Ensuring global food security for millions of people requires urgent attention towards exploring untapped genetic diversity to develop climate-resistant crop varieties. Developing heat stress-tolerant cultivars can be an effective and cost-efficient approach to counter the negative impact of high temperatures on crop quality. Valuable genes and QTNs associated with heat stress tolerance can be found in wild relatives and landraces [17], [18]. In recent times, the advancement in sequencing techniques has provided a wealth of omics information for various crops. This data can be leveraged to identify useful genes or genomic regions linked to beneficial traits from the vast germplasm collections preserved in Gene banks.
We observed a significant positive association between grain iron concentration and grain zinc content under normal sown conditions which in agreement with the results of earlier study [16]. The uptake of iron and zinc in wheat is closely linked, and the transport of one micronutrient can affect the uptake of the other. This may be due to the fact that both iron and zinc share common transporters in the plant, which can result in their co-uptake and co-transport [16]. Therefore, under heat stress, the up-regulation of genes responsible for micronutrients transportation can result in a positive association between iron and zinc in wheat. However, there was a significant negative correlation of GPC with grain amylose content and TSS. Studies have shown that the significant negative correlation between grain protein, amylose content, and total soluble sugar is under high nitrogen availability and/or genotypes with a high potential for protein accumulation. Therefore, managing nitrogen availability during grain development can be a key strategy to balance the allocation of resources between different metabolic pathways and maintain desirable grain quality traits, including amylose content and total soluble sugar. These results are consistent with those of [11], [12], [13]. A negative correlation exists between grain TSS and grain amylose concentration as well. All wheat grain quality parameters respond the same manner under late-sown conditions as they do under normal-sown ones. In contrast to normal-sown situations, the degree of association between the quality parameters in late-sown habitats has increased viz. negative correlation between GPC and amylose has dramatically increased under HS conditions. On the other hand, the positive association between grain iron and zinc concentration has significantly enhanced under such circumstances. All these above results were in agreement with the earlier studies [13], [11], [12], [16].
After performing all the quality control to the raw SNP data obtained through 35K SNP Axiom array, we finally proceed with a total 15805 polymorphic SNPs for association mapping analysis. A total 15805 polymorphic SNPs used as a final no. in the analysis, of which most SNPs (6113) are located in sub-genome B, followed by sub-genome A (5096 SNPs) and sub-genome D (4596 SNPs). To proceed for association mapping analysis, genotype panel was assessed for presence of population structure which is the one of the crucial factor as it minimizes the spurious associations. Population structure analysis revealed the presence of two sub-populations (k = 2) for the current genotypic panel i.e. SP1 (61 lines) and SP2 (65 lines), where among the subpopulations, lines can be further categorized as pure or admixture i.e. SP1 (82% pure and remaining 18% admixtures) & SP2 (75% pure and remaining 25% admixtures). Further, relation among the genotypes was also examined through phylogenetic analysis.
LD is another important factor that always needs to be taken into account while association mapping, particularly when identifying the range of highly associated SNPs. A high LD value indicates that fewer markers are required to cover the genome than a low LD value, which is useful to determine the number of loci necessary for the whole-genome scan [57], [25]. In the present study, population structure in designated wheat germplasm increased the LD values because of admixtures in both subpopulations, resulting in numerous distinct loci displaying significant LD. LD decayed the fastest in sub-genome A, followed by sub-genomes D and B. The faster decay of LD in the A sub-genome of wheat is likely due to a combination of factors related to recombination rates, population history, and gene flow, which was in agreements of results from previous investigations [58], [51].
The development of high-yielding, thermo-tolerant cultivars is anticipated to be accelerated through the discovery of new genes and genomic regions linked to specific bread wheat traits. Given that all-marker effects are concurrently assessed in ML-GWAS models, these are thought to be superior to SL-GWAS methods for mapping of complex traits [54], [51], [39] and were utilized for the current GWAS study. In current study, six different GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, ISIS EM-BLASSO, and pKWmEB) were used to examine the marker-trait association to find out markers associated to the target trait [40],[45], [43], [44], [51]. A total of 67 significant QTNs for five quality parameters with a LOD score ≥ 3 were found in significant marker-trait association. The R2 values of the QTNs ranged from 3to 44%, demonstrating the large range of phenotypic variation seen for these parameters (Table S9). The finding suggests that quality traits are influenced by numerous loci with relatively modest effects, highlighting the intricate genetic regulation of these traits in the early stages of crop development. Additionally, among the six ML-GWAS models used in the analysis, the FASTmrMLM model demonstrated the highest number of associations, whereas the pLARmEB model revealed fewer associations above the threshold. To best of our knowledge there is hardly any study which reported the QTNs for quality parameters especially under HS regime. In the current study, we attempted to identify the novel genomic regions/QTNs/genes for the quality traits under both in normal and late sown conditions. Fortunately, we were successful in identifying 14 annotated and reliable QTNs and their respective candidate genes (Table. 4)
Current study has identified a novel QTN (Qql.iari-1B.1_amy) for grain amylose content, associated with SNP AX-94733833, which is situated on chromosome 1B and is located within the gene TraesCS7D02G208200. This gene encodes a heat shock protein called chaperonin Cpn60/GroEL/TCP-1 family, which is a multi-functional type-I heat shock protein in wheat that helps in protein folding, trafficking and disaggregation under stressful conditions, thus maintaining homeostasis [59]. Similarly, a novel QTN for protein content (Qql.iari-2B.2_pro) was also identified, which was further localized within the gene TraesCS2B02G461500, which codes for P-loop containing nucleoside triphosphate hydrolase (NTPases). In wheat, P-loop containing NTPases are involved in many biological functions such as protein synthesis, plant growth, and stress response. These enzymes hydrolyze nucleoside triphosphates (NTPs) to nucleoside diphosphates (NDPs) and inorganic phosphate (Pi), releasing energy in the process. This energy can be used to drive cellular processes such as DNA replication or protein synthesis. P-loop containing NTPases in wheat also play a crucial role in plant growth and development by regulating cell division and differentiation [60]. For TSS SNP markers AX-94537892 and AX-94810283, associated with QTNs Qql.iari-2A.2_TSS and Qql.iari-2B.3_TSS respectively, were found to be more reliable. Among these two, AX-94537892 was located within the gene, TraesCS2A02G149700, which encodes the NPSN13 protein, a type of SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) protein that regulates vesicle trafficking. This protein is abundant in higher plant species, suggesting that its role in growth and development of wheat during heat stress [61]. Additionally, this gene is also responsible for vesicle-mediated resistance to stripe rust and powdery mildew in wheat [62]. Apart from this, NPSN13 has also been reported in Arabidopsis and rice (Oryza sativa) genomes [63], [64], [65].
Iron and zinc are crucial micronutrients for wheat; vital for its growth and development, and the crop is a significant source of these nutrients for human consumption [66]. We identified 7 novel QTNs in relation to grain iron content in wheat (table 4). The most potent SNP marker (AX-94861766) located on chromosome 3A, associated with the QTN Qql.iari-3A_ Fe, and with the gene TraesCS3A02G046300, which is responsible for coding the Bowman-Birk type proteinase inhibitor (BBI). In wheat, BBI has a crucial function in regulating endogenous proteases during grain development and is induced in response to biotic and abiotic stresses, suggesting a role in plant defense mechanisms [67]. The other proteins that has been encoded by rest of the genes for grain iron content include, phylloplanin-like, basic helix-loop-helix transcription factor (regulator of tapetal programmed cell death, male reproductive development), PTAC2 and tetratricopeptide-like helical domain superfamily. In addition, we have also pinpointed four QTNs related to grain zinc content. The QTN with the highest potency (Qql.iari-6A_ Zn) was associated with the marker AX-94637211 as this particular marker was detected by five out of six models and located on gene TraesCS6A02G406100. This gene encodes a protein with an unknown function, DUF2921, while the other genes encode proteins belonging to the invertase/pectin methylesterase inhibitor domain superfamily and Oligopeptide transporter, OPT superfamily.
The present investigation has successfully validated two potential SNP markers, namely AX-94461119 and AX-95220192, using the KASP approach. Notably, this study represents the first instance of the AX-94461119 marker being linked to grain iron content (Fe) in bread wheat. This marker is located on chromosome 6A and is correlated with the gene TraesCS2A02G484500, responsible for encoding Phylloplanin-like proteins. Similarly, our research marks the initial documentation of the KASP marker AX-95220192 being associated with grain zinc content in bread wheat. This marker is positioned on chromosome 7D and is linked with the gene TraesCS7D02G049000, which codes for an Oligopeptide transporter within the OPT superfamily. Recent studies have indicated the significant involvement of peptide transporters (OPT) in crucial roles during both biotic and abiotic stresses, such as heat stress [68]. This suggests their importance in maintaining wheat quality under adverse environmental conditions. The identified KASP marker holds promise for successful utilization in genetic introgression strategies to enhance or preserve grain zinc content in bread wheat under challenging environmental conditions. It can be employed in the development of heat-tolerant (HT) cultivars through pre-breeding or genetic enhancement, as well as in creating HT bio-fortified varieties by introgressing QTNs/genes from heat-tolerant accessions to cultivated ones.
Our research identified a total of 67 reliable QTNs for various grain quality parameters in bread wheat under HS. These QTNs were distributed across most of the wheat chromosomes, with the exception of a few chromosomes like 7A and 7B, for the five different quality traits. Prior studies have also pinpointed several quality genes/QTNs scattered throughout almost all wheat chromosomes [69], [70]. In our study, we compared the locations of the identified QTNs with those from previous research. However, some comparisons proved challenging due to differences in marker platforms, mapping populations, and the absence of a consensus map for position comparison. Below, we discuss the novel associations for grain quality traits found in this study, alongside those previously reported.
For grain amylose content (GAC), ML-GWAS detected 7 reliable QTNs. Notably, Qql.iari-1B.1_amy (685.09 Mb) and Qql.iari-1D_amy (419.73 Mb), located on chromosomes 1B and 1D, respectively, are in proximity to the markers, QGAsC1B [70] and QGAsC1D [70]. Regarding GPC, we found four QTNs, Qql.iari-1B_pro (5.58 Mb), Qql.iari-2A_pro (715.41 Mb), Qql.iari-3D_pro (305.89 Mb), and Qql.iari-3D.2_pro (426.48 Mb) on chromosomes 1B, 2A, and 3D, respectively, which were close to QTLs, WMC419 [71], QGPC1B, QGPC3A, and QGPC3D [70]. Furthermore, the QTN Qql.iari-6D_pro (363.26 Mb) for GPC reported on chromosome 6D was found to be closely associated with the QTN wsnp_Ex_c1988_3742022 [30]. Additionally, three quantitative trait loci (QTNs) related to grain iron content, Qql.iari-3B_Fe (0.219 Mb) located on chromosome 3B, Qql.iari-4D_Fe (359.11 Mb) on chromosome 4D, and Qql.iari-6A_Fe (560.20 Mb) on chromosome 6A were found to be in close proximity to previously identified QTLs from earlier studies, specifically QFe.caas-3BL, QFe.caas-4DS, and QFe.caas-6AS [72]. Furthermore, five QTNs associated with grain zinc content, Qql.iari-2A_Zn (572.81 Mb) on chromosome 2A, Qql.iari-3B.1_Zn (21.38 Mb), and Qql.iari-3B.2_Zn (4.21 Mb) both on chromosome 3B, Qql.iari-4D_Zn (375.50 Mb) on chromosome 4D, and Qql.iari-6A_Zn (611.57 Mb) on chromosome 6A were found to be in vicinity of QTLs, QZn.caas-2AS, QZn.caas-3BS, QZn.caas-4DS, and QZn.caas-6AS [72]. However, when it comes to grain TSS, there is hardly any identified QTL or QTN in wheat associated with heat stress tolerance.
The ultimate goal of our current study was to identify and validate QTNs/genes/genomic regions in selected wheat germplasm for quality traits under heat stress conditions. The findings of our study can be utilized variously in crop improvement programs. Some of the potential benefits for future prospectus of crop improvement programs are worthy to mention here: Utilization of validated QTNs/genes in marker assisted breeding, value addition to national gene bank by conserving identified trait-specific (HT) wheat germplasm accessions, development of HT cultivars through pre-breeding or genetic enhancement and development of HT bio-fortified varieties by introgression of Fe & Zn QTLs/genes from HT accessions to cultivated ones.