Background: Identification of loci for agronomic traits and characterization of their genetic architecture are crucial in marker-assisted selection (MAS). Genome-wide association studies (GWAS) have increasingly been used as potent tools in identifying marker-trait associations (MTAs). The introduction of new adaptive alleles in the diverse genetic backgrounds may help to improve grain yield of old or newly developed varieties of wheat to balance supply and demand throughout the world. Landraces collected from different climate zones can be an invaluable resource for such adaptive alleles.Results: GWAS was performed using a collection of 298 Iranian bread wheat varieties and landraces to explore the genetic basis of agronomic traits during 2016–2018 cropping seasons under normal (well-watered) and stressed (rain-fed) conditions. A high-quality genotyping by sequencing (GBS) dataset was obtained using either all original single nucleotide polymorphism (SNP, 10938 SNPs) or with additional imputation (46,862 SNPs) based on W7984 reference genome. The results confirm that the B genome carries the highest number of significant marker pairs in both varieties (49,880, 27.37%) and landraces (55,086, 28.99%). The strongest linkage disequilibrium (LD) between pairs of markers was observed on chromosome 2D (0.296). LD decay was lower in the D genome, compared to the A and B genomes. Association mapping under two tested environments yielded a total of 313 and 394 significant (−log₁₀P>3) MTAs for the original and imputed SNP data sets, respectively. Gene ontology results showed that 27 and 27.5% of MTAs of SNPs in the original set were located in protein-coding regions for well-watered and rain-fed conditions, respectively. While, for the imputed data set 22.6 and 16.6% of MTAs represented in protein-coding genes for the well-watered and rain-fed conditions, respectively.Conclusions: Our finding suggests that Iranian bread wheat landraces harbor valuable alleles that are adaptive under drought stress conditions. MTAs located within coding genes can be utilized in genome-based breeding of new wheat varieties. Although imputation of missing data increased the number of MTAs, the fraction of these MTAs located in coding genes were decreased across the different sub-genomes.

Background: Salinity tolerance in wheat is imperative for improving crop genetic capacity in response to the expanding phenomenon of soil salinization. However, little is known about the genetic foundation underlying salinity tolerance at the seedling growth stage of wheat. Herein, a GWAS analysis was carried out by the random-SNP-effect mixed linear model (mrMLM) multi-locus model to uncover candidate genes responsible for salt tolerance at the seedling stage in 298 Iranian bread wheat accessions, including 208 landraces and 90 cultivars. Results: A total of 29 functional marker-trait associations (MTAs) were detected under salinity, 100 mM NaCl (sodium chloride). Of these, seven single nucleotide polymorphisms (SNPs) including rs54146, rs257, rs37983, rs18682, rs55629, rs15183, and rs63185 with R² ≥ 10% were found to be linked with relative water content, root fresh weight, root dry weight, root volume, shoot high, proline, and shoot potassium (K⁺), respectively. Further, a total of 27 candidate genes were functionally annotated to be involved in response to the saline environment. Most of these genes have key roles in photosynthesis, response to abscisic acid, cell redox homeostasis, sucrose and carbohydrate metabolism, ubiquitination, transmembrane transport, chromatin silencing, and some genes harbored unknown functions that all together may respond to salinity as a complex network. For genomic prediction (GP), the genomic best linear unbiased prediction (GBLUP) model reflected genetic effects better than both bayesian ridge regression (BRR) and ridge regression-best linear unbiased prediction (RRBLUP), suggesting GBLUP as a favorable tool for wheat genomic selection. Conclusion: The SNPs and candidate genes identified in the current work can be used potentially for developing salt-tolerant varieties at the seedling growth stage by marker-assisted selection.

Background: Drought is most likely the most significant abiotic stress affecting wheat yield. The discovery of drought-tolerant genotypes is a promising strategy for dealing with the world’s rapidly diminishing water resources and growing population. A genome-wide association study (GWAS) was conducted on 298 Iranian bread wheat landraces and cultivars to investigate the genetic basis of yield, yield components, and drought tolerance indices in two cropping seasons (2018–2019 and 2019–2020) under rainfed and well-watered environments. Results: A heatmap display of hierarchical clustering divided cultivars and landraces into four categories, with high-yielding and drought-tolerant genotypes clustering in the same group. The results of the principal component analysis (PCA) demonstrated that selecting genotypes based on the mean productivity (MP), geometric mean productivity (GMP), harmonic mean (HM), and stress tolerance index (STI) can help achieve high-yield genotypes in the environment. Genome B had the highest number of significant marker pairs in linkage disequilibrium (LD) for both landraces (427,017) and cultivars (370,359). Similar to cultivars, marker pairs on chromosome 4A represented the strongest LD (r² = 0.32). However, the genomes D, A, and B have the highest LD, respectively. The single-locus mixed linear model (MLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) identified 1711 and 1254 significant marker-trait association (MTAs) (-log10 P > 3) for all traits, respectively. A total of 874 common quantitative trait nucleotides (QTNs) were simultaneously discovered by both MLM and mrMLM methods. Gene ontology revealed that 11, 18, 6, and 11 MTAs were found in protein-coding regions (PCRs) for spike weight (SW), thousand kernel weight (TKW), grain number per spike (GN), and grain yield (GY), respectively. Conclusion: The results identified rich regions of quantitative trait loci (QTL) on Ch. 4A and 5A suggest that these chromosomes are important for drought tolerance and could be used in wheat breeding programs. Furthermore, the findings indicated that landraces studied in Iranian bread wheat germplasm possess valuable alleles, that are responsive to water-limited conditions. This GWAS experiment is one of the few types of research conducted on drought tolerance that can be exploited in the genome-mediated development of novel varieties of wheat.

Background: Pre-harvest sprouting (PHS) refers to a phenomenon, in which the physiologically mature seeds are germinated on the spike before or during the harvesting practice owing to high humidity or prolonged period of rainfall. Pre-harvest sprouting (PHS) remarkably decreases seed quality and yield in wheat; hence it is imperative to uncover genomic regions responsible for PHS tolerance to be used in wheat breeding. A genome-wide association study (GWAS) was carried out using 298 bread wheat landraces and varieties from Iran to dissect the genomic regions of PHS tolerance in a well-irrigated environment. Three different approaches (RRBLUP, GBLUP and BRR) were followed to estimate prediction accuracies in wheat genomic selection. Results: Genomes B, A, and D harbored the largest number of significant marker pairs (MPs) in both landraces (427,017, 328,006, 92,702 MPs) and varieties (370,359, 266,708, 63,924 MPs), respectively. However, the LD levels were found the opposite, i.e., genomes D, A, and B have the highest LD, respectively. Association mapping by using GLM and MLM models resulted in 572 and 598 marker-trait associations (MTAs) for imputed SNPs (− log10 P > 3), respectively. Gene ontology exhibited that the pleitropic MPs located on 1A control seed color, α-Amy activity, and PHS. RRBLUP model indicated genetic effects better than GBLUP and BRR, offering a favorable tool for wheat genomic selection. Conclusions: Gene ontology exhibited that the pleitropic MPs located on 1A can control seed color, α-Amy activity, and PHS. The verified markers in the current work can provide an opportunity to clone the underlying QTLs/genes, fine mapping, and genome-assisted selection.Our observations uncovered key MTAs related to seed color, α-Amy activity, and PHS that can be exploited in the genome-mediated development of novel varieties in wheat.

In this study, 12 maize hybrids were planted and evaluated to determine the effect of genotype and genotype-environment interaction (GEI) base GGE (genotype plus genotype-by-environment) using a Graphical biplot technique in four research stations (Arak, Birjand, Shiraz and Karaj) within two years using a Randomized Complete Blocks Design (RCBD). The combined analysis of variance showed that the effects of the environment, genotype and genotype-environment interaction (GEI) were significant in the one percent probability level. GGE biplot results indicated that the first and second principal components (PC1 and PC2) explained more than 83% of the grain performance variation. Simultaneous study of grain performance and hybrid stability using the biplot of average environment coordinates showed that the KSC705 genotype had the highest yield and stability. Polygon view divided the studied areas into two mega-environments (MEs) and identified the best genotypes in each mega-environment (ME). In the first mega-environment (ME1), the Karaj and Shiraz with KSC706 and KSC400 genotypes were detected, and were the best; and in the second mega-environment (ME2), Arak and Birjand with KSC704 and KSC707 genotypes performed better. The biplot graph for the correlation between the genotypes categorized the studied hybrids into four groups positively related to each other based on the angles between vectors. The KSC704 and KSC707 genotypes were desirable in the yield in Shiraz and Karaj and KSC706 were in Arak and Birjand. Additionally, Arak-Birjand, Karaj-Shiraz showed a positive and significant correlation. Birjand and Karaj had most genotype interaction with each other.