**2. Applications of Molecular Markers in Development of Abiotic-Stress-Tolerant Oilseed Crops**

#### *2.1. Drought*

Fresh water scarcity is an emerging global problem. Since agriculture primarily harnesses freshwater, enhancing agricultural output amid restricted water availability is a major challenge [31]. Although improvements in irrigation and tillage methods can be used to conserve water and increase crop yield, supplementary strategies like genetic modification of crops are required for increasing productivity under moisture deficiency conditions [32,33]. Estimates indicate that adverse environmental factors affect about half of the possible crop production, with water shortage being the most severe stress [34–36]. Tolerance to drought is a quantitative attribute that is influenced by numerous genes via a variety of mechanisms in a plant. Under drought stress, expression patterns in genes that are involved in water transport; osmotic balance; oxidative stress; morphological modifications, including root development and reduced leaf area; and damage repair are altered (Figure 2).

A number of studies have provided deeper insights into understanding the molecular basis of drought tolerance in plants [37–40]. Drought alters the growth, physiology, and metabolic activities of plants, which in turn have an adverse impact on the nutritional quality and yield of important oilseed crops around the world [41,42]. In drought stress, it has been observed that plants' enzymatic activity is reduced, which eventually penalizes yield and quality of oilseeds [43]. Under conditions of water deficiency, a decrease in the oil content of soybean seeds has been reported [44]. Genomic resources created using various methods, such as genotyping-by-sequencing (GBS), genome sequencing, genome-wide association studies (GWAS), etc., have given researchers strong tools for characterizing the genetic diversity of oilseed crops, a solid framework for finding new traits, and nextgeneration breeding tools to speed up the development of elite cultivars. Comparative genome analysis is one of the significant advantages of the current growth of genomic data.

**Figure 2.** Cellular morphological and molecular responses in plants help to combat drought stress.

Numerous quantitative trait loci (QTLs) for traits associated with physiological, agronomic, seed composition, and abiotic and biotic stress parameters have been reported in soybean *(Glycine max*) [24,45]. Only a small number of QTLs have, however, so far been linked to characteristics related to drought resistance. Additionally, reported QTLs account for 10% or less of the phenotypic variance for those traits. To date, the majority of research focusing on the identification of QTLs has used small, single populations. Chen et al. [46] discovered QTLs associated with primary root length on chromosome 16 of soybean, which accounts for 30.25% of the variation in phenotype and will help in the development of markers for root-length selection, which is a crucial trait for drought tolerance. In 1996, a research team led by Mian developed an RFLP map in soybean from a population of 120 F4-derived lines of a cross 'Young × PI416937' that identified the multiple QTLs that are associated with leaf ash and water use efficiency (WUE) [47]. For both attributes, authors reported significant (*p* < 0.01) differences at the phenotypic level among the lines. In total, four and six independent RFLP markers were reported to be linked with the said two traits, respectively, and when added together, each set of markers would be responsible for 38 and 53% of the variance in the corresponding traits. A significant QTL was found at marker position cr497-1 on USDA Linkage Group (LG) J, which accounted for 13.2% of the variability in WUE. The scientists also noted that two QTLs were linked to both WUE

and leaf ash and that leaf ash and WUE had a negative correlation (r = −0.40). One QTL associated with RFLP marker A063E for WUE was also detected in the 'Young × P1416937' population; however, the phenotypic effect was merely <10%, according to authors who tested another soybean population derived from F2 progenies developed from the cross of 'S100 × PI41693 [25]. To date, only WUE and leaf ash QTLs have been documented in soybean under water deficit conditions. More extensive research is required in order to find QTLs that affect shoot turgor maintenance and root architecture. Finding novel QTLs and genes, as well as deciphering the mechanism governing how genes behave during drought, could prove to be hugely instrumental in enhancing soybeans' ability to withstand drought stress.

Numerous genes likely associated with drought tolerance have been identified in sunflower (*Helianthus annuus* L.), including *HaDhn1* (sunflower dehydrin gene)*, SunTIP* (sunflower tonoplast intrinsic protein), *HaDhn2*, *Sdi* (sunflower drought induced), *Hahb-4* (sunflower homeobox-leucine zipper gene)*,* and *HAS1* (sunflower, asparagine synthetase) or *HAS1.1*. These genes have been reported to exhibit high levels of expression under drought stress, and it has been speculated that they contribute to the tolerance of sunflower to drought stress [26,48–50]. However, only a handful of studies on sunflower have been conducted to ascertain the development of molecular markers for QTLs linked to drought tolerance [27]. Hervé et al. [28] employed the AFLP linkage map to recognize QTLs for water status (transpiration and leaf water potential), stomatal movements, and net photosynthesis. Using the AFLP linkage map, 19 QTLs were identified, which accounted for 8.8–62.9% of the phenotypic variance for each characteristic. Out of these, two significant QTLs for net photosynthesis were found on linkage group IX [28]. Similar to this, 24 QTLs were discovered in sunflower in well-watered conditions, of which 5 (or around 21%) were also discovered following drought condition. A range of 6% to 29% of phenotypic variance was explained by the QTLs [51].

Safflower (*Carthamus tinctorius* L.) mapping, molecular breeding, and QTL discovery pronouncedly lag behind other oilseed crops due to a lack of genetic data [52]. As a result, there has been very little genetic enhancement of safflower through marker-assisted breeding and linkage of characteristics. In 2010, Tang et al. mapped heat shock protein (HSP) genes by utilizing a cDNA–AFLP linkage study with 192 randomly segregating F2 populations [53]. Genomic and EST-SSR markers, which can be useful for mapping, molecular breeding, and the linkage of desirable QTL traits like drought tolerance, have been developed in safflower by a number of research groups [52,54]. In this direction, an intra-specific F2 population of *Carthamus tinctorius* and an inter-specific BC1 population of *Carthamus tinctorius* × *Carthamus oxyacanthus* were mapped by generating 1142 PCR based markers and 75 RFLP markers to undertake the first major linkage study of the *Carthamus* species. Both of these mapping populations' utilized these EST-SSR markers [55]. Another researcher noted the feasibility of transferring non-genic microsatellite (SSR) markers and gene-based markers from sunflower (*Helianthus annuus* L.) to safflower. These markers comprised resistance gene candidates (RGC)-based markers and intron fragment length polymorphism (IFLP) [56]. In F3 families produced from the hybrid of the tolerant Mex.22- 191 (tolerant) and sensitive IL.111 (sensitive) safflower genotypes under drought stress, QTLs linked to seed yield and its attributes were mapped using SSR and ISSR markers [57]. This study discovered 18 QTLs linked to seed yield and its attributes, including four major QTLs and three linkage groups (2, 4, and 6), which were found to be crucial for safflower's ability to withstand drought.

In spite of large morphological variation observed between germplasm accessions, peanut (*Arachis hypogaea* L.) shows very little genetic variation at the molecular level, as detected by markers like isozymes, RFLPs, and RAPDs [58]. Three independent research groups around the world have invested in the development of microsatellite markers for peanut and have reported up to 200 simple sequence repeats (SSRs) [29,59,60]. About 20% of them can detect peanut polymorphism. Moreover, a genetic map of 191 SSR loci was constructed based on a single mapping population (TAG 24 × ICGV 86031) segregating

for drought and surrogate traits [61]. The QTL Cartographer identified 105 significant impact QTLs (M-QTLs) explaining 3.48 to 33.36 percent of the phenotypic variance (PVE), but the QTL Network only identified 65 M-QTLs that explained 1.3 to 15.0 percent of the PVE. Comparing the two programmes together allowed the identification of 53 common M-QTLs. Additionally, genotype matrix mapping (GMM) identified 186 (8.54–44.72% PVE) and 63 (7.11–21.13% PVE) three and two loci interactions, respectively, while only 8 epistatic QTLs (E-QTL) interactions with 1.7–8.34% PVE were identified by the QTL network. This study led the authors to conclude that the discovery of some major and many minor M-QTLs and QTL × QTL interactions underpinned the complex and quantitative nature of drought tolerance in peanut. It was recommended that genomic selection or markerassisted recurrent selection be used as a breeding strategy for drought tolerance instead of marker-assisted backcrossing [61]. In another related study, a screening of two RIL (recombinant inbred lines) mapping populations, viz., ICGS76 × CSMG84-1 (RIL-2) and ICGS44 × ICGS76 (RIL-3) with 3215 SSR markers, two genetic maps with 119 (RIL-2) and 82 (RIL-3) SSR loci were constructed. Using these aforementioned maps based on two RIL populations and a reference map of 191 SSR loci based on the TAG 24 × ICGV 86,031 RIL population, Gautami et al. [62] constructed a dense consensus map of 293 SSR loci distributed across 20 linkage groups, spanning 2840.8 cm. In addition to a total of 153 M-QTL and 25 E-QTL for drought tolerance, the authors reported the discovery of 16 prospective genomic regions carrying 125 QTL related to biomass, yield, and drought component traits. In summary, this study identified many QTLs with low to moderate phenotypic variance for the complex traits such as biomass, yield, and drought tolerance. These studies potentially provided a direction for additional investigation and exploitation for QTL pyramiding and cloning in the future, though the discovery of major QTL/s for drought tolerance is still awaited.

Sesame is a hardy crop that is well-adapted to drought prone areas. Sesame typically endures drought better than other important food crops [63]. The production of this oil-rich crop is, however, still quite sensitive to droughts that occur during the germination and flowering stages [64,65]. Unfortunately, there are only a few molecular-marker-based studies conducted so far deciphering the genomic regions associated with sesame's tolerance under drought conditions. Dossa et al. [66] conducted a GWAS employing SNP markers for variables interrelated to drought tolerance in 400 different sesame accessions, including landraces and potential modern varieties. This study reported 10 stable QTLs associated with drought-tolerance-linked characteristics located in four linkage groups. Additionally, this study reported two significant pleiotropic QTLs harboring both known as well as unknown genes for drought tolerance, such as *SiTTM3* (*Sesamum indicum* Triphosphate tunnel metalloenzyme 3), *SiABI4* (*Sesamum indicum* ABA insensitive 4)*, SiGOLS1* (*Sesamum indicum* Galactinol synthase 1), *SiNIMIN1* (*Sesamum indicum* NIM1-Interacting 1), and *SiSAM* (*Sesamum indicum S*-adenosylmethionine synthetase). In order to identify candidate genes associated with drought tolerance in the whole genome of sesame, researchers conducted a comparative homology search with three relative species, viz., potato, tomato, and *Arabidopsis* [67]. The authors successfully identified 75 candidate genes (42, 22, and 11 from *Arabidopsis*, potato, and tomato, respectively), which were found to be distributed on the 16 sesame linkage groups. Based on their functional classification, authors divided the genes in two groups. One group consisted of genes that protect the plant against drought effects, while the other included signal transduction genes and transcription factors. Several other studies have also employed molecular markers for QTL mapping and GWAS to unravel the genetic basis of drought tolerance in sesame [68–72].

Although we have witnessed remarkable progress in the field of genomics over the last ten years, the availability of precise and high-throughput phenotyping for drought tolerance traits is still a major challenge for QTL mapping studies. Targeting root architecture, photosynthetic efficiency, osmotic adjustment, relocation of stem reserves, and leaf senescence under drought stress are among the phenotypic features that could benefit the most from the application of MAS. Further, the construction of consensus maps integrating

the QTL information provided by different populations needs more attention. It is certain that molecular-assisted breeding has the potential to more effectively address the problems caused by the diminishing availability and rising cost of irrigation water, as well as the escalating demand for food, fiber, and biomass.

#### *2.2. Salinity*

One of the key abiotic stress challenges influencing the quality and production of food crops globally is soil salinity, which restricts crop plants' growth and development [73,74]. Furthermore, salinity can pose risks to the production of oilseeds by lowering both the yield and quality of the produce. Globally, salt affects >833 million hectares of land [75], and it is believed that 20% of cultivated and 33% of irrigated land are affected [76]. By preventing cell division, enzyme activity, nucleic acid and protein synthesis, and salinity stress negatively impacts seed germination and seedling growth, height, leaf size, leaf number, reproductive structures, seed quantity, seed content, seed weight, and the quality of seed oil [30–44,77–82]. Figure 3 illustrates how a plant also responds at biochemical, molecular, physiological, and morphological levels to salinity stress in order to sustain its growth and production [83]. However, decades of intensive research have led to the improved comprehension of the mechanisms by which salt stress affects crop development and productivity. Indeed, this information may be used to develop genotypes that are salt-tolerant.

**Figure 3.** Plant responses to salinity stress. [Abbreviations: *CIPK*: CBL-interacting protein kinases; *SOS*: salt overly sensitive; *NHX*: sodium/hydrogen antiporter; *CLC*: chloride channel; *APX*: ascorbate peroxidase; *CAT*: catalase; *SOD*: superoxide dismutase; *GR*: glutathione reductase; *LOX*: lipoxygenase; *ERD*: early responsive to dehydration; *GST*: glutathione S-transferase; *P5CR*: pyrroline-5-carboxylate reductase; *P5CS*: pyrroline-5-carboxylate synthetase; *NAC*: NAC transcription factor; *NAP*: nucleosome assembly protein; *ANAC*: *Arabidopsis* NAC transcription factor; *WRKY*: WRKY transcription factors].

Numerous factors, including soil properties, genotypes, and developmental phases, influence how oilseed crops react to salt stress. Although the majority of oilseed species are prone to damage under salt stress, nevertheless a wide range of diversity in terms of salt sensitivity exists among them. While canola, soybean, sunflower, and safflower exhibit moderate to strong tolerance, peanut and linseed are examples of sensitive species [84]. Likewise, it has been observed that amphitetraploid *Brassica* species, such as *B. juncea, B. carinata,* and *B. napus,* are relatively more tolerant against salt stress compared to their progenitors, such as *B. nigra*, *B. rapa*, and *B. oleracea*. Among all the *Brassica* species, *B. napus* is extremely tolerant to salt stress, whereas *B. rapa* and *B. nigra* are extremely sensitive [85]. Since tolerance to salt stress is a physiologically intricate trait, the development of salttolerant genotypes necessitates a comprehensive approach that involves modifying existing cultivars genetically and biotechnologically.

An essential method for localizing the genomic areas that regulate characters related to salt stress tolerance is QTL mapping. QTLs are identified using powerful DNA marker approaches, such as AFLP, RFLP, RAPD, SSR, and SNPs. Successful breeding for salt stress in oilseeds, notably in *Brassica* species, requires the identification of QTL. It is challenging to detect a genetic basis for salinity tolerance in *Brassica* species, since no significant QTL with relation to salinity tolerance in those species has yet been discovered due to the physiological complexity of the salinity response. However, a limited number of studies have demonstrated the utilization of molecular markers in this field. RFLP markers that were used to characterize each line to find salt-tolerance-related QTL in soybean RILs produced S-100 (tolerant cultivar to salinity) and a Tokyo variation (susceptible cultivar to salinity). After that, a single-factor QTL analysis was performed to discover trait-related genomic areas. To improve mapping accuracy, specific genomic areas were flooded with SSR markers. The study found a QTL related to salt tolerance at SSR marker Sat 091 at LG N. In the field, greenhouse, and mixed environments, this QTL was found to be responsible for 41%, 60%, and 79% of salt tolerance, respectively. In fact, the tolerance-related QTL alleles were found to be derived from S-100 through pedigree tracking [86]. Using two RIL populations resulting from the cross between FT-Abyara C01 and Jindou No. 690197, a similar study discovered a substantial salt-tolerance QTL in soybean's molecular linkage group N. This study employed FT-Abyara C01 and Jindou No. 690,197 RIL populations. This QTL accounted for 44.0% to 47.1% of salt tolerance across the two groups [87]. Using a separate linkage group, Chen et al. [88] found a second significant QTL (qppsN.1) between markers Sat 164 and Sat 358 on linkage group G in a cross of Kefeng No.1 (salt-resistant) and Nannong 1138-2 (salt-sensitive) soybeans.

In similar research, Hamwieh and Xu [89] discovered a QTL related to salt-tolerance in soybean on linkage group N with a substantial dominant impact from 225 lines of F2 population produced from a cross of Jackson (PI548657) (salt-resistant cultivar) × JWS156-1 (salt-sensitive wild soybean). This major QTL explained 68.7% of the variance in the salt tolerance rating scale. The authors concluded that both wild and cultivated soybeans carry the conserved QTL related to salt tolerance, which has a significant dominating effect over salt sensitivity.

Most widely used markers in safflower are ISSRs, AFLPs, and RAPDs because they are ideal for crops with little genetic resources, require no prior knowledge, and perform genome scanning with repetitive sequences [90]. Safflower genetic diversity has been documented in numerous investigations employing a combination of phenotypic variation and molecular polymorphism [91–93].

In 2018, Li and his co-researchers used a diversity panel of 490 accessions of sesame (*Sesamum indicum*) to conduct a genome-wide analysis of stress tolerance indices related to sodium-chloride-induced salt stress and PEG-induced drought stress to understand the resulting genetic variants with respect to drought and salinity tolerance at the germination stage [68]. According to this study, under the stresses of drought and salt, respectively, there were 132 and 120 significant SNPs, which further resolved to be associated with 9 and 15 QTLs. There were just two shared QTLs for the response to salt and drought, which were situated in the linkage groups (LGs) 5 and 7, respectively. Authors also reported a total of 13 and 27 potential candidate genes for drought and salt tolerance indices, respectively, which encode transcription factors, osmoprotectants, and antioxidant enzymes and are associated with signal transduction, hormone biosynthesis, or ion sequestration, which were also reported for the drought and salt tolerance indices, respectively.

In an attempt to elucidate the genesis of wild sunflower hybrid's (*H. annuus* × *H. petiolaris*) adaptation to salt stress, Lexer et al. [94] employed EST markers on 11 genes. One EST was mapped to QTL responsible for salt tolerance, which encodes a Ca-dependent protein kinase (*CDPK*) that originated in stress-induced root tissue of *H. annuus;* hence, a plausible adaptive role for Ca-dependent salt tolerance genes in wild sunflower hybrids was

suggested. Another study by the same author on 172 BC2 hybrids between *Helianthus annuus* and *Helianthus petiolaris* planted in the salt marsh habitat of *Helianthus paradoxus* in New Mexico identified 14 QTLs for mineral ion absorption attributes and three for survivability [95]. The previous results that suggested that salt tolerance in Helianthus is achieved through higher Ca<sup>+</sup> absorption, along with stronger exclusion of Na+ and similar mineral ions, were confirmed by mineral ion QTLs mapping to the same place as the survival QTLs (on LG 1, 4, and 17b). In a separate study, researchers evaluated the variability of microsatellites from genomic areas that were neutral in the experimental hybrids with that of microsatellites associated with the three survival QTLs listed above. It was established that populations of the natural hybrid species had significantly less variability according to microsatellites relating to the survival QTLs. However, in parental populations, there was no discernible difference in the levels of diversity between the two microsatellite classes [96].

With the above information in mind, it is evident that considerable effort has been put into identifying the genes or QTLs that contribute to salinity tolerance in oilseed crops; nevertheless, there are presently few reports of cultivars or breeding lines with better salt tolerance that have been successfully developed using molecular markers and MAS technology. The limited use of markers for improving complex traits like salinity tolerance has been attributable to various reasons; however, it is possible to use markers for such complex traits by the identification of reliable QTLs and linked genetic markers. This can be achieved by putting additional efforts, such as conducting mapping experiments in field conditions instead of a greenhouse, so that plants experience actual salt stress in association with other stresses and environmental factors as well, studying the crosstolerance mechanism that exist in plants against various stresses; the identification of QTLs in multiple environments; splitting complex traits, such as salt tolerance, into individual components; and identifying QTLs and markers for such individual components instead of studying salt tolerance as a whole (such as finding QTLs for salinity tolerance at different developmental stages), and finally the pyramiding of such QTLs may pave the way to develop salt-tolerant oilseed crops. This is a challengeable but achievable strategy to follow in order to develop salt tolerance in plants. Table 2 summarizes some of studies that have used molecular markers in the development of resistance to abiotic stresses in oilseed crops.


**Table 2.** List of studies involving MAS for improvement of abiotic stress resistance in oilseeds.


#### **Table 2.** *Cont.*
