analysis

A total of 90 cowpea accessions were genotyped with 11,940 SNP markers. Monomorphic and SNPs with a minor allele frequency of less than 2% were filtered out and 5864 (49%) SNPs were retained for further analysis. Genotypic data were subjected to analyses of molecular variance (AMOVA) and various measures of genetic diversity within and among inferred subpopulations using GenAlex software version 6.5 [ 52 ]. Genetic diversity parameters such as the number of effective alleles per locus (Ne), Shannon’s Information Index (I), gene diversity (He), and the polymorphic information content (PIC) were determined using the protocol of Nei and Li [ 53 ] using GenAlex software version 6.5. The genotypic data were used to obtain a dissimilarity matrix using the Jaccard index as described by Debener [ 54 ]. The matrix was then used to run a cluster analysis based on a neighbor-joining algorithm using the un-weighted pair group method with arithmetic average (UPGMA) in DARwin 6.0 software [ 55 ]. Bootstrap analysis was performed for node construction using 10,000 bootstrap values.

The Bayesian genotypic clustering approach of STRUCTURE 2.3.4 [ 56 ] was used to determine the population structure. An admixture model with independent allele frequencies, without prior population information, was used to simulate the population. The STRUCTURE program was set as follows: a burn-in period length of 100,000, and after burn-in, 100,000 Markov Chain Monte Carlo (MCMC) were used. This model assumes that the genome of each individual is a mixture of genes originating from K unknown ancestral populations. For joint inference of the population substructure, K ranging from 2 to 7 was set up, with ten independent runs for each K. The most probable value of K for each test was detected by ΔK [ 57 ] using the STRUCTURE HARVESTER [ 58 ]. Each individual genotype was grouped into a given cluster using the ‘membership coefficient’ for each cluster interpreted as a probability of membership. The genotype membership was determined by the computer program CLUMPP [ 59 ].

5. Conclusions

The current study revealed the existence of genetic diversity among and within the cowpea accessions analyzed and showed the effectiveness and reliability of SNP markers. The study revealed that 49% of the selected SNP markers were highly polymorphic and efficiently discriminate the tested cowpea accessions. The low heterozygosity and the high inbreeding coefficients observed among cowpea varieties indicate that the accessions reached an acceptable level of homozygosity. The model-based (structure analysis) and distance-based (UPGM) clustering approaches were used in this study. The model-based analysis revealed the presence of four subpopulations at K = 4 whereas the distance-based cluster analysis classified the cowpea accessions into three distinct clusters. The subpopulations identified exhibited the high level of genetic diversity and were moderately differentiated. These subpopulations could serve as heterotic groups and a relevant source of genes for future breeding and selection of diverse cultivars with different traits. Therefore, the results obtained from this study will be highly valuable for the development of new varieties adapted to diverse environments. Consequently, this study will substantially contribute to the utilization, conservation, and broadening of the use of genetic resources of cowpeas for future improvement.

Acknowledgments

The University of KwaZulu-Natal for the support of the project and the Agricultural Research Council for providing plant material, research funds, and research support.

Supplementary Materials

The following supporting information can be downloaded at: www.mdpi.com/article/10.3390/plants11243480/s1 , Table S1: List of cowpea genotypes used in the study and their origin; Figure S1: Population structure analysis using a Bayesian-based approach. Estimation of hypothetical subpopulations using K-values showing the highest Delta k value was observed at the number of populations (K) = 4.

Funding Statement

This research was funded by the National Research Foundation, South Africa, unique grant no: 116344 and the Agricultural Research Council of South Africa.

Author Contributions

Conceptualization: A.S.G., M.T.G. and A.T.M.; formal analysis: M.T.G. and A.B.A.; data validation: M.T.G., A.B.A., A.S.G. and A.T.M.; funding acquisition: A.S.G.; investigation: M.T.G., A.S.G. and A.T.M.; methodology: A.S.G.; project administration: A.S.G.; resources: A.S.G.; supervision: A.S.G. and A.T.M.; writing—original draft, M.T.G.; writing—review and editing, M.T.G., A.S.G., A.B.A. and A.T.M. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

SYSTEMATIC REVIEW article

Exploring chickpea germplasm diversity for broadening the genetic base utilizing genomic resourses.

• 1 Indian Agricultural Research Institute (ICAR), New Delhi, India
• 2 University School of Biotechnology, Guru Gobind Singh Indraprastha University, New Delhi, India
• 3 Department of Genetics and Plant Breeding, University of Agricultural Sciences, Bangalore, Bangalore, India
• 4 Department of Agricultural Sciences, Chandigarh University, Mohali, India
• 5 School of Agricultural Sciences, Sharda University, Greater Noida, India
• 6 Institute of Himalayan Bioresource Technology (CSIR), Pālampur, India
• 7 National Bureau of Plant Genetic Resources (ICAR), New Delhi, India
• 8 International Center for Agriculture Research in the Dry Areas (ICARDA), Giza, Egypt
• 9 Department of Entomology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
• 10 Plant Genome Mapping Laboratory, University of Georgia, Athens, GA, United States

Legume crops provide significant nutrition to humans as a source of protein, omega-3 fatty acids as well as specific macro and micronutrients. Additionally, legumes improve the cropping environment by replenishing the soil nitrogen content. Chickpeas are the second most significant staple legume food crop worldwide behind dry bean which contains 17%–24% protein, 41%–51% carbohydrate, and other important essential minerals, vitamins, dietary fiber, folate, β-carotene, anti-oxidants, micronutrients (phosphorus, calcium, magnesium, iron, and zinc) as well as linoleic and oleic unsaturated fatty acids. Despite these advantages, legumes are far behind cereals in terms of genetic improvement mainly due to far less effort, the bottlenecks of the narrow genetic base, and several biotic and abiotic factors in the scenario of changing climatic conditions. Measures are now called for beyond conventional breeding practices to strategically broadening of narrow genetic base utilizing chickpea wild relatives and improvement of cultivars through advanced breeding approaches with a focus on high yield productivity, biotic and abiotic stresses including climate resilience, and enhanced nutritional values. Desirable donors having such multiple traits have been identified using core and mini core collections from the cultivated gene pool and wild relatives of Chickpea. Several methods have been developed to address cross-species fertilization obstacles and to aid in inter-specific hybridization and introgression of the target gene sequences from wild Cicer species. Additionally, recent advances in “Omics” sciences along with high-throughput and precise phenotyping tools have made it easier to identify genes that regulate traits of interest. Next-generation sequencing technologies, whole-genome sequencing, transcriptomics, and differential genes expression profiling along with a plethora of novel techniques like single nucleotide polymorphism exploiting high-density genotyping by sequencing assays, simple sequence repeat markers, diversity array technology platform, and whole-genome re-sequencing technique led to the identification and development of QTLs and high-density trait mapping of the global chickpea germplasm. These altogether have helped in broadening the narrow genetic base of chickpeas.

1 Introduction

Grain legumes are a key component of the agricultural ecosystem. These plants are a chief member of the most diverse and ecologically crucial botanical families. Legumes play a vital role in crop rotations or intercropping schemes as these plants are capable of nitrogen assimilation through symbiotic relationship with rhizobia. Chickpea ( Cicer arietinum ) is the second most important grain legume after dry bean ( Phaseolus vulgaris L.). Chickpeas have eight pairs of homologous chromosomes (2n = 16) with an estimated genome size of 738 Mb and 28,269 annotated genes ( Varshney et al., 2013 ). The cultivated chickpea is believed to be originated in the Anatolia of Turkey ( Van der Maesen, 1984 ). Vavilov denominated two primary centers of origin for chickpea viz., southwest Asia (Afghanistan) and the Mediterranean with the secondary center of origin as Ethiopia. Since ancient’s times, legumes have been grown for human subsistence. Globally, India is the largest producer and consumer of pulse crops. Pulses are the major source of carbohydrates, proteins, lipids, vitamins, and minerals for people across the globe ( Aykroyd and Doughty, 1982 ). Pulses complement the nutritional quality, bioavailability of nutrients, when consumed along with cereals. Pulses provide 22–24% of protein, which is about twice the amount of wheat and three times the rice. Pulses are one of the cheapest sources of protein and play a very significant role in sustaining nutritional requirements in developing and economically poor countries. They have a low glycemic index (GI) and elicit only a moderate postprandial glycemic response after consumption. As a result, incorporating legumes into one’s diet is advised for glycemic-influenced diabetes control ( Rizkalla et al., 2002 ).

Chickpea is the major source of food and nutrition in the semi-arid tropics. In comparison to other pulses, chickpeas are a rich source of protein and carbohydrates, accounting 80% to the whole mass of dried seeds ( Geervani, 1991 ; Chibbar et al., 2010 ). Chickpea is high in dietary fiber (DF), vitamins, and minerals and is known to lower low-density lipoprotein ( Wood and Grusak, 2007 ). Chickpea has the highest quantity of total DF amongst pulses, which ranges from 18 to 22 g/100 g of raw seed ( Aguilera et al., 2009 ). The soluble and insoluble DF contents of chickpea raw seeds are about 4–8 and 10–18 g/100 g, respectively ( Dalgetty and Baik, 2003 ). It has been demonstrated that chickpeas have more bioavailable protein than other legumes ( Sánchez-Vioque et al., 1999 ; Yust et al., 2003 ). The changes in protein content of pre- and post-dehulled chickpea dried seeds are observed which range from 17%–22% and 25.3%–28.9%, respectively ( Hulse, 1991 ; Badshah et al., 2003 ). Raw chickpea seeds have a total fat content ranging from 2.70 to 6.48% ( Kaur et al., 2005 ; Alajaji and El-Adawy, 2006 ). On an average, raw chickpea seeds give 5.0 mg/100 g Fe, 4.1 mg/100 g Zn, 138 mg/100 g Mg, and 160 mg/100 g Ca. Chickpea is an inexpensive, rich source of folate and tocopherol ( Ciftci et al., 2010 ). The major carotenoids, viz . , β-carotene, lutein, zeaxanthin, β-cryptoxanthin, lycopene, and α-carotene are also found in chickpea.

Globally two types of chickpea cultivars desi or microsperma and Kabuli or macrosperma are cultivated. Generally, Kabuli chickpea is predominantly cultivated in temperate regions like the Mediterranean region that includes Western Asia, Southern Europe, and Northern Africa. However, desi chickpea is raised mainly in the semi-arid tropics ( Malhotra et al., 1987 ; Muehlbauer and Singh, 1987 ) such as Ethiopia and the Indian sub-continent. In general, desi types are characterized by small seeds, angular shape with a rough surface having a dark seed coat and flowers of pink or purple color due to the presence of anthocyanin pigment, whereas Kabuli types are bold seeded owl shaped with smooth surface have beige seed coat and bear white color flowers because of lack of anthocyanin pigment ( Pundir et al., 1985 ). Desi-type chickpeas are generally early maturing and high yielding than the Kabuli type. The desi chickpea is the predominant form cultivated in India occupying approximately 80–85% and the Kabuli chickpea occupies the remaining 15–20% of the total area and production. The chickpea draft genome sequences are already reported for desi ( Jain et al., 2013 ) and Kabuli ( Varshney et al., 2013 ) types.

Chickpeas are majorly grown as rainfed crops since they require less irrigation than other competitive crops such as cereals. However, it can be grown in a wide range of soils and agro-climatic conditions. Chickpea contributes to farming systems’ long-term survival as it plays important roles in crop rotation, mixed and intercropping, soil fertility maintenance through nitrogen fixation, and the release of soil-bound phosphorus; overall it improves the soil ecosystem. Globally, chickpea is grown on 14.842 m ha with an annual production volume of 15.083 m tones having a productivity average of 1,016 kg/ha. Indian contribution to the globe is 73.769% (10.949 m ha) in terms of area and 73.456% (11.080 m tones) production as depicted in Figures 1A,B with average productivity of 1,012 kg/ha ( FAOSTAT 2020 ). Pakistan, Turkey, Australia, Myanmar, Ethiopia, Iran, Mexico, Canada, China, and the United States are among the other significant chickpea producers.

FIGURE 1 . (A) Area and (B) Production of chickpea during 2020 in major producing countries in the world.

Rajasthan, Maharashtra, Madhya Pradesh (MP), Uttar Pradesh (UP), Karnataka, and Andhra Pradesh (AP) are the major states growing chickpea and other pulses in India. Rajasthan is also the highest producer of chickpea in India followed by Maharashtra, MP, UP, and Karnataka; and together contribute to 83% of production and 82% of the area in India ( Figures 2A,B ).

FIGURE 2 . (A) Area and (B) Production of chickpea during 2020 in major producing States in India.

Although the productivity is a little higher than average global productivity, it is lesser than the estimated potential yield, i.e., 6 tones/ha under optimum conditions for the crop (Thudi et al., 2016). Ever-increasing the human population linked with climate change and limited arable land poses a challenge to meet the demands of growing malnutrition and hunger. A lot of efforts had been made by the national and international scientific community to enhance the productivity of chickpeas, but unable to enhance up to a significant level. The reasons underlying are a narrow genetic base and as a result poor genetic gains in the breeding of improved varieties which, leads to the reduction in the yield and its adaptation ( Varshney et al., 2012 ). Devastating pests, pathogens, and increased incidences and severity of abiotic stress amid climate change are the major factors adversely affecting chickpea yield and production. Therefore, diverse sources of variations including wild Cicer species need to be explored for the genetic enhancement of chickpeas.

Chickpea performs better in cooler areas since it is a C-3 plant, implying that C-3 plants are better for the winter season. However, the harvest index (HI) in pulses (15%–20%) is low when compared to cereals (45–50%), which is a concerning issue. It is caused by excessive vegetative growth and can be countered by early dry matter partitioning into seeds ( Saxena and Johansen, 1990 ). Despite continued efforts by national and international chickpea improvement programs for the last several decades, the production and productivity of chickpeas have not increased significantly. Probably, this has happened due to the lack of variability for desired plant ideotypes, resistance sources for devastating pests and pathogens, and less responsive behavior of pulses toward modern agricultural practices and inputs. In general, chickpeas and other pulses are grown as a residual or alternative crop in marginal areas, only if the farmers have met their food/income requirements from high productivity- high input responsive crops such as paddy and wheat. After the onset of the green revolution, pulses were further marginalized in their traditional farming systems and local landrace variability in the farmer’s field was lost. Furthermore, chickpea is subjected to various types of biotic and abiotic stresses, which are blamed for much of the crop’s unstable and low yields ( Reddy, 2016 ).

In the production of chickpea, there has been a considerable risk of abiotic stresses. Crop failure is frequently attributed to moisture and temperature stresses, which leave the greatest impact on grain yield. Drought and heat stresses cause forced maturity, resulting in reduced yield. For example, the terminal drought stress in the Mediterranean region when chickpea is grown in the spring season. Drought along with heat stress alone annually reduces productivity by up to 70%. Another major problem in chickpea production is soil salinity and alkalinity. High levels of salinity and alkalinity in both semi-arid tropics and irrigated sections of the Indo-Gangetic plains are a major problem, as most of the pulses are highly sensitive to salinity and alkalinity. Another abiotic factor that limits chickpea grain yield is cold, particularly in temperate regions. Yield is further affected by lack of highly resistant sources in the cultivated gene pool for many of the devastating pathogens and biotic stresses such as dry root rot, ascochyta blight, collar rot, botrytis grey mold (BGM) and Helicoverpa species further aggravate the situation ( Reddy, 2016 ). In India, more than 250 insect species have been documented to be harmful to pulses including the chickpea crop.

To achieve higher and stable productivity, it is crucial to breed superior crop varieties with high yield, improved nutrition, disease, and pest resistance to meet the rising global demands. The genetic gains of chickpea and other legume crops are very less as compared to other crops, the reason behind this is the narrow genetic base. To meet the future demand, we have to accelerate genetic gains which are a cyclic process of identifying new variants, carrying selection, and fixing desirable traits. Further, to sustain higher genetic gain for a longer duration, infusion of genetic diversity in modern varieties from landraces and wild Cicer species is required. Genomics, high throughput precision phenotyping tools, and artificial intelligence can help in making a desired selection, and in achieving accelerated genetic gain while reducing genetic diversity loss ( Varshney et al., 2018 ).

2 Narrow Genetic Base—A Major Bottleneck in Chickpea

Chickpeas have an inherently narrow genetic base as the crop had been subjected to a series of major genetic bottlenecks such as natural selection driven by biotic and abiotic stresses, farmers’ selection pressure (domestication syndrome effect), the introduction of a small set of variability (founder effect), utilization of a very small proportion of variability in the breeding of modern cultivars, etc. ( Abbo et al., 2003 ). Chickpea is a self-fertilization crop, which enhances the probability of loss of variability particularly rare alleles/traits in a population during the selection processes, leading to further narrowing of the chickpea genetic base. Some of the other major factors causing narrowed genetic base of chickpea are areas given below:

• Restricted distribution of wild progenitors of chickpea ( C. reticulatum is restricted to a small area in SE turkey) ( Abbo et al., 2003 ), which obstructs the gene flow from the wild to the cultivated types.

• Founder effect: similar to any other Neolithic crops, chickpea crop is of monophyletic origin from its wild progenitor and only a limited amount of variability is spread to other parts of the world, causing a genetic bottleneck and narrowed genetic base ( Ladizinsky, 1985 ).

• Domestication syndrome: wild progenitors have ordained to cultivated forms after passing through various genetic modifications and acquiring a combination of traits which might have led to the disappearance of many genes/alleles responsible for input response and higher gain yield ( Jain et al., 2014 ).

• The change from autumn to spring sowing in chickpea: in the Early Bronze Age, the shift of chickpea sowing from autumn to spring to avoid certain biotic stresses, i.e., ascochyta blight. This was possible through the selection for vernalization response in chickpea wild progenitor species; which must have caused a drastic loss of genetic diversity ( Abbo et al., 2003 ).

• The replacement of the land races by elite cultivars produced by modern plant breeding methods which are often developed by genetically similar parental lines and most of the breeding programs shares a limited set of parental lines ( Tanksley and McCouch, 1997 ).

Crop improvement mainly relies on the genetic matter available for exploration through the methods of plant breeding, i.e., classical and molecular breeding. The repeated use of the same germplasm has made very less contribution to the development of the new cultivars. Hence, it could be inferred that chickpea has a narrow genetic base and prompt measures for the transfer of targeted traits from wild Cicer species to cultivated one should be taken up by properly evaluating, characterizing, identifying, and utilizing the available germplasm during hybridization programs ( Varshney et al., 2021 ).

In cereals, the amount of yield improvement achieved by breeding is substantially more than chickpea and other pulses. This is probably because the crops have not faced such a harsh bottleneck, and have a comparative broader genetic base ( Abbo et al., 2003 ). The drawback of chickpea breeding programs is their narrow genetic base and unavailability of high input responsive cultivars. In order to develop high-yielding lines, chickpea genetic resources are needed to be explored to broaden the genetic base. Genetic diversity is a major contributor to selection-induced genetic gain, therefore, poor genetic diversity in chickpeas is the major limiting factor in enhancing chickpea yield. As a result, expanding the genetic base of chickpeas is critical for enhancing breeding efficiency. Chickpea wild species are an important genetic resource, especially for biotic and abiotic stress resistance and nutritional quality. Chickpea mutants with novel features like brachytic growing behavior ( Gaur et al., 2008 ), more than three flowers per node–the cymose inflorescence ( Gaur and Gour, 2002 ), determinate ( Hegde, 2011 ), and semi-determinate growth habit ( Harshavardhan et al., 2019 ; Ambika et al., 2021 ) with the potential to generate futuristic plant types have been identified. In addition, several relevant agro-morphological features and key biotic factors in a variety of wild annual Cicer species have been discovered and proposed for their introgressions into the cultivated gene pool to expand the genetic basis ( Singh et al., 2014 ). Therefore, there is an emergent need to strengthen research efforts for identifying useful breeding techniques to enhance the genetic base of chickpea for enhancing genetic gains and finally chickpea yield. One of the greatest challenges in boosting grain legume output is the availability of high-quality seed and other inputs, which is lagging in the chickpea crop and only possible through infusing more and more variability in seed chain systems ( David et al., 2002 ).

3 Sources of Genetic Diversity and Broadening of Chickpeagenetic Base

In the past, crop improvement has led to narrowing down of the genetic base resulting in low genetic gains and increased risk of genetic vulnerability. In order to overcome the genetic bottlenecks and create superior gene pools, broadening the genetic base through pre-breeding is required to enhance the utility of germplasm. To attain sustainable growth in chickpeas, new sources of genes need to be identified and incorporated into high-yielding cultivars. The systematic evaluation, characterization, and utilization of wild species-specific targeted genes, to overcome the drawbacks of the abiotic and biotic stresses by broadening the genetic base of chickpea cultivars, are the emergent and immediate requirements. Broadening of the genetic base is now necessary and useful and it is well recognized in all crops mainly in chickpeas and other pulse crops.

The genetic base of cultivated chickpeas is limited ( Kumar and Gugita, 2004 ). Breeders are unwilling to employ exotic germplasm because of linkage drag and/or loss of adaptive gene complex, which necessitates a prolonged time for developing cultivars. As a result, breeders prefer to focus on adapted and improved materials; while ignoring wild relatives, landraces, and exotic germplasm accessible in gene banks ( Nass and Paterniani, 2000 ); thus, further narrowing the genetic base and expanding the gap between available genetic resources and their use in breeding programs ( Marshall, 1989 ). However, substantial diversity among specified parental lines is critical for the success of any breeding program, particularly when the traits to be improved are quantitative, highly variable, and exhibit high G × E interactions.

3.1 Sources for Broadening of Genetic Base

There are several sources that could be used for broadening of the genetic base in chickpea to overcome the bottleneck of biotic and abiotic stress in the scenario of changing climatic conditions. Tolerance may be contained in the wild relatives, landraces, advanced breeding materials, initial breeding material, and high-yielding cultivars ( Meena et al., 2017 ). Landraces and wild progenitors have been used for the introgression of various abiotic and biotic stress tolerant gene(s). Mini core germplasm ( Upadhyaya et al., 2013 ) along with several varieties and cultivars have been screened intensively for various biotic and abiotic stresses and used for numerous tolerances in chickpeas.

3.1.1 Sources of Chickpea Genetic Diversity: Cicer Wild Relatives

The genus Cicer currently comprises 44 species ( Table 1 ) containing 10 annuals and 34 perennials ( van der Maesenet al., 2007 ). C. turcicum is the recent most identified wild Cicer species endemic to Southeast Anatolia (Turkey) ( Toker et al., 2021 ). This is an annual species, and with sequence similarity based on the internal transcribed spacer (ITS) region, it appears that C. turcicum is a sister species of C. reticulatum and C. echinospermum , both of which gives fertile progenies when crossed with the cultivated species. Utilization of the new species in the chickpea improvement program will have a great impact on the genetic base broadening. C. arietinum is the only species that is extensively recognized as cultivated species. Cicer reticulatum is identified as a probable ancestor of chickpea ( Ladizinsky and Adler, 1976a ). The cultivated chickpea is believed to be originated in the Anatolia of Turkey ( Van der Maesen, 1984 ). Vavilov specified two primary centres of origin for chickpea, southwest Asia and the Mediterranean with the secondary center of origin as Ethiopia. The chickpea closely associated species viz.; C. bijugum , C. echinospermum , and C. reticulatum are widely distributed across southeastern Turkey and neighboring Syria ( Ladizinsky and Adler, 1975 ; Ladizinsky, 1998 ). However, several Cicer species are restricted to particular geographic areas such as C. bijugum in Syria and Turkey, C. anatolicum in Armenia and Turkey, C. macracanthum in Pakistan, C. microphyllum in India and Pakistan, and so on. C. arietinum is a cultivated species that can’t colonize without human assistance. C. reticulatum and C. bijugum grow naturally in weedy habitats (fallow lands, road sides, cultivated fields of wheat, and other territories not grabbed by human beings or livestock), C. pungens and C. yamashitae are found in mountain slopes among rubbles, C. montbretia and C. floribundum are distributed on forest soils, in broad leaf or pine forests and C. microphyllum grows naturally in stony and desert areas of the Himalayas in India ( Chandel, 1984 ). Different Cicer species and their distributions are presented in Table 1.

TABLE 1 . List of Cicer species and their distribution.

The primary gene pool constitutes domesticated chickpea, C. arietinum, and the immediate progenitor, C. reticulatum , the species which are easily crossable with regular gene exchange. They differ either by a reciprocal inversion, a paracentric inversion or by the location of chromosomal satellites ( Ladizinsky, 1998 ). The C. echinospermum represents a secondary gene pool and is crossable with cultivated chickpea, but gives reduced pollen fertility in the hybrids and their advanced generations. The tertiary gene pool contained remnant 6 annual and 34 perennial species having poor crossing compatibility with cultivated chickpea and requiring advanced approaches for gene transfer. Wild lines of chickpeas are very good sources of the genes/QTLs for the development of varieties which could be climate-resilient and tolerant to most of the biotic and abiotic stresses ( Table 2 ). These lines consist of different species of chickpea of the primary, secondary, and tertiary gene pool ( Figure 3 ). The resistance transfer from wild species poses several problems such as cross incompatibility, hybrid sterility, hybrid inevitability, and linkage of undesirable traits.

TABLE 2 . Sources of desirable traits in Cicer species for introgression into elite genetic background of chickpea to broaden genetic base.

FIGURE 3 . Chickpea gene pool concept and their crossing compatibility.

3.1.2 Sources of Chickpea Genetic Diversity: Gene Bank Collections and Introductions

The primary goal of a germplasm collection is to capture a significant amount of genetic variation, conserve, and enhance utilization ( Singh and Singh, 1997 ). The first exploration expedition, led by the United States Department of Agriculture’s Regional Pulse Improvement, was conducted in India in the 1970s, collecting almost 7,000 chickpea accessions. In India, systematic explorations to expand chickpea germplasm began only after the establishment of the National Bureau of Plant Genetic Resources (NBPGR) in 1976. In India, the area surveyed for chickpea germplasm collection included regions of Rajasthan, Odisha, Maharashtra, Gujarat, eastern parts of Arunachal Pradesh, Bihar, and southern parts of Tamil Nadu and Karnataka ( Singh and Singh, 1997 ). The awareness about the wild Cicer species as rich sources of genes/alleles not just for biotic and abiotic stresses, but also for superior agro-morphological features, has sparked a lot of interest in the researchers ( Van der Maesen and Pundir, 1984 ). Chickpea collection displays variations in plant height, foliage color, pod size, pod bearing habit, seed coat texture, seed coat surface, seed color, and seed size ( Singh et al., 2001 ; Archak et al., 2016 ). Madhya Pradesh collections were double podded, large-seeded (kabuli type), and tuberculated seeded (desi type) with short and medium duration ( Pundir and Reddy, 1989 ; Pundir et al., 1990 ). NBPGR has introduced valuable germplasm material from many agroecological zones throughout the world. Some of the potential exotic Cicer arietinum germplasm exhibit significant levels of resilience to biotic and abiotic stresses. The imports of Cicer wild species ( C. canariense, C. anatolicum, C. oxyodon, C. bijugum, C. reticulatum, C. pinnatifidum and C. judaicuni ) have received special attention for use in breeding programs. The majority of the introductions came from International Center for Agricultural Research in the Dry Areas (ICARDA). Other important introduction sources included Spain, Afghanistan, The Former Soviet Union, Iran, United States, Morocco, and Greece. Some of the introduced chickpea lines made significant contributions to the genetic enhancement and pre-breeding, mainly for resistance to Fusarium wilt, Ascochyta blight, leaf miner, cyst nematode, cold, drought, earliness, tall stature, and bold seeds. The important chickpea germplasm collections, including wild species that have been preserved in ex-situ collections in various gene banks around the world ( Table 3 ).

TABLE 3 . Ex-situ conservation of Cicer accessions in the world.

3.1.3 Sources of Chickpea Genetic Diversity: Landraces and Cultivated Varieties

Landraces are locally adapted cultivars that evolved in a diverse range of environmental conditions and are maintained generation after generation by farmers and local seed systems. The landraces are the goldmines for trait identification for various biotic and abiotic stresses viz.; drought, salinity and cold. These land races could be exploited in breeding programs for introgression of useful genes/QTLs and enhancing the genetic variability in the modern chickpea cultivars.

The tolerance variation depends on various factors viz.; climatic factors, genotypes, seed attributes, and seed compositions. The most important prerequisite is seedling salinity tolerance since this attribute facilitates the establishment and growth of tolerant genotypes in saline soils. The roles of seed yield, yield components, pods per plant, number of seeds, in vitro pollen germination, pollen viability, and in vivo pollen tube development to assess the reproductive successful outcome of chickpea under saline stress were investigated ( Turner et al., 2013 ). The increased salt tolerance, as measured under salty ambient by relative yield, was correlated positively with increased shoot biomass, number of pods, and seeds. Pollen viability, in vitro pollen germination, and in vivo pollen tube growth were uninfluenced by salty ambient in either of the tolerant or sensitive genotypes but pod abortion was relatively higher in salt-sensitive genotypes. Genotypes ICCV-00104, ICCV-06101, CSG-8962, and JG-62 showed a minimum reduction in seedling characters in salt stress conditions. Similar findings were reported by Samineni et al., 2011 , while studying chickpea seedlings under saline stress. Flowering terminates at temperatures below 15°C as reported in Australia ( Siddique and Sedgley, 1986 ), India ( Savithri et al., 1980 ; Srinivasan et al., 1999 ) and the Mediterranean ( Singh and Ocampo 1993 ). It was observed that, when average daily temperature remained below 15°C, plants produced flowers but did not set pods. However, scientists at International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) could develop numerous breeding materials (e.g., ICCV series 88502, 88503, 88506, 88510, and 88516) that are capable to set pods at 12°C–15°C lower average daily temperatures. A pollen selection was applied in Australia to transfer chilling tolerance from ICCV 88516 to chilling sensitive cultivars, leading to the development and release of two chilling tolerant cultivars namely Sonali and Rupali ( Clarke and Siddique, 2004 ). Minicore germplasm was screened for drought tolerance and a few germplasm accessions viz.; ICC series 1356, 3512, 4872, 13523, and 15697 with deeper root systems were identified. The Germplasm accession ICC8261 had the highest root length density, an extremely high root/shoot ratio and rooting depth in both Rabi and Kharif seasons. ICC4958, which is a source used as a deep and large root system parent or check in most drought avoidance studies, was reported to be an extremely prolific rooting genotype. The new genotypes identified could be used as valuable alternative sources for diversification of mapping populations with varying characters and growth durations to obtain the required polymorphism for successfully mapping root traits in chickpeas.

3.2 Approaches for Broadening the Genetic Base

Broadening of the genetic base, up to now, has utilized the techniques of classical breeding viz.; hybridization, segregation, back crossing, cyclic population improvement, pedigree selection among selfed progenies. However, wild relatives couldn’t be utilized because of inter-specific hybridization barriers, limited data for specific traits, and linkage drag. With the advent of molecular breeding techniques, new biotechnological methods, which are being applied for the identification of the QTLs for the traits of interest and needs to be incorporated through various techniques of pre-breeding which are used in transferring useful genes from the exotic or wild species into the high-yielding cultivars. The halted speed of chickpea breeding due to narrow genetic diversity could be fastened by employing wild relatives as a valuable source of new genes and alleles to be further exploited by breeders for allelic richness and broadening of chickpea germplasm. Thus, comprehensive approaches could be utilized for broadening the genetic base in chickpea and other grain legume crops as depicted ( Figure 4 ).

FIGURE 4 . Comprehensive approach for broadening the genetic base of chickpea.

Chickpea’s limited genetic base is a major source of anxiety for chickpea breeding programs, as genetic variability is a major contributor to selection-induced genetic gain. As a result, expanding the genetic base of chickpeas is critical for enhancing breeding efficiency. Chickpea wild species are an important genetic resource, especially for biotic and abiotic stress resistance and nutritional quality. Chickpea mutants with novel features like brachytic growing behavior ( Gaur et al., 2008 ), more than three flowers per node—the cymose inflorescence ( Gaur and Gour, 2002 ), determinate ( Hegde, 2011 ), upright peduncle podding ( Singh et al., 2013 ) and semi-determinate growth habit ( Harshavardhan et al., 2019 ; Ambika et al., 2021 ) with the potential to generate futuristic plant types have been identified. In addition, several relevant agro-morphological features and key biotic factors in a variety of wild annual Cicer species have been discovered and proposed for their introgressions into the cultivated gene pool to expand the genetic basis ( Singh et al., 2014 ). Some of the useful agro-morphological traits including major biotic and abiotic stresses are presented in Tables 2 , 4 . There is an emergent need to strengthen research efforts for identifying useful breeding techniques to enhance the genetic base of chickpeas.

TABLE 4 . Sources of resistance to abiotic and biotic stresses as reported by various workers after evaluating the chickpea mini core collection.

3.2.1 Utilization of Adapted and Un-Adapted Germplasm for Traits Discovery and Broadening the Genetic Base

Pre-breeding offers an unparallel opportunity for the introgression of desired genes and gene combinations from exotic germplasm into genetic backgrounds easily employed by breeders with minimal linkage drag ( Sharma et al., 2013 ). Comprehensive broadening of the genetic base through incorporation is the most suitable method when new genetic variabilities for quantitative traits are required, latest and most reliable methods could be optical contribution selection (OCS) based pre-breeding, haplotype-based genomic approaches, and genomic predictions ( Varshney et al., 2021 ). To achieve the highest level of yield, the existing variability among indigenous germplasm has been used. Wild Cicer species and exotic germplasm lines include valuable alleles that, if discovered, can aid in breaking yield barriers and improving resistance to various stresses for crop yield stability ( Labdi et al., 1996 ; Tayyar and Waines, 1996 ; Ahmad and Slinkard, 2003 ; Ahmad et al., 2005 ).

Several inter-specific crosses between Cicer arietinum and its annual wild relatives have been attempted in the context of wild Cicer species usage. There is no evidence of successful hybridization between a perennial Cicer species and Cicer arietinum . Ladizinsky and Adler (1976b) reported inter-specific crosses amongst C. arietinum, C. reticulatum and C. cuneatum for the first time. Several researchers have successfully attempted inter-specific hybrids between Cicer arietinum and Cicer echinospermum ( Verma et al., 1990 ; Singh and Ocampo, 1993 ; Pundir and Mengesha, 1995 ). Numerous crossings between Cicer arietinum as the female parent and Cicer reticulatum, C. echinospermum, C. judaicum, C. bijugum , and C. pinnatifidum as the male parent have been conducted ( Verma et al., 1990 ). Van Dorrestein et al., 1998 aimed to cross C. arietinum with C. judaicum and C. bijugum . Badami et al., 1997 used an embryo rescue strategy to successfully hybridize C. arietinum with C. pinnatifidum . Inter-specific crosses have resulted in the development of certain pre-breeding lines at IIPR, Kanpur, and PAU, Ludhiana ( Singh et al., 2012 ). Singh et al. (2015) attempted inter-specific crosses and the results revealed a high level of heterosis for the number of pods and seed yield per plant in the F 1 generation. Three cross-combinations viz.; Pusa 1103 x ILWC 46, Pusa 256 x ILWC 46, and Pusa 256 x ILWC 239 demonstrated significantly increased variability for crucial yield related characteristics.

Adoption and harmonizing conventional and modern approaches like molecular breeding, physiological breeding, biotechnological methods, high throughput genomics, and phenomics will aid in the broadening of the genetic base and release of high-yielding varieties which will be tolerant to various biotic and abiotic stresses. Several mapping populations could be developed for the identification of trait-specific QTLs and can be introgressed into high-yielding cultivars for enhancing the gene pool of chickpea.

3.2.2 Bi-Parental Populations for Broadening Genetic Bases

Two inbred lineages are generally crossed in bi-parental populations to generate one or more segregating progenies ( Xu et al., 2017 ). This is the basic approach of combining desired traits in a genotype through ongoing breeding programs. Parents are chosen for a trait of interest based on their genetic and phenotypic diversity allowing the reconstruction of progeny genomes from founder haplotypes to find genomic areas related to the target trait ( Dell’Acqua et al., 2015 ). Bi-parental crosses derived populations capture only a modest impression of the genetic determinants that influence targeted traits in the species and suffer from a lack of diversity owing to the limited genetic base of both parents. Therefore, while the approach is indispensable for any breeding program, genetic diversity must not be reduced in the selection process, to sustain genetic gains for a longer duration. Molecular tools such as re-sequencing technologies and other cost-effective genotyping technologies, which can scan the whole genome, may be useful in the identification of diverse parental lines having the target traits of interest. The utilization of such parental lines will enhance the genetic diversity in the released varieties without compromising the desired yield gain. High-throughput precision phenotyping, genomic selection, and identification of superior haplotypes may further accelerate the breeding cycle and boost the genetic diversity in farmers’ fields to enhance the crop resilience toward the biotic and abiotic stresses. In addition, the QTLs detected in the two-parent population may not be expressed in other genetic origins ( Rakshit et al., 2012) . Mallikarjuna et al., 2017 , utilized F2 populations derived from four crosses (ICCV96029 x CDC frontier, ICC5810 x CDC frontier, BGD 132 x CDC frontier, ICC 16641 x CDC frontier) and found major QTLs corresponding to flowering time genes.

3.2.3 Multi-Parent Populations for Broadening Genetic Bases

Multi-parental and germplasm populations, on the other hand, may offer solutions to bi-parental and germplasm populations’ major flaws. Throughout the history of scientific crop improvement multi-parental populations or multi-parental cross designs (MpCD) have been generated in a range of crop species. Adaptation to crops that are difficult to artificially hybridize, multi-parental populations are created by making crossings amongst more than two inbred founder lines, which serve as a link between association mapping (GWAS) and traditional bi-parental crosses. While such populations are able to combine and reveal better allelic combinations, transgressive segregants, and simultaneously genetic diversity in the progenies are also enhanced. Multi-parent populations also are more efficient in increasing mapping resolution, if they are used for high-density genotyping using advanced high-throughput genomic technologies ( Rakshit et al., 2012 ). This unique technique dramatically improves mapping resolution by merging numerous founder parents with higher phenotypic and genetic diversity. Thanks to the evolution of more powerful techniques, multi-parental populations can now be utilized in numerous genetic mapping studies ( Mackay and Powell, 2007 ; Huang et al., 2015 ). Here, the emphasis is on MAGIC populations, which are RILs of fine-scale mosaic panels, although numerous MpCD other forms are also available. Thus, MAGIC populations are considered as a growing and next-generation powerful resource for plant genetics mapping, combining variation and high genetic recombination to analyze complex traits’ structure and enhance crop improvement techniques. In various model crop species, MAGIC populations have been generated illustrating their potential to find polymorphisms for underlying QTLs or genes of importance for useful complex traits. There are already MAGIC like or MAGIC populations obtainable in numerous crop species, viz., cereals, legumes, vegetables, fruit trees, and industrial crops with many more in the other works and because of their large genetic foundation, MAGIC populations could be used for discovery of QTL(s) and gene (s), enhancement of breeding populations, introduction and development and of novel genotypes ( Pascual et al., 2015 ). Multi-parent populations such as multiparent advanced generation intercross (MAGIC) populations have gained a tremendous popularity among researchers and breeders. Such populations, along with enhancing genetic diversity, also make it easier to examine the genomic framework and their relationships with phenotypic traits.

3.2.4 Molecular Markers Based Approaches for Broadening Genetic Bases

Since the advent of molecular markers, these tools have played an indispensable role in understanding genetic diversity, phylogenetic relationship, background, and foreground selection in molecular and conventional breeding programs. Recent advances in genomics, coupled with high throughput and precise phenotyping, have made it easier to identify genes that regulate important agronomic attributes. Genetic variability such as multiple podding per peduncle, multiple seeds per pod, upright podding, tall and erect genotypes, and several other traits for biotic stress tolerance are rare, and incorporating these traits to the major cultivars helps in enhancing the variability in the gene pool. These traits could be used in combination with tools for genomics to expedite the generation of crops with higher genetic variability with better agronomic traits, improved resilience to climate change, and nutritional values ( Pourkheirandish et al., 2020 ). Exploring the marker-assisted selection (MAS) technique along with other biotechnological tools can boost genetic diversity and simultaneously enhancing the yield in chickpeas ( Varshney et al., 2005 ; Varshney et al., 2009 ).

Genomic advancements have aided in understanding the complex trait’s mechanisms affecting chickpeas economically important characters’ genetic architecture as well as productivity in order to speed up breeding programs ( Roorkiwal et al., 2020 ). In chickpea, a number of markers and trait relationships and dense genetic maps have allowed MAS to become a routine practice in crop breeding programs ( Kulwal et al., 2011 ; Madrid et al., 2013 ; Ali et al., 2016 ; Caballo et al., 2019 ). Single nucleotide polymorphism (SNP) allelic variants on 27 ortholog candidate genes were utilized for the GWAS study, and potential candidate genes such as PIN1 , TB1 , BA1/LAX1 , GRAS8 , and MAX2 were identified for branch number in chickpea utilizing highly diverse chickpea germplasm ( Bajaj et al., 2016 ). The gene for double podding per peduncle was linked to Tr44 and Tr35 on linkage group 6 ( Cho et al., 2002 ). Saxena et al. (2014) has mapped four traits viz. 100-seed weight, pod, number of branches per plant and plant hairiness, using simple sequence repeats (SSRs) and SNP markers. There are several other examples of utilization of molecular makers for the identification of traits and underlying genes/QTLs in chickpea such as 100-seed weight ( Das et al., 2015 ; Kujur et al., 2015b ), resistance to Helicoverpa armigera ( Sharma et al., 2005 ), pod number ( Das et al., 2016 ), flowering time ( Srivastava et al., 2017 ), plant height ( Parida et al., 2017 ), photosynthetic efficiency traits ( Basu et al., 2019 ), etc. Furthermore, comprehending the chickpea developmental processes’ regulations has been facilitated by the framework offered due to discoveries of new microRNAs (miRNAs) and their expression patterns ( Jain et al., 2014 ).

For genomic investigations and crop improvement, numerous polymorphic molecular markers that could be exposed to high-throughput analysis are sought. On the basis of isozyme analysis, Cicer arietinum is most closely related to C. reticulatum , followed by C. echinospermum, C. bijugum, C. pinnatifidum, C. judaicum, C. chorassanicum, C. yamashitae and C. cuneatum ( Ahmad et al., 1992 ). Cicer reticulatum and Cicer echinospermum were grouped together in the same cluster; Cicer chorassanicum and Cicer yamashitae were grouped together in another cluster; Cicer bijugum , Cicer judaicum , and Cicer pinnatifidum were grouped together in the third different cluster; and Cicer cuneatum alone formed the fourth different cluster based on the analysis of RAPD markers ( Ahmad, 1999 ; Sudupak et al., 2002 ). An AFLP analysis for the same Cicer species also confirmed the same pattern ( Sudupak et al., 2004 ). RAPD and ISSR fingerprinting demonstrate that C. arietinum cultivars had the narrowest genetic variation while its wild C. reticulatum accessions had much greater genetic variation, which could be used in chickpea improvement ( Rao et al., 2007 ). The widespread use of molecular markers in chickpea genetics and breeding began with the introduction of SSR markers. The draft genome sequence of chickpea identified approximately 48,000 SSRs appropriate for PCR primer design for use as genetic markers ( Varshney et al., 2013 ), whereas a draft sequence of C. reticulatum (PI 4889777) spanning 327.07 Mb was assembled to the eight linkage groups with 25,680 protein-coding genes ( Gupta et al., 2017 ).

A variety of comparatively new marker systems have recently been introduced including sequence-based SNP and hybridization-based diversity array technology (DArT) markers which offer medium to high-throughput genotyping and are simple to automate. Two sets of Axiom®CicerSNP array have been developed in chickpea, one was including 50,590 probes distributed on all eight linkage groups as described by Roorkiwal et al. (2014) and the second multispecies SNP chip includes chickpea along with other pulses using markers that can be imputed up to whole-genome (800,000 markers) was developed by AgriBio, Centre for AgriBioscience Melbourne, Australia (personal communication).

To date, several studies have been published using DArT and SNP chips. We highlight the 5397 polymorphic DArT markers identified from a pool of 15,360 developed markers utilizing 94 different chickpea genotypes ( Thudi et al., 2011 ). The low genetic diversity was unraveled between wild Cicer and cultivated species through DArT markers ( Roorkiwal et al., 2014 ). Although transcriptome investigation of chickpea and its wild progenitors detected thousands of SNPs ( Coram and Pang, 2005 ; Varshney et al., 2009 ; Gujaria et al., 2011 ; Agarwal et al., 2012 ; Bajaj et al., 2015b ; Kujur et al., 2015a ). These SNPs and markers can be utilized by chickpea breeders in MAS-assisted breeding programs.

3.2.5 Trait Identificationin Legumes for Broadening Genetic Bases

3.2.5.1 trait identification through sequencing.

With the advancement in the next-generation sequencing (NGS)-based approaches, trait mapping has become an easy job to do. Not only are these technologies time-saving but also cutting the cost at basal levels. The genetic mapping is based on recombination (the exchange of DNA sequence between sister chromatids during meiosis) and the distance between the markers measured by cM representing approximately 1% of the recombination frequency, while the physical map is based on the alignment of the DNA sequences, with the distance between markers measured in base pairs. However, the high-resolution physical maps serve as the scaffold for genome sequence assembly to identify the most accurate distance between the markers and the genes linked in addition to exploring the potential candidate gene(s) linked to desired traits. The trait mapping through sequencing approaches may be categorized into two classes 1) Sequencing of complete populations for trait mapping and 2) Sequencing of pooled samples for trait mapping. Using composite interval mapping a high-density genetic map consisting of 788 SNP markers spanning through 1125cMalong with the identification of 77 QTLs for 12 traits was reported ( Jha et al., 2021 ). Similarly, several QTLs were mapped for several other traits like flowering time ( Mallikarjuna et al., 2017 ; Jha et al., 2021 ), plant height ( Kujur et al., 2016 ; Barmukh et al., 2021 ), and primary branches ( Barmukh et al., 2021 ).

3.2.5.2 Trait Identification Through Sequencing of Complete Populations

It primarily consists of the genotyping by sequencing (GBS) and whole-genome re-sequencing (WGRS) mapping populations, both of which yield genome-wide SNPs. GBS is popular because it is inexpensive and provides a lot of genetic data. The discovery of a large number of genome-wide SNPs has facilitated rapid diversity assessment, trait mapping, GS and GWAS in a variety of crop by employing GBS—a potential strategy. A chickpea genetic variation map was developed using whole-genome sequencing technique and genomes were characterized at the sequence level, observing variations in 3,171 cultivated and 195 wild accessions and construction of a pan-genome to explain the genomic diversity across wild progenitors and cultivated chickpea ( Varsheny et al., 2021 ). The 16 mapping populations segregating for different abiotic (drought, heat, salinity), biotic stress (Fusarium wilt, Aschochyta blight, BGM & Helicoverpa armigera ) and protein contents along with their 35 chickpea parental genotypes were re-sequenced in order to exploit the genetic potential for chickpea improvement ( Thudi et al., 2016 ). Genetic analysis, fine-tuning of genomic areas, and production of genetic maps are facilitated by re-sequencing ( Kujur et al., 2015b ; Li et al., 2015 ). Chickpea is one of the best examples of crops in which GBS was used to identify 828 SNPs in addition to the previously mapped SSRs. The creation of these detailed genetic maps aids in the discovery of QTLs in chickpea that controls yield, drought tolerance, and seed weight. It is quite useful for locating QTL hotspots. Moving on to the second promising strategy, WGRS has been found to be more useful in finding candidate genes than GWAS ( Jaganathan et al., 2015 ; Varshney et al., 2014 ).

3.2.5.3 Trait Identification Through Pooled Sequencing

The analysis is done on the basis of the pooled population through the inclusion of BSR-Seq, Indel-Seq, Mut-Map, QTL-Seq, and Seq-BSA the five major approaches. The “QTL-Seq” is the first and foremost promising technique to have been successfully employed with larger crop plant genomes. This strategy has been used to pinpoint the blast resistance and seedling vigor governing genomic areas in rice, flowering QTLs in cucumber, fruit weight and locule number loci in tomatoes and successfully applied for localization of QTLs/candidate genes for 100 seed weight in chickpea ( Takagi et al., 2013 ; Li et al., 2015 ). The “MutMap” is a robust and simple NGS-based approach, first of all which was applied for the identification of EMS-induced interesting candidate genes in rice. Crossing of selected mutant plants with wild types, which reduces background noise—the fundamental benefit, is the necessity of mapping the population created for the MutMap experimental strategies. Consequently, using extreme pool samples derived from segregating populations coupled to a wild parent the genome-wide SNP index is calculated. The third method, known as “Seq-BSA,” is a straightforward and reliable NGS-based strategy for identifying potential SNPs in specific genomic regions ( Takagi et al., 2013 ). Employing QTL-seq pipelines utilizing parent with high-value trait as reference parent assemblage, genome-wide SNP indexes of both extreme bulks are calculated in the third method. The fourth strategy, “Indel-Seq” which is mostly focused on insertions and deletions, has also emerged as a potential trait mapping approach. To date, the proposed methodologies for identifying genomic regions have relied on the discovery of SNPs followed by the use of various statistical approaches to recognize candidate genomic gene/regions. However, in all approaches, the relevant genomic region-specific existing Indels have not been targeted for trait mapping but ignored. The fact that the Indels reported in the candidate genes are found in most of the cloned genes in rice and other crops and makes this strategy more practicable. The strength of the RNA-seq and BSA were combined for enhancing the strength to find candidate genes for the targeted characteristic—a novel genetic mapping approach as the fifth strategy, dubbed as “Bulked segregant RNA-Seq (BSR-Seq)”. This method has been used to successfully identify the glossy3 genes in maize. RNA-seq-based investigations will be cheaper than WGRS at higher coverage; hence, this strategy has more cost savings. We believe that, given the benefits of RNA-Seq, this approach will be effective for legumes with larger genomes ( Liu et al., 2012 ; Trick et al., 2012 ). Thus, chickpea breeders utilize these generated informations in chickpea MAS-assisted breeding programs.

3.2.6 Transcriptomics Utilization for Broadening the Genetic Bases

Work on legumes focused on building libraries of cDNAs, gene expression profiling, the manufacture of expressed sequence tags (EST), and in silico extraction of EST data sets’ functional information even before sequences of the genome achievability. Transcriptome sequencing has been employed in other functional genomics methodologies, viz., genome annotation, gene expression profiling, and non-coding RNA identification employed transcriptome sequencing ( Morozova and Marra, 2008 ). In recent years, for generating a large number of transcript reads from a variety of developing and distress-responsive tissues in several leguminous crops through several low-cost sequencing systems has already been established, viz., an improved transcriptome assembly, utilizing FLX/454 sequencing together with Sanger ESTs comprised 103,215 Transcript Assembly Contigs (TACs) with an average contig length of 459 base pairs in chickpea ( Hiremath et al., 2011 ). Employing various sequencing technologies or a combination of two or more sequencing technologies created by transcriptome assemblies provides useful transcriptomic resources such as functional markers, EST-SSRs, Spanning Regions (ISRs), SNPs, Introns, and so on in soybean and common bean 1,682 and 4,099 SNPs, respectively ( Deschamps and Campbell, 2012 ), ESTs comprising of 103,215 Transcript Assembly Contigs (TACs) in chickpea (Hiremath et al., 2011) can be utilized by the breeders to achieve a better grasping of the molecular underpinnings of distress tolerance and as a result more stress-tolerant beans as well chickpea cultivars may be produced and narrow genetic base may be broadened.

3.2.7 Proteomics and Metabolomics for Broadening the Genetic Bases

New datasets for crop plants can be created by exploiting the opportunities of advancement in “omics” technologies. The advancements will result in a greater integrated association of “omics” data and crop improvement resulting in the evolution from genomic assisted breeding (GAB) to omics assisted breeding (OAB) in the future ( Langridge and Fleury, 2011 ) that can also be utilized for broadening the genetic bases in chickpea.

3.2.7.1 Proteomics Approaches

Increased proteome coverage and advancements in quantitative evaluations have benefitted plant proteome composition, modulation, and alterations of developmental phases including stress–response mechanisms. Proteomic pipelines are rapidly being used in crop research notably to investigate crop-specific features and stress response mechanisms. Proteome mapping, comparative proteomics, discovery of post-translational modifications (PTMs), and protein–protein interaction networks are key topics of plant proteomics ( Vanderschuren et al., 2013 ). In chickpea the comparative root proteomic analysis for the effect of drought and its tolerance in hydroponics using 2D gel electrophoresis coupled with MALDI-TOF revealed eight categories of protein-based on their functional annotation viz.; proteins involved in carbon and energy metabolism, proteins involved in stress response, ROS metabolism, signal transduction, secondary metabolism, nitrogen and amino acid metabolism ( Gupta and Laxman, 2020 ). High-throughput protein quantification has benefited from advancements in accuracy, speed, mass spectrometry (MS) utilizations in terms of sensitivity, and software tools. Gel-based or gel-free, shot-gun, and label-based (isotopic/isobaric) or label-free quantitative proteomics platforms have emerged as a result of developments in MS technology for high-throughput protein quantifications ( Abdallah et al., 2012 ; Hu et al., 2015 ). In legume crops, comparative proteomics approaches and differential expression analyses have given understanding of distress responses including dehydration, and early phases of cold stress in chickpeas ( Pandey et al., 2008 ) and can be effectively integrated into genomic-assisted breeding programs for broadening the narrow genetic bases.

3.2.7.2 Metabolomics Approaches

In plant metabolic engineering, targeted reverse genetic methods and high-throughput metabolite screening have the advantage of providing a better understanding of metabolic networks on a larger scale in relation to developmental stages of phenotypes and the ability to screen out undesirable traits ( Fernie and Schauer, 2009 ). The literature describes two major metabolomics profiling methodologies that use nuclear magnetic resonance (NMR) and MS. A combination of many analytical techniques generated from one of the MS was frequently used to obtain a larger range of numerous metabolites in plants ( Arbona et al., 2013 ). Flow injection-based analysis with Fourier Transform Infrared spectroscopy and MS (FIA/MS) are two further approaches. The identification of new metabolic QTLs and candidates for the desired traits are made possible by combining metabolomics data, transcriptomics data, high-throughput phenotypes, and bioinformatics platforms to profile large genetically varied populations and increase the accuracy of targeted gene identification. To boost yields and broaden the narrow genetic bases, metabolomics is utilized in conjunction with a genomic-assisted selection and introgression techniques, minimizing the time spent in uncovering new characteristics and allelic mutations ( Fernie and Schauer, 2009 ).

3.2.8 Pan Genomics

Recent developments in genome sequencing technologies have revolutionized the crop improvement programs. Now the whole-genome sequencing (WGS) is not limited to one or two individuals, but a large set of accessions of a species (pangenome) including their crop wild relatives (super-pangenome) are the whole genome sequenced to unravel the full potential of the species for the crop improvement programs. Once the pangenome information is available, the genomic segments/genes lacking in cultivated germplasm can be identified and introgressed in cultivated germplasm to enhance the genetic variability. The total number of genes of a species are collectively known as its pan-genome. It was observed from several evidences that a sole organism can’t contain all the genes of a species due to variability present in the genomic sequences. The desirable features of an ideal pan-genome are completeness (i.e., contains all functional genes), stability (i.e., unique catechistic features), comprehensibility (i.e., contains all the genomic information of all the species or individuals), and efficacy (i.e., organized data structure). Pangenome information of a species helps in the identification of desired alleles, rare alleles, presence or absence of a traits in a species. Recently a chickpea pangenome of 592.58 Mb was constructed which containsa total of 29,870 genes ( Varshney et al., 2021 ). The pan-genome was constructed using whole-genome sequencing using 3,366 comprising 3,171 cultivated and 195 wild accessions. Assembly was done by combining the CDC frontier reference genome including 53.60 Mb from cultivated chickpea inclusive of 2.93 Mb from ICC 4958 and 5.28 Mb from 28 accessions of C. reticulatum . This pan-genome analysis revealed useful information on genomic regions more often selected during the domestication process, superior haplotypes, and targets for purging deleterious alleles. The new genes identified encoding responses to oxidative stress, response to stimuli, heat shock proteins, cellular response to acidic pH, and response to cold, which could have a possible contribution to the adaptation of chickpea.

3.2.9 QTL Mappings, Their Introgression and Utilization for Broadening the Genetic Bases

The utility of the fundamental assumption of locus finding by co-segregation of characteristics with markers is enhanced by new permutations of QTL mapping ( Table 5 ). However, the definition of a trait can now be expanded beyond whole-organism phenotypes to include phenotypes like the amount of RNA transcript or protein produced by a specific gene because these phenotypes have more typical organismal characteristics viz.; yield in corn are polygenic and QTL mapping works in these situations. Transcript abundance is regulated not only by cis-acting regions like the promoter but also by Transacting transcription factors that may or may not be related. Similarly, local variation at the coding gene and distant variation mapping to other areas of the genome control protein abundance. Local variation is most likely made up of cis variations that regulate transcript levels. Polymorphisms for the protein’s stability or control could be another local mechanism. Distant variation, on the other hand, could comprise upstream regulatory control areas ( Upadhyaya et al., 2016 ).

TABLE 5 . List of QTLs for various traits in chickpea.

Quantitative trait loci (QTLs) conferring resistance to biotic and abiotic stresses have been applied in chickpeas in the last 2 decades and the molecular markers closely associated with these loci are also located ( Santra et al., 2000 ). For example, several QTLs conferring Ascochyta blight resistance are identified, and several MAS (SCY17 and SCAE19) were reported as the best markers linked to AB-resistant genes. These two markers were validated on different populations ( Iruela et al., 2006 ; Imtiaz et al., 2008 ; Madrid et al., 2014 ). More recently, three major conserved quantitative trait loci (QTLs) that confer AB resistance have been reported, two on chromosome Ca2 and one on chromosome Ca4. These QTLs explained a maximum of 18.5%, and 25% of the total variation. In total, 27 predicted genes were located in chromosome IV close to these QTL (Hamwieh et al., Unpublished data).

The 20 QTLs and candidate genes associated with seed traits were also identified in chickpeas using the GBS approach ( Pavan et al., 2017 ). In pigeon pea, the GBS-based mapping of two RIL populations led to the identification of QTLs and candidate genes for resistance to fusarium wilt (FW) and sterility mosaic disease (SMD) ( Saxena et al., 2017 ) in addition to restoration of fertility (Rf) ( Saxena et al., 2018 ), using GWAS drought tolerance-related traits in chickpea (Kale et al., 2015), flowering time control, seed development and pod dehiscence in pigeon pea ( Varshney et al., 2017 ) have been mapped. The GBS has been utilized in the fine mapping of the “ QTL-hotspot ” region for drought tolerance-related traits in chickpeas ( Kale et al., 2015 ). In the case of chickpea, QTL seq approach has successfully identified a major genomic region (836,859–872,247 bp) on Ca1 chromosome which was further narrowed down to a 35-kb region harboring six candidate genes for 100 seed weight ( Das et al., 2015 ).

Plant breeding can help in solving the global problem of micronutrient deficiencies in a cost-effective and long-term manner. The development of biofortified chickpea varieties is aided by evaluating cultivars for micronutrient contents and identifying quantitative trait loci (QTLs)/genes and markers. The F 2:3 derived population resulting from a cross between MNK-1 and Annigeri-1 was dissected employing the GBS technique and concentrations of Fe and Zn were examined with the goal of determining the responsible genetic areas ( Vandemark et al., 2018 ). The researchers mapped 839 SNPs on an intra-specific genetic linkage map covering a total distance of 1,088.04 cM with a marker density of 1.30 cM. By combining linkage map data with phenotypic data from the F2:3 populations a total of 11 QTLs for seed Fe concentration on CaLG03, CaLG04, and CaLG05 with phenotypic variance varying from 7.2% (CaqFe3.4) to 13.4% (CaqFe3.4; CaqFe4.2). On CaLG04, CaLG05, and CaLG08 along with eight QTLs for seed Zn concentration with explained phenotypic variances ranging from 5.7% (CaqZn8.1) to 13.7% (CaqZn4.3) were discovered ( Pandey et al., 2016 ).

The identification of marker-trait association between a genetic marker and a trait of interest is the initial stride in crop breeding utilizing molecular breeding/genomics assisted breeding. For initial experiments, linkage maps were created employing F 2 populations. The inter-specific cross C. arietinum (ICC 4958) x C. reticulatum (PI 489777) was employed to create the first recombinant inbred lines (RILS) mapping population which is now being used as a chickpea reference mapping population for genome mapping ( Nayak et al., 2010 ). Maps created from intra-specific mapping populations have a smaller number of markers (<250 markers) and poorer genome coverage (<800 cM) due to minimal variation in the cultivated chickpea. Consensus genetic maps were also created utilizing both inter and intra-specific mapping populations.

The genetic mapping of QTLs affecting resistance to various diseases, and also vital agronomical traits, in chickpea are extensively documented. Santra et al. (2000) identified two quantitative trait loci (QTL1 and QTL2) that give resistance to Ascochyta blight. These QTLs were predicted to be responsible for overall phenotypic variance (34.4%, 14.6%), respectively ( Santra et al., 2000 ; Tekeoglu et al., 2002 ). Comparative protein profiling of wild chickpeas and induced mutants was carried out in order to measure genetic diversity between mutants and parental genotypes ( Patil and Kamble, 2014 ). Kujur et al. (2016) reported candidate genes and natural allelic variations for QTLs determining plant height, which was followed by the discovery of QTLs for heat distress response ( Paul et al., 2018 ) as well as photosynthetic efficiency attributes for boosting seed yield in chickpea using GWAS and expression profiling ( Basu et al., 2019 ). These discoveries have opened up new paths for analysis and comprehensive characterization of wild Cicer species, which will help in harnessing unidentified allelic variations to extend the genetic foundation of cultivars.

Molecular markers have been discovered for gene(s)/QTL(s) linked to abiotic stress resistances, viz., drought tolerance ( Molina et al., 2008 ; Rehman et al., 2012 ), salinity resilience ( Vadez et al.,2012 ), biotic stresses, viz., Ascochyta blight ( Milla´n et al., 2003 ; Iruela et al., 2006 ; Aryamanesh et al., 2010 ; Garg et al., 2019 ), Fusarium wilt ( Cobos et al., 2005 ; Gowda et al., 2009 ; Sabbavarapu et al., 2013 ) and botrytis gray mold ( Anuradha et al., 2011 ) along with seed characteristics ( Gowda et al., 2009 ) in chickpea. These technologies can be employed to improve chickpea genetics and breeding as well as to explain the variety of the chickpea genome and domestication events. Furthermore, genomic selection has been presented as a promising strategy for enhancing traits that are influenced by a large number of gene (s)/QTL (s) ( Bajaj et al., 2015a ; Bajaj et al., 2015b ). Both phenotypic and genotypic data sets are employed in this approach to determine genomic estimated breeding values (GEBV) of improved progenies.

3.2.10 Genome-Wide Association Studies for Broadening the Genetic Bases

GWAS have become one of the most important genetic methods for analyzing complicated trait QTLs and underlying genes. Many studies have shown that GWAS can be used to map more authentically new genes implicated in complex agronomic variables in plants. Given this, linkage disequilibrium (LD), population substructure, and imbalanced allele frequencies are the key drawbacks of GWAS. Many markers associated with tolerance to abiotic stresses have been also reported in chickpea. In brief, the germplasm of 186 chickpea genotypes has been genotyped with 1856 DArTseq markers. The association with the salinity tolerance in the field (Arish, Sinai, Egypt) and the greenhouse by using hydroponic system at 100 mM NaCl concentration indicated one locus on chromosome Ca4 at 10,618,070 bp associated with salinity tolerance, in addition to another locus-specific to the hydroponic system on chromosome Ca2 at 30,537,619 bp. The gene annotation analysis revealed the location of rs5825813 within the Embryogenesis-associated protein (EMB8-like), while the location of rs5825939 is within the Ribosomal Protein Large P0 (RPLP0) ( Ahmed et al., 2021 ). Utilizing such markers in practical breeding programs can effectively improve the adaptability of current chickpea cultivars in saline soil.

Besides the above-mentioned reports, GWAS has also been conducted for yield and related traits in chickpea ( Li et al., 2021 ), root morphological traits ( Thudi et al., 2021 ), nutrient content ( Diapari et al., 2014 ; Sab et al., 2020 ) and abiotic tolerance traits ( Thudi et al., 2014 ; Samineni et al., 2022 ). Thus, the associated genomic regions identified through GWAS could be used for breeding programs to improve yield-related traits, nutrient content, and biotic and abiotic stress tolerance in chickpea. Recently, in other studies, we have accomplished GWAS for nodule numbers in chickpea by conducting multi-locational phenotypic evaluations and have identified seven significant SNP IDs (Kumar et al. unpublished data).

3.2.11 Genetic Engineering for Broadening Genetic Bases

Genetic engineering has been widely utilized to select resistant gene(s) ( Table 6 ) from various resources and transmit them to selected plants to introgress resistance to various abiotic as well as biotic challenges. Various genes are now being deployed in pulses using Agrobacterium -mediated ( Eapen et al., 1987 ; Krishnamurthy et al., 2000 ; Sharma K. K. et al., 2006 ), particle gun bombardment ( Kamble et al., 2003 ; Indurker et al., 2007 ), electroporation of intact axillary buds ( Chowrira et al., 1996 ) electroporation and PEG mediated transformation using protoplasts ( Köhler et al., 1987a ; Köhler et al., 1987b ). The most widely used method for developing transgenics in pulse crops is Agrobacterium mediated explant transformation. To generate transgenic plants, numerous transgenes from various sources have been introduced into pulse crops.

TABLE 6 . List of engineered genes/traits in chickpea.

Transgenic chickpea is developed either by gene gun ( Kar et al., 1997 ; Husnain et al., 2000 ; Tewari-Singh et al., 2004 ; Indurker et al., 2007 ) or Agrobacterium -mediated method ( Kar et al., 1997 ; Sanyal et al., 2005 ; Biradar et al., 2009 ; Acharjee et al., 2010 ; Asharani et al., 2011 ; Mehrotra et al., 2011 ; Ganguly et al., 2014 ). Important target traits for transgenic plant development in chickpea are insect pest resistance including α amylase inhibitor genes and lectin genes ( Dita et al., 2006 ), Cry genes from Bacillus thuringiensis , protease inhibitor genes, disease resistance including transfer of genes such as chitinase gene, antifungal protein genes or stilbene synthase gene for fungal resistance, coat protein genes of viruses for viral resistance and bacterial resistance from T 4 lysozyme gene ( Eapen, 2008 ), various abiotic stresses like salinity, drought, mineral toxicities, cold, temperature, etc., seed proteins, plant architecture, and RNA interference technology could be used to increase carotenoids and flavanoids by engineering metabolic pathways to decrease the effect of endogenous genes ( Eapen, 2008 ).

As presented in Table 7 transformation through Agrobacterium with the cry1Ab/Ac gene in chickpea has resulted in resistance to Helicoverpa armigera ( Lawo et al., 2008 ; Ganguly et al., 2014 ). Bombardment of calli with DNA-coated tungsten particles resulted in somatic embryogenesis and the subsequent generation of transgenic chickpea ( Husnain et al., 2000 ). Other researchers have also reported on the use of transgenic chickpea as a drought-tolerant and pest-resistant cultivar ( Bhatnagar-Mathur et al., 2009 ; Khatodia et al., 2014 ; Kumar et al., 2014 ).

TABLE 7 . Genetic transformation of chickpea.

3.2.12 Bioinformatic Molecular Data Bases/Resources for Broadening Genetic Bases

The recent data reports on leguminous genomics and transcriptomics have forced the creation of an exhaustive model of legume genomics and transcriptomics databases. Readily available data through online database portals are playing a significant role in research and development. LegumeIP ( http://plantgrn.noble.org/LegumeIP/ ), an integrative database for comparative genomics and transcriptomics of model legumes, for use in studying gene function and genome evolution in this center-stage plant family including the genome sequences of M. truncatula , G. max and L. japonicas and two reference plant species, i.e., A. thaliana and Populus trichocarpa were employed ( Li et al., 2012 ). The Legume Information System (LIS; https://legumeinfo.org ) ( Dash et al., 2016 ) gives users access to genetic and genomic data for model legumes. KnowPulse ( https://knowpulse.usask.ca ) for chickpea, common bean, field pea, fababean, and lentil, focuses on diversity data and gives information on germplasm, genetic markers, sequence variants, and phenotypic traits ( Sanderson et al., 2019 ).

The construction of bioinformatics databases ( Table 8 ) for the chickpea gene pool, according to recent breakthroughs in computational genomics, will permit users to visualize and extract chickpea genomics data in order to learn comparative genomics, annotate gene function, and investigate novel transcription factors ( Doddamani et al., 2015 ; Verma et al., 2015 ; Gayali et al., 2016 ). Many databases have been built for chickpea, including CicArMiSatDB ( https://cegresources.icrisat.org/CicArMiSatDB/ ) for SSR markers ( Doddamani et al., 2014 ), CicArVarDB ( https://cegresources.icrisat.org/cicarvardb/ ) for SNPs and QTLs, and Chickpea Transcriptome Database ( Verma et al., 2015 ). Furthermore, a few years ago, the PLncPRO tool was developed to acquire unique insights into the rising importance of long noncoding RNAs in response to various abiotic challenges in chickpea ( Singh et al., 2017 ).

TABLE 8 . Bioinformatics resources for chickpea.

There are also other molecular databases developed in other pulse crops which are useful in comparative genomics studies. Some of the important databases are highlighted as further. The PIgeonPEa Microsatellite DataBase (PIPEMicroDB) program ( http://cabindb.iasri.res.in/pigeonpea/ ) stores a catalogue of microsatellites retrieved from the pigeon pea genome ( Sarika et al., 2013 ). The adaptation of this program for chromosome-based search may be utilized for QTL markers for crop improvement and mapping of genes. With the fast development of publicly available Affymetrix GeneChip Medicago Genome Array Gene Chip data from cell types, a wide range of tissues, growth conditions, and stress treatments, the legume research group is in need of an efficient bioinformatics system to assist efforts to analyze the Medicago genome through functional genomics. The MtGEA ( Medicago truncatula Gene Expression Atlas) website ( http://bioinfo.noble.org/gene-atlas/ ) now includes additional gene expression data and genome annotation ( He et al., 2009 ). The Medicago truncatula Genome Database ( http://www.medicagogenome.org ) houses a diverse collection of genomic data sets ( Krishnakumar et al., 2015 ). RNA-Seq Atlas (Seq-Atlas) for Glycine max ( http://www.soybase.org/soyseq ) gathers RNASeq data from a variety of tissues and offers new techniques for analyzing huge transcriptome data sets produced from next-generation sequencing ( Severin et al., 2010 ). SoyBase ( https://www.soybase.org/ ), the USDA-ARS soybean genetic database, is a comprehensive library of professionally maintained soybean genetics, genomics, and related data resources ( Grant et al., 2010 ). The Lotus japonicus Gene Expression Atlas (LjGEA: http://ljgea.noble.org/ ) provides a global picture of gene expression in organ systems of the species including roots, nodules, stems, petioles, leaves, flowers, pods, and seeds. It enables versatile, multifaceted transcriptome analysis ( Verdier et al., 2013 ).

3.2.13 Genome Editing for Broadening Genetic Bases

Genome editing promises giant leaps forward in broadening the genetic bases research. Targeted DNA integration into known locations in the genome has potential advantages over the random insertional events typically achieved using conventional means of genetic modification. The gene of interest is positioned near the T-DNA left border which is responsible for the insertion of plant cell. Molecular biologists can now more accurately target any gene of interest because advances in genome editing tools such as zinc-finger nucleases (ZFNs), homing endonuclease and transcription activator-like effector nucleases (TALENs) could possibly be exploited for genomics-assisted selection toward accelerated genetic gains ( Shan et al., 2013 ; Bortesi and Fischer, 2015 ), while more advancements in chickpea enhancement using these cutting-edge approaches are still awaited. In chickpea, the 4-coumarate ligase (4CL) and Reveille 7 (RVE7) genes were selected as genes associated with drought tolerance for CRISPR/Cas9 editing in chickpea protoplast. The knockout of these selected genes in the chickpea protoplast showed high-efficiency editing was achieved for RVE7 gene in vivo compared to the 4CL gene ( Badhan et al., 2021 ). These methods, however, are costly and time-consuming since they need complex procedures that require protein engineering. Unlike first-generation genome editing techniques, CRISPR/Cas9 genome editing is straightforward to design and clone and the same Cas9 can theoretically be used with various guide RNAs targeting many places in the genome. Several proof-of-concept demonstrations in crop plants using the primary CRISPR-Cas9 module, and numerous customized Cas9 cassettes have been used to improve target selectivity and reduce off-target cleavage. Thus, the applications of genome editing techniques in chickpea research have great potential ( Mahto et al., 2022 ).

4 Integrating Various Omics Approaches for Broadening the Chckpea Genetic Base

The technological advances that transformed chickpea from an orphan crop to a genomic resource enriched crop in the post-genomics era, Re-sequencing efforts using WGRS have led to the dissection of genetic diversity, population structure, domestication patterns, linkage disequilibrium and the unexploited genetic potential for chickpea improvement ( Varshney et al., 2019 ). Modern genomics technologies have the potential to speed up the process for trait mapping, gene discovery, marker development and molecular breeding, in addition to enhancing the rate of productivity gains in chickpea. Integration of genome-wide sequence information with precise phenotypic variation allows capturing accessions with low-frequency variants that may be responsible for essential phenotypes such as yield components, abiotic stress tolerance, or disease resistance ( Roorkiwal et al., 2020 ). NGS technology has resulted in the development and application of a wide variety of molecular markers for chickpea improvement ( Kale et al., 2015 ; Varshney et al., 2018 ). Over the past decade, more than 2000 simple sequence repeat (SSR) markers, 15,000 features-based diversity array technology (DArT) platform, and millions of SNP markers have been developed for chickpea ( Varshney 2016 ). The revolution in NGS technologies has enabled sequencing to be performed at a higher depth (whole-genome re-sequencing), mid-depth (skim sequencing), or lower depth (genotyping by sequencing, RAD-Seq). Integrating omics data from multiple platforms such as transcriptomics, proteomics and metabolomics are paramount to bridging the genome-to-phenome gap in crop plants and ultimately identifying the phenotype based on their genetics. applications of genomic technologies for bridging the genotype–phenotype gap in chickpea ( Figure 5 ). With the availability of the reference genome, these genetic resources can be subjected to whole-genome re-sequencing (WGRS) or high- to low-density genotyping, based on the objective of the study, using the available genotyping platforms (e.g., genotyping by sequencing, GBS; array-based genotyping). Analysis at the transcriptome, proteome, and metabolome levels can be performed to gain novel insights into the candidate genes and biological processes involved. Using a genomics approach Fusarium wilt resistance WR 315 Annigeri 1 foc4 has been Released as “Super Annigeri 1′ for commercial cultivation in India Mannur et al. (2019) .

FIGURE 5 . Integrating various approaches for broadening the genetic base.

5 Conclusion and Future Perspective

With the employment of modern “Omics” technologies in combination with traditional methods, it is now possible to overcome yield limits, and achieve higher genetic gains ensuring high output for chickpea production and quality features. Chickpea land races and wild Cicer species are the goldmines of beneficial genes influencing desired traits of interest for biotic, abiotic, and yield component features. Identification of novel sources of desired traits, QTLs or alleles through extensive evaluation and utilization of landraces and wild Cicer species will have a greater impact on developing chickpeas for better climate resilience and higher yield. Many desirable features from primary and secondary gene pools in wild Cicer species have been successfully transmitted into cultivated cultivars using both traditional and modern procedures. The wealth of new omics approaches and growing resources offer great potential to transform chickpea breeding in the near future. An integrated application of chickpea “Omics”, classical and modern breeding methods, marker-assisted selection, and biotechnological application promises for the broadening of the chickpea genetic base and introgression of new genes for crop traits for higher productivity will lead to next-generation chickpea varieties.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

RK conceptualized and supervised the manuscript writing. RS, CS, Ambika, BC, RM, RP, and AG collected the related literature and contributed to the original writing. VG, Gayacharan, AH, HU, and RK extended their help in the inference, review, and editing of the manuscript. All authors went through the final manuscript draft and approved it.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer RS declared a shared affiliation with the author Gayacharan to the handling editor at the time of review.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

ICARDA authors received support from CWANA integrated initiative.

Abbo, S., Berger, J., and Turner, N. C. (2003). Viewpoint: Evolution of Cultivated Chickpea: Four Bottlenecks Limit Diversity Andconstrain Adaptation. Funct. Plant Biol. 30 (10), 1081–1087. doi:10.1071/fp03084

PubMed Abstract | CrossRef Full Text | Google Scholar

Abdallah, C., Dumas-Gaudot, E., Renaut, J., and Sergeant, K. (2012). Gel-based and Gel-free Quantitative Proteomics Approaches at a Glance. Int. J. Plant Genomics 2012, 494572. doi:10.1155/2012/494572

Acharjee, S., Sarmah, B. K., Kumar, P. A., Olsen, K., Mahon, R., Moar, W. J., et al. (2010). Transgenic Chickpeas ( Cicer Arietinum L.) Expressing a Sequence-Modified cry2Aa Gene. Plant Sci. 178 (3), 333–339. doi:10.1016/j.plantsci.2010.02.001

CrossRef Full Text | Google Scholar

Agarwal, G., Jhanwar, S., Priya, P., Singh, V. K., Saxena, M. S., Parida, S. K., et al. (2012). Comparative Analysis of Kabuli Chickpea Transcriptome with Desi and Wild Chickpea Provides a Rich Resource for Development of Functional Markers. PLoS ONE 7, e52443. doi:10.1371/journal.pone.0052443

Aguilera, Y., Esteban, R. M., Benítez, V., Mollá, E., and Martín-Cabrejas, M. A. (2009). Starch, Functional Properties, and Microstructural Characteristics in Chickpea and Lentil as Affected by Thermal Processing. J. Agric. Food Chem. 57 (22), 10682–10688. doi:10.1021/jf902042r

Ahmad, F., and Slinkard, A. E. (2003). Limitations to Bridge-Species Facilitated Alien Gene Transfers in Chickpea: Pre-fertilization Events [ Cicer Arietinum L.]. J. Genet. Breed. (Italy) .

Google Scholar

Ahmad, F., Gaur, P. M., and Slinkard, A. E. (1992). Isozyme Polymorphism and Phylogenetic Interpretations in the Genus Cicer L. Theor. Appl. Genet. 83 (5), 620–627. doi:10.1007/bf00226907

Ahmad, F., Gaur, P. M., and Croser, J. (2005). Chickpea ( Cicer Arietinum L.). Genet. Resour. Chromosome Eng. crop Improve.-Grain Legumes 1, 187–217. doi:10.1201/9780203489284.ch7

Ahmad, S., Khan, M. A., Sahi, S. T., and Ahmad, R. (2013). Evaluation of Chickpea Germplasm against Ascochyta Rabiei (Pass) Lab. J. Anim. Plant Sci. 23 (2), 440–443.

Ahmad, F. (1999). Random Amplified Polymorphic DNA (RAPD) Analysis Reveals Genetic Relationships Among the Annual Cicer Species. Theor. Appl. Genet. 98 (3), 657–663. doi:10.1007/s001220051117

Ahmed, S. M., Alsamman, A. M., Jighly, A., Mubarak, M. H., Al-Shamaa, K., et al. (2021). Genome-wide Association Analysis of Chickpea Germplasms Differing for Salinity Tolerance Based on DArTseq Markers. Plos one 16 (12), e0260709. doi:10.1371/journal.pone.0260709

Alajaji, S. A., and El-Adawy, T. A. (2006). Nutritional Composition of Chickpea ( Cicer Arietinum L.) as Affected by Microwave Cooking and Other Traditional Cooking Methods. J. Food Compos. Anal. 19, 806–812. doi:10.1016/j.jfca.2006.03.015

Ali, L., Deokar, A., Caballo, C., Tar’an, B., Gil, J., Chen, W., et al. (2016). Fine Mapping for Double Podding Gene in Chickpea. Theor. Appl. Genet. 129 (1), 77–86. doi:10.1007/s00122-015-2610-1

Ambika, , , Hegde, V., Nimmy, M. S., Bharadwaj, C., Tripathi, S., Singh, R. K., et al. (2021). Unraveling Genetics of Semi-determinacy and Identification of Markers for Indeterminate Stem Growth Habit in Chickpea ( Cicer Arietinum L.). Sci. Rep. 11 (1), 1–8. doi:10.1038/s41598-021-01464-3

Anuradha, C., Gaur, P. M., Pande, S., Gali, K. K., Ganesh, M., Kumar, J., et al. (2011). Mapping QTLs for Resistance to Botrytis Grey Mould in Chickpea. Euphytica 182 (1), 1–9. doi:10.1007/s10681-011-0394-1

Arbona, V., Manzi, M., Ollas, C. D., and Gómez-Cadenas, A. (2013). Metabolomics as a Tool to Investigate Abiotic Stress Tolerance in Plants. Int. J. Mol. Sci. 14 (3), 4885–4911. doi:10.3390/ijms14034885

Archak, S., Tyagi, R. K., Harer, P. N., Mahase, L. B., Singh, N., Dahiya, O. P., et al. (2016). Characterization of Chickpea Germplasm Conserved in the Indian National Genebank and Development of a Core Set Using Qualitative and Quantitative Trait Data. Crop J 4 (5), 417–424. doi:10.1016/j.cj.2016.06.013

Aryamanesh, N., Nelson, M. N., Yan, G., Clarke, H. J., and Siddique, K. H. M. (2010). Mapping a Major Gene for Growth Habit and QTLs for Ascochyta Blight Resistance and Flowering Time in a Population between C. Arietinum and C. reticulatum . Euphytica 173, 307–331. doi:10.1007/s10681-009-0086-2

Asharani, B. M., Ganeshaiah, K. N., Raja, A., Kumar, V., and Makarla, U. K. (2011). Transformation of Chickpea Lines with Cry1X Using in Planta Transformation and Characterization of Putative Transformants T1 Lines for Molecular and Biochemical Characters. J. Plant Breed. Crop Sci. 3 (16), 413–423. doi:10.5897/jpbcs11.074

Aykroyd, W. R., and Doughty, J. (1982). “Legumes in Human Nutrition,” in FAO Food and Nutrition Paper ( FAO of the United Nations ), 20, 1–152.

PubMed Abstract | Google Scholar

Badami, P. S., Mallikarjuna, N., and Moss, J. P. (1997). Interspecific Hybridization between I and C. Pinnatifidum . Plant Breed. 116 (4), 393–395. doi:10.1111/j.1439-0523.1997.tb01019.x

Badhan, S., Ball, A. S., and Mantri, N. (2021). First Report of CRISPR/Cas9 Mediated DNA-free Editing of 4CL and RVE7 Genes in Chickpea Protoplasts. Int. J. Mol. Sci. 22 (1), 396. doi:10.3390/ijms22010396

Badshah, A., Khan, M., Bibi, N., Khan, M., and Ali, S. (2003). Quality Studies of Newly Evolved Chickpea Cultivars. Adv. FoodSci. 25 (3), 96–99.

Bajaj, D., Saxena, M. S., Kujur, A., Das, S., Badoni, S., Tripathi, S., et al. (2015a). Genome-wide Conserved Non-coding Microsatellite (CNMS) Marker-Based Integrative Genetical Genomics for Quantitative Dissection of Seed Weight in Chickpea. J. Exp. Bot. 66, 1271–1290. doi:10.1093/jxb/eru478

Bajaj, D., Das, S., Badoni, S., Kumar, V., Singh, M., Bansal, K. C., et al. (2015b). Genome-wide High-Throughput SNP Discovery and Genotyping for Understanding Natural (Functional) Allelic Diversity and Domestication Patterns in Wild Chickpea. Sci. Rep. 5, 12468. doi:10.1038/srep12468

Bajaj, D., Upadhyaya, H. D., Das, S., Kumar, V., Gowda, C. L. L., Sharma, S., et al. (2016). Identification of Candidate Genes for Dissecting Complex Branch Number Trait in Chickpea. Plant Sci. 245, 61–70. doi:10.1016/j.plantsci.2016.01.004

Barmukh, R., Soren, K. R., Madugula, P., Gangwar, P., Shanmugavadivel, P. S., Bharatwaj, C., et al. (2021). Construction of a High-Density Genetic Map and QTL Analysis for Yield, Yield Components and Agronomic Traits in Chickpea ( Cicer Arietinum L.). Plos one 16 (5), e0251669. doi:10.1371/journal.pone.0251669

Basandrai, A. K., Macleod, W. J., Siddique, K., Pande, S., and Payne, P. (2006). Evaluation of Wild Chickpea Germplasm against Australian Isolates of Botrytis Cinerea the Casual Organism of Botrytis Grey Mold of Chickpea. BGM Newsl.

Basandrai, A. K., Pande, S., and Basandrai, D. (2008). “Recent Advance in Host-Parasite Interaction, Chickpea Ascochyta Rabaie and Botrytis Cinerea Systems,” in Crop Improvement, Strategies and Applications . Editors R. C. Setia, H. Nayyar, and N. Setia (New Delhi India: IK International ), 265–301.

Basu, U., Bajaj, D., Sharma, A., Malik, N., Daware, A., Narnoliya, L., et al. (2019). Genetic Dissection of Photosynthetic Efficiency Traits for Enhancing Seed Yield in Chickpea. Plant, Cell Environ. 42 (1), 158–173. doi:10.1111/pce.13319

Benzohra, I. E., Bendahmane, B. S., Benkada, M. Y., and Labdi, M. (2014). Evaluation of Wild Cicer Species Accessions for Resistance to Three Pathotypes of Ascochyta Rabiei (Pass.) Labr.in Algeria. Afr. J. Micro. Res. 8 (20), 2022–2029. doi:10.5897/AJMR2014.6628

Berger, J. D., Buck, R., Henzell, J. M., and Turner, N. C. (2005). Evolution in the Genus Cicer—Vernalisation Response and Low Temperature Pod Set in Chickpea (C. Arietinum L.) and its Annual Wild Relatives. Aust. J. Agric. Res. 56 (11), 1191–1200. doi:10.1071/ar05089

Bhatnagar-Mathur, P., Vadez, V., Jyotsna, D. M., Lavanya, M., Vani, G., and Sharma, K. K. (2009). Genetic Engineering of Chickpea with the P5CSF129A Gene for Osmoregulation with Implications on Drought Tolerance. Mol. Breed. 23, 591–606. doi:10.1007/s11032-009-9258-y

Biradar, S. S., Sridevi, O., and Salimath, P. M. (2009). Genetic Enhancement of Chickpea for Pod Borer Resistance through Expression of CryIAc Protein. Karnataka J. Agric. Sci. 22 (3), 467–470.

Bortesi, L., and Fischer, R. B. (2015). The CRISPR/Cas9 System for Plant Genome Editing and beyond. Biotech. Adv. 33, 41–52. doi:10.1016/j.biotechadv.2014.12.006

Caballo, C., Castro, P., Gil, J., Millan, T., Rubio, J., and Die, J. V. (2019). Candidate Genes Expression Profiling during Wilting in Chickpea Caused by Fusarium Oxysporum F. Sp. Ciceris Race 5. PLoSONE 14 (10), e0224212. doi:10.1371/journal.pone.0224212

Canci, H., and Toker, C. (2009). Evaluation of Annual Wild Cicer Species for Drought and Heat Resistance under Field Conditions. Genet. Resour. Crop Evol. 56 (1), 1–6. doi:10.1007/s10722-008-9335-9

Chakraborty, J., Sen, S., Ghosh, P., Sengupta, A., Basu, D., and Das, S. (2016). Homologous Promoter Derived Constitutive and Chloroplast Targeted Expression of Synthetic cry1Ac in Transgenic Chickpea Confers Resistance against Helicoverpa Armigera . Plant Cell Tiss. Org. Cult. 125, 521–535. doi:10.1007/s11240-016-0968-7

Chandel, K. P. S. (1984). A Note on the Occurrence of Wild Cicer Microphyllum Benth. And its Nutrient Status. Intl. Chickpea Newsl. 10, 4–5.

Chibbar, R. N., Ambigaipalan, P., and Hoover, R. (2010). Molecular Diversity in Pulse Seed Starch and Complex Carbohydrates and its Role in Human Nutrition and Health. Cereal Chem. 87, 342–352. doi:10.1094/cchem-87-4-0342

Cho, S., Kumar, J., Shultz, J. L., Anupama, K., Tefera, F., and Muehlbauer, F. J. (2002). Mapping Genes for Double Podding and Other Morphological Traits in Chickpea. Euphytica 128 (2), 285–292. doi:10.1023/a:1020872009306

Chowrira, G. M., Akella, V., Fuerst, P. E., and Lurquin, P. F. (1996). Transgenic Grain Legumes Obtained by in Planta Electroporation-Mediated Gene Transfer. Mol. Biotechnol. 5 (2), 85–96. doi:10.1007/bf02789058

Ciftci, H., Ozkaya, A., Cevrimli, B. S., and Bakoglu, A. (2010). Levels of Fat-Soluble Vitamins in Some Foods. Asian J. Chem. 22, 1251–1256.

Clarke, H. J., and Siddique, K. H. M. (2004). Response of Chickpea Genotypes to Low Temperature Stress during Reproductive Development. Field Crops Res. 90 (2-3), 323–334. doi:10.1016/j.fcr.2004.04.001

Cobos, M. J., Fernandez, M., Rubio, J., Kharrat, M., Moreno, M. T., Gil, J., et al. (2005). A Linkage Map of Chickpea ( Cicer Arietinum L.) Based on Populations from Kabuli X Desi Crosses: Location of Genes for Resistance to Fusarium Wilt Race 0. Theor. Appl. Genet. 110, 1347–1353. doi:10.1007/s00122-005-1980-1

Collard, B. C. Y., Ades, P. K., Pang, E. C. K., Brouwer, J. B., and Taylor, P. W. J. (2001). Prospecting for Sources of Resistance to Ascochyta Blight in Wild Cicer Species. Australas. Plant Pathol. 30 (3), 271–276. doi:10.1071/ap01036

Collard, B. C. Y., Pang, E. C. K., Ades, P. K., and Taylor, P. W. J. (2003). Preliminary Investigation of QTLs Associated with Seedling Resistance to Ascochyta Blight from Cicer Echinospermum , a Wild Relative of Chickpea. Theor. Appl. Genet. 107 (4), 719–729. doi:10.1007/s00122-003-1297-x

Coram, T. E., and Pang, E. C. K. (2005). Isolation and Analysis of Candidate Ascochyta Blight Defence Genes in Chickpea. Part I. Generation and Analysis of an Expressed Sequence Tag (EST) Library. Physiol. Mol. Plant Pathol. 66, 192–200. doi:10.1016/j.pmpp.2005.08.003

Dalgetty, D. D., and Baik, B. K. (2003). Isolation and Characterization of Cotyledon Fibres from Peas, Lentils, and Chickpea. Cereal Chem. 80, 310–315. doi:10.1094/cchem.2003.80.3.310

Das, S., Upadhyaya, H. D., Bajaj, D., Kujur, A., Badoni, S., Kumar, V., et al. (2015). Deploying QTL-Seq for Rapid Delineation of a Potential Candidate Gene Underlying Major Trait-Associated QTL in Chickpea. DNA Res. 22 (3), 193–203. doi:10.1093/dnares/dsv004

Das, S., Singh, M., Srivastava, R., Bajaj, D., Saxena, M. S., Rana, J. C., et al. (2016). mQTL-Seq Delineates Functionally Relevant Candidate Gene Harbouring a Major QTL Regulating Pod Number in Chickpea. DNA Res. 23 (1), 53–65. doi:10.1093/dnares/dsv036

Das, A., Datta, S., Thakur, S., Shukla, A., Ansari, J., Sujayanand, G. K., et al. (2017). Expression of a Chimeric Gene Encoding Insecticidal Crystal Protein Cry1Aabc of Bacillus Thuringiensis in Chickpea ( Cicer Arietinum L.) Confers Resistance to Gram Pod Borer ( Helicoverpa Armigera Hubner). Front. Plant Sci. 8, 1423. doi:10.3389/fpls.2017.01423

Dash, S., Campbell, J. D., Cannon, E. K., Cleary, A. M., Huang, W., Kalberer, S. R., et al. (2016). Legume Information System (LegumeInfo. Org): a Key Component of a Set of Federated Data Resources for the Legume Family. Nucl. Acids Res. 44 (D1), D1181–D1188. doi:10.1093/nar/gkv1159

David, S., Mukandala, L., and Mafuru, J. (2002). Seed Availability, an Ignored Factor in Crop Varietal Adoption Studies: a Case Study of Beans in Tanzania. J. Sustain. Agric. 21, 5–20. doi:10.1300/j064v21n02_03

Dell’Acqua, M., Gatti, D. M., Pea, G., Cattonaro, F., Coppens, F., Magris, G., et al. (2015). Genetic Properties of the MAGIC Maize Population: A New Platform for High-Definition QTL Mapping in Zea mays . Genome Biol. 16, 1–23. doi:10.1186/s13059-015-0716-z

Deschamps, S., and Campbell, M. A. (2012). Genetic Variant Discovery and its Use in Genome Characterization of Agronomically Important Crop Species. Mol. markers plants , 137–167. doi:10.1002/9781118473023.ch9

Devasirvatham, V., Gaur, P. M., Mallikarjuna, N., Tokachichu, R. N., Trethowan, R. M., and Tan, D. K. (2012). Effect of High Temperature on the Reproductive Development of Chickpea Genotypes under Controlled Environments. Funct. Plant Biol. 39 (12), 1009–1018. doi:10.1071/fp12033

Diapari, M., Sindhu, A., Bett, K., Deokar, A., Warkentin, T. D., and Tar’an, B. (2014). Genetic Diversity and Association Mapping of Iron and Zinc Concentrations in Chickpea ( Cicer Arietinum L.). Genome 57 (8), 459–468. doi:10.1139/gen-2014-0108

Dita, M. A., Rispail, N., Prats, E., Rubiales, D., and Singh, K. B. (2006). Biotechnology Approaches to Overcome Biotic and Abiotic Stress Constraints in Legumes. Euphytica 147 (1), 1–24. doi:10.1007/s10681-006-6156-9

Doddamani, D., Mohan, A. V. S. K., Katta, M. A. V. S. K., Khan, A. W., Agarwal, G., Shah, T. M., et al. (2014). CicArMiSatDB: the Chickpea Microsatellite Database. BMC Bioinform. 15, 212. doi:10.1186/1471-2105-15-212

Doddamani, D., Khan, A. W., Katta, M. A. V. S. K., Agarwal, G., Thudi, M., Ruperao, P., et al. (2015). CicArVarDB: SNP and InDel Database for Advancing Genetics Research and Breeding Applications in Chickpea. Database 19, 2015. doi:10.1093/database/bav078

Eapen, S., Köhler, F., Gerdemann, M., and Schieder, O. (1987). Cultivar Dependence of Transformation Rates in Moth Bean after Co-cultivation of Protoplasts with Agrobacterium Tumefaciens . Theor. Appl. Genet. 75 (1), 207–210. doi:10.1007/bf00249165

Eapen, S. (2008). Advances in Development of Transgenic Pulse Crops. Biotechnol. Adv. 26 (2), 162–168. doi:10.1016/j.biotechadv.2007.11.001

Eker, T., Erler, F., Adak, A., Imrek, B., Guven, H., Tosun, H. S., et al. (2018). Screening of Chickpea Accessions for Resistance against the Pulse Beetle, Callosobruchus Chinensis L. (Coleoptera: Bruchidae). J. Stored Prod. Res. 76, 51–57. doi:10.1016/j.jspr.2017.12.007

FAOSTAT (2020). FAOSTAT data base, 2020. Available at: http://faostat.fao.org/site (Accessed March 26, 2022)

Fernie, A. R., and Schauer, N. (2009). Metabolomics-assisted Breeding: a Viable Option for Crop Improvement? Trends Genet. 25 (1), 39–48. doi:10.1016/j.tig.2008.10.010

Ganguly, M., Molla, K. A., Karmakar, S., Datta, K., and Datta, S. K. (2014). Development of Pod Borer-Resistant Transgenic Chickpea Using a Pod-specific and a Constitutive Promoter-Driven Fused cry1Ab/Ac Gene. Theor. Appl. Genet. 127 (12), 2555–2565. doi:10.1007/s00122-014-2397-5

Garg, V., Khan, A. W., Kudapa, H., Kale, S. M., Chitikineni, A., Qiwei, S., et al. (2019). Integrated Transcriptome, Small RNA and Degradome Sequencing Approaches Provide Insights into Ascochyta Blight Resistance in Chickpea. Plant Biotechnol. J. 17, 914–931. doi:10.1111/pbi.13026

Gaur, P. M., and Gour, V. K. (2002). A Gene Producing One to Nine Flowers Per Flowering Node in Chickpea. Euphytica 128, 231–235. doi:10.1023/a:1020845815319

Gaur, P. M., Gour, V. K., and Srinivasan, S. (2008). An Induced Brachytic Mutant of Chickpea and its Possible Use in Ideotype Breeding. Euphytica 159, 35–41. doi:10.1007/s10681-007-9454-y

Gayali, S., Acharya, S., Lande, N. V., Pandey, A., Chakraborty, S., and Chakraborty, N. (2016). CicerTransDB 1.0: a Resource for Expression and Functional Study of Chickpea Transcription Factors. BMC Plant Biol. 16, 169. doi:10.1186/s12870-016-0860-y

Geervani, P. (1991). “Utilization of Chickpea in India and Scope for Novel and Alternative Uses,” in Proceedings of a Consultants Meeting , Patancheru, AP , 27–30 March 1989 ( ICRISAT. ), 47–54.

Ghanti, S. K. K., Sujata, K. G., Kumar, B. V., Karba, N. N., Janardhan Reddy, K., Rao, M. S., et al. (2011). Heterologous Expression of P5CS Gene in Chickpea Enhances Salt Tolerance without Affecting Yield. Biol. Plant. 55, 634. doi:10.1007/s10535-011-0161-0

Golla, S. K., Rajasekhar, P., Akbar, S. M. D., and Sharma, H. C. (2018). Proteolytic Activity in the Midgut of Helicoverpa Armigera (Noctuidae: Lepidoptera) Larvae Fed on Wild Relatives of Chickpea, Cicer Arietinum . J. Econ. Entomol. 111 (5), 2409–2415. doi:10.1093/jee/toy160

Gowda, S. J. M., Radhika, P., Kadoo, N. Y., Mhase, L. B., and Gupta, V. S. (2009). Molecular Mapping of Wilt Resistance Genes in Chickpea. Mol. Breed. 24, 177–183. doi:10.1007/s11032-009-9282-y

Grant, D., Nelson, R. T., Cannon, S. B., and Shoemaker, R. C. (2010). SoyBase, the USDA-ARS Soybean Genetics and Genomics Database. Nucl. Acids Res. 38 (Suppl. l_1), D843–D846. doi:10.1093/nar/gkp798

Greco, N., and Vito, M. D. (1993). Breeding for Stres Tolerance in Cool-Season Food Legumes , Editor K. B. Singh K. B., and M. C. Saxena (Chichester, United Kingdom: John Wiley & Sons ), 157–166.

Gujaria, N., Kumar, A., Dauthal, P., Dubey, A., Hiremath, P., Bhanu Prakash, A., et al. (2011). Development and Use of Genic Molecular Markers (GMMs) for Construction of a Transcript Map of Chickpea ( Cicer Arietinum L.). Theor. Appl. Genet. 122, 1577–1589. doi:10.1007/s00122-011-1556-1

Gupta, R., and Laxman, S. (2020). tRNA Wobble-Uridine Modifications as Amino Acid Sensors and Regulators of Cellular Metabolic State. Curr. Genet. 66 (3), 475–480. doi:10.1007/s00294-019-01045-y

Gupta, S., and Parihar, A. K. (2015). “Broadening the Genetic Base of Pulse Crops,” in Pulses Challenges and Opportunities Under Changing Climatic Scenario, (Kanpur, UP, India: ISPRD ), 86–101.

Gupta, S., Nawaz, K., Parween, S., Roy, R., Sahu, K., Kumar, P. A., et al. (2017). Draft Genome Sequence of Cicer Reticulatum L., the Wild Progenitor of Chickpea Provides a Resource for Agronomic Trait Improvement. DNA Res. 24, 1–10. doi:10.1093/dnares/dsw042

Hajyzadeh, M., Turktas, M., Khawar, K. M., and Unver, T. (2015). miR408 Overexpression Causes Increased Drought Tolerance in Chickpea. Gene 555, 186–193. doi:10.1016/j.gene.2014.11.002

Harshavardhana, Y. S., Hegde, V., Tripathi, S., Raje, R. S., Jain, P. K., Gaikwad, K., et al. (2019). Genetics of Semi-determinacy and Identification of Molecular Marker Linked to Dt1 Locus in Chickpea ( Cicer Arietinum L.). Indian J. Genet. Pl. Br. 79 (Suppl. 01), 270–275. doi:10.31742/ijgpb.79s.1.18

Haware, M. P., Nene, Y. L., Pundir, R. P. S., and Rao, J. N. (1992). Screening of World Chickpea Germplasm for Resistance to fusarium Wilt. Field Crops Res. 30 (1-2), 147–154. doi:10.1016/0378-4290(92)90063-f

He, J., Benedito, V. A., Wang, M., Murray, J. D., Zhao, P. X., Tang, Y., et al. (2009). The Medicago Truncatula Gene Expression Atlas Web Server. BMC Bioinfo. 10 (1), 1–9. doi:10.1186/1471-2105-10-441

Hegde, V. S., Tripathi, S., Bharadwaj, C., Agrawal, P. K., and Choudhary, A. K. (2018). Genetics and Genomics Approaches to Enhance Adaptation and Yield of Chickpea ( Cicer Arietinum L.) in Semi-arid Environments. Sabrao J. Breed. Genet. 50 (2), 217–241.

Hegde, V. S. (2011). Morphology and Genetics of a New Found Determinate Genotype in Chickpea. Euphytica 182, 35–42. doi:10.1007/s10681-011-0447-5

Hiremath, P. J., Farmer, A., Cannon, S. B., Woodward, J., Kudapa, H., Tuteja, R., et al. (2011). Large-Scale Transcriptome Analysis in Chickpea ( Cicer Arietinum L.), an Orphan Legume Crop of the Semi-arid Tropics of Asia and Africa. Plant Biotechnol. J. 9 (8), 922–931. doi:10.1111/j.1467-7652.2011.00625.x

Hu, J., Rampitsch, C., and Bykova, N. V. (2015). Advances in Plant Proteomics toward Improvement of Crop Productivity and Stress Resistancex. Front. Plant Sci. 6, 209. doi:10.3389/fpls.2015.00209

Huang, B. E., Verbyla, K. L., Verbyla, A. P., Raghavan, C., Singh, V. K., Gaur, P., et al. (2015). MAGIC Populations in Crops: Current Status and Future Prospects. Theor. Appl. Genet. 128 (6), 999–1017. doi:10.1007/s00122-015-2506-0

Hulse, J. H. (1991). “Nature, Composition and Utilization of Pulses,” in Uses of Tropical Grain Legumes, Proceedings of a Consultants Meeting , Patancheru, AP , 27–30 March 1989 ( ICRISAT ), 11–27.

Husnain, T., Fatima, T., Islam, R., and Riazuddin, S. (2000). Plant Regeneration and Expression of Beta-Glucorinidase Gene in Hypocotyls Tissue of Chickpea ( Cicer Arietinum L.). Pak. J. Biol. Sci. 3, 842–845. doi:10.3923/pjbs.2000.842.845

ICRISAT (2009). ICISAT Archival Report . Hyderabad, India: International Crop Research Institute for the Semi-Arid Tropics . sci985.0011183X002500030047x.

Imtiaz, M., Materne, M., Hobson, K., van Ginkel, M., and Malhotra, R. S. (2008). Molecular Genetic Diversity and Linked Resistance to Ascochyta Blight in Australian Chickpea Breeding Materials and Their Wild Relatives. Aust. J. Agric. Res. 59, 554–560. doi:10.1071/ar07386

Indurker, S., Misra, H. S., and Eapen, S. (2007). Genetic Transformation of Chickpea ( Cicer Arietinum L.) with Insecticidal Crystal Protein Gene Using Particle Gun Bombardment. Plant Cell Rep. 26, 755–763. doi:10.1007/s00299-006-0283-6

Infantino, A., Porta-Puglia, A., and Singh, K. B. (1996). Screening Wild Cicer Species for Resistance to fusarium Wilt. Plant Dis. 80, 42–44. doi:10.1094/pd-80-0042

Iruela, M., Rubio, J., Barro, F., Cubero, J. I., Millan, T., and Gil, J. (2006). Detection of Two Quantitative Trait Loci for Resistance to Ascochyta Blight in an Intra-specific Cross of Chickpea ( Cicer Arietinum L.): Development of SCAR Markers Associated with Resistance. Theor. Appl. Genet. 112, 278–287. doi:10.1007/s00122-005-0126-9

Jaganathan, D., Thudi, M., Kale, S., Azam, S., Roorkiwal, M., Gaur, P. M., et al. (2015). Genotyping-by-sequencing Based Intra-specific Genetic Map Refines a ‘‘QTL-Hotspot” Region for Drought Tolerance in Chickpea. Mol. Genet. Genom. 290 (2), 559–571. doi:10.1007/s00438-014-0932-3

Jain, M., Misra, G., Patel, R. K., Priya, P., Jhanwar, S., Khan, A. W., et al. (2013). A Draft Genome Sequence of the Pulse Crop Chickpea ( Cicer Arietinum L.). Plant J 74 (5), 715–729. doi:10.1111/tpj.12173

Jain, M., Chevala, V. V., and Garg, R. (2014). Genome-wide Discovery and Differential Regulation of Conserved and Novel microRNAs in Chickpea via Deep Sequencing. J. Exp. Bot. 65, 5945–5958. doi:10.1093/jxb/eru333

Jaiswal, H. K., and Singh, B. D. (1989). Analysis of Gene Effects for Yield and Certain Yield Traits in Crosses between Cicer Arietinum L. And C. reticulatum Ladz. Indian J. Genet. Plant Breed. 49 (1), 9–17.

Jha, U. C., Nayyar, H., Palakurthi, R., Jha, R., Valluri, V., Bajaj, P., et al. (2021). Major QTLs and Potential Candidate Genes for Heat Stress Tolerance Identified in Chickpea ( Cicer Arietinum L.). Front. Plant Sci. 12, 655103. doi:10.3389/fpls.2021.655103

Kaiser, W. J., Alcala-Jimenez, A. R., Hervas-Vargas, A., Trapero-Casas, J. L., and Jimenez-Diaz, R. M. (1994). Screening of Wild Cicer Species for Resistance to Races 0 to 5 of Fusarium Oxysporum F. Sp. Ciceris. Plant Dis. 78, 962–967. doi:10.1094/pd-78-0962

Kale, S. M., Jaganathan, J., Ruperao, P., Chen, C., Punna, R., Kudapa, H., et al. (2015). Prioritization of Candidate Genes in QTL-Hotspot Region for Drought Tolerance in Chickpea (Cicer Arietinum L.). Sci. Rep. 5, 15296. doi:10.1038/srep15296

Kamble, S., Misra, H. S., Mahajan, S. K., and Eapen, S. (2003). A Protocol for Efficient Biolistic Transformation of Mothbean ( Vigna Aconitifolia L. Jacq. Marechal. ). Plant Mol. Biol. Rep. 21 (4), 457–458. doi:10.1007/bf02772595

Kar, S., Basu, D., Das, S., Ramkrishnan, N. A., Mukherjee, P., Nayak, P., et al. (1997). Expression of cry1Ac Gene of Bacillus Thuringiensis in Transgenic Chickpea Plants Inhibits Development of Pod Borer ( Heliothis Armigera ) Larvae. Transgenic Res. 6, 177–185. doi:10.1023/a:1018433922766

Kashiwagi, J., Krishnamurthy, L., Upadhyaya, H. D., Krishna, H., Chandra, S., Vadez, V., et al. (2005). Genetic Variability of Drought-Avoidance Root Traits in the Mini-Core Germplasm Collection of Chickpeas ( Cicer Arietinum L.). Euphytica 146 (3), 213–222. doi:10.1007/s10681-005-9007-1

Kashiwagi, J., Krishnamurthy, L., Singh, S., and Upadhyaya, H. D. (2006b). Variation of SPAD Chlorophyll Meter Readings (SCMR) in the Mini-Core Germplasm Collection of Chickpeas. J. SAT Agric. Res. 2 (1), 1–3.

Kashiwagi, J., Krishnamurthy, L., Upadhyaya, H. D., and Gaur, P. M. (2008). Rapid Screening Technique for Canopy Temperature Status and its Relevance to Drought Tolerance Improvement in Chickpea. J. SAT Agric. Res. 6, 105–114.

Kashiwagi, J., Upadhyaya, H. D., and Krishnamurthy, L. (2010). Significance and Genetic Diversity of SPAD Chlorophyll Meter Reading in Chickpea Germplasm in the Semi-arid Environments. J. Food Legumes 23 (2), 99–105.

Kaur, S., Chhabra, K. S., and Arora, B. S. (1999). Incidence of Helicoverpa Armigera (Hubner) on Wild and Cultivated Species of Chickpea. Int. Chickpea Pigeonpea Newsl. 6, 18–19.

Kaur, M., Singh, N., and Sodhi, N. S. (2005). Physicochemical, Cooking, Textural and Roasting Characteristics of Chickpea ( Cicer Arietinum L.) Cultivars. J. Food Eng. 69, 511–517. doi:10.1016/j.jfoodeng.2004.09.002

Kaur, L., Tripathi, H. S., Vishwa, D., Reddy, M. V., Singh, G., and Kharkwal, M. C. (2008). “Management of Diseases in Food Legumes,” in Proc. fourth Int. Food Legumes Res. Confer. , New Delhi, India , 608–637. 1.

Kaur, L., Sirari, A., Kumar, D., Sandhu, J. S., Singh, S., Kapoor, K., et al. (2013). Combining Ascochyta Blight and Botrytis Grey Mould Resistance in Chickpea through Interspecific Hybridization. Phytopathol. Mediterr. 52 (1), 157–165. doi:10.14601/Phytopathol_Mediterr-11651

Khatodia, S., Kharb, P., Batra, P., and Chowdhury, V. K. (2014). Development and Characterization of Transgenic Chickpea ( Cicer Arietinum L.) Plants with cry1Ac Gene Using Tissue Culture Independent Protocol. Int. J. Adv. Res. 2, 323–331.

Knights, E. J., Southwell, R. J., Schwinghamer, M. W., and Harden, S. (2008). Resistance to Phytophthora Medicaginis Hansen and Maxwell in Wild Cicer Species and its Use in Breeding Root Rot Resistant Chickpea ( Cicer Arietinum L.). Aust. J. Agric. Res. 59 (4), 383–387. doi:10.1071/ar07175

Köhler, F., Golz, C., Eapen, S., Kohn, H., and Schieder, O. (1987a). Stable Transformation of Moth Bean Vigna Aconitifolia via Direct Gene Transfer. Plant Cell Rep. 6 (4), 313–317. doi:10.1007/BF00272007

Köhler, F., Golz, C., Eapen, S., and Schieder, O. (1987b). Influence of Plant Cultivar and Plasmid-DNA on Transformation Rates in Tobacco and Moth Bean. Plant Sci. 53 (1), 87–91.

Krishnakumar, V., Kim, M., Rosen, B. D., Karamycheva, S., Bidwell, S. L., Tang, H., et al. (2015). MTGD: The Medicago Truncatula Genome Database. Plant Cell Physiol. 56, 1e1. doi:10.1093/pcp/pcu179

Krishnamurthy, K. V., Suhasini, K., Sagare, A. P., Meixner, M., De Kathen, A., Pickardt, T., et al. (2000). Agrobacterium Mediated Transformation of Chickpea ( Cicer Arietinum L.) Embryo Axes. Plant Cell Rep. 19 (3), 235–240. doi:10.1007/s002990050005

Krishnamurthy, L., Kashiwagi, J., Gaur, P. M., Upadhyaya, H. D., and Vadez, V. (2010). Sources of Tolerance to Terminal Drought in the Chickpea ( Cicer Arietinum L.) Minicore Germplasm. Field Crops Res. 119 (2-3), 322–330. doi:10.1016/j.fcr.2010.08.002

Krishnamurthy, L., Gaur, P. M., Basu, P. S., Chaturvedi, S. K., Tripathi, S., Vadez, V., et al. (2011a). Large Genetic Variation for Heat Tolerance in the Reference Collection of Chickpeas ( Cicer Arietinum L.) Germplasm. Plant Genet. Resour. 9 (1), 59–69. doi:10.1017/s1479262110000407

Krishnamurthy, L., Turner, N. C., Gaur, P. M., Upadhyaya, H. D., Varshney, R. K., Siddique, K. H. M., et al. (2011b). Consistent Variation across Soil Types in Salinity Resistance of a Diverse Range of Chickpea ( Cicer Arietinum L.) Genotypes. J. Agron. Crop Sci. 197 (3), 214–227. doi:10.1111/j.1439-037x.2010.00456.x

Kujur, A., Bajaj, D., Upadhyaya, H. D., Das, S., Ranjan, R., Shree, T., et al. (2015a). Employing Genome-wide SNP Discovery and Genotyping Strategy to Extrapolate the Natural Allelic Diversity and Domestication Patterns in Chickpea. Front. Plant Sci. 6, 162. doi:10.3389/fpls.2015.00162

Kujur, A., Upadhyaya, H. D., Shree, T., Bajaj, D., Das, S., Saxena, M. S., et al. (2015b). Ultra-high Density Intra-specific Genetic Linkage Maps Accelerate Identification of Functionally Relevant Molecular Tags Governing Important Agronomic Traits in Chickpea. Sci. Rep. 5 (1), 1–13. doi:10.1038/srep09468

Kujur, A., Upadhyaya, H. D., Bajaj, D., Gowda, C. L. L., Sharma, S., Tyagi, A. K., et al. (2016). Identification of Candidate Genes and Natural Allelic Variants for QTLs Governing Plant Height in Chickpea. Sci. Rep. 6, 27968. doi:10.1038/srep27968

Kulwal, P. L., Thudi, M., and Varshney, R. K. (2011). “Genomics Interventions in Crop Breeding for Sustainable Agriculture,” in Encyclopaedia of Sustainability Science and Technology ( Springer ). doi:10.1007/978-1-4419-0851-3

Kumar, S., and Gugita, S. (2004). “How Wide the Genetic Base of Pulse Crops Is,” in Proc. Nat. Simp. Crop Diversification Natur. Resour. Manag. , Kanpur, India . Editors M. Ali, B. B. Singh, S. Kumar, and V. Dhar, 211–217. Pulses in New Perspective

Kumar, K. B., Mahalakshmi, S. L., Kumar, S. M., and Devi, B. P. (2014). Optimized Agrobacterium-Mediated Genetic Transformation in Chickpea ( Cicer Arietinum L.) Cultivar Swetha (ICCV-2). Trends Biosci. 7, 2237–2244.

Labdi, M., Robertson, L. D., Singh, K. B., and Charrier, A. (1996). Genetic Diversity and Phylogenetic Relationships Among the Annual Cicer Species as Revealed by Isozyme Polymorphism. Euphytica 88 (3), 181–188. doi:10.1007/bf00023889

Ladizinsky, G., and Adler, A. (1975). Origin of Chickpea as Indicated by Seed Protein Electrophoresis. Israel J. Bot. 24, 183–189. doi:10.1007/bf00132919

Ladizinsky, G., and Adler, A. (1976a). The Origin of Chickpea Cicer Arietinum L. Euphytica 25 (1), 211–217. doi:10.1007/bf00041547

Ladizinsky, G., and Adler, A. (1976b). Genetic Relationships Among the Annual Species of Cicer L. Theor . Appl. Genet. 48 (4), 197–203. doi:10.1007/bf00527371

Ladizinsky, G. (1985). Founder Effect in Crop-Plant Evolution. Econ. Bot. 39 (2), 191–199. doi:10.1007/bf02907844

Ladizinsky, G. (1998). Plant Evaluation under Domestication. Dordrecht Springer , 1–254. doi:10.1007/978-94-011-4429-2

Langridge, P., and Fleury, D. (2011). Making the Most of ‘omics’ for Crop Breeding. TRENDS Biotechnol. 29 (1), 33–40. doi:10.1016/j.tibtech.2010.09.006

Lawo, N. C., Mahon, R. J., Milner, R. J., Sarmah, B. K., Higgins, T. J. V., and Romeis, J. (2008). Effectiveness of Bacillus Thuringiensis Transgenic Chickpeas and the Entomopathogenic Fungus Metarhizium Anisopliae in Controlling Helicoverpa Armigera (Lepidoptera: Noctuidae). Appl. Environ. Microbiol. 74, 4381–4389. doi:10.1128/aem.00484-08

Li, J., Dai, X., Liu, T., and Zhao, P. X. (2012). LegumeIP: an Integrative Database for Comparative Genomics and Transcriptomics of Model Legumes. Nucl. Acids Res. 40 (D1), D1221–D1229. doi:10.1093/nar/gkr939

Li, H., Rodda, M., Gnanasambandam, A., Aftab, M., Redden, R., Hobson, K., et al. (2015). Breeding for Biotic Stress Resistance in Chickpea: Progress and Prospects. Euphytica 204 (2), 257–288. doi:10.1007/s10681-015-1462-8

Li, Y., Ruperao, P., Batley, J., Edwards, D., Davidson, J., Hobson, K., et al. (2017). Genome Analysis Identified Novel Candidate Genes for Ascochyta Blight Resistance in Chickpea Using Whole Genome Re-sequencing Data. Front. Plant Sci. 8, 359. doi:10.3389/fpls.2017.00359

Li, Y., Ruperao, P., Batley, J., Edwards, D., Martin, W., Hobson, K., et al. (2021). Genomic Prediction of Preliminary Yield Trials in Chickpea: Effect of Functional Annotation of SNPs and Environment. Plant Genome 15, e20166. doi:10.1002/tpg2.20166

Liu, Z., Crampton, M., Todd, A., and Kalavacharla, V. (2012). Identification of Expressed Resistance Gene-like Sequences by Data Mining in 454-derived Transcriptomic Sequences of Common Bean (Phaseolus vulgaris L.). BMC plant Biol. 12 (1), 1–9. doi:10.1186/1471-2229-12-42

Mackay, I., and Powell, W. (2007). Methods for Linkage Disequilibrium Mapping in Crops. Trends plant Sci. 12 (2), 57–63. doi:10.1016/j.tplants.2006.12.001

Madrid, E., Chen, W., Rajesh, P. N., Castro, P., Millan, T., and Gil, J. (2013). Allele-specific Amplification for the Detection of Ascochyta Blight Resistance in Chickpea. Euphytica 189, 183–190. doi:10.1007/s10681-012-0753-6

Madrid, E., Seoane, P., Claros, M. G., Barro, F., Rubio, J., Gil, J., et al. (2014). Genetic and Physical Mapping of the QTL AR3 Controlling Blight Resistance in Chickpea ( Cicer Arietinum L). Euphytica 198 (1), 69–78. doi:10.1007/s10681-014-1084-6

Mahto, R. K., Ambika, , , Singh, C., Chandana, B. S., Singh, R. K., Verma, S., et al. (2022). Chickpea Biofortification for Cytokinin Dehydrogenase via Genome Editing to Enhance Abiotic-Biotic Stress Tolerance and Food Security. Front. Genet. 13, 900324. doi:10.3389/fgene.2022.900324

Malhotra, R. S., Pundir, R. P. S., and Slinkard, A. E. (1987). “Genetic Resources of Chickpea,” in The Chickpea . Editors M. C. Saxena, and K. B. Singh (Aberystwyth: CAB Int. Cambrian News Ltd ), 67–81.

Mallikarjuna, B. P., Samineni, S., Thudi, M., Sajja, S. B., Khan, A. W., Patil, A., et al. (2017). Molecular Mapping of Flowering Time Major Genes and QTLs in Chickpea ( Cicer Arietinum L.). Front. Plant Sci. 8, 1140. doi:10.3389/fpls.2017.01140

Mannur, D. M., Babbar, A., Thudi, M., Sabbavarapu, M. M., Roorkiwal, M., Yeri, S. B., et al. (2019). Super Annigeri 1 and Improved JG 74: Two Fusarium Wilt-Resistant Introgression Lines Developed Using Marker-Assisted Backcrossing Approach in Chickpea ( Cicer Arietinum L.). Mol. Breed. 39 (1), 1–13. doi:10.1007/s11032-018-0908-9

Maqbool, M. A., Aslam, M., and Ali, H. (2017). Breeding for Improved Drought Tolerance in Chickpea ( Cicer Arietinum L.). Plant Breed. 136 (3), 300–318. doi:10.1111/pbr.12477

Marshall, D. R. (1989). “Limitations to the Use of Germplasm Collections,” in The Use of Plant Genetic Resources . Editor A. H. D. Brown, O. H. Frankel, D. R. Marshall, and J. T. Williams (New York: Cambridge University Press ), 105–120.

Meena, A. K., Gurjar, D., and Kumhar, B. L. (2017). Pre-breeding Is a Bridge between Wild Species and Improved Genotypes—A Review. Chem. Sci. Rev. Lett. 6 (22), 1141–1151. doi:10.20546/ijcmas.2017.606.007

Mehrotra, M., Singh, A. K., Sanyal, I., Altosaar, I., and Amla, D. V. (2011). Pyramiding of Modified cry1Ab and cry1Ac Genes of Bacillus Thuringiensis in Transgenic Chickpea ( Cicer Arietinum L.) for Improved Resistance to Pod Borer Insect Helicoverpa Armigera . Euphytica 182 (1), 87–102. doi:10.1007/s10681-011-0501-3

Milla´n, T., Rubio, J., Iruela, M., Daly, K., Cubero, J. I., and Gil, J. (2003). Markers Associated with Ascochyta Blight Resistance in Chickpea and Their Potential in Marker-Assisted Selection. Field Crops Res. 84, 373–384.

Molina, C., Rotter, B., Horres, R., Udupa, S., Besser, B., Bellarmino, L., et al. (2008). SuperSAGE: the Drought Stress-Responsive Transcriptome of Chickpea Roots. BMC Genom. 9 (1), 553. doi:10.1186/1471-2164-9-553

Morozova, O., and Marra, M. A. (2008). Applications of Next-Generation Sequencing Technologies in Functional Genomics. Genomics 92 (5), 255–264. doi:10.1016/j.ygeno.2008.07.001

Muehlbauer, F. J., and Singh, K. B. (1987). “Genetics of Chickpea,” in The Chickpea . Editors M. C. Saxena, and K. B. Singh (Wallingford: CAB Int. ), 99–126.

Mulwa, R. M. S., Kimurto, P. K., and Towett, B. K. (2010). “Evaluation and Selection of Drought and Pod Borer ( Helicoverpa Armigera ) Tolerant Chickpea Genotypes for Introduction in Semi-arid Areas of Kenya.,” in Second RUFORUM Biennial Meeting , Entebbe, Uganda , 20 - 24 September 2010 .

Nass, L. L., and Paterniani, E. (2000). Pre-breeding: a Link between Genetic Resources and Maize Breeding. Sci. Agric. 57, 581–587. doi:10.1590/s0103-90162000000300035

Nayak, S. N., Zhu, H., Varghese, N., Datta, S., Choi, H., Horres, R., et al. (2010). Integration of Novel SSR and Gene-Based SNP Marker Loci in the Chickpea Genetic Map and Establishment of New Anchor Points with Medicago Truncatula Genome. Theor. Appl. Genet. 120, 1415–1441. doi:10.1007/s00122-010-1265-1

Nene, Y. L., and Haware, M. P. (1980). Screening Chickpea for Resistance to Wilt. Plant Dis. 64 (4), 379–380. doi:10.1094/pd-64-379

Nguyen, T. T., Taylor, P. W. J., Redden, R. J., and Ford, R. (2004). Genetic Diversity Estimates in Cicer Using AFLP Analysis. Plant Breed. 123 (2), 173–179. doi:10.1046/j.1439-0523.2003.00942.x

Pande, S. K. H. M. S., Siddique, K. H. M., Kishore, G. K., Bayaa, B., Gaur, P. M., Gowda, , et al. (2005). Ascochyta Blight of Chickpea ( Cicer Arietinum L.): a Review of Biology, Pathogenicity, and Disease Management. Aust. J. Agric. Res. 56 (4), 317–332. doi:10.1071/ar04143

Pande, S., Galloway, J., Gaur, P. M., Siddique, K. H. M., Tripathi, H. S., Taylor, P., et al. (2006). Botrytis Grey Mould of Chickpea: a Review of Biology, Epidemiology, and Disease Management. Aust. J. Agric. Res. 57 (11), 1137–1150. doi:10.1071/ar06120

Pande, S., Sharma, M., Gaur, P. M., and Gowda, C. L. L. (2010). Host Plant Resistance to Ascochyta Blight of Chickpea . Hyderabad, India: ICRISAT . Information Bulletin No.82.

Pandey, A., Chakraborty, S., Datta, A., and Chakraborty, N. (2008). Proteomics Approach to Identify Dehydration Responsive Nuclear Proteins from Chickpea ( Cicer Arietinum L.). Mol. Cell. Proteomics 7 (1), 88–107. doi:10.1074/mcp.m700314-mcp200

Pandey, M. K., Roorkiwal, M., Singh, V. K., Ramalingam, A., Kudapa, H., Thudi, M., et al. (2016). Emerging Genomic Tools for Legume Breeding: Current Status and Future Prospects. Front. Plant Sci. 7, 455. doi:10.3389/fpls.2016.00455

Parameshwarappa, S. G., and Salimath, P. M. (2008). Field Screening of Chickpea Genotypes for Drought Resistance. Karnataka J. Agric. Sci. 21 (1), 113–114.

Parida, S. K., Srivastava, R., and Bajaj, D. (2017). A Genome-wide mQTL-Seq Scan Identifies Potential Molecular Signatures Regulating Plant Height in Chickpea. Plant Mol. Biol. Report. 35 (2), 273–286. doi:10.1007/s11105-016-1021-z

Pascual, L., Desplat, N., Huang, B. E., Desgroux, A., Bruguier, L., Bouchet, J. P., et al. (2015). Potential of a Tomato MAGIC Population to Decipher the Genetic Control of Quantitative Traits and Detect Causal Variants in the Resequencing Era. Plant Biotechnol. J. 13 (4), 565–577. doi:10.1111/pbi.12282

Patil, A. S., and Kamble, G. C. (2014). Comparative Protein Profiling Study in Wild Chickpea and its Induced Mutants. Experiment 18, 1297–1303.

Paul, P. J., Samineni, S., Thudi, M., Sajja, S. B., Rathore, A., Das, R. R., et al. (2018). Molecular Mapping of QTLs for Heat Tolerance in Chickpea. Int. J. Mol. Sci. 19, 2166. doi:10.3390/ijms19082166

Pavan, S., Lotti, C., Marcotrigiano, A. R., Mazzeo, R., Bardaro, N., Bracuto, V., et al. (2017). A Distinct Genetic Cluster in Cultivated Chickpea as Revealed by Genome-wide Marker Discovery and Genotyping. Plant Genome 10 (2), 1–9. doi:10.3835/plantgenome2016.11.0115

Pourkheirandish, M., Golicz, A. A., Bhalla, P. L., and Singh, M. B. (2020). Global Role of Crop Genomics in the Face of Climate Change. Front. Plant Sci. 11, 922. doi:10.3389/fpls.2020.00922

Pundir, R. P. S., and Mengesha, M. H. (1995). Cross Compatibility between Chickpea and its Wild Relative, Cicer Echinospermum Davis. Euphytica 83 (3), 241–245. doi:10.1007/bf01678136

Pundir, R. P. S., and Reddy, K. N. (1989). Induction, Genetics and Possible Use of Glabrousness in Chickpea. Euphytica 42 (1), 141–144. doi:10.1007/bf00042625

Pundir, R. P. S., Rao, N. K., and van der Maesen, L. J. G. (1985). Distribution of Qualitative Traits in the World Germplasm of Chickpea ( Cicer Arietinum L.). Euphytica 34, 697–703. doi:10.1007/bf00035406

Pundir, R. P. S., Mengesha, M. H., and Reddy, K. N. (1990). Leaf Types and Their Genetics in Chickpea ( Cicer Arietinum L.). Euphytica 45 (3), 197–200. doi:10.1007/bf00032985

Rakshit, S., Ganapathy, K. N., Gomashe, S. S., Rathore, A., Ghorade, R. B., Nagesh Kumar, M. V., et al. (2012). GGE Biplot Analysis to Evaluate Genotype, Environment and Their Interactions in Sorghum Multi-Location Data. Euphytica 185 (3), 465–479. doi:10.1007/s10681-012-0648-6

Rao, L. S., Usha Rani, P., Deshmukh, P. S., Kumar, P. A., and Panguluri, S. K. (2007). RAPD and ISSR Fingerprinting in Cultivated Chickpea ( Cicer Arietinum L.) and its Wild Progenitor Cicer Reticulatum Ladizinsky. Genet. Resour. Crop Evol. 54 (6), 1235–1244. doi:10.1007/s10722-006-9104-6

Reddy, A. A. (2016). Pulse Production Technology: Status and Way Forward. Econ. Political Wkly. 44 (52), 73–80. doi:10.2139/ssrn.1537540

Rehman, A. U., Malhotra, R. S., Bett, K., Tar’an, B., Bueckert, R., and Warkentin, T. D. (2012). Mapping QTL Associated with Traits Affecting Grain Yield in Chickpea ( Cicer Arietinum L.) under Terminal Drought Stress. Crop Sci. 51, 450–463.

Rizkalla, S. W., Bellisle, F., and Slama, G. (2002). Health Benefits of Low Glycaemic Index Foods, Such as Pulses, in Diabetic Patients and Healthy Individuals. Br. J. Nutr. 88, 255–262. doi:10.1079/bjn2002715

Robertson, L. D., Singh, K. B., and Ocampo, B. (1995). A Catalog of Annual Wild Cicer Species . Beirut, Lebanon: International Center for Agricultural Research in the Dry Areas ICARDA .

Robertson, L. D., Ocampo, B., and Singh, K. B. (1997). Morphological Variation in Wild Annual Cicer Species in Comparison to the Cultigen. Euphytica 95 (3), 309–319. doi:10.1023/a:1003004516921

Roorkiwal, M., Von Wettberg, E. J., Upadhyaya, H. D., Warschefsky, E., Rathore, A., and Varshney, R. K. (2014). Exploring Germplasm Diversity to Understand the Domestication Process in Cicer Spp. Using SNP and DArT Markers. PLoS One 9 (7), e102016. doi:10.1371/journal.pone.0102016

Roorkiwal, M., Bharadwaj, C., Barmukh, R., Dixit, G. P., Thudi, M., Gaur, P. M., et al. (2020). Integrating Genomics for Chickpea Improvement: Achievements and Opportunities. Theor.Appl. Genet. 133, 1703–1720. doi:10.1007/s00122-020-03584-2

Sab, S., Lokesha, R., and Mannur, D. M. (2020). Genome-Wide SNP Discovery and Mapping QTLs for Seed Iron and Zinc Concentrations in Chickpea (Cicer Arietinum L.). Front. Nutr. 7, 559120. doi:10.3389/fnut.2020.559120

Sabbavarapu, M. M., Sharma, M., Chamarthi, S. K., Swapna, N., Rathore, A., Thudi, M., et al. (2013). Molecular Mapping of QTLs for Resistance to Fusarium Wilt (Race 1) and Ascochyta Blight in Chickpea ( Cicer Arietinum L.). Euphytica 193 (1), 121–133. doi:10.1007/s10681-013-0959-2

Saeed, A., and Darvishzadeh, R. (2017). Association Analysis of Biotic and Abiotic Stresses Resistance in Chickpea (Cicer spp.) Using AFLP Markers. Biotechnol. Biotechnol. Equip. 31 (4), 698–708. doi:10.1080/13102818.2017.1333455

Samineni, S., Siddique, K. H., Gaur, P. M., and Colmer, T. D. (2011). Salt Sensitivity of the Vegetative and Reproductive Stages in Chickpea ( Cicer Arietinum L.): Podding Is a Particularly Sensitive Stage. Environ. Exp. Bot. 71 (2), 260–268. doi:10.1016/j.envexpbot.2010.12.014

Samineni, S., Mahendrakar, M. D., Hotti, A., Chand, U., Rathore, A., and Gaur, P. M. (2022). Impact of Heat and Drought Stresses on Grain Nutrient Content in Chickpea: Genome-wide Marker-Trait Associations for Protein, Fe and Zn. Environ. Exp. Bot. 194, 104688. doi:10.1016/j.envexpbot.2021.104688

Sánchez-Vioque, R., Clemente, A., Vioque, J., Bautista, J., and Millán, F. (1999). Protein Isolates From Chickpea ( Cicer arietinum L.): Chemical Composition, Functional Properties and Protein Characterization. Food Chem. 64 (2), 237–243.

Sandhu, J. S. (2004). Pers. Communication . Punjab Agric. Univ., Ludhiana, Punjab .

Sanderson, L. A., Caron, C. T., Tan, R., Shen, Y., Liu, R., and Bett, K. E. (2019). KnowPulse: A Web-Resource Focused on Diversity Data for Pulse Crop Improvement. Front. Plant Sci. 10, 965. doi:10.3389/fpls.2019.00965

Santra, D. K., Tekeoglu, M., Ratnaparkhe, M., Kaiser, W. J., and Muehlbauer, F. J. (2000). Identification and Mapping of QTLs Conferring Resistance to Ascochyta Blight in Chickpea. Crop Sci. 40, 1606–1612. doi:10.2135/cropsci2000.4061606x

Sanyal, I., Singh, A. K., Kaushik, M., and Amla, D. V. (2005). Agrobacterium-mediated Transformation of Chickpea ( Cicer Arietinum L.) with Bacillus Thuringiensis cry1Ac Gene for Resistance against Pod Borer Insect Helicoverpa Armigera . Plant Sci. 168 (4), 1135–1146. doi:10.1016/j.plantsci.2004.12.015

Sarika, S., Arora, V., Iquebal, M. A., Rai, A., and Kumar, D. (2013). PIPEMicroDB: Microsatellite Database and Primer Generation Tool for Pigeonpea Genome. Database 2013, bas054. doi:10.1093/database/bas054

Savithri, K. S., Ganapathy, P. S., and Sinha, S. K. (1980). Sensitivity to Low Temperature in Pollen Germination and Fruit-Set in Cicer Arietinum L. J. Exp. Bot. 31 (2), 475–481. doi:10.1093/jxb/31.2.475

Saxena, N. P., and Johansen, C. (1990). “Realized Yield Potential in Chickpea and Physiological Considerations for Further Genetic Improvement,” in Proceedings of the International Congress of Plant Physiology (New Delhi: Indian Society for Plant Physiology and Biochemistry, Indian Agricultural Research Institute ), 279–288.1

Saxena, M. S., Bajaj, D., Das, S., Kujur, A., Kumar, V., Singh, M., et al. (2014). An Integrated Genomic Approach for Rapid Delineation of Candidate Genes Regulating Agro-Morphological Traits in Chickpea. DNA Res. 21, 695–710. doi:10.1093/dnares/dsu031

Saxena, R., K., Kale, S., M., Kumar, V., Parupalli, S., Joshi, S., Singh, V., K., et al. (2017). Genotyping-by-sequencing of Three Mapping Populations for Identification of Candidate Genomic Regions for Resistance to Sterility Mosaic Disease in Pigeonpea. Sci. Rep. 7 (1), 1–10. doi:10.1038/s41598-017-01535-4

Saxena, R., K., Patel, K., Sameer Kumar, C., V., Tyagi, K., Saxena, K.,B., and Varshney, R., K. (2018). Molecular Mapping and Inheritance of Restoration of Fertility (Rf) in A4 Hybrid System in Pigeonpea (Cajanus Cajan (L.) Millsp.). Theor. Appl. Genet. 131, 1605–1614. doi:10.1007/s00122-018-3101-y

Serraj, R., Krishanamurthy, L., and Upadhyay, H. D. (2004). Screening Chickpea Minicore Germplasm for Tolerance to Soil Salinity. Int. Chickpea Pigeonpea Newsl. (ICPN) 11, 29–32.

Severin, A. J., Woody, J. L., Bolon, Y. T., Joseph, B., Diers, B. W., Farmer, A. D., et al. (2010). RNA-seq Atlas of Glycine Max: a Guide to the Soybean Transcriptome. BMC Plant Biol. 10 (1), 1–16. doi:10.1186/1471-2229-10-160

Shah, T. M., Haq, M. A., Atta, B. M., Alam, S. S., and Ali, H. (2005). Evaluation of Cicer Species for Resistance to Ascochyta Blight. Pak. J. Bot. 37 (2), 431.

Shan, Q., Wang, Y., Li, J., Zhang, Y., Chen, K., Liang, Z., et al. (2013). Targeted Genome Modification of Crop Plants Using a CRISPR-Cas System. Nat. Biotechnol. 31, 686–688. doi:10.1038/nbt.2650

Sharma, H. C., Pampapathy, G., Lanka, S. K., and andRidsdill-Smith, T. J. (2005). Exploitation of Wild Cicer Reticulatum Germplasm for Resistance to Helicoverpa Armigera . J. Econ. Entomol. 98 (6), 2246–2253. doi:10.1093/jee/98.6.2246

Sharma, S., Upadhyaya, H. D., Roorkiwal, M., Varshney, R. K., and Gowda, C. L. (2013). “Chickpea,” in Genetic and Genomic Resources of Grain Legume Improvement , 81–111. doi:10.1016/b978-0-12-397935-3.00004-9

Sharma, H. C., Bhagwat, M. P., Pampapathy, G., Sharma, J. P., and Ridsdill-Smith, T. J. (2006). Perennial Wild Relatives of Chickpea as Potential Sources of Resistance to Helicoverpa Armigera . Genet. Resour. Crop Evol. 53 (1), 131–138. doi:10.1007/s10722-004-1951-4

Sharma, K. K., Lavanya, M., and Anjaiah, V. (2006). Agrobacterium-mediated Production of Transgenic Pigeonpea ( Cajanus Cajan L. Millsp.) Expressing the Synthetic Bt cry1Ab Gene. In Vitro Cellular. Dev. Biol. Plant. 42 (2), 165–173. doi:10.1079/ivp2005730

Sharma, H. C. (2004). A Little Help from Wild: Exploiting Wild Relatives of Chickpea for Resistance to Helicoverpa Armigera . Patancheru, India: ICRISAT .

Siddique, K., and Sedgley, R. (1986). Chickpea (Cicer-Arietinum-L), a Potential Grain Legume for Southwestern Australia - Seasonal Growth and Yield. Aust. J. Agric. Res. 37, 245–261. doi:10.1071/Ar9860245

Sillero, J. C., Moreno-Alías, I., and Rubiales, D. (2012). Identification and Characterization of Resistance to Rust (Uromyces Ciceris-Arietini (Grognot) Jacz. And Boyd) in a Germplasm Collection of Cicer Spp. Euphytica 188 (2), 229–238. doi:10.1007/s10681-012-0709-x

Singh, K. B., and Ocampo, B. (1993). Interspecific Hybridization in Annual Cicer Species. J. Genet. Breed. 47 (3), 199–204.

Singh, K. B., and Reddy, M. V. (1993). Resistance to Six Races of Ascochyta Rabiei in the World Germplasm Collection of Chickpeas. Crop Sci. 33 (1), 186–189. doi:10.2135/cropsci1993.0011183x003300010033x

Singh, K. B., and Weigand, S. (1994). Identification of Resistant Sources in Cicer Species to Liriomyza cicerina . Genet. Res. Crop Evol. 41, 75–79.

Singh, K. B., and Singh, O. (1997). “Prospects of Creating Higher Yield Potential in Chickpea,” in Recent Advances in Pulses Research (Kanpur: Indian Society of Pulses Research and Development, IIPR ), 247, 9.

Singh, K. B., and Weigand, S. (1994). Identification of Resistant Sources in Cicer Species to Liriomyza Cicerina. Genet. Resour. Crop Evol. 41 (2), 75–79. doi:10.1007/bf00053051

Singh, K. B., Malhotra, R. S., and Saxena, M. C. (1990). Sources for Tolerance to Cold in Cicer Species. Crop Sci. 30 (5), 1136–1138. doi:10.2135/cropsci1990.0011183x003000050036x

Singh, K. B., Malhotra, R. S., and Saxena, M. C. (1995). Additional Sources of Tolerance to Cold in Cultivated and Wild Cicer Species. Crop Sci. 35 (5), 1491–1497. doi:10.2135/cropsci1995.0011183x003500050037x

Singh, K. B., Ocampo, B., and Robertson, L. D. (1998). Diversity for Abiotic and Biotic Stress Resistance in the Wild Annual Cicer Species. Genet. Resour. Crop Evol. 45 (1), 9–17. doi:10.1023/a:1008620002136

Singh, N. P., Krishna, R., and Kumar, R. (2001). An Assay of Effects of Different Traits on Chickpea Grain Yield. Ann. Agric. Res. New Ser. 22 (4), 564–569.

Singh, S., Gumber, R. K., Joshi, N., and Singh, K. (2005). Introgression from Wild Cicer Reticulatum to Cultivated Chickpea for Productivity and Disease Resistance. Plant Breed. 124 (5), 477–480. doi:10.1111/j.1439-0523.2005.01146.x

Singh, A. K., Singh, R. V., Bharati, R. C., Chandra, N., Kumar, C., and Dimree, S. K. (2010). Introduction of Wild and Weedy Relatives of Crop Plants in India. Environ. Ecol. 28 (3A), 1715–1721.

Singh, R. P., Singh, I., Singh, S., and Sandhu, J. S. (2012). Assessment of Genetic Diversity Among Interspecific Derivatives in Chickpea. J. Food Legumes 25 (2), 150–152.

Singh, M., Dahiya, O. P., Singh, N., Nizar, A., Dutta, M., Tyagi, R. K., et al. (2013). Identification of an Upright Peduncle and Podding Genotype in Chickpea Germplasm Conserved in the National Gene Bank. Indian J. Plant Genet. Resour. 26 (2), 166–168.

Singh, M., Bisht, I. S., Dutta, M., Kumar, K., Basandrai, A. K., Kaur, L., et al. (2014). Characterization and Evaluation of Wild Annual Cicer Species for Agro-Morphological Traits and Major Biotic Stresses under Northwestern Indian Conditions. Crop Sci. 54, 229–239. doi:10.2135/cropsci2013.04.0225

Singh, M., Kumar, K., Bisht, I. S., Dutta, M., Rana, M. K., Rana, J. C., et al. (2015). Exploitation of Wild Annual Cicer Species for Widening the Gene Pool of Chickpea Cultivars. Plant Breed. 134 (2), 186–192. doi:10.1111/pbr.12254

Singh, U., Khemka, N., Rajkumar, M. S., Garg, R., and Jain, M. (2017). PLncPRO for Prediction of Long Non-coding RNAs (lncRNAs) in Plants and its Application for Discovery of Abiotic Stress-Responsive lncRNAs in Rice and Chickpea. Nucl. Acids Res. 45, e183. doi:10.1093/nar/gkx866

Singh, G. (1982). Screening Chickpea for Gray Mold Resistance. Inter. Chickpea Newsl. 7, 13–14.

Srinivasan, A., Saxena, N. P., and Johansen, C. (1999). Cold Tolerance during Early Reproductive Growth of Chickpea ( Cicer Arietinum L.): Genetic Variation in Gamete Development and Function. Field Crops Res. 60 (3), 209–222. doi:10.1016/s0378-4290(98)00126-9

Srivastava, R., Bajaj, D., Malik, A., Singh, M., and Parida, S. K. (2016). Transcriptome Landscape of Perennial Wild Cicer Microphyllum Uncovers Functionally Relevant Molecular Tags Regulating Agronomic Traits in Chickpea. Sci. Rep. 6 (1), 1–17. doi:10.1038/srep33616

Srivastava, R., Upadhyaya, H. D., Kumar, R., Daware, A., Basu, U., Shimray, P. W., et al. (2017). A Multiple QTL-Seq Strategy Delineates Potential Genomic Loci Governing Flowering Time in Chickpea. Front. Plant Sci. 8, 1105. doi:10.3389/fpls.2017.01105

Sudupak, M., Akkaya, S., and Kence, A. (2002). Analysis of Genetic Relationships Among Perennial and Annual Cicer Species Growing in Turkey Using RAPD Markers. Theor. Appl. Genet. 105 (8), 1220–1228. doi:10.1007/s00122-002-1060-8

Sudupak, M. A., Akkaya, M. S., and Kence, A. (2004). Genetic Relationships Among Perennial and Annual Cicer Species Growing in Turkey Assessed by AFLP Fingerprinting. Theor.Appl. Genet. 108, 937–944. doi:10.1007/s00122-003-1505-8

Takagi, H., Abe, A., Yoshida, K., Kosugi, S., Natsume, S., Mitsuoka, C., et al. (2013). QTL-seq: Rapid Mapping of Quantitative Trait Loci in Rice by Whole Genome Resequencing of DNA from Two Bulked Populations. Plant J. 74 (1), 174–183. doi:10.1111/tpj.12105

Tan, G. Z., Das Bhowmik, S. S., Hoang, T. M., Karbaschi, M. R., Long, H., Cheng, A., et al. (2018). Investigation of Baseline Iron Levels in Australian Chickpea and Evaluation of a Transgenic Biofortifcation Approach. Front. Plant Sci. 9, 788. doi:10.3389/fpls.2018.00788

Tanksley, S. D., and McCouch, S. R. (1997). Seed Banks and Molecular Maps: Unlocking Genetic Potential from the Wild. science 277 (5329), 1063–1066. doi:10.1126/science.277.5329.1063

Taran, B., Warkentin, T. D., Vandenberg, A., and Holm, F. A. (2010). Variation in Chickpea Germplasm for Tolerance to Imazethapyr and Imazamox Herbicides. Can. J. Plant Sci. 90 (1), 139–142. doi:10.4141/cjps09061

Tayyar, R. I., and Waines, J. G. (1996). Genetic Relationships Among Annual Species of Cicer (Fabaceae) Using Isozyme Variation. Theor. Appl. Genet. 92 (2), 245–254. doi:10.1007/bf00223381

Tekeoglu, M., Rajesh, P. N., and Muehlbauer, F. J. (2002). Integration of Sequence Tagged Microsatellite Sites to Chickpea Genetic Map. Theor. Appl. Genet. 105, 847–854. doi:10.1007/s00122-002-0993-2

Tewari-Singh, N., Sen, J., Kiesecker, H., Reddy, V. S., Jacobsen, H. J., and Mukherjee, S. G. (2004). Use of a Herbicide or Lysine Plus Threonine for Non Antibiotic Selection of Transgenic Chickpea. Plant Cell Rep. 22, 576–583. doi:10.1007/s00299-003-0730-6

Thompson, J. P., Reen, R. A., Clewett, T. G., Sheedy, J. G., Kelly, A. M., Gogel, B. J., et al. (2011). Hybridisation of Australian Chickpea Cultivars with Wild Cicer Spp. Increases Resistance to Root-Lesion Nematodes ( Pratylenchus Thornei and P. Neglectus ). Australas. Plant Pathol. 40 (6), 601–611. doi:10.1007/s13313-011-0089-z

Thudi, M., Bohra, A., Nayak, S. N., Varghese, N., Shah, T. M., Penmetsa, R. V., et al. (2011). Novel SSR Markers from BAC-End Sequences, DArT Arrays and a Comprehensive Genetic Map with 1291 Marker Loci for Chickpea ( Cicer Arietinum L.). PLoS ONE 6 (11), e27275. doi:10.1371/journal.pone.0027275

Thudi, M., Upadhyaya, H. D., Rathore, A., Gaur, P. M., Krishnamurthy, L., Roorkiwal, M., et al. (2014). Genetic Dissection of Drought and Heat Tolerance in Chickpea through Genome-wide and Candidate Gene-Based Association Mapping Approaches. Plos One 9 (5), e96758. doi:10.1371/journal.pone.0096758

Thudi, M., Khan, A. W., Kumar, V., Gaur, P. M., Katta, K., Garg, V., et al. (2016). Whole Genome Re-sequencing Reveals Genome-wide Variations Among Parental Lines of 16 Mapping Populations in Chickpea ( Cicer Arietinum L.). BMC plant Biol. 16 (1), 53–64. doi:10.1186/s12870-015-0690-3

Thudi, M., Chen, Y., Pang, J., Kalavikatte, D., Bajaj, P., Roorkiwal, M., et al. (2021). Novel Genes and Genetic Loci Associated with Root Morphological Traits, Phosphorus-Acquisition Efficiency and Phosphorus-Use Efficiency in Chickpea. Front. Plant Sci. 12, 636973. doi:10.3389/fpls.2021.636973

Toker, C., Canci, H., and Yildirim, T. O. L. G. A. (2007). Evaluation of Perennial Wild Cicer Species for Drought Resistance. Genet. Resour. crop Evol. 54 (8), 1781–1786. doi:10.1007/s10722-006-9197-y

Toker, C., Berger, J., Eker, T., Sari, D., Sari, H., Gokturk, R. S., et al. (2021). Cicer Turcicum : a New Cicer Species and its Potential to Improve Chickpea. Front. Plant Sci. 12, 587. doi:10.3389/fpls.2021.662891

Toker, C. (2005). Preliminary Screening and Selection for Cold Tolerance in Annual Wild Cicer Species. Genet. Resour. Crop Evol. 52 (1), 1–5. doi:10.1007/s10722-005-1743-5

Trick, M., Adamsk, N., M., Mugford, S., G., Jiang, C., C., Febrer, M., and Uauy, C. (2012). Combining SNP Discovery from Next-Generation Sequencing Data with Bulked Segregant Analysis (BSA) to Fine-Map Genes in Polyploidy Wheat. BMC Plant Biol. 12 (1), 1–17. doi:10.1186/1471-2229-12-14

Turner, N. C., Colmer, T. D., Quealy, J., Pushpavalli, R., Krishnamurthy, L., Kaur, J., et al. (2013). Salinity Tolerance and Ion Accumulation in Chickpea ( Cicer Arietinum L.) Subjected to Salt Stress. Plant Soil 365 (1), 347–361. doi:10.1007/s11104-012-1387-0

Upadhyaya, H. D., Dronavalli, N., Gowda, C. L. L., and Singh, S. (2011). Identification and Evaluation of Chickpea Germplasm for Tolerance to Heat Stress. Crop Sci. 51 (5), 2079–2094. doi:10.2135/cropsci2011.01.0018

Upadhyaya, H. D., Dronavalli, N., Dwivedi, S. L., Kashiwagi, J., Krishnamurthy, L., Pande, S., et al. (2013). Mini Core Collection as a Resource to Identify New Sources of Variation. Crop Sci. 53 (6), 2506e2517. doi:10.2135/cropsci2013.04.0259

Upadhyaya, H. D., Bajaj, D., Das, S., Kumar, V., Gowda, C. L. L., Sharma, S., et al. (2016). Genetic Dissection of Seed-Iron and Zinc Concentrations in Chickpea. Sci. Rep. 6 (1), 1–12. doi:10.1038/srep24050

Vadez, V., Krishnamurthy, L., Serraj, R., Gaur, P. M., Upadhyaya, H. D., Hoisington, D. A., et al. (2007). Large Variation in Salinity Tolerance in Chickpea Is Explained by Differences in Sensitivity at the Reproductive Stage. Field crops Res. 104 (1-3), 123–129. doi:10.1016/j.fcr.2007.05.014

Vadez, V., Krishnamurthy, L., Thudi, M., Anuradha, C., Colmer, T. D., and Turner, N. C. (2012). Assessment of ICCV 2 X JG 62 Chickpea Progenies Shows Sensitivity of Reproduction to Salt Stress and Reveals QTL for Seed Yield and Yield Components. Mol. Breed. 30, 9–21. doi:10.1007/s11032-011-9594-6

Van der Maesen, L. J. G., and Pundir, R. P. S. (1984). Availability and Use of Wild Cicer Germplasm. FAO / IBPGR Plant Gen. Resour. Newsl. 57, 19–24.

Van der Maesen, L. J. G., Maxted, N., Javadi, F., Coles, S., and Davies, A. M. R. (2007). “Taxonomy of the Genus Cicer Revisited,” in Chickpea Breed. Management (Wallingford: CAB Int. ), 14–46.

Van der Maesen, L. J. G. (1984). “Taxonomy, Distribution and Evolution,” in Genetic Resources and Their Exploitation: Chickpea, Faba Bean and Lentils . Editors J. R. Witcombe, and W. Erskine ( The Hague, Netherlands , 95–101. doi:10.1007/978-94-009-6131-9_9

Van Dorrestein, B., Baum, M., and Malhotra, R. S. (1998). “Interspecific Hybridization between Cultivated Chickpea ( Cicer Arietinum L.) and the Wild Annual Species C. Judaicum Boiss, C. Pinnatifidum Jaub. And Sp. and C. Bijugum KH. Rech,” in Proceedings of the 3rdEuropean Grain Legume Conference , 362.

Vandemark, G. J., Grusak, M. A., and McGee, R. J. (2018). Mineral Concentrations of Chickpea and Lentil Cultivars and Breeding Lines Grown in the US Pacific Northwest. Crop J. 6 (3), 253–262. doi:10.1016/j.cj.2017.12.003

Vanderschuren, H., Lentz, E., Zainuddin, I., and FGruissem, W. (2013). Proteomics of Model and Crop Plant Species: Status, Current Limitations and Strategic Advances for Crop Improvement. J. Proteome. 93, 5–19. doi:10.1016/j.jprot.2013.05.036

Varshney, R. K., Graner, A., and Sorrells, M. E. (2005). Genomic-assisted Breeding for Crop Improvement. Trends Plant Sci. 10, 621–630. doi:10.1016/j.tplants.2005.10.004

Varshney, R. K., Hiremath, P. J., Lekha, P., Kashiwagi, J., Balaji, J., Deokar, A. A., et al. (2009). A Comprehensive Resource of Drought- and Salinity-Responsive ESTs for Gene Discovery and Marker Development in Chickpea ( Cicer Arietinum L.). BMC Genom 10 (1), 1–18. doi:10.1186/1471-2164-10-523

Varshney, R. K., Ribaut, J. M., Buckler, E. S., Tuberosa, R., Rafalski, J. A., and Langridge, P. (2012). Can Genomics Boost Productivity of Orphan Crops? Nat. Biotechnol. 30 (12), 1172–1176. doi:10.1038/nbt.2440

Varshney, R. K., Song, C., Saxena, R. K., Azam, S., Yu, S., Sharpe, A. G., et al. (2013). Draft Genome Sequence of Chickpea ( Cicer Arietinum L.) Provides a Resource for Trait Improvement. Nat. Biotechnol. 31 (3), 240–246. doi:10.1038/nbt.2491

Varshney, R. K., Thudi, M., Nayak, S. N., Gaur, P. M., Kashiwagi, J., Krishnamurthy, L., et al. (2014). Genetic Dissection of Drought Tolerance in Chickpea ( Cicer Arietinum L.). Theor. Appl. Genet. 127 (2), 445–462. doi:10.1007/s00122-013-2230-6

Varshney, R., K., Saxena, R., K., Upadhyaya, H., D., Khan, A. W., Yu, Y., Kim, C., et al. (2017). Whole-genome Resequencing of 292 Pigeonpea Accessions Identifies Genomic Regions Associated with Domestication and Agronomic Traits. Nat. Genet. 49, 1082–1088. doi:10.1038/ng.3872

Varshney, R. K., Thudi, M., Pandey, M. K., Tardieu, F., Ojiewo, C., Vadez, V., et al. (2018). Accelerating Genetic Gains in Legumes for the Development of Prosperous Smallholder Agriculture: Integrating Genomics, Phenotyping, Systems Modelling and Agronomy. J. Exp. Bot. 69 (13), 3293–3312. doi:10.1093/jxb/ery088

Varshney, R., K., Pandey, M. K., Bohra, A., Singh, V. K., Thudi, M., and Saxena, R. K. (2019). Toward the Sequence-Based Breeding in Legumes in the Post-genome Sequencing Era. Theor. Appl. Genet. 132, 797–816. doi:10.1007/s00122-018-3252-x

Varshney, R. K., Roorkiwal, M., Sun, S., Bajaj, P., Chitikineni, A., Thudi, M., et al. (2021). A Chickpea Genetic Variation Map Based on the Sequencing of 3,366. Genomes. Nat. 599 (7886), 622–627. doi:10.1038/s41586-021-04066-1

Varshney, R. K. (2016). Exciting Journey of 10 Years from Genomes to Fields and Markets: Some Success Stories of Genomics-Assisted Breeding in Chickpea, Pigeonpea and Groundnut. Plant Sci. 242, 98–107. doi:10.1016/j.plantsci.2015.09.009

Verdier, J., Torres-Jerez, I., Wang, M., Andriankaja, A., Allen, S. N., He, J., et al. (2013). Establishment of the Lotus Japonicus Gene Expression Atlas (LjGEA) and its Use to Explore Legume Seed Maturation. Plant J. 74 (2), 351–362. doi:10.1111/tpj.12119

Verma, M. M., Sandhu, J. S., Rraar, H. S., and Brar, J. S. (1990). Crossability Studies in Different Species of Cicer (L.). Crop Improv. 17 (2), 179–181.

Verma, M., Kumar, V., Patel, R. K., Garg, R., and Jain, M. (2015). CTDB: an Integrated Chickpea Transcriptome Database for Functional and Applied Genomics. PLoS ONE 10, e0136880. doi:10.1371/journal.pone.0136880

Wood, J. A., and Grusak, M. A. (2007). “Nutritional Value of Chickpea,” in Chickpea Breeding and Management . Editors S. S. Yadav, R. Redden, W. Chen, and B. Sharma (Wallingford: CAB International ), 101–142.

Xu, Y., Obielodan, M., Sismour, E., Arnett, A., Alzahrani, S., and Zhang, B. (2017). Physicochemical, Functional, Thermal and Structural Properties of Isolated Kabuli Chickpea Proteins as Affected by Processing Approaches. Int. J. Food Sci. 52 (5), 1147–1154. doi:10.1111/ijfs.13400

Yust, M. M., Pedroche, J., and Giron-Calle, J. (2003). Production of ACE Inhibitory Peptides by Digestion of Chickpea Legumin with Alcalase. J. Food Chem. 81, 363–369. doi:10.1016/s0308-8146(02)00431-4

Zaman-Allah, M., Jenkinson, D. M., and Vadez, V. (2011a). Chickpea Genotypes Contrasting for Seed Yield under Terminal Drought Stress in the Field Differ for Traits Related to the Control of Water Use. Funct. Plant Biol. 38 (4), 270–281. doi:10.1071/fp10244

Zaman-Allah, M., Jenkinson, D. M., and Vadez, V. (2011b). A Conservative Pattern of Water Use, rather Than Deep or Profuse Rooting, Is Critical for the Terminal Drought Tolerance of Chickpea. J. Exp. Bot. 62 (12), 4239–4252. doi:10.1093/jxb/err139

Keywords: broadening the genetic base, cicer, genetic diversity (GD), gene editing, multiple resistance, omics, QTL mapping, wild chickpea utilization

Citation: Singh RK, Singh C, Ambika , Chandana BS, Mahto RK, Patial R, Gupta A, Gahlaut V, Gayacharan , Hamwieh A, Upadhyaya HD and Kumar R (2022) Exploring Chickpea Germplasm Diversity for Broadening the Genetic Base Utilizing Genomic Resourses. Front. Genet. 13:905771. doi: 10.3389/fgene.2022.905771

Received: 27 March 2022; Accepted: 24 June 2022; Published: 04 August 2022.

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*Correspondence: Rajendra Kumar, [email protected]

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Development of ssr markers and evaluation of genetic diversity of endangered plant saussurea involucrata.

1. Introduction

2. materials and methods, 2.1. plant sampling and preservation, 2.2. identification and development of genomic ssrs, 2.3. pcr amplification and electrophoresis detection, 2.4. data analysis, 3.1. analysis of the distribution of ssrs in the genome of s. involucrata, 3.2. linkage disequilibrium tests, 3.3. genetic diversity of the s. involucrata, 3.4. genetic relationship and population structure analysis, 4. discussion, 4.1. genetic diversity of s. involucrata, 4.2. genetic differentiation of s. involucrata, 4.3. conservation of s. involucrata, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Rawat, U.; Agarwal, N. Biodiversity: Concept, threats and conservation. Environ. Conserv. J. 2015 , 16 , 19–28. [ Google Scholar ] [ CrossRef ]
• Pauls, S.U.; Nowak, C.; Bálint, M.; Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 2013 , 22 , 925–946. [ Google Scholar ] [ CrossRef ]
• Jump, A.S.; Marchant, R.; Peñuelas, J. Environmental change and the option value of genetic diversity. Trends Plant Sci. 2009 , 14 , 51–58. [ Google Scholar ] [ CrossRef ]
• Ellegren, H.; Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 2016 , 17 , 422–433. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• De Kort, H.; Prunier, J.G.; Ducatez, S.; Honnay, O.; Baguette, M.; Stevens, V.M.; Blanchet, S. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 2021 , 12 , 516. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chik, W.-I.; Zhu, L.; Fan, L.-L.; Yi, T.; Zhu, G.-Y.; Gou, X.-J.; Tang, Y.-N.; Xu, J.; Yeung, W.-P.; Zhao, Z.-Z.; et al. Saussurea involucrata : A review of the botany, phytochemistry and ethnopharmacology of a rare traditional herbal medicine. J. Ethnopharmacol. 2015 , 172 , 44–60. [ Google Scholar ] [ CrossRef ]
• He, L.; Fan, S. From Saussurea bracteata to Saussurea tianschanica . For. Hum. 2018 , 4 , 66–73. [ Google Scholar ]
• Dai, P. Study on Reproductive Ecology of Saussurea involucrata . Master’s Thesis, Xinjiang Agricultural University, Urumqi, China, 2008. [ Google Scholar ]
• Xie, X.; Zhu, Y. Survival wisdom of Saussurea involucrata . Xinjiang For. 2023 , 3 , 31–32. [ Google Scholar ] [ CrossRef ]
• Yi, T.; Chen, H.-B.; Zhao, Z.-Z.; Jiang, Z.-H.; Cai, S.-Q.; Wang, T.-M. Identification and Determination of the Major Constituents in the Traditional Uighur Medicinal Plant Saussurea involucrata by LC-DAD-MS. Chromatographia 2009 , 69 , 537–542. [ Google Scholar ] [ CrossRef ]
• Zhai, K.; Wang, C. Research progress of Saussurea involucrata . Hubei Agric. Sci. 2009 , 48 , 2869–2873. [ Google Scholar ]
• Zhuang, L.; Li, W. Utilization, development and protection of Saussurea involucrata resources in Xinjiang. Resour. Environ. Arid. Areas 2006 , 2 , 195–202. [ Google Scholar ] [ CrossRef ]
• Kuo, C.-L.; Agrawal, D.-C.; Chang, H.-C.; Chiu, Y.-T.; Huang, C.-P.; Chen, Y.-L.; Huang, S.-H.; Tsay, H.-S. In vitro culture and production of syringin and rutin in Saussurea involucrata (Kar. et Kir.)—An endangered medicinal plant. Bot. Stud. 2015 , 56 , 12. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, R.; Liu, J.; Liu, S.; Guan, S.; Jiao, P. Characterization of the complete chloroplast genome of Saussurea involuerata (Compositae), an endangered species endemic to China. Mitochondrial DNA Part B 2020 , 5 , 511–512. [ Google Scholar ] [ CrossRef ]
• Yuan, X.F.; Dai, Z.H.; Wang, X.D.; Zhao, B. Assessment of genetic stability in tissue-cultured products and seedlings of Saussurea involucrata by RAPD and ISSR markers. Biotechnol. Lett. 2009 , 31 , 1279–1287. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wei, S.; Yang, W.; Wang, X.; Hou, Y. High genetic diversity in an endangered medicinal plant, Saussurea involucrata (Saussurea, Asteraceae), in western Tianshan Mountains, China. Conserv. Genet. 2017 , 18 , 1435–1447. [ Google Scholar ] [ CrossRef ]
• Hu, L.; Lu, T.; Wang, X.; Wang, J.; Shi, W. Conservation Priorities and Demographic History of Saussurea involucrata in the Tianshan Mountains and Altai Mountains. Life 2023 , 13 , 2209. [ Google Scholar ] [ CrossRef ]
• Zhang, B.; Yao, Y.; Cheng, W.; Zhou, C.; Lu, Z.; Chen, X.; Alshir, K.; ErDowlet, I.; Zhang, L.; Shi, Q. Human-Induced Changes to Biodiversity and Alpine Pastureland in the Bayanbulak Region of the East Tienshan Mountains. Mt. Res. Dev. 2002 , 22 , 383–389. [ Google Scholar ] [ CrossRef ]
• Tautz, D.; Renz, M. Simple sequences are ubiquitous repetitive components of eukaryotic genomes. Nucleic Acids Res. 1984 , 12 , 4127–4138. [ Google Scholar ] [ CrossRef ]
• Litt, M.; Luty, J.A. A hypervariable microsatellite revealed by in vitro amplification of a dinucleotide repeat within the cardiac muscle actin gene. Am. J. Hum. Genet. 1989 , 44 , 397–401. [ Google Scholar ]
• Tóth, G.; Gáspári, Z.; Jurka, J. Microsatellites in different eukaryotic genomes: Survey and analysis. Genome Res. 2000 , 10 , 967–981. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Amiteye, S. Basic concepts and methodologies of DNA marker systems in plant molecular breeding. Heliyon 2021 , 7 , e08093. [ Google Scholar ] [ CrossRef ]
• Yang, H.; Li, X.; Liu, D.; Chen, X.; Li, F.; Qi, X.; Luo, Z.; Wang, C. Genetic diversity and population structure of the endangered medicinal plant Phellodendron amurense in China revealed by SSR markers. Biochem. Syst. Ecol. 2016 , 66 , 286–292. [ Google Scholar ] [ CrossRef ]
• Jiao, Y.; Li, X.-W.; Chai, M.-L.; Jia, H.-J.; Chen, Z.; Wang, G.-Y.; Chai, C.-Y.; van de Weg, E.; Gao, Z.-S. Development of simple sequence repeat (SSR) markers from a genome survey of Chinese bayberry ( Myrica rubra ). BMC Genom. 2012 , 13 , 201. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sethy, N.K.; Shokeen, B.; Edwards, K.J.; Bhatia, S. Development of microsatellite markers and analysis of intraspecific genetic variability in chickpea ( Cicer arietinum L.). Theor. Appl. Genet. 2006 , 112 , 1416–1428. [ Google Scholar ] [ CrossRef ]
• Du, Q.; Wang, B.; Wei, Z.; Zhang, D.; Li, B. Genetic Diversity and Population Structure of Chinese White Poplar ( Populus tomentosa ) Revealed by SSR Markers. J. Hered. 2012 , 103 , 853–862. [ Google Scholar ] [ CrossRef ]
• Ramakrishnan, M.; Ceasar, S.A.; Duraipandiyan, V.; Al-Dhabi, N.A.; Ignacimuthu, S. Assessment of genetic diversity, population structure and relationships in Indian and non-Indian genotypes of finger millet ( Eleusine coracana (L.) Gaertn) using genomic SSR markers. SpringerPlus 2016 , 5 , 120. [ Google Scholar ] [ CrossRef ]
• Gil, J.; Um, Y.; Kim, S.; Kim, O.T.; Koo, S.C.; Reddy, C.S.; Kim, S.-C.; Hong, C.P.; Park, S.-G.; Kim, H.B.; et al. Development of Genome-Wide SSR Markers from Angelica gigas Nakai Using Next Generation Sequencing. Genes 2017 , 8 , 238. [ Google Scholar ] [ CrossRef ]
• Liu, F.; Hong, Z.; Xu, D.; Jia, H.; Zhang, N.; Liu, X.; Yang, Z.; Lu, M. Genetic Diversity of the Endangered Dalbergia odorifera Revealed by SSR Markers. Forests 2019 , 10 , 225. [ Google Scholar ] [ CrossRef ]
• Wang, S.-Q. Genetic diversity and population structure of the endangered species Paeonia decomposita endemic to China and implications for its conservation. BMC Plant Biol. 2020 , 20 , 510. [ Google Scholar ] [ CrossRef ]
• Serrote, C.M.L.; Reiniger, L.R.S.; Silva, K.B.; Rabaiolli, S.M.d.S.; Stefanel, C.M. Determining the Polymorphism Information Content of a Molecular Marker. Gene 2020 , 726 , 144175. [ Google Scholar ] [ CrossRef ]
• Falk, D.A. Integrated Strategies for Conserving Plant Genetic Diversity. Ann. Mo. Bot. Gard. 1990 , 77 , 38–47. [ Google Scholar ] [ CrossRef ]
• Escudero, A.; Iriondo, J.M.; Torres, M. Spatial analysis of genetic diversity as a tool for plant conservation. Biol. Conserv. 2003 , 113 , 351–365. [ Google Scholar ] [ CrossRef ]
• Yu, Y.-L.; Wang, H.-C.; Yu, Z.-X.; Schinnerl, J.; Tang, R.; Geng, Y.-P.; Chen, G. Genetic diversity and structure of the endemic and endangered species Aristolochia delavayi growing along the Jinsha River. Plant Divers. 2021 , 43 , 225–233. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gaudeul, M.; Taberlet, P.; Till-Bottraud, I. Genetic diversity in an endangered alpine plant, Eryngium alpinum L. (Apiaceae), inferred from amplified fragment length polymorphism markers. Mol. Ecol. 2000 , 9 , 1625–1637. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, Y.-Y.; Liao, L.; Li, W.; Li, Z.-Z. Genetic diversity and population structure of the endangered alpine quillwort Isoetes hypsophila Hand.-Mazz. revealed by AFLP markers. Plant Syst. Evol. 2010 , 290 , 127–139. [ Google Scholar ] [ CrossRef ]
• Milarska, S.E.; Androsiuk, P.; Bednarek, P.T.; Larson, K.; Giełwanowska, I. Genetic variation of Cerastium alpinum L. from Babia Góra, a critically endangered species in Poland. J. Appl. Genet. 2023 , 64 , 37–53. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, Y.; Liang, Q.; Hao, G.; Chen, C.; Liu, J. Population genetic analyses of the endangered alpine Sinadoxa corydalifolia (Adoxaceae) provide insights into future conservation. Biodivers. Conserv. 2018 , 27 , 2275–2291. [ Google Scholar ] [ CrossRef ]
• Yun, S.A.; Kim, S.-C. Genetic diversity and structure of Saussurea polylepis (Asteraceae) on continental islands of Korea: Implications for conservation strategies and management. PLoS ONE 2021 , 16 , e0249752. [ Google Scholar ] [ CrossRef ]
• Zawko, G.; Krauss, S.L.; Dixon, K.W.; Sivasithamparam, K. Conservation genetics of the rare and endangered Leucopogon obtectus (Ericaceae). Mol. Ecol. 2001 , 10 , 2389–2396. [ Google Scholar ] [ CrossRef ]
• Booy, G.; Hendriks, R.J.J.; Smulders, M.J.M.; Van Groenendael, J.M.; Vosman, B. Genetic Diversity and the Survival of Populations. Plant Biol. 2000 , 2 , 379–395. [ Google Scholar ] [ CrossRef ]
• Young, A.G.; Brown, A.H.D.; Zich, F.A. Genetic Structure of Fragmented Populations of the Endangered Daisy Rutidosis leptorrhynchoides . Conserv. Biol. 1999 , 13 , 256–265. [ Google Scholar ] [ CrossRef ]
• Friedman, J.; Barrett, S.C.H. High Outcrossing in the Annual Colonizing Species Ambrosia artemisiifolia (Asteraceae). Ann. Bot. 2008 , 101 , 1303–1309. [ Google Scholar ] [ CrossRef ]
• Ferrer, M.M.; Eguiarte, L.E.; Montaña, C. Genetic structure and outcrossing rates in Flourensia cernua (Asteraceae) growing at different densities in the South-western Chihuahuan Desert. Ann. Bot. 2004 , 94 , 419–426. [ Google Scholar ] [ CrossRef ]
• Xiao, Y.; Jiang, X.; Lu, C.; Liu, J.; Diao, S.; Jiang, J. Genetic Diversity and Population Structure Analysis in the Chinese Endemic Species Michelia crassipes Based on SSR Markers. Forests 2023 , 14 , 508. [ Google Scholar ] [ CrossRef ]
• Duminil, J.; Fineschi, S.; Hampe, A.; Jordano, P.; Salvini, D.; Vendramin, G.G.; Petit, R.J. Can population genetic structure be predicted from life-history traits? Am. Nat. 2007 , 169 , 662–672. [ Google Scholar ] [ CrossRef ]
• Hmeljevski, K.V.; Wolowski, M.; Forzza, R.C.; Freitas, L. High outcrossing rates and short-distance pollination in a species restricted to granitic inselbergs. Aust. J. Bot. 2017 , 65 , 315–326. [ Google Scholar ] [ CrossRef ]
• Szczecińska, M.; Sramko, G.; Wołosz, K.; Sawicki, J. Genetic Diversity and Population Structure of the Rare and Endangered Plant Species Pulsatilla patens (L.) Mill in East Central Europe. PLoS ONE 2016 , 11 , e0151730. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Storfer, A. Gene flow and endangered species translocations: A topic revisited. Biol. Conserv. 1999 , 87 , 173–180. [ Google Scholar ] [ CrossRef ]
• Govindaraju, D.R. Relationship between Dispersal Ability and Levels of Gene Flow in Plants. Oikos 1988 , 52 , 31–35. [ Google Scholar ] [ CrossRef ]
• Gerber, S.; Chadœuf, J.; Gugerli, F.; Lascoux, M.; Buiteveld, J.; Cottrell, J.; Dounavi, A.; Fineschi, S.; Forrest, L.L.; Fogelqvist, J.; et al. High rates of gene flow by pollen and seed in oak populations across Europe. PLoS ONE 2014 , 9 , e85130. [ Google Scholar ] [ CrossRef ]
• Tang, J.; Fan, X.; Milne, R.I.; Yang, H.; Tao, W.; Zhang, X.; Guo, M.; Li, J.; Mao, K. Across two phylogeographic breaks: Quaternary evolutionary history of a mountain aspen ( Populus rotundifolia ) in the Hengduan Mountains. Plant Divers. 2024 , 46 , 321–332. [ Google Scholar ] [ CrossRef ]
• Su, R. Study on tourism climate resources and comfort in Hejing County. Rural. Technol. 2017 , 19 , 87–88. [ Google Scholar ] [ CrossRef ]
• Stronen, A.V.; Norman, A.J.; Wal, E.V.; Paquet, P.C. The relevance of genetic structure in ecotype designation and conservation management. Evol. Appl. 2022 , 15 , 185–202. [ Google Scholar ] [ CrossRef ]
• Dobrowski, S.Z. A climatic basis for microrefugia: The influence of terrain on climate. Glob. Change Biol. 2011 , 17 , 1022–1035. [ Google Scholar ] [ CrossRef ]
• Lande, R.; Barrowclough, G.F. Effective population size, genetic variation, and their use in population management. Viable Popul. Conserv. 1987 , 87 , 87–124. [ Google Scholar ]
• Schemske, D.W.; Husband, B.C.; Ruckelshaus, M.H.; Goodwillie, C.; Parker, I.M.; Bishop, J.G. Evaluating Approaches to the Conservation of Rare and Endangered Plants. Ecology 1994 , 75 , 584–606. [ Google Scholar ] [ CrossRef ]
• Li, J.; Cai, S. Advances in chemical and pharmacological studies of Saussurea involucrata . Chin. J. Pharm. 1998 , 8 , 3–6. [ Google Scholar ]
• Chen, F.; Yang, Y. Advances in species, habitat distribution and chemical constituents of Saussurea involucrata in China. Bot. Bull. 1999 , 5 , 561–566. [ Google Scholar ] [ CrossRef ]
• Aiguly, S.; Adiri, S. The research status of Hami ‘Snow Mountain Flower King’— S. involucrata . Chin. J. Ethn. Med. 2016 , 22 , 26–28. [ Google Scholar ]
• Wei, S.; Wu, Y. Research progress on endangered medicinal plant Saussurea involucrate resources. J. Minzu Univ. China (Nat. Sci. Ed.) 2014 , 23 , 10–15. [ Google Scholar ]
• Feng, J. Protection and Development of Saussurea involucrata . For. Pract. Technol. 2004 , 10 , 33. [ Google Scholar ] [ CrossRef ]

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Hu, L.; Wang, J.; Wang, X.; Zhang, D.; Sun, Y.; Lu, T.; Shi, W. Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata . Biomolecules 2024 , 14 , 1010. https://doi.org/10.3390/biom14081010

Hu L, Wang J, Wang X, Zhang D, Sun Y, Lu T, Shi W. Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata . Biomolecules . 2024; 14(8):1010. https://doi.org/10.3390/biom14081010

Hu, Lin, Jiancheng Wang, Xiyong Wang, Daoyuan Zhang, Yanxia Sun, Ting Lu, and Wei Shi. 2024. "Development of SSR Markers and Evaluation of Genetic Diversity of Endangered Plant Saussurea involucrata " Biomolecules 14, no. 8: 1010. https://doi.org/10.3390/biom14081010

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Global Journal of Agricultural Research (GJAR)

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Genetic Diversity and Its Impact in Enhancement of Crop Plants

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Citation: Temesgen Begna, Hayilu Gichile and Werkissa Yali (2022) Genetic Diversity and Its Impact in Enhancement of Crop Plants, Global Journal of Agricultural Research , Vol.10, No.2, pp.13-25

Plant breeding is a science that focuses on the development of new plant varieties in a systematic and ongoing method. It takes advantage of genetic variation among individuals within a plant species and combines desired traits to produce new and improved varieties. Plant breeding depends on genetic variety, and new variation is critical for introducing interesting characteristics into breeding programs. Genetic erosion is a term used to describe the loss of variation in crops as a result of agricultural modernization. Plant breeding has had a significant impact on food production and will continue to play an important role in ensuring global food security. Plant breeding can be broadly described as changes in plants caused by human use, ranging from unintended modifications generated by the introduction of agriculture through the use of molecular tools for precision breeding. Plant breeding based on observed variation by selection of plants based on natural variants appearing in nature or within traditional varieties; plant breeding based on controlled mating by selection of plants presenting recombination of desirable genes from different parents; and plant breeding based on monitored recombination by selection of specific genes or marker profiles. Continuous use of traditional breeding methods in a given species may reduce the gene pool from which cultivars are derived, making crops more susceptible to biotic and abiotic challenges and impeding future advancement. Plant breeding’s primary objectives are ‘high yield, high quality, and quantity, extension of climate and soil adaptability ability, and tolerance or resistance to pests and diseases.’ Plant breeders achieve these goals by exploiting genetic differences between plants. The range of the genetic base, as measured by genetic diversity, is a limiting factor in successful adaptation to environmental conditions and plant breeding. Many challenges in plant breeding require genetic variation, which can be found in the biodiversity of plant genetic resources such as breeding lines, landraces, primitive forms, wilds and wild relatives, and weed races.

Keywords: genetic diversity; adaptation; molecular marker; diversity analysis; gene pyramiding

This work by European American Journals is licensed under a  Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License

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Combining genetic diversity data with demographic information reveals extinction risks of natural populations

by University of Helsinki

Genetic diversity, a key pillar of biodiversity, is crucial for conservation. But can snapshot estimates of genetic diversity reliably indicate population extinction risk? New research shows that genome-wide genetic diversity is a strong predictor of extinction risk, but only when confounding factors are accounted for.

The paper is published in the journal Proceedings of the National Academy of Sciences .

As species face increasing environmental pressures , their populations often decline, leading to a loss of genetic diversity . This reduction in genetic variation can have serious consequences, including increased inbreeding and a diminished capacity to adapt to changing conditions.

Genome-wide genetic diversity is often used as an indicator of species' vulnerability to extinction . However, recent studies have suggested that genetic diversity does not always predict population viability.

The collaborative research sought to clarify under what circumstances genetic diversity can accurately predict extinction risk. The findings suggest that while genetic diversity is indeed linked to extinction risk, the strength of this relationship varies depending on other factors such as population size and the potential for rescue through dispersal.

The importance of integrating demographic data

The study highlights the dangers of relying solely on genetic data to assess population viability. "Our research demonstrates that inferences about the role of genetic diversity in extinction risk must be informed by demographic and environmental data," explains Professor Marjo Saastamoinen, senior author of the paper.

"For instance, we observed a strong negative relationship between genetic diversity and extinction risk, but this correlation was largely driven by underlying population size. Without accounting for demographic factors, we would have drawn misleading conclusions."

Dr. Michelle DiLeo, the leading author of the study, cautions, "Had we focused only on genetic diversity, we might have incorrectly assumed its effects on extinction risk were uniform across different populations and environments. Conversely, ignoring the interactions between genetics and demographics would have led us to underestimate the importance of genetic diversity in explaining extinction risk.

"Our results suggest that both genetic diversity and demographic factors, such as population size, population trends and immigration, must be considered in conservation strategies.

"Not all populations with low genetic diversity were doomed to extinction, as they were rescued by dispersal from other populations."

Recommendations for conservation

Given that most species are data-deficient, the researchers emphasize the need for strategic data collection to inform conservation efforts. They recommend focusing on three key pieces of information: estimates of genome-wide or neutral genetic diversity, population size trends, and the potential for rescue via dispersal.

Population size trends and population connectivity are already used in some global biodiversity frameworks, but more work is needed to integrate these metrics with genetic data for a comprehensive assessment of species vulnerability.

The study also underscores the importance of maintaining connectivity among populations to mitigate the risks associated with low genetic diversity in the face of environmental change.

Journal information: Proceedings of the National Academy of Sciences

Provided by University of Helsinki

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Relationship analysis and genetic diversity of tea Camellia sinensis germplasm from illegitimate seeds based on morphological characters

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Abstract. Maxiselly Y, Bakti C, Murgayanti, Ernah, Wahyudin AA, Prayoga MK, Karuniawan A. 2024. Relationship analysis and genetic diversity of tea Camellia sinensis germplasm from illegitimate seeds based on morphological characters . Biodiversitas 25: 3486-3495. Tea ( Camellia sinensis (L.) O. Kuntze) is a plant with self-incompatible traits and requires significant efforts to assemble superior clones. The generation of the superior clone is from seed-derived genetic material that demands comprehensive information on population variance, genetic diversity, and the level of relationship among accessions. Therefore, this research aimed to determine population variances, genetic diversity, and relationships of tea germplasm accessions based on morphological characteristics. The experiment was conducted from August 2023 to January 2024 at the Indonesian Research Institute Tea and Cinchona, Mekarsari Village, Pasir Jambu District, Bandung Regency, West Java, Indonesia. An experimental method, "no layout design," was used to identify morphological traits of 36 characters in 50 selected accessions from the population. Observational data were analyzed to determine variance values, principal component analysis (PCA), heatmap correlation, and cluster analysis using XLSTAT software. The results showed a wide variation of 50% among the 36 characters, with genetic diversity in the 2 Principal Components (PC) at 38.99%. The cluster analysis of accessions had an Euclidean distance range, measuringa measure of the straight line distance between two points of 2.23 to 12.86. This variation was caused by a high correlation of some characteristics, such as leaf length, width, and leaf area, as the potential to develop the new tea clone.

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What chocolate secrets lie in the genes of ecuador’s cocoa crops.

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Cesar Tapia in the field at a Universidad Técnica del Norte experimental station in August 2024.

In Ecuador, researchers are conserving the genetic legacy of cocoa ( Theobroma cacao ), the raw material of chocolate.

Cocoa (or cacao ) is derived from the fermented and dried seeds of the Theobroma cacao tree and though the largest production areas are now in Africa, wild relatives and ancestral varieties abound in Colombia, Peru and Ecuador . It is also one Ecuador's key crops, with production growing at an average annual rate of 15 percent since 2014 .

Cesar Guillermo Tapia Bastidas , head of Ecuador's national department of phytogenetic resources at the country's National Institute of Agricultural Research, explains that their germplasm bank consists of approximately 28,000 accessions that are conserved in the field, in vitro, cryopreservation and cold storage.

"The cocoa collection, which consists of approximately 2000 accessions that are conserved in the field at the Pichilingue Experimental Station, Litoral Sur Experimental Station and Central Amazonian Experimental Station, began in the 1950s as an inter-institutional initiative with colleagues from the United States, and grew over time with the support of various donors and the Ecuadorian government," he says.

Small producers in Ecuador continue to face challenges of improving production and finding sustainable supply chains , but the genebank is key in preserving Ecuador's highly regarded varieties.

"This collection is one of the largest in Latin America with very important materials such as fine aroma cacao," he says, adding that to preserve the cacao collection, the national genebank is upgrading all its conservation efforts with support from the Biodiversity for Opportunities, Livelihoods and Development program, which supports over a dozen national genebanks in ­Africa, Asia and Latin America.

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Tapia explains that the motivation for starting the germplasm bank is that underutilized crops, such as grains, tubers and high Andean roots, were being lost in the 1980s.

"The bank conserves native and exotic agrobiodiversity of grains, tubers and high Andean roots, Amazonian and tropical fruit trees, forest species, medicinal plants and export crops such as cocoa, coffee, bananas, African palm, achiote, among others," he says.

Tapia explains that the gene bank is a source of genes for breeding programs and for obtaining improved varieties with high productivity, resistant to pests and diseases and with important nutritional quality, while also helping to return these preserved varieties to agricultural communities, to encourage biodiversity, support agro-tourism and develop value-added products.

"In a nutshell, genetic resources for food and agriculture are what is feeding the world but unfortunately there is still a lack of awareness and knowledge to use and conserve them," he says, "I think the biggest challenge is to avoid the genetic erosion that these genetic resources suffer and the next step in the near future will be to develop the red book of native agrobiodiversity of Ecuador that will allow us to alert about the loss of this important diversity that has been domesticated by our people for more than 10,000 years."

ECUDORIAN CACAO

Growing Up in Ecuador

Tapia is from Ecuador's Pichincha province and growing up was passionate about nature and the mountains.

"Working with this biodiversity made me realize where to go professionally and my love for the agrobiodiversity of my country began to grow, taking into account that I am in a mega-biodiverse country," he says, "Almost immediately that I became involved in the life of indigenous and farming communities and their biodiverse production systems, I began to observe a significant loss of native varieties of species of importance for food security.

Tapia explains that this was approximately 30 years ago, when crops such as quinoa, amaranth, melloco, mashua, oca were still considered weeds instead of the superfoods they are known as today.

"I graduated from the University with a degree in Agricultural Engineering, I did my thesis on minor Andean tubers (melloco, oca, mashua) at INIAP where I continue to work until today," he says. "Today we have a germplasm bank that is the largest in the country, conserving agrobiodiversity of many crops of importance for food security, for export and with very important nutritional characteristics."

Tapia explains that researchers from the Global South play a fundamental role in the generation of technology that contributes to the solution of global problems.

"It is also important that researchers participate in politics mainly in the generation of laws," he says, adding that he also participated in the negotiation at the international level on biodiversity policies as a representative of Ecuador.

"I do not believe that researchers should only be dedicated to the development of technologies but should be active and critical participants in policies for the care of our planet and its biodiversity," Tapia says.

Cesar Tapia during the annual seed fair in Cotacachi, Ecuador, which Cesar and his team started over ... [+] 20 years ago. (August 2024).

Brazil Nuts in Colombia

In neighboring Colombia, Diana Medellín-Zabala has been studying the Brazil nut plant family (Lecythidaceae), including helping to describe a new species.

The trade in Brazil nut ( Bertholletia excelsa ) is worth $299 million but there's so many other species in the larger family, which includes about 215 species across Central and South America

Medellin-Zabala, a Colombian biologist Ph.D. candidate at the University of Michigan, USA, explains that it is important to understand why this particular group of plants is found in unique ecosystems such as the Amazon basin, the Guiana Shield in northern South America, and Colombia's Choco biogeographic region along its Pacific coast.

"This group is one of the 20 most-species rich families of trees in the Amazon forest, and third in terms of biomass providing important ecological services such as carbon sequestration and food resources for pollinators and seed dispersers," she says, "Besides its center of diversification is in the Amazon basin, there are remarkable species occurring in other ecoregions like the inter-Andean valleys, the Chocó Biogeographic region, and even the Andes (about 2000 meters above sea level), which are regions with different geological history, climate variables, and biotic dynamics."

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COBRE Phase III Offering Pilot Project Grants

The center will be accepting applications until 5 pm est on september 13, 2024 ..

The Center of Biomedical Research Excellence (COBRE) Phase III on Dietary Supplements and Inflammation (CDSI) funded by NIH is pleased to announce an open call for Pilot Project grant proposals.

Information on the Pilot Project grants and how to apply can be found on the Center for Dietary Supplements and Inflammation webpage .

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