Search

Cultivated beet (Beta vulgaris L. ssp. vulgaris) originated from sea beet (B. vulgaris ssp. maritima (L.) Arcang), a wild beet species widely distributed along the coasts of the Mediterranean Sea and Atlantic Ocean, as well as northern Africa. Understanding the evolution of sea beet will facilitate its efficient use in sugarbeet improvement. We used SNPs (single nucleotide polymorphisms) covering the whole genome to analyze 599 sea beet accessions collected from the north Atlantic Ocean and Mediterranean Sea coasts. All B. maritima accessions can be grouped into eight clusters with each corresponding to a specific geographic region. Clusters 2, 3 and 4 with accessions mainly collected from Mediterranean coasts are genetically close to each other as well as to Cluster 6 that contained mainly cultivated beet. Other clusters were relatively distinct from cultivated beets with Clusters 1 and 5 containing accessions from north Atlantic Ocean coasts, Clusters 7 and Cluster 8 mainly have accessions from northern Egypt and southern Europe, and northwest Morocco, respectively. Distribution of B. maritima subpopulations aligns well with the direction of marine currents that was considered a main dynamic force in spreading B. maritima during evolution. Estimation of genetic diversity indices supported the formation of B. maritima subpopulations due to local genetic drift, historic migration, and limited gene flow. Our results indicated that B. maritima originated from southern Europe and then spread to other regions through marine currents to form subpopulations. This research provides vital information for conserving, collecting, and utilizing wild sea beet to sustain sugarbeet improvement. [ABSTRACT FROM AUTHOR]

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 coexpression 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. [ABSTRACT FROM AUTHOR]

Sugar beet (Beta vulgaris L. ssp. vulgaris Doell.) was originally selected from white fodder beet in the 1780s and was then specifically bred for sucrose production. The relatively recent inception of the crop has led to a narrow genetic base that has bottlenecked sustainable improvement. To evaluate the potential of publicly available germplasm for sugar beet improvement, genetic diversity analysis with SNPs (single‐nucleotide polymorphisms) covering the whole genome of sugar beet was conducted using 1936 publicly available germplasm lines in the United States. The results confirmed the narrow genetic base of sugar beet and identified germplasm accessions with inherently greater diversity that were mostly accessions of wild sea beet (B. vulgaris ssp. maritima (L.) Arcang.), the progenitor species of white fodder beet. These wild accessions displayed a distinct genetic relationship from cultivated sugar beet lines, indicating their high potential for broadening sugar beet genetic diversity. Analysis of historic resistance evaluations also suggested a higher potential of B. vulgaris ssp. maritima accessions to be used as sources of resistance to major diseases and insects of sugar beet. A genome‐wide association study using historic evaluation data identified genomic regions significantly associated with disease and insect resistance. However, genomic regions associated with resistance to nematode, insects, or diseases vectored by insects were of low significance, indicating the need for additional research to allow for a more precise evaluation of germplasm responses to insects. The research confirms that accessions of B. vulgaris ssp. maritima are potentially valuable for improving biotic stress resistance and broadening the genetic base of sugar beet. Core Ideas: Analyzed genetic diversity of publicly available Beta vulgaris germplasm.Potential of the available germplasm for sustainable sugar beet improvement.Association mapping to identify genomic regions associated with pest resistance using historic evaluation data. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms (SNPs) have been widely used for gene identification. Allelic discrimination for an individual SNP with high reliability and flexibility is critical for the accurate detection of beneficial genes linked to specific SNP sites. Several SNP genotyping platforms have been developed but most exclusively rely on fluorescence signals for allelic differentiation. Genotyping via a fluorescence signal can have a lower accuracy if strong background signal noise is present, a common challenge associated with crop genetics. The semi-thermal asymmetric reverse PCR (STARP) marker system introduces extra SNPs in its forward primers to ensure specificity of the PCR reaction and adds a 4-nucleotide insertion into one universal primer to create fragment length polymorphism among STARP markers, which makes SNP allelic discrimination possible through either fluorescence signals or traditional gel electrophoresis. The STARP marker system is preferable for SNP genotyping in crops such as sugarbeet (Beta vulgaris ssp. Vulgaris L.) that exhibit strong background signal noise during PCR reactions due to an abundant repetitive sequence and high levels of heterozygosity in the genome. In this study, SNPs among sugarbeet lines were detected through genotype-by-sequencing (GBS) and confirmed by sequencing PCR products containing SNP sites. STARP primers were designed, and they generated STARP markers clearly discriminated by SNP alleles among sugarbeet plants either through a fluorescence signal or fragment length polymorphism. In addition, by prolonging 5-nucleotide in an allele-specific forward primer F2 that increased fragment length polymorphism of STARP markers from 4-bp to 9-bp, genotyping individual SNPs can be performed using user-friendly agarose gels. This research resulted in the development of a STARP marker platform for the flexible genotyping of individual SNPs of sugarbeet as well as an improved STARP technique for easy SNP allelic discrimination that also has utilities in other plant species. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Okra (Abelmoschus esculentusMoench) is valued for its edible green seed pods. Twenty-five okra genotypes were sown to determine the genetic variability for 10 yield- and quality-related traits. Minor differences between genotypic and phenotypic variance indicated low environmental variance. All traits were positively correlated with yield per plant except for photosynthetic-active radiation and carbohydrate percentage, which exhibited a negative association. Cluster analysis grouped genotypes in three clusters; cluster 1 consisted of five genotypes; cluster 2 consisted of 13, and cluster 3 consisted of seven. The three principal components explained 63.5% of total existing genetic variability. Selection from genotypes in cluster 1 had high genetic diversity, which results in high pod yield. The genotype Diksha could be used for developing plants with good quality fruit. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Two experiments were conducted to develop an efficient maize regeneration system using mature and immature embryos of four maize elite lines (Agaiti-85, Golden, Soneri and Sultan). The comparative studies revealed that all maize lines were responsive to callus and plant regeneration from mature and immature embryos. The culture medium Chu's N6 was used in both experiments. Immature embryos efficiently produced callus and regenerated plants therefore, when cultured on medium supplemented with 2,4-D, kinetin, 6-BAP and IBA at concentrations of 2 mL L-1, 0.2, 0.2 and 0.3 g L-1, respectively. However, callus-to-plant regeneration system for mature embryos required these growth regulators at higher concentrations (5 mL L-1, 0.4 g L-1, 0.5 g L-1 and 0.6 g L-1, respectively). The regeneration frequencies ranged from 40 to 75% using immature embryos, compared to 55 to 80% from immature embryos of all elite lines of maize under study. [ABSTRACT FROM AUTHOR]

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 &#95;Cd and low Seed &#95;Cd ), (2) hyper-translocators (i.e. low Leaf &#95;Cd and high Seed &#95;Cd ), and (3) moderate lines (i.e. low Leaf &#95;Cd and low Seed &#95;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., (© 2020 The Authors. The Plant Genome published by Wiley Periodicals, Inc. on behalf of Crop Science Society of America.)