Analysis of genetic diversity and genome-wide association study for drought tolerance related traits in Iranian bread wheat.

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.

Exploring genomic regions involved in bread wheat resistance to leaf rust at seedling/adult stages by using GWAS analysis.

BACKGROUND: Global wheat productivity is seriously challenged by a range of rust pathogens, especially leaf rust derived from Puccinia triticina. Since the most efficient approach to control leaf rust is genetic resistance, many efforts have been made to uncover resistance genes; however, it demands an ongoing exploration for effective resistance sources because of the advent of novel virulent races. Thus, the current study was focused on detecting leaf rust resistance-related genomic loci against the P. triticina prevalent races by GWAS in a set of Iranian cultivars and landraces. RESULTS: Evaluation of 320 Iranian bread wheat cultivars and landraces against four prevalent rust pathotypes of P. triticina (LR-99-2, LR-98-12, LR-98-22, and LR-97-12) indicated the diversity in wheat accessions responses to P. triticina. From GWAS results, 80 leaf rust resistance QTLs were located in the surrounding known QTLs/genes on almost chromosomes, except for 1D, 3D, 4D, and 7D. Of these, six MTAs (rs20781/rs20782 associated with resistance to LR-97-12; rs49543/rs52026 for LR-98-22; rs44885/rs44886 for LR-98-22/LR-98-1/LR-99-2) were found on genomic regions where no resistance genes previously reported, suggesting new loci conferring resistance to leaf rust. The GBLUP genomic prediction model appeared better than RR-BLUP and BRR, reflecting that GBLUP is a potent model for genomic selection in wheat accessions. CONCLUSIONS: Overall, the newly identified MTAs as well as the highly resistant accessions in the recent work provide an opportunity towards improving leaf rust resistance.

Genome-wide association study to identify genomic loci associated with early vigor in bread wheat under simulated water deficit complemented with quantitative trait loci meta-analysis.

A genome-wide association study (GWAS) was used to identify associated loci with early vigor under simulated water deficit and grain yield under field drought in a diverse collection of Iranian bread wheat landraces. In addition, a meta-quantitative trait loci (MQTL) analysis was used to further expand our approach by retrieving already published quantitative trait loci (QTL) from recombinant inbred lines, double haploids, back-crosses, and F2 mapping populations. In the current study, around 16%, 14%, and 16% of SNPs were in significant linkage disequilibrium (LD) in the A, B, and D genomes, respectively, and varied between 5.44% (4A) and 21.85% (6A). Three main subgroups were identified among the landraces with different degrees of admixture, and population structure was further explored through principal component analysis. Our GWAS identified 54 marker-trait associations (MTAs) that were located across the wheat genome but with the highest number found in the B sub-genome. The gene ontology (GO) analysis of MTAs revealed that around 75% were located within or closed to protein-coding genes. In the MQTL analysis, 23 MQTLs, from a total of 215 QTLs, were identified and successfully projected onto the reference map. MQT-YLD4, MQT-YLD9, MQT-YLD13, MQT-YLD17, MQT-YLD18, MQT-YLD19, and MQTL-RL1 contributed to the highest number of projected QTLs and were therefore regarded as the most reliable and stable QTLs under water deficit conditions. These MQTLs greatly facilitate the identification of putative candidate genes underlying at each MQTL interval due to the reduced confidence of intervals associated with MQTLs. These findings provide important information on the genetic basis of early vigor traits and grain yield under water deficit conditions and set the foundation for future investigations into adaptation to water deficit in bread wheat.

Genotype and environment interaction and stability of grain yield and oil content of rapeseed cultivars.

Investigating the interaction of genotype and environment in multi-environment experiments (MET) is one of the reliable techniques to demonstrate the most stable and compatible cultivars. The main contribution of this study is to evaluate the stability and compatibility of rapeseed cultivars using additive main effects and multiplicative interaction (AMMI) and genotype plus genotype environment interaction (GGE) bi-plot methods for grain yield and oil content. For this purpose, an experiment in a randomized complete block design (RCBD) with three replications was conducted for 10 rapeseed cultivars across 10 environments (five regions in 2 years). Hence, the proposed technique can be used to identify the superior cultivars corresponding to the multivariant properties including yield and oil content. To do so, a case-study analysis was conducted over rapeseed, while more than 96% of the data variance for grain yield and more than 94% of the data variance for oil content were explained based on the AMMI model. According to the AMMI model, it was observed that the "Zarfam" and "Licord" genotypes were introduced as favorable genotypes for grain yield and oil content, respectively. "Karaj1" and "Sanandaj1" were selected as the superior environments for yield trait, "Kashmar2" for oil content, and "Licord" and "Kashmar2" were identified as the superior genotypes and environment for oil content, respectively. Graphical GGE bi-plot illustrated that "Hyola401," "Okapi," and "Sarigol" for grain yield and "Option500" and "Sunday" for oil content were identified as stable and high-yield genotypes. "Sanandaj1" for grain yield and "Karaj2" for oil content were identified as environments with high differentiation and screening power.

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions.

BACKGROUND: The markers detected by genome-wide association study (GWAS) make it possible to dissect genetic structure and diversity at many loci. This can enable a wheat breeder to reveal and used genomic loci controlling drought tolerance. This study was focused on determining the population structure of Iranian 208 wheat landraces and 90 cultivars via genotyping-by-sequencing (GBS) and also on detecting marker-trait associations (MTAs) by GWAS and genomic prediction (GS) of wheat agronomic traits for drought-tolerance breeding. GWASs were conducted using both the original phenotypes (pGWAS) and estimated breeding values (eGWAS). The bayesian ridge regression (BRR), genomic best linear unbiased prediction (gBLUP), and ridge regression-best linear unbiased prediction (rrBLUP) approaches were used to estimate breeding values and estimate prediction accuracies in genomic selection. RESULTS: Population structure analysis using 2,174,975 SNPs revealed four genetically distinct sub-populations from wheat accessions. D-Genome harbored the lowest number of significant marker pairs and the highest linkage disequilibrium (LD), reflecting different evolutionary histories of wheat genomes. From pGWAS, BRR, gBLUP, and rrBLUP, 284, 363, 359 and 295 significant MTAs were found under normal and 195, 365, 362 and 302 under stress conditions, respectively. The gBLUP with the most similarity (80.98 and 71.28% in well-watered and rain-fed environments, correspondingly) with the pGWAS method in the terms of discovered significant SNPs, suggesting the potential of gBLUP in uncovering SNPs. Results from gene ontology revealed that 29 and 30 SNPs in the imputed dataset were located in protein-coding regions for well-watered and rain-fed conditions, respectively. gBLUP model revealed genetic effects better than other models, suggesting a suitable tool for genome selection in wheat. CONCLUSION: We illustrate that Iranian landraces of bread wheat contain novel alleles that are adaptive to drought stress environments. gBLUP model can be helpful for fine mapping and cloning of the relevant QTLs and genes, and for carrying out trait introgression and marker-assisted selection in both normal and drought environments in wheat collections.

Genome-Wide Association Study (GWAS) and genome prediction of seedling salt tolerance in bread wheat (Triticum aestivum L.).

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.

Genome-wide association mapping for wheat morphometric seed traits in Iranian landraces and cultivars under rain-fed and well-watered conditions.

Seed traits in bread wheat are valuable to breeders and farmers, thus it is important exploring putative QTLs responsible for key traits to be used in breeding programs. GWAS was carried out using 298 bread wheat landraces and cultivars from Iran to uncover the genetic basis of seed characteristics in both rain-fed and well-watered environments. The analyses of linkage disequilibrium (LD) between marker pairs showed that the largest number of significant LDs in landraces (427,017) and cultivars (370,359) was recorded in genome B, and the strongest LD was identified on chromosome 4A (0.318). LD decay was higher in the B and A genomes, compared to the D genome. Mapping by using mrMLM (LOD > 3) and MLM (0.05/m, Bonferroni) led to 246 and 67 marker-trait associations (MTAs) under rain-fed, as well as 257 and 74 MTAs under well-watered conditions, respectively. The study found that 3VmrMLM correctly detected all types of loci and estimated their effects in an unbiased manner, with high power and accuracy and a low false positive rate, which led to the identification of 140 MTAs (LOD > 3) in all environments. Gene ontology revealed that 10 and 10 MTAs were found in protein-coding regions for rain-fed and well-watered conditions, respectively. The findings suggest that landraces studied in Iranian bread wheat germplasm possess valuable alleles, which are responsive to water-limited conditions. MTAs uncovered in this study can be exploited in the genome-mediated development of novel wheat cultivars.

Genome-wide association mapping and genomic prediction for pre­harvest sprouting resistance, low α-amylase and seed color in Iranian bread 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.

Characterization of Dynamic Regulatory Gene and Protein Networks in Wheat Roots Upon Perceiving Water Deficit Through Comparative Transcriptomics Survey.

A well-developed root system benefits host plants by optimizing water absorption and nutrient uptake and thereby increases plant productivity. In this study we have characterized the root transcriptome using RNA-seq and subsequential functional analysis in a set of drought tolerant and susceptible genotypes. The goal of the study was to elucidate and characterize water deficit-responsive genes in wheat landraces that had been through long-term field and biochemical screening for drought tolerance. The results confirm genotype differences in water-deficit tolerance in line with earlier results from field trials. The transcriptomics survey highlighted a total of 14,187 differentially expressed genes (DEGs) that responded to water deficit. The characterization of these genes shows that all chromosomes contribute to water-deficit tolerance, but to different degrees, and the B genome showed higher involvement than the A and D genomes. The DEGs were mainly mapped to flavonoid, phenylpropanoid, and diterpenoid biosynthesis pathways, as well as glutathione metabolism and hormone signaling. Furthermore, extracellular region, apoplast, cell periphery, and external encapsulating structure were the main water deficit-responsive cellular components in roots. A total of 1,377 DEGs were also predicted to function as transcription factors (TFs) from different families regulating downstream cascades. TFs from the AP2/ERF-ERF, MYB-related, B3, WRKY, Tify, and NAC families were the main genotype-specific regulatory factors. To further characterize the dynamic biosynthetic pathways, protein-protein interaction (PPI) networks were constructed using significant KEGG proteins and putative TFs. In PPIs, enzymes from the CYP450, TaABA8OH2, PAL, and GST families play important roles in water-deficit tolerance in connection with MYB13-1, MADS-box, and NAC transcription factors.

Dissection of the genetic basis of genotype-by-environment interactions for grain yield and main agronomic traits in Iranian bread wheat landraces and cultivars.

Understanding the genetic basis of performance stability is essential to maintain productivity, especially under severe conditions. In the present study, 268 Iranian bread wheat landraces and cultivars were evaluated in four well-watered and two rain-fed conditions for different traits. According to breeding programs, cultivars were in a group with a high mean and stability in terms of GY, GN, and SW traits, while in terms of PH, they had a low mean and high stability. The stability of cultivars and landraces was related to dynamic and static stability, respectively. The highest number of marker pairs and lowest LD decay distance in both cultivars and landraces was observed on the B genome. Population structure differentiated indigenous cultivars and landraces, and the GWAS results for each were almost different despite the commonalities. Chromosomes 1B, 3B, 7B, 2A, and 4A had markers with pleiotropic effects on the stability of different traits. Due to two rain-fed environments, the Gene Ontology (GO) confirmed the accuracy of the results. The identified markers in this study can be helpful in breeding high-performance and stable genotypes and future breeding programs such as fine mapping and cloning.

Proteomic analysis of drought stress response mechanism in soybean (Glycine max L.) leaves.

Knowledge of the physiological and molecular mechanisms of drought responses is fundamental for developing genetically drought tolerant and high yielding crops. To understand molecular mechanism of drought tolerance of soybean (Glycine max L.), we compared leaf proteome patterns of in two genotypes GN-3074 (drought tolerant) and GN-2032 (drought-sensitive) under drought stress during vegetative stage. Proteins were extracted from leaves of well-watered and drought-treated plants by using the trichloroacetic acid (TCA)-acetone precipitation method and analyzed by two-dimensional polyacrylamide gel electrophoresis. Out 488 reproducibly detected and analyzed on two-dimensional electrophoresis gels, 26 proteins showed significant changes in at least one genotype. The identification of 20 differentially expressed proteins using mass spectrometry revealed a coordinated expression of proteins involved in cellular metabolisms including photosynthesis, oxidative stress defense, respiration, metabolism process, signal transduction, phosphorus transduction, and methyl transduction which enable plant to cope with drought conditions. The most identified proteins involved in photosynthesis and oxidative stress defense system. The up-regulation of several photosynthetic proteins and also high abundance of oxidative stress defense proteins in GN-3074 genotypes as compare to GN-2032 genotypes might reflect the fact that drought tolerance of GN-3074 is due to effective photosynthetic machinery and more defense against oxidative stress. Our results suggest that soybean plant might response to drought stress by applying efficiently stay-green mechanism through coordinated gene expression during vegetative stage.

A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars.

The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method (ridge regression-best linear unbiased prediction, genomic best-linear unbiased predictions, and Bayesian ridge regression), training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.

Genome-wide association study in diverse Iranian wheat germplasms detected several putative genomic regions associated with stem rust resistance.

Stem rust is one of the most important diseases, threatening global wheat production. Identifying genomic regions associated with resistance to stem rust at the seedling stage may contribute wheat breeders to introduce durably resistant varieties. Genome-wide association study (GWAS) approach was applied to detect stem rust (Sr) resistance genes/QTLs in a set of 282 Iranian bread wheat varieties and landraces. Germplasms evaluated for infection type and latent period in four races of Puccinia graminis f. sp. tritici (Pgt). A total of 3 QTLs for infection type (R2 values from 9.54% to 10.76%) and 4 QTLs for the latent period (R2 values from 8.97% to 12.24%) of studied Pgt races were identified in the original dataset. However, using the imputed SNPs dataset, the number of QTLs for infection type increased to 10 QTLs (R2 values from 8.12% to 11.19%), and for the latent period increased to 44 QTLs (R2 values from 9.47% to 26.70%). According to the results, the Iranian wheat germplasms are a valuable source of resistance to stem rust which can be used in wheat breeding programs. Furthermore, new information for the selection of resistant genotypes against the disease through improving marker-assisted selection efficiency has been suggested.

A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat.

BACKGROUND: High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat. RESULTS: The reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively. CONCLUSIONS: This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs.

Genotypic variation for cadmium tolerance in common bean (Phaseolus vulgaris L.).

Given the limitation of crop production in Cd-polluted areas, the identification and selection of plant genotypes tolerant to Cd stress are of great significance. In the present work, we show the existence of genotypic variation for Cd tolerance in common bean. The laboratory screening of 25 bean genotypes indicated a significant positive correlation of the mean productivity (MP) and the geometric mean productivity (GMP) with plant fresh weight both in control and Cd-treated plants. A principal component analysis further confirmed this variation and, together with other analyses, led to the selection of genotypes G-11867, Taylor, Emerson, and D-81083 as tolerant genotypes. A total of six bean genotypes with different degrees of Cd tolerance were selected, and their long-term physiological responses to Cd (0, 45, and 90 mg/kg soil) were evaluated. Increasing Cd concentrations led to higher Cd accumulation both in roots and shoots, and to significant rises in the levels of the oxidative stress biomarkers malondialdehyde (MDA), dityrosine (D-T), and 8-hydroxy-2'-deoxyguanosine (8-OH-2'-dG). Remarkable reductions in plant hormone levels and chlorophyll contents, as well as in dry and fresh weight, were observed in Cd-treated plants. Among the examined genotypes, Emerson, Taylor, and G-11867 were found to be more tolerant to Cd owing to lower Cd accumulation and lower oxidative stress levels, as well as higher chlorophyll and hormone contents. Our results contribute to the understanding of the physiological and biochemical basis of Cd tolerance in bean plants and may therefore, be useful for breeding programs directed towards obtaining bean varieties showing low Cd accumulation.

Genome-wide association study of agronomic traits in bread wheat reveals novel putative alleles for future breeding programs.

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 (-log10 P >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.

Imputation accuracy of wheat genotyping-by-sequencing (GBS) data using barley and wheat genome references.

Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat (Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references (Chinese Spring survey sequence, W7984, and IWGSC RefSeq v1.0) and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.

Study of QTLs linked to awn length and their relationships with chloroplasts under control and saline environments in bread wheat.

INTRODUCTION: Some studies in wheat showed that awns may have a useful effect on yield, especially under drought stress. Up to this time few researches has identified the awn length QTLs with different effect in salinity stress. OBJECTIVE: The primary objective of this study was to examine the additive (a) and the epistatic (aa) QTLs involve in wheat awns length in control and saline environments. METHODS: A F7 RIL population consisting of 319 sister lines, derived from a cross between wheat cultivars Roshan and Falat (seri82), and the two parents were grown in two environments (control and Saline) based on an alpha lattice design with two replications in each environment. At flowering, awn length was measured for each line. For QTL analysis, the linkage map of the ''Roshan × Falat'' population was used, which included 748 markers including 719 DArT, 29 simple sequenced repeats (SSRs). Additive and pleiotropic QTLs were identified. In order to reveal the relationship between the identified QTL for awns length and the role of the gene or genes that it expresses, the awns length locus location and characteristics of its related CDS, gene, UTRs, ORF, exons and Introns were studied using ensemble plant ( http://plants.ensembl.org/Triticum_aestivum ). Furthermore, the promoter analysis has been done using NSITE-PL. RESULTS: We identified 6 additive QTLs for awn length by QTL Cartographer program using single-environment phenotypical values. Also, we detected three additive and two epistatic QTLs for awn length by the QTLNetwork program using multi-environment phenotypical values. Our results showed that none of the additive and epistatic QTLs had interactions with environment. One of the additive QTLs located on chromosome 4A was co-located with QTLs for number of sterile spikelet per spike in both environment and number of seed per spike in control environment. COCLUSION: Studies of the locus linked to the awns length QTL revealed the role of awn and its chloroplasts in grain filing during abiotic stress could be enhanced by over expression of some genes like GTP-Binding proteins which are enriched in chloroplasts encoded by genes included wPt-5730 locus.

Identification of SSR and retrotransposon-based molecular markers linked to morphological characters in oily sunfl ower (Helianthus annuus L.) under natural and water-limited states.

Sunflower is an important source of edible oil. Drought is known as an important factor limiting the growth and productivity of field crops in most parts of the world. Agricultural biotechnology mainly aims at developing crops with higher tolerance to the challenging environmental conditions, such as drought. This study examined a number of morphological characters, along with relative water content (RWC) in 100 inbred sunflower lines. A 10 × 10 simple lattice design with two replications was employed to measure the mentioned parameters under natural and water-limited states during two successive years. In molecular trial, 30 simple sequence repeat (SSR) primer pairs, as well as 14 inter-retrotransposon amplified polymorphism (IRAP) and 14 retrotransposon-microsatellite amplified polymorphism (REMAP) primer combinations were used for DNA fingerprinting of the lines. Most of the examined characters had lower average values under water-limited than natural states. Maximum and minimum reductions were observed in the cases of yield and oil percentage, respectively. The broad-sense heritabilities for all the examined characters were 0.20-0.73 and 0.10-0.34 under natural and water-limited states, respectively. In the studied samples, 8.97% of the 435 possible locus pairs of the SSRs represented significant linkage disequilibrium (LD) levels. In the association analysis using SSR markers, 22 and 21 markers were identified (P ≤ 0.05) for the studied characters under natural and water-limited states, respectively. The corresponding values were 50 and 37 using retrotransposon-based molecular markers. Some detected markers were communal between the characters under water-limited and natural states. This was in line with the phenotypic correlations detected between the characters. Communal markers facilitate the simultaneous selection of several characters and can thus improve the efficacy of selection based on markers in the plant-breeding activities.