Establishment of gmQTL and smQTL regions
To gain deeper insight into the control of leaf rust resistance in wheat, a meta-QTL analysis was performed based on the numerous QTLs conferring leaf rust resistance identified in the literature from various independent studies. The first step to identifying consensus regions via meta-QTL analysis is the projection of the original QTLs onto a consensus or reference map.
A feature of the consensus map and QTL database was that the B genome reported the highest marker saturation, and thus, the highest number of QTLs was mapped to this genome, which is in agreement with previous studies characterizing genetic diversity and unravelling complex traits for disease resistance in bread wheat (Li et al. 2015; Soriano and Royo, 2015; Wang et al. 2014). The D genome presented a lower number of QTLs, as previously found in other meta-QTL analyses for disease resistance in wheat (Soriano and Royo, 2015; Venske et al. 2019; Liu et al. 2020b; Zheng et al. 2020). Furthermore, no QTLs were found on chromosome 6D, as discovered in previous meta-QTL analysis studies on leaf rust and Fusarium head blight diseases in wheat (Soriano and Royo, 2015; Zheng et al. 2020). A possible explanation for the limited QTLs located on the D genome across various disease studies could be the low level of polymorphism associated with the D genome. In this study, a larger number (81.4%) of QTLs was projected onto the consensus map compared to the fewer number of QTLs projected in a previous meta-QTL analysis for leaf rust (Soriano ad Royo, 2015) (44%). A possible reason could be due to the different consensus maps used. In this study, we used a high-density consensus map that combined SSRs and markers obtained from high-throughput genotyping platforms, in contrast to the consensus map used in the previous study from Soriano and Royo (2015). Consequently, the number of genomic regions (gmQTLs) discovered in this study was higher than those reported in Soriano and Royo (2015), at 75 and 48, respectively. For the gmQTLs discovered in this study, the confidence interval ranged from 0.03 cM to 25.23 cM, which was significantly reduced by more than 50% from the confidence interval of the original QTLs, ranging from 1.14 cM to 173.11 cM. In addition, in the present study, the physical position of the gmQTLs was reported due to the release of the wheat genome sequence (IWGSC, 2018), improving the mapping resolution of the genome regions (smQTLs) and helping the identification of candidate genes. These analyses enhance the results provided by studies published prior to the release of the genome sequence. In this study, we discovered 7 smQTLs incorporating at least five original QTLs and having a confidence interval of less than 10 Mb, making them the most promising for candidate gene identification.
Colocalization of smQTLs with leaf rust resistance genes and traits
To strengthen the location of smQTLs discovered in this study, a search for colocalization of leaf rust resistance genes and smQTLs was performed. More than 61 leaf rust genes have been mapped and documented in wheat (Kim et al. 2020), and 4 of them have been cloned (Hafeez et al. 2021). A total of six leaf rust genes (Lr13, Lr14a, Lr46, Lr68, Lr63, Lr60, Lr42, and Lr41) were found to colocalize with smQTLs. Interestingly, the colocalization of Lr13, Lr14a, and Lr46 with smQTL2B.5, smQTL7B.3, and smQTL1B.4 on chromosomes 2B, 7B, and 1B, respectively, in this study was in agreement with the results obtained by Soriano and Royo (2015). As reported by these authors, Lr68 was found to have a tight association with MQTL33 (colocalized with Lr14a) on chromosome 7B; however, in this study, smQTL7B.3 colocalized with both leaf rust genes (Lr14a and Lr68), thus confirming the usefulness of using highly saturated consensus maps for meta-QTL analysis. The gene Lr14a, known to confer adult plant resistance, is thought to have evolved from emmer wheat cv. Yaroslav (McFadden, 1930) and is associated with the stem rust and powdery mildew resistance genes Sr17 and Pm5. Lr68, on the other hand, confers seedling resistance to the majority of P. triticina isolates with low to medium infection types and is linked to small but noticeable leaf tip necrosis (Herrera-Foessel et al. 2012). Consequently, the smQTL7B.3 region not only confers seedling and adult plant resistance to leaf rust but also constitutes a region of multiple disease resistance. Additionally, smQTL1D.2 colocalized with two leaf rust resistance genes (Lr60 and Lr42). Hiebert et al. (2008) found that Lr60 is 13.5 cm distal to Lr21, which would position Lr60 and Lr42 approximately 40 cm apart (Huang et al. 2003; Somers et al. 2004). The association between Lr60 and Lr42 has not been confirmed, but in this study, we discovered that both genes were located in the same smQTL, with a confidence interval of 8.8 Mb. This supports possible linkage between the two genes; however, a genetic linkage test needs to be carried out to corroborate this claim. Furthermore, Lr60 is known to confer seedling resistance, while Lr42 confers adult plant resistance. Thus, the smQTL1D.2 region has the potential to provide more qualitative resistance against leaf rust. Additionally, smQTL1B.4 colocalized with Lr46, a gene known to increase the latent period and reduce the frequency of infection and uredinial size in a similar manner to Lr34 (Drijepondt and Pretorius, 1989; William et al. 2003). There is also a tight linkage between Lr46 and a stripe rust gene (Yr29), which is similar to the linkage between Lr34 and Yr18 (McIntosh, 1992; Singh 1992). Consequently, the smQTL1D.2 region confers resistance to both leaf and stripe rust in wheat, thus making this region a hotspot for selecting multiple disease resistance in wheat.
Most of the smQTLs discovered in this study clustered QTLs conferring two or more resistance traits. This phenomenon was also discovered by Kolmer et al. (2018) using RILs, suggesting that they experienced higher disease severity levels and leaf rust responses. In another study, Ren et al. (2012) also discovered that maximum disease severity had a significant association with the area under the disease progress curve (AUDPC) across diverse environments, and this finding was in agreement with previous studies (Wang et al. 2005b; Liang et al. 2006; Lan et al. 2009). Consequently, this result indicates the possibility of replacing AUDPC with MDS. A possible explanation for this could be that when two or more traits are mapped to the same region, they are most likely under the same genetic control, as suggested by Lu et al. (2017). Furthermore, effects arising from tight linkage and pleiotropism could also be a possible explanation.
Candidate genes within hcmQTLs and their role in leaf rust resistance
The search for candidate genes was extended to hcmQTLs within 20 Mb, thus yielding 15 hcmQTLs. hcmQTLs also have a small confidence interval compared to smQTLs, thus making them more reliable and useful for QTL selection in breeding programmes. The hcmQTLs harboured a total of 2240 genes, after which 92 DEGs were narrowed down. Two main types of disease resistance are used in breeding programmes: seedling resistance and adult plant resistance. Thus, the analysis of the expression of the candidate genes across different tissues and developmental stages can inform us of their potential role in seedling or adult plant resistance. Five out of the 92 genes expressed across the three transcriptomic data sets, TraesCS7D02G212800, TraesCS6A02G073300, TraesCS2B02G104200, TraesCS1D02G003700, and TraesCS2D02G021300, showed moderate expression in the first leaf sheath at the seedling stage. TraesCS7D02G212800 and TraesCS2B02G104200 encode a receptor-like kinase (RLK) and protein kinase family protein, respectively, and both proteins play a crucial role in contributing to disease resistance in wheat. Plant protein kinases, as well as receptor-like kinases, govern the detection and activation of diverse developmental and physiological signals, particularly those involved in defence and symbiosis (Rentel et al. 2004; AbuQamar et al. 2008; Fu et al. 2009; Garcia et al. 2012). Prior studies found that various RLK genes coding wheat leaf rust kinases (WLRKs) were conserved in wheat, with the most studied member of the WLRK family being LRK10, which is genetically linked to the Lr10 locus (Feuillet et al. 1997, 1998, 2001). Gu et al. (2020), in a recent study, uncovered an RLK gene that plays an important role in resistance to P. triticina infection and has a positive regulatory effect on the hypersensitive reaction (HR) cell death process induced by P. triticina. TraesCS6A02G073300, encoding a 3-ketoacyl-CoA synthase, has been reported to harbour quantitative trait nucleotides (QTNs) in close proximity to leaf rust genes in wheat (Fatima et al. 2020). The 50S ribosomal protein L28 encoded by TraesCS1D02G003700 belongs to the ribosomal protein family, and members of this family have been shown to confer tolerance against fungal pathogens in plants (Yang et al. 2013). Furthermore, TraesCS7D02G217700, encoding a glycosyltransferase, was highly and moderately expressed in the first leaf blade and leaf sheath, respectively, at the seedling stage. According to Bolton et al. (2008), two pathogen-responsive genes encoding glycosyltransferases were shown to be upregulated under leaf rust infection. At the adult stage, TraesCS1D02G004600, encoding a cytochrome P450, was expressed in the flag leaf blade at both the dough and ripening stages. Different studies have reported the role played by cytochrome P450 in the host response to disease, which included the response to Fusarium head blight (FHB) disease in wheat (Walter et al. 2008, 2011). The pathogen-responsive gene encoding cytochrome P450 has been shown to be differentially expressed under leaf rust infection in wheat (Bolton et al. 2008). Additionally, Bolton et al. (2008) reported that gene models coding for the same protein as some of the hcmQTLs discovered in this study were upregulated under leaf infection. All gene models coding for serine-threonine protein kinases and cytochrome P450 were upregulated in all treatments.
Breeding implications for leaf rust resistance
The primary use of MQTLs for breeding purposes is the development of improved varieties with enhanced yield that are resistant to diseases via marker-assisted selection (MAS). MQTLs with the smallest confidence interval (CIs) have been harnessed effectively for MAS because they incorporate multiple QTLs, as reported for disease resistance in maize (Xiang et al. 2012; Wang et al. 2016), grain yield-associated traits in rice (Wu et al. 2016; Carrijo et al. 2017), seed quality in soybean (Qi et al. 2017) and anthesis time in wheat (Griffiths et al. 2009). To this end, the smQTLs were refined to 15 hcmQTLs, each of them incorporating at least 5 original QTLs and having a physical interval lower than 20 Mb and a genetic interval lower than 10 cM. In addition, meta-QTL analysis can be used to identify regions that confer resistance to more than one disease, and the marker information can be used for MAS (Ali et al. 2013). In this study, the hcmQTLs1B.4 region was identified to confer resistance to leaf and stripe rusts, thus making it a potential region to exploit for multiple disease resistance in wheat. Furthermore, breeding for durable resistance is desired in major breeding programmes. Durable resistance remains effective against a pathogen for a significant number of years (Johnson, 1981; Johnson, 1984). The combination of seedling resistance and adult plant resistance has been proven to confer prolonged resistance over several years (Kolmer and Oelke, 2006). In addition, various studies have ascribed durable leaf resistance to adult plant resistance rather than to seedling resistance (Figlan et al. 2020). Therefore, hcmQTL1D.2 discovered in this study can be harnessed to confer durable resistance in wheat, as it incorporates genes conferring both seedling and adult plant resistance. Another useful approach that could be harnessed in breeding for leaf rust resistance in wheat is gene pyramiding. Gene pyramiding involves incorporating multiple desired genes into a single variety. Gene pyramiding is broadly acknowledged by breeders, plant pathologists and farmers to improve disease resistance in wheat (Chen and Kang, 2017). A major requirement for gene pyramiding is to identify various QTLs or genes conferring resistance and then incorporate them into a high-yielding cultivar (Singh 1992). In several instances, this technique has been utilized in crops. For instance, long-term resistance was conferred when diverse genes were pyramided with leaf rust genes (Kolmer, 1996; Bhawar et al. 2011; Aboukhaddour et al. 2020; Babu et al. 2020). Additionally, in barley, MAS combined with gene pyramiding has been used to introgress resistance loci against stripe rust into numerous lines (Toojinda et al. 1998, 2000; Castro et al. 2003a, b; Richardson et al. 2006). To this end, the hcmQTLs discovered in this study can be utilized and exploited for gene pyramiding via MAS to bolster the resistance of wheat against leaf rust.