Genetic and Genomics Resources of Cross-Species Vigna Gene Pools for Improving Biotic Stress Resistance in Mungbean (Vigna radiata L. Wilczek)

Abstract

Mungbean (Vigna radiata L. Wilczek) is an important short-duration grain legume of the genus Vigna that has wider adaptability across agro-climatic regions and soil types. Significant strides have been made towards the development of superior, high-yielding, and climate resilient cultivars in mungbean. A number of donors for various traits to have been deployed in introgression breeding. However, the use of common sources of resistance to different biotic stresses may lead to boom and bust cycles due to the appearance of new races or biotypes. Therefore, broadening the genetic base using wild and exotic plant genetic resources may offer a better quality of durable resistance. Many crop wild relatives (CWRs) confer a high degree of resistance against multiple diseases. Recently, several agronomically important genes have been mapped using inter-specific populations, which are being deployed for the improvement of mungbean. In such a situation, tagging, mapping, and exploiting genes of interest from cross-species donors for stress tolerance will offer novel genetic variations. This will also provide increased opportunities for the selection of desirable types. Advances in genomics and transcriptomics have further made it easy to tag the cross-compatible resistance loci and study their expression for delineating the mechanism of resistance. The comparative analysis of omics technology also helps in understanding the evolution and offers a scope for using cross-specific target genes for mungbean improvement. This review focuses on the effective utilization of cross-species cultivated and wild relatives as well as their omics resources for breeding multiple disease-resistant mungbean cultivars.

1. Introduction

Mungbean is one of the most important grain legumes among the pulses, which is globally suited to tropical and sub-tropical climates. It is mainly grown in Asia, Africa, and Latin America [1,2], and consumed as food, feed, and green manuring crop [3,4]. It has a wide adaptability to an array of environmental and soil conditions including extreme environments. Its short life cycle (55–70 days), low input requirements, and nitrogen-fixing ability further augment the importance of this crop [5]. It also improves the soil health and enhances the yield potential of subsequent crops. Owing to these features, there is ample scope for the vertical as well as horizontal expansion of mungbean worldwide. However, this crop is significantly affected by various biotic and abiotic stresses [6]. Climate change is leading to the evolution of new races and biotypes of different pathogens, which are posing a major threat to mungbean production and productivity [7]. Yellow mosaic disease (YMD), Cercospora leaf spot (CLS), powdery mildew (PM), and web blight (WB) are major causes of concern among the diseases (Figure 1), affecting the crop yield up to 100% under severe conditions [8] as well as deteriorating the grain quality.

Among viral diseases, yellow mosaic disease (YMD) is the most devastating one in mungbean. Three different species of Begomoviruses, namely mungbean yellow mosaic India virus (MYMIV), mungbean yellow mosaic virus (MYMV), and horsegram yellow mosaic virus (HgYMV) are recognized as the causal agents of YMD in mungbean in different parts of the world [9,10]. YMD has a wide host range among leguminous crops, including mungbean, urdbean, mothbean, soybean, etc.

YMD causes considerable change in the plant system depending upon the growth stages. The plants infected with YMD generally show the yellowing or chlorosis of leaves followed by necrosis, shortening of internodes, and the severe stunting of plants with few flowers and deformed pods producing immature and shriveled seeds [11]. The symptoms of disease appear after the preliminary multiplication of the virus particles while the coat protein (CP) reveals its expression even when slight viral residue is present in a plant [12]. After virus multiplication, its movement from cell to cell is mediated through movement proteins (MPs). These proteins can be divided into two broad categories: (1) proteins inducing structural changes in the plasmodesma and (2) proteins binding with viral nucleic acid to form a complex that travels through the plasmodesma [13]. The International Committee on the Taxonomy of Viruses (ICTV) has accepted the CP-gene sequence as a desirable marker for virus identity, even when a full-length genomic sequence was not available. Viral particles associated with YMD were found to be isometric and geminate, 18–30 nm in size with two single-stranded DNA molecules (DNA A and DNA B) of 2726 and 2775 nucleotides, respectively [14,15]. YMD is persistently transmitted by whitefly [16,17]. The seed transmission of YMD in mungbean and urdbean has been ruled out due to DNase activity in the seeds during the germination process, which is able to cleave the viral DNA. MYMV infection has been reported to result in the malfunctioning of polyphenol metabolism by increasing the total sugars, free amino acids, and total phenols in the susceptible varieties, and only ortho-dihydroxy phenol and flavonols in the resistant variety as compared to their respective healthy ones [18]. Pathogenic stress involves the cascade of signals initiated upon involving salicylic acid (SA), that contributes immensely in the systemic acquired resistance (SAR) [19]. SA is a naturally occurring phenolic compound and a key component in activating resistance pathways against a wide range of pathogens [20]. Hence, resistance to virus infection can be induced by activating the pathogenesis-related proteins by spraying chemicals including salicylic acid (SA) [21] that can inhibit the virus translocation in plant cells [22,23,24]. However, the deployment of resistant varieties is the most effective measure to manage this disease. Unfortunately, the pace of development of high-yielding resistant varieties to the newly emerging or re-emerging Geminiviruses is rather slow and many varieties exhibit divergence in subsequent generations.

The major fungal diseases which infect the mungbean crop are powdery mildew (Erysiphe spp.) [25], anthracnose (Colletotrichum spp.) [26], Cercospora leaf spot (Cercospora spp.) [27], and web blight (Rhizoctonia solani) [28] that cause yield losses approximately ranging from 20 to 60% [29]. Yield losses due to Cercospora leaf spot were reported from 23 to 96% in field trials conducted in different states of India [30]. The foliar diseases cause a significant yield-reduction, especially when incidence takes place at an early crop growth stage [31]. Erysiphae polygoni is an obligate biotroph on leaves and fruits that infects cells of the epidermal layer from which nutrients are extracted by the pathogen through a haustorium [32]. The anthracnose disease of mungbean was first reported in Assam, India [33]. It causes considerable damage by reducing seed quality and yield [34]. The average seed yield loss of 40.18% and stalk yield loss of 46.90% were noticed due to anthracnose of mungbean [35]. The process of infection/penetration by Colletotrichum spp. is thought to be triggered by cutin-degrading enzymes such as cutinase, which have been suggested to participate in carbon acquisition for saprophytic growth [36]. These were also presumed to play a role in fungal penetration [37,38] and in surface adhesion [39]. Approximately 30–40% of yield loss due to web blight was reported from Rajasthan (Anonymous 2000) and 20–40% seedling mortality due to Rhizoctonia infection was reported from central India [40]. The web blight pathogen was reported in mungbean under the name Thanatephorus cucumeris (frank) Donk [41] which is a teleomorph stage of Rhizoctonia solani. The use of different cultural practices such as crop rotation, planting dates, and plant residue management apart from crop diversification are recommended as effective strategies for managing crop diseases [42]. Next-generation breeding approaches can achieve this with a precise understanding of gene action involved for the resistance and availability of resistance genes in germplasm. Hence, the identification of molecular markers associated with major genes/QTL and their exploitation of host resistance need to be focused upon.

A diverse range of insect pests cause serious damage to mungbean from the seedling to maturity stages and even under storage conditions. The whitefly, Bemisia tabaci, is the most significant because it not only causes damage by direct feeding but also spreads the YMD causing viruses [43]. Thrips, Megalurothrips distalis, which is found in many parts of Asia, Africa, South and Central America, and Australia [44], is considered a serious pest of cowpea, chickpea, pea, and lentil [45]. Seedlings that are severely infested by thrips struggle to thrive. Flowering thrips invade and cause significant damage during flowering and pod formation. Under severe conditions, thrips cause flower drop, the deformation of pods, and ultimately a reduction in yield. Blister beetles (Mylabris spp.) wreak havoc on the flowers, particularly the second and third flushes. Bihar hairy caterpillar, Spilosoma obliqua, is a polyphagous pest that feeds on mungbean along with several other hosts [46,47]. Spotted pod borer, Maruca vitrata (Fabr.); spiny pod borer, Etiella zinckenella Tretsche; gram caterpillar, Helicoverpa armigera (Hubner) are the key pod borers; however, blue butterfly, Lampides boeticus, and Catechrysops cnejus Fabr. are also considered major insect-pests affecting crop growth at one time or another. Bruchids belonging to the genus Callosobruchus are the most common insect pests of stored mungbean and their related species [48]. Twenty species of seed beetle in the Bruchidae family were reported as pests, usually in stored legume seeds [49]—in which C. maculatus, C. chinensis, and C. analis are most common [48], causing up to 100% losses within 3–6 months if not controlled [50]. Likewise, pulse beetle (Callosobruchus sp.) causes up to 80% yield loss of stored grains under severe conditions [51].

Various cultural and management strategies have been adopted by different workers to manage these biotic stresses. However, host plant resistance (HPR) remains the most preferred strategy for the management of the biotic stresses, although it requires robust donors for introgression breeding. A number of potential donors have been reported for various diseases and insect-pest resistance. Few of them have been utilized in breeding programs. Nonetheless, despite the transfer of a few useful genes among the cultivated genotypes, the breakdown of resistance is the most common phenomenon due to the narrow genetic base of the commercial cultivars. Therefore, broadening the genetic base will strengthen our efforts towards combating these emerging challenges. The crop wild relatives (CWR) are known to possess useful genes and cryptic genetic variations, which can be introgressed in a cultivated gene-pool to improve the yield, adaptation, and stress tolerance [52,53,54,55]. Furthermore, recent advances in distant hybridization and prebreeding techniques through hormonal manipulations, embryo rescue, use of mentor pollen, etc. provide an increased opportunity to introgress useful traits/genes/loci from even the secondary and tertiary gene pool into the cultivated background.

In recent years, the development of genomic tools and their deployment for molecular breeding have gained momentum after the decoding of the whole genome reference sequence of mungbean [56,57,58]; to date, six Vigna species such as mungbean [56], urdbean [59], cowpea [60], V. stipulacea [61], adzukibean [62], and beach pea [63] have been sequenced, suggesting that the genome size of different Vigna species ranges from 416 to 1394 Mb. Additionally, the partial sequences from other Vigna species are also available in the database. The cross-specific gene flow is also a way to mine the desirable alleles/genes for crop improvement [64]. A relatively recent approach, genotyping by sequencing, is conducive to the identification of useful genes that could be targeted for the improvement of cultivated species. The genome, transcriptome, metabolome, and proteome need to be inter linked with high throughput phenome data [65]. Various researchers sequenced the genome and transcriptome for gene discovery and their expression analysis under various stresses, however, cloning and characterization can stabilize its effective utilization. The genome-wide identification and expression of various important gene family candidates and transcriptional factors are important to understanding the proteome function [66], as noteworthy progress has also been made in the genetic transformation of mungbean. Therefore, we need to integrate all the omics approaches together with breeding efforts to accelerate the genetic gains. This review aimed to elaborate such novel efforts and provide an insight into the improvement of mungbean. It further advocates exploring and utilizing novel cross-specific cultivated and wild genetic and omics resources for improving the resistance to diseases and major insect-pests.

2. Approaches to Combat Biotic Stresses

2.1. Pathogen Characterization and Screening

Extensive information on the causal organism of a disease, its pathotypes, mode of transmission, and inheritance pattern are of utmost significance since a single disease may be caused by many pathogen species. Sometimes, mixed infection may also lead to the appearance of common symptoms. For example, YMD, the most devastating mungbean disease [65,67], is caused by three different viruses with similar yellow mosaic symptoms. Even the inheritance of resistance of these pathogens is different. For example, MYMIV in black gram is governed by a single dominant gene [68], while the recessive monogenic inheritance pattern of MYMV was also reported by previous workers [69,70]. This suggests that different strategies must be adopted to improve disease resistance. Pathogens also change their host plants during the course of time for better survivability. However, the symptoms in alternate host may be changed. Haq et al. [71] studied the infectivity of MYMIV and MYMV in the black gram, green gram, French bean, and cowpea crops. They also studied the infectivity through recombinants of the virus. The MYMIV and MYMV caused yellow mosaic symptoms in black gram and green gram, whereas in cowpea and French bean, these caused stunting, mild leaf curl, and leaf deformation. These two pathogens are also able to cause stunting, downwards leaf curl, puckering, and necrotic symptoms in French bean. Similar symptoms were also recorded through agro-inoculation with recombinants of MYMIV–MYMV in French bean, whereas these recombinants were not able to cause any disease in mungbean and urdbean. The leaf curl disease caused by MYMIV in French bean was reported by Patwa et al. [72]. It was indicated that the pathogen characterization and screening of breeding materials against the specific pathogens are very important steps for tagging potential donors, which help in making appropriate strategies for enhancing the resistance. Secondly, screening techniques also play an important role. In most of the reports, diseases were screened under high disease pressure in the fields at hot spots. As we know, the disease unfolds with the host–pathogen interaction depending on the environment and time period. In a vector-transmitted disease, the population of the vector is also affected by the environment. Hence, the artificial screening technique is required, which gives us more appropriate results. However, in some cases, artificial screening is difficult. In multi-location disease phenotyping, we may notice that the severity of the disease is often changed across locations and the researchers pool the data for selecting the resistant donors for breeding program. Therefore, the selection criteria also need to be changed and instead of pooling the disease severity data, maximum severity may be taken in to consideration because, if the environment is appropriate, the disease severity will be maximal. Under artificial screening, the pathogen load is very important. The molecular techniques will also help in performing proper disease phenotyping. The standardization of methods for detecting the pathogen load through quantitative real-time PCR will also be helpful. Likewise, in the screening experiments of bruchids, a few researchers identified the species and reported species-specific-resistant donors. The bruchids also had a very wide host range and their life cycle and growth depended on the seed size and nutrients present in the crops. The crop-wise and species-wise bruchid damage was also different. This relationship also needs to be explored. The wider host range, species variation, and co-adoption to host also enable the crossing of specific breeds and the development of new biotypes. Attention should also be paid to this aspect. Aidbhavi et al. [48] advocated species-specific molecular markers for the identification of three bruchid species. The development of more molecular markers for detecting the hybrid progenies of different bruchid species will help incite the development new biotypes. Hence, the integration of molecular tools and techniques will be helpful in accelerating our screening procedure and appropriate phenotyping.

2.2. Understanding the Genetics of Pathogen-Specific Biotic Stress Resistance

Knowledge about the inheritance of disease resistance greatly helps in improving the resistance status of crops. The simply inherited traits are easy to transfer from one background to another using morphological markers or classical breeding techniques. However, resistance to most diseases is governed by quantitatively inherited genes, and it is challenging to the breeders to pyramid them and improve the resistance status. A number of resistance sources in the secondary and tertiary gene pools were reported by various workers [73,74,75]. The genes from a cultivated background within the crop species are easy to transfer. However, the resistance breakdown is a very common phenomenon due to the pathogen evolution and narrow genetic base. To improve the durability of resistance against the pathogen or insect-pest, the pyramiding of target genes from different backgrounds is required. This is made possible through the exploitation of novel variations from cross-specific gene pools in breeding programs. The use of wild relatives in breeding programs also influences the inheritance of traits or resistance. Although the trait inheritance of cross-species members and CWRs is less studied (

Table 1. Inheritance of disease/insect resistance in Vigna and its derivatives.

Stress	Crop	Population	Approach	Genetics	Marker Used	Reference
MYMIV	Mungbean	RILs of KPS 2 × NM 10-12-1	Linkage mapping	QTL	SSR	[76]
		BM6 × BM1	Linkage mapping	QTL	SSR	[77]
		A panel of 130 genotypes	Association mapping	QTL	SSR	[73]
	Black gram	AKU 9904 × DPU 88-31	BSA	Single dominant gene	SSR	[68]
	Black gram × ricebean	KUG253 × Mash114	QTL-seq	Major QTL	SNP	[78]
MYMV	Mungbean	KMG 189 × VBN (Gg) 2	Segregation analysis	Single recessive gene	--	[69]
		KMG 189 × VBN (Gg) 2	Segregation analysis	Single recessive gene/linkage mapping	SCAR	[70]
		NM92 × VC2272, 6601 × VC2272, 6601 × Pusa Baisakhi, VC3902A × NM92, VC3902A × ML-5, NM92 × Pusa Baisaki, VC 1560D × 6601, VC 1560D × NM92	F2 segregation analysis	--	--	[79]
		NM 92 × NM 98	F2 segregation analysis	--	--	[80]
		VBN(Gg)2 × KMG189	F2 segregation analysis	Single recessive gene	--	[70]
	Black gram	MDU1 × Mash 1008	Linkage mapping	QTLs	SSR	[81]
		MDU 1 × TU 68	Linkage mapping	Major QTL	SSR	[82]
	Ricebean × mungbean	TNAU Red × VRM (Gg) 1	Segregation analysis	Single recessive gene	--	[69]
	Mungbean × ricebean	VRM (Gg) 1 × TNAU RED	GBS approach	One major QTL and three minor QTLs	SNP	[83]
YMV (causal virus not identified)	Black gram	IC436656 × KKB14045	Segregation analysis and linkage mapping	Single recessive gene	SSR	[84]
Cercospora leaf spot	Mungbean	KPS1×V4718	Linkage mapping	QTL	SSR	[85]
		Kamphaeng Saen 1 × V4718	Fine mapping	QTL	SNP	[86]
		Kopergaon × HUM12; Kopergaon × ML 1720	GMA	Quantitative inheritance	--	[87]
		KPS1 × V4718	GBS	QTL	SNP	[86]
Powdery mildew	Mungbean	Kamphaeng Saen 1 × VC6468-11-1A (RILs)	Linkage mapping	Two QTLs	SSR	[88]
		Berken × ATF 3640	Linkage mapping	Major QTL	RFLP	[89]
		Chai Nat 72 × V4758	Segregation analysis and BSA	QTL	ISSR-RGA	[90]
	Cowpea	ZN 016 × Zhijiang282	Linkage mapping	Major QTL	SSR and SNP	[91]
Bruchid (C. chinansis)	Mungbean	V2709	--	Single dominant gene	RAPD/SSR/STS	[92]
Callosobruchus spp.	Mungbean × V. sublobata	VC2778A × TC 1966	Comparative genomics	Major QTL	SNP	[93]
C. Chinansis; C. maculatus		KPS1 × V2709	Segregation analysis and linkage mapping	Major QTL	SNP	[94]
	V. vexillata	TVNu 240 × TVNu 1623	SLAF sequencing	QTL	SNP	[95]
Bruchid (C. maculatus)	Urdbean × Vigna mungo var. sylvestris	TU 94-2 × Vigna mungo var. sylvestris	Segregation analysis	QTLs	RAPD, ISSR, SSR	[96]
	Urdbean × Vigna mungo var. sylvestris	BC48 × TC2210	Linkage mapping	Two QTLs	SNP	[97]
	Urdbean	MDU1 × TU 68	Linkage mapping	QTL	SSR	[82]

), it needs to be explored for its potential use in mungbean breeding programs.

2.3. Exploring Cross-Specific Newer Gene Pools for Potential Donors

The genus Vigna is a highly diverse group comprising more than 200 species. Mungbean lies among the lowermost taxa and gene flow from other Vigna species to mungbean has already been reported [98], and while a few of the genes have been transferred from the wild species within the primary gene pool, the success is limited so far due to the selection bias against alien alleles. Most Vigna crops and their wild relatives are diploid and self-pollinated in nature with some exceptions (Creole bean). Considerable variability in wild relatives has been reported for yield-related and adaptive traits including biotic and abiotic stress resistance. The ploidy level also plays an important role in the success of alien gene transfer through distant hybridization [99]. The potential donors might be utilized in a breeding program for improving the biotic stress resistance in mungbean (

Table 2. Potential sources of disease/insect resistance in Vigna species.

Disease/Pest	Donor Genotypes	Species	Method	References
MYMIV	IC277021	V. sylvestris	Field screening	[100]
	IC248326, IC248326, IC248343	V. vexillata	Field screening
	LRM/13-43, LRM/13-32, LRM/13-34, IC276983, IC331436, IC331454, IC331456, Trichy local, Kumur local, IIPRW17-3	V. trilobata	Field screening
	RBL-50, IC251445, PRR 2007-2, PRR 2008-2, RB-5-1, IC251439, IC251442, IC251446, IC251447, IC528878, IC197812, IIPRW 17-1,	V. umbellata	Field screening
	PRR 2008-2	V. umbellata	Field screening
	LRM/13-11, LRM/13-33, TMV-1, LRM/13-26, LRM/13-37, LRM/13-38, LRM/13-36,	V. aconitifolia	Field screening
	IC331450	V. hainiana	Field screening
	Trichy Local-1, Trichy Local-2	V. stipulaceae	Field screening
	TCR-20	V. glaberesense	Field screening
	TCR-7	V. umbellata	Field screening
	TU-68	V. mungo	Bioassay and GC–MS analysis	[101]
	TCR-79,TCR-82, TCR-239,	V. radiata	Field screening	[66]
	TCR-7, TCR-238,JAP/10-36,TCR-110,JAP/10-47,TCR-160, TCR-88,JAP/10-51, NSB 007,	V. sublobata	Field screening
	TCR-64,LRM/13-43, LRM/13-34, LRM/13-32, LRM/13-24, LRM/13-30, ZAP/10-5, ZAP/10-7, ZAP/10-9,TCR-192, TCR-305, TCR-319, TCR-320, TCR-513,LRM/13-44, LRM/13-33, LRM/13-26, LRM/13-38, LRM/13-36, LRM/13-37,	V. trilobata	Field screening
	TCR-254,TCR-390,	V. sylvestris	Field screening
	TCR-314,TCR-315,TCR-24, TCR-29	V. haniana	Field screening
	TCR-20	V. glabrascens	Field screening
	TLC1, TLC2	V. stipulaceae	Field screening
	RBL-1, TCR-93, PRR-2007-2, PRR-2008-2, RB-5-1, TCR-91, TCR-87, TCR-90, TCR-94, TCR-95, TCR-279,	V. umbellata	Field screening
MYMIV	IC-546453, IPU 11-02, IC-548278, IC-43647, COBG-653, Pant-Urd-19, UR-218, Shekhar-2, STY-2289, VBG-04-008, IPU 31-1, PDU-19, PDU-3, IPU 99-211, PLU-110, DPU88-31	V. mungo	Field screening	[102]
	UPU 8335, IPU 99-205, PGRU 95004, SPS 43		Field screening	[103]
Powdery mildew	LBG 645, LBG 17, IC-281977	V. mungo	Field and artificial screening	[104]
Callosobruchus maculatus	V2802BG, V2709, BSR-GG-1-49-3-1, BSRGG-1-56-2-2, BSR-GG-1-160-5-3, BSR-GG-1-170-2-4, BSR-GG-1-198-1-4	V. radiata	Bioassay	[105]
Bruchid (C. analis)	IC251439, IC251442, PRR 2007-2, IC251440, TCR 279	V. Umbellata	Bioassay	[48]
	JAP/10-51	V. trinervia
	IC251435, IC553527, IC553526, JAP/10-7,IC349701	V. trilobata
	TMV 1, LRM 13-44	V. aconitifolia
	Trichy Local 1	V. stipulacea
	Kumur Local	V. khandalensis
	Mung seed 1, IC571775, IC251434	V. radiata
	IC251390IC251387	V. mungo
	IC210580	V. pilosa
Bruchid (C. maculatus)	Mung Seed-1, IC251426A, IC251426B	V. radiata	Bioassay	[48]
	IC247408	V. dalzelliana
	IC248326, IC248343	V. vexillata
	Kumur Local	V. khandalensis
	JAP/10-51	V. trinervia
	IC210575	V. pilosa
	IC251394, IC251390, IC251385, IC251387	V. mungo
	JAP/10-5, IC251435, IC553527	V. trilobata
	IC251439, IC251442, PRR 2007-2, IC251440	V. umbellata
Bruchid (C. chinesis)	Kumur Local	V. khandalensis	Bioassay	[48]
	IC251397, IC251390, IC251385, IC251387	V. mungo
	IC247408	V. dalzelliana
	IC248326, IC248343	V. vexillata
	IC251439, IC251442, PRR 2007-2, IC251440	V. umbellata
	JAP/10-51	V. trinervia
	IC247407	V. trinervia var. bournei
	IC210575	V. pilosa
	Mung seed-1	V. radiata

).

In Vigna species, the hybridization between mungbean and urdbean is routinely practiced for mungbean improvement. The derivatives obtained from such crosses exhibit many desirable features such as resistance to biotic and abiotic stresses, synchronous maturity, and improved seed quality traits [65]. “Meha” was the first interspecific mungbean variety which comprised MYMIV resistance transferred from V. mungo. Recently, “IPM 312-20” and “Tripura Mung-1” were developed through mungbean × urdbean interspecific hybridization. In similar ways, other wild species in addition to urdbean may also be utilized in breeding programs for improving the stress resistance and developing new mungbean varieties.

2.4. Characterizing Vigna Diversity: From Conventional to Omics Approaches

A high yield coupled with enhanced stress resistance and added newer traits of adoption need to be a major focus for the genetic improvement of crops. The prime traits for improvement include durable resistance to diseases and insect-pests. Despite the continued yield improvement through conventional breeding, the new biotechnological techniques will be needed to maximize the probability of success. Molecular marker technology and genomic resources offer great promise for crop improvement. Owing to the genetic linkage, DNA markers can be used to detect the presence of allelic variation in the genes and increase the efficiency of breeders. The effective utilization of these markers as a tool in plant breeding accelerates marker-assisted selection (MAS). Several workers have utilized various marker systems to decipher the genetic variation in the Vigna gene pool [52,73,83,99,106]. Similarly, many of the marker systems display the recent trend of detecting genetic variations and accelerating molecular breeding. As mungbean is compatible to cross with many other Vigna species, cross-specific genomic resources are also very important for its improvement. Banni et al. [107] evaluated a panel of 178 adzukibean genotypes using 39 polymorphic SSRs and a gene flow study suggested their effective utilization for improving the related Vigna species. Kaewwongwal et al. [108] evaluated 520 cultivated and 14 wild accessions of black gram for diversity assessment using 22 SSR markers and found that black gram is closely related to the mungbean and ricebean, indicating that the useful genes from black gram and ricebean may be effectively utilized for mungbean improvement. Several researchers utilized cross-specific markers for the mapping and tagging of important traits in mungbean [73,109]. Zhao et al. [110] used the fluorescent-labeled SSR markers to detect genetic variations among 151 mungbean varieties. In the post-genome sequence era, SNP markers are now frequently utilized in understanding the linkage disequilibrium of the mungbean population. Noble et al. [111] characterized a mungbean panel comprising 466 cultivated genotypes and 16 wild accessions to demonstrate its utility through pilot genome-wide association study for seed coat color. They detected approximately 22,000 genome-wide SNPs and used them to understand the genetic diversity, population structure, and linkage disequilibrium (LD) of mungbean. Recently, Wu et al. [112] identified 6486 SNPs on a panel of 95 mungbean genotypes. These SNP markers will be utilized in marker-assisted breeding after its successful validation.

2.5. Highlights of Vigna Genomic Resources

Various Vigna crops have now been sequenced starting with mungbean as a model [56] (Figure 2). Before the decoding of genome sequence information, the researchers used transferable markers from other Vigna species in the analysis of mungbean [113] and other legumes [68,114]. Kang et al. [56] sequenced a mungbean pure line, namely VC1973A, and its relatives V. reflexo-pilosa var. glabra and V. radiata var. sublobata to construct a draft genome sequence. The estimated genome size ranged from 579 Mb to approximately 968 Mb (V. reflexo-pilosa var. glabra). In addition, they also sequenced a wild relative, namely TC1966, a V. radiata var. sublobata accession with the estimated genome size of 501 Mb. The available mungbean whole-genome sequence information will further boost genomics research in Vigna species and accelerate mungbean breeding programs. Jiao et al. [57] re-sequenced two accessions of mungbean, namely Salu and AL127, to map the lma locus and identify 236,998 single-nucleotide polymorphisms and 8896 insertion/deletions (InDels). Following the whole genome sequencing of mungbean, the genome sequencing of other Vigna crops was accelerated (Figure 2) and many species have been sequenced to date. The genome size of adzukibean (V. angularis var. angularis) was estimated and approximately 75% genome assembly was drafted [62]. The assembly produced 3883 scaffolds with a proper read coverage statistics of the sequencing libraries, including the pseudo-library from the Newbler assembly and the N50 length of the scaffolds was 703 kb. The sum of the scaffold length was approximately 443 Mb. A total of 4524 segregating SNP sites were identified. Using the MAKER pipeline, 26,857 high-confidence genes were predicted in the adzukibean genome, among which 15,976 were located on 11 pseudo chromosomes. The 9196 orthologs between V. angularis var. angularis and V. radiata var. radiata showed persistent tissue specificity, suggesting that the gene functions were extensively retained. Lonardi et al. [60] presented the draft genome of cowpea cv. IT97K-499-35, which is approximately 519 Mbp against the estimated size of 640 Mbp. A total of 29,773 protein-coding loci were annotated, along with 12,514 alternatively spliced transcripts. Based on annotation, an estimated 49.5% of the genome is composed of transposable elements (39.2%), simple sequence repeats (4.0%), and unidentified low-complexity sequences (5.7%). Takahashi et al. [61] sequenced the whole genome of V. stipulacea cv. JP245503 and generated 19.6 Gbp of subreads using 52 SMRT cells with a coverage of approximately 387.7 Mbp (87.9%) of the estimated genome size of approximately 445.1 Mbp. Based on the gene models and transcript as well as protein alignments, a total of 26,038 protein coding genes was predicted. Kaul et al. [115] reported the ricebean (V. umbellata) draft genome sequences, and estimated there were approximately 31,276 highly confidential genes with a functional coverage of 96.08%. The genome assembly was found to be closer to adzukibean, followed by mungbean and cowpea. A draft genome sequence of V. marina cv. ANBp-14-03 was generated through the NGS platform and approximately 23.7 Gb of sequence data were generated. The assembly containing 68,731 scaffolds gave an N50 length of 10,272 bp and the assembled sequences totaled 365.6 Mb. A total of 35,448 SSRs, including 3574 compound SSRs, were identified. Genome analysis identified 50,670 genes with a mean coding sequence length of 1042 bp. Phylogenetic analysis revealed the highest sequence similarity with V. angularis, followed by V. radiata. The comparison with the V. angularis genome revealed 16,699 SNPs and 2253 InDels and the comparison with the V. radiata genome revealed 17,538 SNPs and 2300 InDels. Souframanien et al. [59] constructed the draft genome sequence of black gram (V. mungo L. Hepper) cv. PU-31 through hybrid genome assembly with Illumina reads and third-generation Oxford Nanopore sequencing technology. The final de-novo whole genome of black gram is  ~ 475 Mb (82% of the genome) and has a maximum scaffold length of 6.3 Mb with a scaffold N50 of 1.42 Mb. A total of 42,115 genes were identified, among which approximately 80.60% of the predicted genes were annotated. Approximately 50% of the assembled sequence was composed of repetitive elements. A total of 166,014 SSRs, including 65,180 compound SSRs, were identified. Recently, Ambreen et al. [116] carried out the long read-based draft genome sequencing of black gram cv. Uttara and identified the disease and seed-related genes. Thy tagged 119 NBS-encoding genes which might be associated with various biotic stress resistance. The close resemblance of many of the Vigna species with mungbean will provide an insight into delineating the mechanism of stress tolerance and their effective utilization in mungbean breeding.

Recently, Ha et al. [58] constructed the near-complete genome sequence of mungbean with a size of 475 Mb. They identified several misassembled pseudo-molecules on Chr03, Chr04, Chr05, and Chr08 in the previous draft assembly. The Chr03, Chr04, and Chr08 were assembled into one chromosome, and Chr05 was broken into two segments in the improved reference genome assembly, providing more accurate information. The genomic information of the Vigna gene pools represents an important resource for accelerating the genomics-assisted improvement of mungbean through cross-specific alien sources.

2.6. Tagging, Mapping, and Exploiting QTL

During the process of plant breeding, a high yield coupled with enhanced stress tolerance, yield stability, and sustainability should be a major focus for crop improvement. The prime traits include durable resistance to diseases, insect-pests and tolerance to abiotic stresses [73]. Despite the continued yield improvement through breeding and biotechnological interventions, desirable genes from wild genetic resources must be introduced to maximize the probability of success. Genomic resources and molecular marker technology offer great promise for plant breeding. Owing to genetic linkage, DNA markers can be used to detect the presence of allelic variations and increase the breeding efficiency. The effective utilization of these markers as a tool in plant breeding accelerates marker-assisted selection (MAS). Various types of plant populations are used for the mapping and tagging of genes/QTLs, thus governing various developmental traits as well as biotic and abiotic stresses. The nature of the mapping population is very important for detecting its power for mapping. In general, F₂ segregating populations originate from the extreme phenotype for a trait used for the mapping and tagging of loci governing the trait of interest. Additionally, backcross (BCF₂) populations and some fixed populations such as doubled haploids (DH), recombinant inbred lines (RILs), near isogenic lines (NILs), nested association mapping (NAM) population, and multiple advanced generations inter-cross (MAGIC) populations are frequently used for the purpose of mapping. However, these populations are developed using parents with extreme phenotypes, and therefore, only bi-parental segregation occurs, which is the major limitation of linkage mapping [117]. In recent years, exploring QTLs by association analysis has been one of the effective approaches in quantitative genetics, which performs the rapid and fine-mapping of the target locus [73]. Thomas [111] developed a mapping panel of mungbean, which consisted of 30 crosses including four interspecific crosses using V. sublobata and advanced to F₅ generation for mapping complex traits such as drought and heat tolerance.

Association mapping has the ability to detect more QTLs because it uses a diverse germplasm that has more allelic diversity and the occurrence of several random events because of its parental evolution history than bi-parental population. Many researchers used marker-trait association through association mapping studies for mapping various important traits, such as fiber quality in cotton [118], MYMIV resistance in soybean [119], agronomic and flowering traits in lentil [120], seed coat color in mungbean [111], MYMIV resistance in mungbean [73], and agronomic traits in an MYMIV-resistant panel of mungbean [67]. Previously, researchers believed that the identified markers associated with QTLs from preliminary mapping studies were directly used in MAS. However, in the recent past, it has become widely accepted that QTL confirmation and validation is required [121]. The use of donor parents in developing a mapping population and background of molecular markers also affects the detection of QTLs. Most studies showed that the use of cross-specific molecular markers in the identification of QTLs exhibited the efficiency of cross-specific resources. Kitsanachandee et al. [76] detected five QTLs for MYMIV resistance, explaining 6.24–27.93% phenotypic variations in mungbean. Kasettranan et al. [88] detected two QTLs for powdery mildew resistance in mungbean through cross-specific markers. Recently, Singh et al. [73] identified three linked loci through the AM-approach in mungbean using the cross-specific markers of adzukibean. The effectiveness of cross-specific genomics resources for mungbean improvement was evident. Mathivathana et al. [83] mapped the major QTL on LG-4 through genotyping by a sequencing (GBS) approach using the mapping population developed between mungbean × ricebean. Somta et al. [97] identified two QTLs such as qVmunBr6.1 and qVmunBr6.2. as new loci for C. maculatus resistance in Vigna mungo var. Silvestris that suggested that a lectin receptor kinase and chitinase are candidates for qVmunBr6.2. Mariyammal et al. [122] identified 12 QTLs in two environments through the mapping of an RIL population developed by crossing mungbean × ricebean. Venkataramana et al. [123] identified two major QTLs, namely Cmpd1.5 and Cmpd1.6, mapped within 11.9 cM and 13.0 cM of the flanking markers, which accounted for 67.3 and 77.4% of the variance for seed damage due to pulse beetle. Subramaniyan et al. [82] performed the linkage mapping in the population of urdbean for bruchid resistance and explained 17.01% of the genetic variation. This QTL was flanked with the SSR marker CEDG302 and GMES1248. Dhaliwal et al. [78] mapped a major QTL having 70% phenotypic variation for MYMIV resistance in the RILs of black gram × ricebean. They identified three competitive allele-specific (KASP) markers tightly linked to MYMIV that originated from serine threonine kinase, UBE2D2 and BAK1/BRI1-ASSOCIATED RECEPTOR KINASE genes. This indicates the possibility to identify and exploit cross-specific QTLs/genes. Previous studies suggested that V. umbellata, V. sylvistris and V. sublobata were less affected by the disease and insect-pests, and might prove as useful resource for improving biotic stress tolerance in mungbean.

2.7. Expanding Genomic Regions for Tagging New Candidate Genes

The identification of QTLs through various approaches for target traits is routine these days, although cloning and characterization remain limited to date [67]. Expanding the genomic regions associated with QTLs/loci will offer a means of tagging candidates for target traits. Mathivathanal et al. [83] identified five QTLs with phenotypic variation explained (PVE) from 10.11 to 20.04 for MYMV resistance using ricebean as a donor parent. The QTL qMYMV4-1 was found to be a major and stable QTL for MYMV. They also expanded the genomic regions of qMYMV4-1 and identified 16 candidate genes. Mariyammal et al. [122] also mapped the bruchid-resistant QTLs on chromosome 5 in mungbean × ricebean RILs population and identified 16 candidate genes. These candidate genes may have an important role in imparting resistance against MYMV and bruchid (Figure 3,

Table 3. The candidate genes associated with major disease/insect-pest resistance.

Gene Name	LG	Stress	Crop	Functional Characterization	Reference
Vradi04g06770	04	MYMV	Mungbean × ricebean	Protein kinase superfamily protein (serine/threonine kinase activity)	[83]
Vradi04g06840	04			Small GTP-binding protein (disease resistance protein)/ leucine-rich repeat/P-loop containing nucleoside triphosphate hydrolase
Vradi04g06900	04			Zinc finger, RING/FYVE/PHD-type (RING finger protein 165-like
Vradi04g06950	04			Receptor-like kinase/leucine-rich repeats
Vradi04g06960	04			Zinc finger, RING/FYVE/PHD-type (U-box domain-containing protein 15-like)
Vradi04g07000	04			Protein kinase superfamily protein (serine/threonine kinase activity)
Vradi04g07100	04			WRKY family transcription factor
Vradi04g07130	04			WRKY family transcription factor
Vradi04g07220	04			MYB transcription factor MYB64
Vradi04g07240	04			Transcription factor bHLH79-like (basic helix–loop–helix (bHLH) domain)
Vradi04g0727	04			MYB transcription factor MYB183
Vradi04g07290	04			DNA/RNA helicase, DEAD/DEAH box type, N-terminal/P-loop containing nucleoside triphosphate hydrolase
Vradi04g07440	04			Zinc finger, RING/FYVE/PHD-type (U-box domain-containing protein 38-like)
Vradi04g07450	04			Jasmonic acid carboxyl methyltransferase (SAM-dependent carboxyl methyltransferase)/ methyltransferase activity
Vradi04g07490	04			Zinc finger, RING/FYVE/PHD-type (RING-H2 finger protein 2B)
Vradi04g07540	04			Cytochrome P450 (oxidation-reduction process)
Vradi05g09450	05	Bruchid		WRKY family transcription factor	[122]
Vradi05g09480	05			Kelch repeat F-box protein
Vradi05g09650	05			Aminoacyl-tRNA synthetase
Vradi05g09830	05			Flavin-binding monooxygenase family protein
Vradi05g09990	05			Cellulose synthase family protein
Vradi05g10080	05			Ethylene-responsive transcription factor (ERF)
Vradi05g10110	05			F-box family protein (leucine-rich repeat)
Vradi05g10130	05			Ascorbate peroxidase (Peroxidase activity)
Vradi05g10140	05			Receptor-like serine/threonine-protein kinase
Vradi05g10200	05			Chloroplastic ATP synthase
Vradi05g10210	05			Protein kinase superfamily protein/protein kinase activity/ATP binding/protein phosphorylation
Vradi05g10410	05			Cellulose synthase family protein
Vradi05g10460	05			Protein kinase superfamily protein/concanavalin A-like lectin/glucanase/protein phosphorylation
Vradi05g10480	05			Ethylene-responsive transcription factor (ERF)
Vradi05g10500	05			Zinc finger protein 1-like (zinc finger, C2H2)
Vradi05g10560	05			Calmodulin-binding transcription activator 4-like isoform X5
Vradi05g10580	05			Calmodulin-binding transcription activator 4 isoform X3 protein
Vigan.05G027700	05	C. maculatus	Black gram	Zinc finger RING/FYVE/PHD-type, CTLH/CRA C-terminal to LisH motif	[82]
Vigan.05G028300	05			Leucine-rich repeat—N-terminal, protein kinase family
Vigan.05G029200	05			Pathogenesis-related protein 1- like/cysteine-rich secretory protein allergen V5/Tpx-1 family
Vigan.05G030000	05			Myc-type basic helix–loop–helix (bHLH) typ
Vigan.05G030500	05			Zinc finger proteins (C2H2 type)
Vigan.05G031900	05			Protein kinase family (serine–threonine/tyrosine-protein kinase catalytic), Concanavalin A-like lectin/glucanase subgroup
Vigan.05G035600	05			F-box family protein
Vigan.05G036000	05			Diacylglycerol kinase catalytic protein, ATP-NAD kinase-lik
Vigan.05G036200	05			Target SNARE site (coiled-coil structure), syntaxin N-terminal
Vigan.05G038400	05			Toll/interleukin-1 receptor homology (TIR)
Vigan.05G042200	05			Ubiquitin-conjugating enzyme E2/RWD-like
Vigan.05G044400	05			Leucine-rich repeat (malectin-like carbohydrate binding)
Vigan.05G046400	05			Glutaredoxin-like protein/Thioredoxin-like fold
Vigan.05G046700	05			NB-LRR family proteins
Vigan.05G048300	05			Pathogenesis-related genes transcriptional activator (PTI5)
Vigan.05G049800	05			Chloramphenicol acetyltransferase-like
Vigan.05G056900	05			Ankyrin repeat-containing protein
Vigan.05G066300	05			F-box proteins/Kelch repeat type 1
Vigan.05G075700	05			F-box/kelch-repeat protein At3g23880-like isoform X1
Vigan.08G002100	08			Bi-functional inhibitor/seed storage helical protein
Vigan.08G002200	08			Lipid-transfer protein DIR1 family
Vigan.08G002600	08			Myc-type basic helix–loop–helix (bHLH) type
Vigan.08G003600	08			Zinc finger CCCH-type K homology protein
Vigan.08G003900	08			Cytochrome P450 conserved protein
Vigan.08G004400	08			Protein kinase super family (serine–threonine-dual specificity protein)/Concanavalin A-like lectin/glucanase subgroup
Vigan.08G004700	08			Basic-leucine zipper protein/transcription factor TGA-like
VrPGIP1, VrPGIP 2	05	Callosobruchus chinensis and Callosobruchus maculatus	Green gram	Polygalacturonase-inhibiting protein	[94,124]
Vradi04g00919	04	C. chinensis		polygalacturonase inhibitor	[125]
Vradi05g03810	05	Callosobruchus spp.	V. radiata >× V. sublobata	Resistant-specific protein-1	[93]
Vradi05g03830	05			Resistant-specific protein-2
Vradi05g03840	05			Resistant-specific protein-2
Vradi05g03860	05			Resistant-specific protein-2
Vradi05g03870	05			Resistant-specific protein-1
Vradi05g03880	05			Resistant-specific protein-1
Vradi05g03930	05			Resistant-specific protein-2
Vradi05g03940	05			Polygalacturonase inhibitor
Vradi05g03950	05			Polygalacturonase inhibitor

).

Recently, Subramaiyan et al. [82] identified 19 candidate genes on LG-5 and 07 candidate genes on LG-8 of urdbean. The expression analysis and further characterization of these candidate genes will offer the scope to utilize the selected genes for mungbean improvement using cross-species. In a similar way, we also need to develop mapping populations using cross-species and wild relatives for tagging potential QTLs/genes. This will help in utilizing useful genes from another Vigna gene pool for enhancing the stress tolerance. Limited reports are available in the identification and characterization of candidate genes from the QTL region, which need to be focused upon. This will help develop noble functional markers to accelerate the molecular breeding for enhancing the stress tolerance.

2.8. Comprehensive RNA-Seq Approach

The RNA-seq approach deals with the complete set of RNA transcripts produced by the genome of an individual in the cell/tissue under specific conditions. It is emerging as a promising technique to analyze the expression pattern of genes, which helps to understand the first layer function of a particular gene [126]. Various methods have been adopted earlier such as cDNAs-AFLP, differential display-PCR (DD-PCR), SSH, etc. However, these techniques provided low resolution. The introduction of advanced techniques such as microarrays, digital gene expression profiling, NGS, RNAseq, SAGE, etc. made it more effective to understand the candidate genes. The mapping of tagged candidate genes against the available decoded whole genome sequences of the crops and their relatives provided an insight into their structural and functional diversity. Wang et al. [127] emphasized single-molecule sequencing (SMS) as an emerging state-of-the-art technique for gene discovery and annotation. Baruah et al. [128] performed the expression of 20 defense related genes through qPCR, in which 12 genes showed up-regulation, whereas 8 showed down-regulation upon bruchid oviposition. Some major defense genes such as defensin, PR gene, and LOX were highly expressed in the oviposited population as compared with the non-oviposited ones. The Blast2GO analysis indicated the role of certain enzymes related to secondary metabolites, aromatic amino acid, and primary amino acid metabolism in activating defense mechanism. Lin et al. [129] performed transcriptome and proteome analysis and three DEGs/DPs, including resistant-specific protein (g39185), gag/pol polyprotein (g34458), and aspartic proteinase (g5551) were identified, which encode a protein containing a BURP domain. Liu et al. [130] compared the genomic and transcriptomic data in mungbean against bruchid resistance and 91 DEGs were identified upon bruchid infestation. They found 408 nucleotide variations (NVs) between bruchid-resistant and -susceptible lines in regions spanning 2 kb (kilo base pairs) of the promoters of 68 DEGs. Furthermore, 282 NVs were identified on exons of 148 sequence-changed-protein genes (SCPs). Raizada and Jegadeesan [59] performed the comparative transcriptome analysis of the developing seeds of wild and cultivated black gram with contrasting phenotypes for three traits, bruchids infestation, YMD, and seed size, in which 715 DEGs were re-annotated. Das et al. (2021) performed the transcriptomic analysis and stated that the bruchid ovipositioning-mediated defense response in black gram is induced by SA signaling pathways and defense genes, and such defensin genes could be potential candidates for resistance to bruchids [131]. These reports demonstrate the role of transcriptomics in terms of stress responses and development for crops. The RNA-seq approach also proved to be one of the powerful techniques of transcriptomics to develop genic-SSR or functional markers that can be linked to phenotypic trait variations. Kumar et al. [132] recently performed the transcriptomic analysis in lentils under heat stress and identified the DEGs and developed the genic SSR markers. To understand the differential expression profiles in response to specific stress in different crop species, the comparative transcriptomic approach is one of the viable options. Due to the close resemblance of Vigna species and their cross transferability of genes through classical and molecular approaches, the comparative transcriptomics will prove a way to understand the function of target genes. This approach will also help select the candidate genes from different cross-species to mungbean for their pyramiding. Collectively, all these transcriptomic techniques could be helpful for mungbean improvement.

2.9. Gene-Based Functional Markers

Functional markers can be developed through many approaches such as transcriptomics, NGS, TILLING, homologous recombinant (HR), association mapping, allele mining, etc. [133]. The transcriptome sequences available in databases provide a cost-effective and valuable source of the development of new molecular markers. Gupta and Gopalkrishna [134] developed 1071 SSRs in cowpea unigene sequences. Primer pairs were successfully designed for 803 SSR motifs and 102 SSR markers were finally characterized and validated. Gupta et al. [135] identified 12,596 EST sequences from mungbean and developed 1848 in silico EST-SSRs. One hundred randomly selected primer pairs were further used for characterization. These EST-SSRs might be useful for the QTL mapping of target traits and can be further utilized for the functional characterization and development of functional markers. This approach is more effective than utilizing the random markers in crop improvement. Baruah et al. [128] developed EST-SSRs associated with MYMV in black gram. However, the development of gene-based functional markers (FMs) is limited in Vigna species, and needs to be accelerated. The advantage of FMs over other molecular markers is the close genomic resemblance between an FM and a phenotypic trait. Therefore, FMs may facilitate the direct selection of genes associated with the phenotype, which serves to increase the selection efficiency for crop improvement. Furthermore, there is no need to use the flanking markers during gene introgression. Therefore, these markers would play an important role in marker-assisted selection strategies.

2.10. Developing Potential SCARs

A variety of molecular markers have been developed and utilized in crop improvement. PCR-based molecular markers are believed to be cost-effective and require less DNA. Pratap et al. [55], Kumari et al. [100], and many more workers assessed the molecular diversity through cross-specific molecular markers in the efficiency and effectiveness of these markers in crop breeding. Singh et al. [67,73] used the molecular markers from adzukibean in mapping loci for MYMIV and agronomic traits in the population of mungbean. The development of SCAR markers is one of the ways to develop the tightly linked markers to the trait of species. Souframanien and Gopalakrishna [136] used the RAPD markers and the marker ISSR8111357 was sequenced and the SCAR-YMV1 linked to MYMV was designed. Dhole and Reddy [137] developed the marker OPB-07600 which was closely linked (6.8 cM) with an MYMV resistance gene. Recently, Zhang and Panthee [138] developed the codominant SCAR marker linked to the resistant genes in tomato for gene pyramiding. Feng et al. [139] developed SCoT-based SCAR markers to authenticate the species of the genus Physalis. These SCAR markers were also found to be effective in discriminating the genotypes of the related species, which might be utilized in cross-specific alien gene transfer. Some of the wild species are closely related to mungbean as well as each other, which require robust taxonomy-based phenotyping for their identification. Gore et al. [140] identified two genotypes of the section Aconitifoliae (V. trilobata and V. stipulacea) using 47 descriptive traits. The SCAR marker can help easily identify those species and accelerate their effective utilization. Zheng et al. [141] developed the SCAR markers for discriminating the toxic genotypes to non-toxic ones in Dendrobium officinale. This indicated the potential of SCAR markers in Vigna improvement.

2.11. Marker-Assisted Breeding

Marker-assisted selection (MAS) refers to use of DNA markers to assist phenotypic selection towards crop improvement. The markers, such as RAPD, SCoT, ISSR, SSR, and SNPs were effectively utilized in breeding programs. The identification and exploitation of tightly linked markers to the trait of interest has led to practical achievements in terms of varietal development with enhanced efficiency and accuracy in shorter time periods. NGS technologies also led to remarkable advancements providing ultra-throughput sequences for plant genotyping. To further broaden the usages of sequencing technologies to large crop genomes or the non-availability of reference sequences, GBS has been developed for marker discovery. In recent days, the bioinformatic pipelines were deployed to identify the candidate genes [66]. These candidates might be useful for developing the functional markers for MAS programs for mungbean improvement. Many QTLs/genes from cross-specific donors using the RIL population for biotic stress resistance were identified. However, their cloning, characterization, and introgression in high-yielding varieties for improving the biotic stress tolerance are rather limited. Wu et al. [142] successfully introgressed the bruchid resistant gene VrPGIP-2 in KPS-1 from V2802 using VrBR-SSR013 and DMB-SSR-158 as foreground markers. Mariyammal et al. [122] identified 15 candidate genes for bruchid resistance in mungbean × ricebean population. Recently Chen et al. [93] identified a bruchid-resistant gene Vradi05g03810 encoding the probable resistant-specific protein from TC1966 in the background of mungbean. Mathivathana et al. [83] identified 19 candidate genes on chr.04 in the mungbean × ricebean population for MYMV resistance. After mapping these genes, the genes can be tagged and introgressed in mungbean to improve the bruchid resistance (Figure 4). The identification of resistant genes from different sources will also help in gene pyramiding to improve the durability of resistance.

3. Conclusions and Way Forward

Mungbean productivity is globally influenced by a wide array of biotic and abiotic constraints, many of which cause significant yield losses. Numerous efforts have been made in recent decades to enhance the biotic stress resistance, especially that to yellow mosaic disease and leaf spot through breeding approaches. However, success has been rather disproportionately limited due to the complexity of stress responses. Resistance breakdown has also been commonly observed for biotic stresses. Therefore, gene identification and its characterization, and the exploitation of QTLs/genes by related species through marker technologies will help effectively address this challenge. Many of the wild relatives of Vigna were reported as highly resistant to different diseases as well as insect-pests, and can therefore be deployed for this purpose. The availability of the reference genome and transcriptome sequences and resequencing data are now being used to assess the germplasm diversity analysis, genetic linkage mapping, genome-wide association studies, marker development, and marker-assisted selection to improve the biotic stress resistance. The decoding of the whole genome sequence of mungbean and its relatives also offers great scope to undertake the genome-wide analysis of biotic stress encoding gene families, as well as many more transcriptional factors for enhancing the stress response. Gene expression studies in different tissues during the normal course of development and under stress environments were undertaken, and the information generated might be useful to understand the stress responses. This review provides updates on the potential donors, the possibility of cross-species gene flow, genomics, and transcriptomics advancements, showing that the marker technologies for the effective utilization of cross-species alien genes/QTLs need to be utilized to enhance biotic stress resistance in mungbean.