Transcriptomic and metabolomic changes in postharvest sugarbeet roots reveal widespread metabolic changes in storage and identify genes potentially responsible for respiratory sucrose loss.

Endogenous metabolism is primarily responsible for losses in sucrose content and processing quality in postharvest sugarbeet roots. The genes responsible for this metabolism and the transcriptional changes that regulate it, however, are largely unknown. To identify genes and metabolic pathways that participate in postharvest sugarbeet root metabolism and the transcriptional changes that contribute to their regulation, transcriptomic and metabolomic profiles were generated for sugarbeet roots at harvest and after 12, 40 and 120 d storage at 5 and 12°C and gene expression and metabolite concentration changes related to storage duration or temperature were identified. During storage, 8656 genes, or 34% of all expressed genes, and 225 metabolites, equivalent to 59% of detected metabolites, were altered in expression or concentration, indicating extensive transcriptional and metabolic changes in stored roots. These genes and metabolites contributed to a wide range of cellular and molecular functions, with carbohydrate metabolism being the function to which the greatest number of genes and metabolites classified. Because respiration has a central role in postharvest metabolism and is largely responsible for sucrose loss in sugarbeet roots, genes and metabolites involved in and correlated to respiration were identified. Seventy-five genes participating in respiration were differentially expressed during storage, including two bidirectional sugar transporter SWEET17 genes that highly correlated with respiration rate. Weighted gene co-expression network analysis identified 1896 additional genes that positively correlated with respiration rate and predicted a pyruvate kinase gene to be a central regulator or biomarker for respiration rate. Overall, these results reveal the extensive and diverse physiological and metabolic changes that occur in stored sugarbeet roots and identify genes with potential roles as regulators or biomarkers for respiratory sucrose loss.

Exploring the Genetic Diversity and Population Structure of Wheat Landrace Population Conserved at ICARDA Genebank.

Landraces are considered a valuable source of potential genetic diversity that could be used in the selection process in any plant breeding program. Here, we assembled a population of 600 bread wheat landraces collected from eight different countries, conserved at the ICARDA's genebank, and evaluated the genetic diversity and the population structure of the landraces using single nucleotide polymorphism (SNP) markers. A total of 11,830 high-quality SNPs distributed across the genomes A (40.5%), B (45.9%), and D (13.6%) were used for the final analysis. The population structure analysis was evaluated using the model-based method (STRUCTURE) and distance-based methods [discriminant analysis of principal components (DAPC) and principal component analysis (PCA)]. The STRUCTURE method grouped the landraces into two major clusters, with the landraces from Syria and Turkey forming two clusters with high proportions of admixture, whereas the DAPC and PCA analysis grouped the population into three subpopulations mostly according to the geographical information of the landraces, i.e., Syria, Iran, and Turkey with admixture. The analysis of molecular variance revealed that the majority of the variation was due to genetic differences within the populations as compared with between subpopulations, and it was the same for both the cluster-based and distance-based methods. Genetic distance analysis was also studied to estimate the differences between the landraces from different countries, and it was observed that the maximum genetic distance (0.389) was between the landraces from Spain and Palestine, whereas the minimum genetic distance (0.013) was observed between the landraces from Syria and Turkey. It was concluded from the study that the model-based methods (DAPC and PCA) could dissect the population structure more precisely when compared with the STRUCTURE method. The population structure and genetic diversity analysis of the bread wheat landraces presented here highlight the complex genetic architecture of the landraces native to the Fertile Crescent region. The results of this study provide useful information for the genetic improvement of hexaploid wheat and facilitate the use of landraces in wheat breeding programs.

QTL Mapping of Adult Plant Resistance to Stripe Rust in a Doubled Haploid Wheat Population.

Stripe rust caused by Puccinia striiformis Westend. f. sp. tritici. is a major bread wheat disease worldwide with yield losses of up to 100% under severe disease pressure. The deployment of resistant cultivars with adult plant resistance to the disease provides a long-term solution to stripe rust of wheat. An advanced line from the International Winter Wheat Improvement Program (IWWIP) 130675 (Avd/Vee#1//1-27-6275/Cf 1770/3/MV171-C-17466) showed a high level of adult plant resistance to stripe rust in the field. To identify the adult plant resistance genes in this elite line, a mapping population of 190 doubled haploid (DH) lines was developed from a cross between line 130675 and the universal stripe rust-susceptible variety Avocet S. The DH population was evaluated at precision wheat stripe rust phenotyping platform, in Izmir during 2019, 2020, and 2021 cropping seasons under artificial inoculations. Composite interval mapping (CIM) identified two stable QTLs QYr.rcrrc-3B.1, and QYr.rcrrc-3B.2, which were detected in multiple years. In addition to these two QTLs, five more QTLs, QYr.rcrrc-1B, QYr.rcrrc-2A, QYr.rcrrc-3A, QYr.rcrrc-5A, and QYr.rcrrc-7D, were identified, which were specific to the cropping year (environment). All QTLs were derived from the resistant parent, except QYr.rcrrc-3A. The significant QTLs explained 3.4-20.6% of the phenotypic variance. SNP markers flanking the QTL regions can be amenable to marker-assisted selection. The best DH lines with high yield, end-use quality, and stripe rust resistance can be used for further selection for improved germplasm. SNP markers flanking the QTL regions can aid in identifying such lines.

Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan.

Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV's for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.

Genetic Diversity and Population Structure Analysis of Triticum aestivum L. Landrace Panel from Afghanistan.

Landraces are a potential source of genetic diversity and provide useful genetic resources to cope with the current and future challenges in crop breeding. Afghanistan is located close to the centre of origin of hexaploid wheat. Therefore, understanding the population structure and genetic diversity of Afghan wheat landraces is of enormous importance in breeding programmes for the development of high-yielding cultivars as well as broadening the genetic base of bread wheat. Here, a panel of 363 bread wheat landraces collected from seven north and north-eastern provinces of Afghanistan were evaluated for population structure and genetic diversity using single nucleotide polymorphic markers (SNPs). The genotyping-by-sequencing of studied landraces after quality control provided 4897 high-quality SNPs distributed across the genomes A (33.75%), B (38.73%), and D (27.50%). The population structure analysis was carried out by two methods using model-based STRUCTURE analysis and cluster-based discriminant analysis of principal components (DAPC). The analysis of molecular variance showed a higher proportion of variation within the sub-populations compared with the variation observed as a whole between sub-populations. STRUCTURE and DAPC analysis grouped the majority of the landraces from Badakhshan and Takhar together in one cluster and the landraces from Baghlan and Kunduz in a second cluster, which is in accordance with the micro-climatic conditions prevalent within the north-eastern agro-ecological zone. Genetic distance analysis was also studied to identify differences among the Afghan regions; the strongest correlation was observed for the Badakhshan and Takhar (0.003), whereas Samangan and Konarha (0.399) showed the highest genetic distance. The population structure and genetic diversity analysis highlighted the complex genetic variation present in the landraces which were highly correlated to the geographic origin and micro-climatic conditions within the agro-climatic zones of the landraces. The higher proportions of admixture could be attributed to historical unsupervised exchanges of seeds between the farmers of the central and north-eastern provinces of Afghanistan. The results of this study will provide useful information for genetic improvement in wheat and is essential for association mapping and genomic prediction studies to identify novel sources for resistance to abiotic and biotic stresses.

Genome-wide association study of resistance to PstS2 and Warrior races of Puccinia striiformis f. sp. tritici (stripe rust) in bread wheat landraces.

Stripe or yellow rust, caused by Puccinia striiformis Westend. f. sp. tritici is a major threat to bread wheat production worldwide. The breakdown in resistance of certain major genes and newly emerging aggressive races of stripe rusts pose serious concerns in all main wheat growing areas of the world. To identify new sources of resistance and associated QTL for effective utilization in future breeding programs an association mapping (AM) panel comprising of 600 bread wheat landraces collected from eight different countries conserved at ICARDA gene bank were evaluated for seedling and adult plant resistance against the PstS2 and Warrior races of stripe rust at the Regional Cereal Rust Research Center (RCRRC), Izmir, Turkey during 2016, 2018 and 2019. A set of 25,169 informative SNP markers covering the whole genome were used to examine the population structure, linkage disequilibrium and marker-trait associations in the AM panel. The genome-wide association study (GWAS) was carried out using a Mixed Linear Model (MLM). We identified 47 SNP markers across 19 chromosomes with significant SNP-trait associations for both seedling stage and adult plant resistance. The threshold of significance for all SNP-trait associations was determined by the false discovery rate (q) ≤ 0.05. Three genomic regions (QYr.1D_APR, QYr.3A_seedling and QYr.7D_seedling) identified in this study do not correspond to previously reported Yr genes or QTL, suggesting new genomic regions for stripe rust resistance.

Genome-wide association study identifies five new cadmium uptake loci in wheat.

Cadmium (Cd) toxicity is a serious threat to future food security and health safety. To identify genetic factors contributing to Cd uptake in wheat, we conducted a genome-wide association study with genotyping from 90K SNP array. A spring wheat diversity panel was planted under normal conditions and Cd stress (50 mg Cd/kg soil). The impact of Cd stress on agronomic traits ranged from a reduction of 16% in plant height to 93% in grain iron content. Individual genotypes showed a considerable variation for Cd uptake and translocation subdividing the panel into three groups: (1) hyper-accumulators (i.e. high Leaf_Cd and low Seed_Cd ), (2) hyper-translocators (i.e. low Leaf_Cd and high Seed_Cd ), and (3) moderate lines (i.e. low Leaf_Cd and low Seed_Cd ). Two lines (SKD-1 and TD-1) maintained an optimum grain yield under Cd stress and were therefore considered as Cd resistant lines. Genome-wide association identified 179 SNP-trait associations for various traits including 16 for Cd uptake at a significance level of P < .001. However, only five SNPs were significant after applying multiple testing correction. These loci were associated with seed-cadmium, grain-iron, and grain-zinc: qSCd-1A, qSCd-1D, qZn-2B1, qZn-2B2, and qFe-6D. These five loci had not been identified in the previously reported studies for Cd uptake in wheat. These loci and the underlying genes should be further investigated using molecular biology techniques to identify Cd resistant genes in wheat.