Variations in MAS

The numerous molecular methods used in MAS include marker-assisted backcrossing (MABC), gene pyramiding, marker-assisted recurrent selection (MARS), and genomic selection (GS). In order to characterize genetic material and select individuals in the early segregating generation, these techniques have been applied in plant breeding, speeding up and improving the precision of the breeding cycle [157–159]. The genomics-assisted breeding (GAB) techniques MABC, MARS, and GS have recently been applied to breeding superior chickpea varieties with increased yield and resistance/tolerance to adverse climatic conditions [160].

#### Marker-Assisted Backcrossing (MABC)

MABC, a backcrossing technique, is made possible by molecular markers [161]. It expedites both the selection process and the genetic recovery of the recipient parents. By transferring the gene of choice or quantitative trait loci (QTLs) from the donor parent, this method is frequently used to eradicate undesirable features, such as disease and pest susceptibility, anti-nutritional factor, etc. from high-yielding cultivated varieties [162]. Foreground selection, background selection, and recombinant selection are the three steps of MABC.

Two high-yielding Desi cultivars viz., Annigeri 1 and JG 74, were employed in a collaborative effort between the University of Agricultural Sciences (UAS-Raichur) and Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Jabalpur, India, to increase FW resistance using the MABC method. In Central and South India, both grown species demonstrated high susceptibility to Fusarium wilt race 4 (*FOC* 4) and decreased production. This led to the development of two novel resistant varieties, namely, "Super Annigeri 1" and "enhanced JG 74", by introgressing a genomic region that imparts resistance to *FOC* 4, utilizing MABC and WR 315 as the donor parent [163].

The two primary factors limiting the output of chickpeas are Ascochyta blight (AB) and Fusarium wilt (FW). Using a step-by-step MABC strategy, a superior chickpea cultivar, C 214, was given dual resistance [164]. The *FOC* 1 gene for FW and two quantitative trait loci (QTL) regions, ABQTL-I and ABQTL-II, were targeted for introgression to produce

resistant lines. Employing foreground selection with six markers related to *FOC*1 and eight markers linked to both QTLs, it is now possible to choose plants with desirable alleles in several segregating generations. To find a plant with high recurrent parent genome recovery, background selection employing 40 uniformly distributed SSR markers was performed, in addition to foreground selection. After three backcrosses and three rounds of selfing, 22 BC3F4 lines for FW and 14 MABC lines for AB were acquired [165]. Three resistant lines for FW and seven resistant lines for AB have been identified phenotypically using this line.

#### Marker-Assisted Gene Pyramiding (MAGP)

One of the contemporary MAS methods used to produce MAGPs is the pyramiding of different genes. Two or more genes are picked for pyramiding simultaneously in MAGP. Gene pyramiding has been performed using an array of methods, including backcrossing, recurrent selection, complicated crossing, and multiple-parent crossing [166].

#### Marker-Assisted Recurrent Selection (MARS)

Recurrent selection, where two genes are chosen at a time for pyramiding, is an effective method used in plant breeding to improve quantitative traits through continuous crossing and selection processes [167]. The breeding cycle is slowed down by environmental changes, which have a negative effect on the breeder's ability to select. At each generational level, molecular markers are employed for the intended features in MARS. Every cycle of crossing and selection in this case involves selectively crossing specific plants. The selection is made utilizing phenotypic data and marker scores. As a result, it accelerates the breeding or selection cycle and boosts the efficiency of recurrent selection. MARS is a forward breeding approach that has been extensively used for polygenic traits such as agricultural production and resistance to different biotic and abiotic stresses [168].

#### *7.2. Genetic Mapping and QTL Technique*

Studying the genetics of quantitative traits is crucial in the field of plant biotechnology. Complex quantitative features can be found in many plant species in nature. We now have better knowledge of these complicated traits. The section of the genome known as a QTL is linked to a quantitative trait's influence [169]. Quantitative trait loci are made up of a single gene or a group of linked genes that affect phenotypes. One or more genes that influence quantitative traits have been identified using molecular markers and advanced statistical methods, together with specific chromosome loci. These identified loci are known as QTLs. For the attributes of yield, quality, insect-pest resistance, abiotic stress tolerance, and environmental adaptation in chickpea, QTL mapping is conducted. When identifying connected QTLs in a population with segregating traits, it is essential to select parents with a variety of genetic backgrounds and to hybridize parental lines that differ in one or more of their quantitative traits [170].

Genetic maps are created by employing the segregation and recombination principles of Mendelian genetics. They may demonstrate how close together chromosomes and DNA producers are within an organism. This level of parental differentiation in the population is crucial for the creation of genetic maps. Crop breeding and genetic mapping are closely related, and many crop breeding populations have already undergone genetic mapping [171]. Building genetic maps based on molecular markers that are easy to produce, highly repeatable, co-dominant, and specific to recognized linkage groups is greatly desired for breeding purposes. Because the length of each marker is the most crucial component, maps created using AFLPs, RAPDs, and ISSRs have limited transferability between populations and pedigrees within a species [171,172].

The identification and mapping of genes that impact chickpea resistance to different races of *FOC* have been made easier thanks to the use of DNA marker technology. In two mapping populations, CA 2156-JG 62 and CA 2139-JG 62, Halila et al. [172] discovered a second gene, *FOC*02/*FOC*02, which is flanked by markers TS47 and TA59 on LG2. Jendoubi

et al. [173] used nearly isogenic lines (NILs) to finely map the *FOC*01/*FOC*01 gene on LG5 within a 2 cM interval. An SSR-based QTL analysis of the F2:3 population (C 214 × WR 315) identified two QTLs on LG6 for *FOC*1 resistance: FW-Q-APR-6-1 and FW-Q-APR-6-2 [174].

The first genetic maps of the chickpea were created using isozymes from F2 populations resulting from interspecific crosses. Following this, additional maps were created by various study groups. One of these maps included QTLs related to flowering time, agronomic traits, and Ascochyta blight resistance [175]. Other characteristics included double pod, growth habit, and Fusarium wilt resistance [176]. To map the *FOC*-3 resistance gene and connect it to the *FOC*-1, *FOC*-3, and *FOC*-4 resistance genes, RAPD, STS, ISSR, and STMS markers were used. At 0.6 cm from the *FOC*-3 gene, the STMS marker TA96 was found, but the STMS markers TA27 and CS27A co-segregated with TA96. Additionally, the authors found a link between *FOC*-3, *FOC*-1, and *FOC*-4. While *FOC*-1 and *FOC*-4 were mapped close together at 1.1 cm, *FOC*-3 appeared to be associated with them at distances of 9.8 cm and 8.7 cm, respectively.

Using the SSR marker TA103, *FOC*1 was introduced from WR 315 to C 214. Earlier, scientists discovered *FOC*1 flanked by the SSRs TA110 and H3A12 on LG2. On LG2, the genes for *FOC*2 (TA96-H3A12) and *FOC*3 (TA194-H1B06y) were also discovered. However, according to Jingade and Ravikumar [177], a major QTL (GSSR 18-TC14801) on LG1 for *FOC*1 resistance can account for up to 71% of phenotypic variance (PV). Moreover, a sizable QTL (FW-Q-APR-2-1) was found on CaLG02, and two smaller QTLs (FW-QAPR-4-1 and FW-Q-APR-6-1, respectively) were found on CaLG4 and CaLG6, indicating resistance to *FOC*1 and *FOC*3 [178]. It has been determined that the resistance loci on LG2 are either monogenic or oligogenic with respect to *FOC* 5. With the help of SNP and SSR markers, the possible LG2 genomic area was recently reduced to 820 kb [179].

Moreover, two distinct genes that provide race 0 resistance have been identified and labeled. The first resistance gene, *FOC01*/*FOC01*, was flanked by two markers, i.e., OPJ20600 and TR59, on linkage group 3 (LG3), which corresponds to LG2. In an F2:3 mapping population of "C 214" × "WR 315", Sabbavarapu et al. [174] recently revealed two unique QTLs for race 1A (*FW-Q-APR6-1* and *FW-Q-APR-6-2*). The second gene (*FOC02*/*FOC02*) was located on LG2, and the STMS markers TS47 and TA59 were located on each side of it (Table 4). All additional wilt pathogen resistance genes were found in linkage group 2, except for *FOC*-01 and two QTLs for race 1A.

Numerous studies have shown that four genes, including *FOC*-1, *FOC*-3, *FOC*-4, and *FOC*-5, should be in the same linkage group [180]. Five resistance genes, viz., *FOC*-1, *FOC*-2, *FOC*-3, *FOC*-4, and *FOC*-5, were found to be clustered in chickpea. On LG2, a cluster of five genes covering 8.2 cm was discovered. The resistance gene cluster was 2.952 Mb in size, where 1 cm equals 360 kb. Among the five genes, *FOC*-1 and *FOC*-5 were separated by 2.0 cm, but *FOC*-5 was separated from *FOC-3* by 3.4 cm. It was determined that 5.4 cm separated *FOC*-1 from *FOC*-3. There was a 1.0 cm distance between *FOC*-3 and *FOC*-2 and a 1.8 cm distance between *FOC*-2 and *FOC*-4. At the extremities of the cluster, 8.2 cm separated two genes (*FOC*-1and *FOC*-4). It was observed that gene order and map distances were more accurate because only one source of resistance to five genes was utilized, and the mapping population descended from an intraspecific cross.

The discovered QTLs for various traits can be utilized in genomics-assisted breeding using modern techniques, such as marker-assisted backcrossing, the introgression of superior alleles from wild species through advanced backcross QTL, marker-assisted recurrent selection, and genome-wide selection. Garg et al. [178] constructed a genetic map for resistance to Fusarium wilt on 188RILs gene rated from a cross between JG 62 and ICCV 05530, and identified five QTLs for resistance, with explained phenotypic variance ranging from 6.63 to 31.55 percent. Out of the five QTLs found, three QTLs onCaLG02 and one minor QTL each on CaLG04 and CaLG06 were mapped for race1.


**Table 4.** List of various QTLs contributing to Fusarium wilt in chickpea.

According to molecular mapping investigations, resistance genes for pathogen races 0, 1, 2, 3, and 4 have been found on LG2 of the chickpea map. Due to the grouping of six resistance genes, LG/2 is a hotspot for Fusarium wilt resistance. In order to employ MAS and better understand the molecular mechanism of resistance, strongly related markers for some of the genes have been found and verified in various genetic backgrounds [181]. Race 5 resistance gene near-isogenic lines have been created, which can be used for map-based cloning and fine mapping.

*FW-Q-APR-2-1*, a significant QTL for race 1, was identified on CaLG02. Additionally, minor QTLs on CaLG04 (*FW-Q-APR4-1*) and CaLG06 were detected (*FW-Q-APR-6-1*). For race 3 of an FW discovered in Ludhiana, a significant QTL was discovered on CaLG02 (*FW-Q-APR-2-1*) and CaLG04 (*FW-Q-APR4-1*). Since the primary QTLs for races 1 and 3 on CaLG02 shared flanking markers, i.e., TR19 and H2B061, it is possible that the same genomic regions regulate resistance to these two races.

#### *7.3. Genome Sequencing*

A few decades after the Sanger DNA sequencing method was created, deep, highthroughput, in-parallel DNA sequencing techniques known as next-generation sequencing (NGS) were created. Amplification libraries, also known as amplified sequencing libraries, are required for second-generation sequencing methods. It is now possible to perform single-molecular sequencing by employing third-generation sequencing, without the timeconsuming and expensive amplification libraries. Research teams may now create de novo draught genome sequences for every organism of interest, with the help of bioinformatics tools and the synchronized rapid advancement of NGS technology. These technologies can be applied to whole-transcriptome shotgun sequencing (WTSS, also known as RNA sequencing (RNA-seq)) [182], targeted (TS) or candidate gene sequencing (CGS) [183,184], whole-exome sequencing (WES) [185], and methylation sequencing (MeS) [186].

Genome sequencing is being transformed due to advances inhigh-throughput technology. The intense rivalry among new sequencing techniques has led to some incredible advancement. The essential concepts of the best-known sequencing platforms are: ABI/SOLiD sequencing, Roche/454 Life Sciences sequencing, and Solexa/Illumina sequencing.

Prior to 2013, the chickpea was recognized as an orphan crop due to a lack of genetic data. However, the first draughts of the genomes of the Desi and Kabuli chickpea investigations were released in 2013 [187]. The development of high-throughput sequencing and next-generation technologies laid the foundation for the sequencing of the chickpea genome. A thorough map of deviation in 3171 cultivated and 195 wild accessions was produced by Varshney et al. [188] to provide resources for breeding and research on chickpea genomics.

The creation of genetic resources is still crucial for molecular or genomics-assisted breeding. Unfortunately, there has been delayed development of genetic resources for this important crop of legumes. Chickpea genomic resources have significantly increased in recent years due to next-generation sequencing (NGS) initiatives and their use in genomics research [188]. The discovery of the candidate gene(s)/genomic regions controlling disease resistance may be made possible by the availability of whole-genome sequence information in different plant species, including chickpea. Williams et al. [189] and Srivastava et al. [190] reported on the virulence-related genes *FOC* (*FOC*-38-1) and Fop (Fop-37622), which have provided fresh information that has increased our comprehension of the pathogenicity of FW and the evolution of the host–pathogen interaction in legume species.

The use of NGS technology has led to the creation of numerous molecular markers for the advancement of chickpeas [188]. In the past, millions of SNP markers, 2000 SSR markers, and more than 15,000 feature-based diversity array technology (DArT) platform markers have been produced for chickpea. The NGS revolution has made it possible to perform sequencing at different depths, including whole-genome re-sequencing, skim sequencing, and low-depth sequencing (genotyping via sequencing, RAD-Seq).

#### **8. Multi-Omics Approaches**

Several interesting omics technologies have evolved during the past few decades. The information gathered using these omics techniques may be combined with genetic information to alter a variety of biological processes involved in chickpea breeding. These omics-based techniques have been proven to be useful for examining the molecular and genetic foundations of crop development by modifying DNA, proteins, metabolites, transcript levels, and mineral nutrients against negative environmental and physiological stress responses [191]. Numerous omics methods have disclosed each corresponding molecular bi-

ological aspect integrated with plant systems, including metagenomics, genomics, transcriptomics, metabolomics, proteomics, ionomics, and phenomics [192]. High-throughput and speedy data creation for transcriptomes, genomes, proteomes, metabolomes, epigenomes and phenomes has been made possible by the development of next-generation sequencing (NGS) technology [193]. The integration of different omics techniques under physiological and environmental stress could reveal gene networks and activities [15]. The use of omics provides a systems biology approach to comprehending the intricate relationships between genes, proteins, and metabolites within the phenotype. In order to preserve and develop crops, this integrated approach largely relies on computational analysis, bioinformatics, chemical analytical procedures, and many different biological disciplines [194]. For the purpose of finding possible candidate genes and their pathways, the integration of various omics methods may prove useful. Omics allows for a deeper understanding of the processes behind the complex architecture of numerous phenotypic features with agricultural importance. Thus, the integration of multi-omics approaches may be beneficial to identify the mechanisms behind the expression of simple and quantitative traits such as higher yield and disease resistance. Omics approaches are also important for understanding the inheritance of these traits [195]. This information is significant in the development of biotic stress-resistant cultivars through the introgression of desired traits to maintain the sustainable production of different crops, including chickpea. For example, metabolomics may help in the identification of the up-and down-regulation of different metabolites that are important for defense systems in plants [196].

#### *8.1. Transcriptomics/Gene Expression Studies*

Differential gene expression in chickpea plants infected with Fusarium wilt, as well as plants without infection, comparatively offers a wealth of resources for the functional analysis of resistance-related genes and their application in breeding for long-lasting wilt resistance. In chickpea, various studies have been conducted to identify differentially expressed genes. Using cDNA-RAPD and cDNA-AFLP techniques, Nimbalkar et al. [182] identified differentially expressed genes in chickpea during root infection by *Fusarium oxysporum* f. sp. ciceri race 1. Based on a cDNA template and decamer primers, the former discovered nine transcripts that were differently expressed in the infection-resistant chickpea variety. In total, 273 of the 2000 transcript-derived fragments (TDFs) displayed differential expression in infected chickpea stems. Only 13.65% of the TDFs were differentially expressed during the pathogen infection process in chickpea roots, while the remaining 86% did not vary in expression (Table 5). In a study, Saable et al. [196] identified 162 DEGs that belonged to defense signaling pathways. Using this sequence, other studies have also been carried out to discover differentially expressed genes (DEGs). Ashraf et al. [197] discovered 6272 DEGs that belonged to stress-responsive genes in chickpea through RNA blot analysis during wilt infection with race 1. Gupta et al. [198] race 1 induced redox state alterations in chickpea. Recently, Priyardashni et al. [199] analyzed the expression of NBS-LRR and WRKY genes in chickpea infected with Fusarium wilt, causing a fungal pathogen.


**Table 5.** Differentially expressed genes (DEGs) contributing to FW resistance in chickpea.

The transcriptome, or the complete collection of RNA transcripts produced by an organism's genome in a cell or tissue, is the subject of the study of transcriptomics [208]. To study how genes are expressed in response to various stimuli over an extended period, a dynamic technique called transcriptome profiling has grown in popularity [209,210]. By enabling the researcher to examine the differential expression of genes in vitro, this method aids in the clarification of a gene's basic function. To analyze transcriptome dynamics, at first, conventional profiling approaches, such as differential display-PCR (DD-PCR), SSH, and cDNAs-AFLP, were used; however, these methods had poor resolution [211]. The use of microarrays, digital gene expression profiling, NGS, RNA seq, and SAGE for RNA expression profiling was soon made possible through the development of truthful techniques [212,213]. A breakthrough technique for advancing transcriptomics uses in situ RNA-seq, often referred to as in situ ligation, to sequence RNA in living cells or tissues [214]. A second method called spatially resolved transcriptomics uses spatial information to detect gene expression within cells or tissues in order to provide a detailed molecular description of physiological processes in living things [215]. One of the better techniques for creating genic-SSR markers that can be connected to phenotypic features associated with candidate genes is RNA-seq.

Before the discovery of digital transcriptome profiling, expressed sequence tags (ESTs), cDNA-AFLP, and cDNA-RAPD were mostly employed to identify the gene(s) involved in plant defense mechanisms and plant–pathogen interactions [216–220]. In recent years, the transcriptome analysis of the four chickpea cultivars, viz., JG 62, ICCV 2, K 850, and WR 315, allowed the genomic regions regulating FW resistance to have "big effect" SNPs and Indels [221,222]. The chickpea from the cross ILC 3279 × WR 315 was functionally validated for the genomic area determining *FOC* (race 5) resistance [223]. In this experiment, resistant and sensitive NILs were generated. Three novel candidate genes, i.e., LOC101495941, LOC101509359, and LOC101510206 (encoding the MATE family protein, MADS-box transcription factor, and serine hydroxymethyl-transferase, respectively) and two previously known candidate genes, i.e., LOC101490851 and LOC101499873 (encoding chaperonin) were related to defense activity against FW, recognized via differential gene expression analysis at twenty-four hours post inoculation (hpi) [224]. Numerous transcripts associated with distinct TFs were found to be differently expressed in JG 62 and WR 315 in response to FW (race 1) infection. Through sugar metabolism and cellular transporters, defense signaling against FW was activated in chickpea [224].
