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19 results for “wheat breeding”
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
Supplementary data: Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?
<p>Agronomic and quality data of organic wheat (<em>Triticum aestivum</em>), mean comparisons and supplementary figures related to the publication "Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?" by Grausgruber et al. (2024) published in the Journal of Cereal Science.</p>
CIMMYT bread wheat breeding lines
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Wheat breeding phenotyping and yield dataset
<p>This is a wheat breeding phenotyping and yield dataset, including canopy height (CH, m), canopy volume (CV, m3), and leaf area index (LAI) collected in the field; vegetation index (VI) generated by multispectral data acquired by UAV remote sensing; trial site weather (Weather); and yield (Yield, kg). The data comes from field trials.</p> <p>Data acquisition and processing are described in the relevant part of the manuscript.</p> <p>This dataset will be continuously updated in the future to help breeders carry out their work efficiently and accelerate the breeding process of excellent varieties.</p>
A haplotype-led approach to increase the precision of wheat breeding
<p>Crop productivity must increase at unprecedented rates to meet the needs of the growing worldwide population. Exploiting natural variation for the genetic improvement of crops plays a central role in increasing productivity. Although current genomic technologies can be used for high-throughput identification of genetic variation, methods for efficiently exploiting this genetic potential in a targeted, systematic manner are lacking. Here, we developed a haplotype-based approach to identify genetic diversity for crop improvement using genome assemblies from 15 bread wheat (<em>Triticum aestivum</em>) cultivars. We used stringent criteria to identify identical-by-state haplotypes and distinguish these from near-identical sequences (~99.95% identity). We showed that each cultivar shares ~59 % of its genome with other sequenced cultivars and we detected the presence of extended haplotype blocks containing hundreds to thousands of genes across all wheat chromosomes. We found that genic sequence alone was insufficient to fully differentiate between haplotypes, as were commonly used array-based genotyping chips due to their gene centric design. We successfully used this approach for focused discovery of novel haplotypes from a landrace collection and documented their potential for trait improvement in modern bread wheat. This study provides a framework for defining and exploiting haplotypes to increase the efficiency and precision of wheat breeding towards optimising the agronomic performance of this crucial crop.</p>
Data from: Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs
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A haplotype-led approach to increase the precision of wheat breeding
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Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Climate change may outpace current wheat breeding yield improvements in North America
<p>The raw data in Climate change may outpace current wheat breeding yield improvements in North America</p>
Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
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Supporting data for: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics
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Ms3 dominant genetic male sterility for wheat improvement with molecular breeding
<p>Genetic dominant male sterility (DMS) has not been widely used as a breeding tool in wheat (<i>Triticum aestivum</i> L.), although DMS-facilitated backcross, mass selection, half-sib selection, and S<sub>1</sub> family recurrent selection strategies have been described, and <i>Ms2-</i>facilitated recurrent selection has been used in China. Our objective was to revisit these strategies using the tools of molecular breeding. Development of a mechanism for seedling identification of sterile progeny was a key component of designing practical DMS-facilitated molecular breeding systems. The DMS gene <i>Ms3</i> was previously localized to the centromeric region of chromosome 5A. The centromeric location is an advantage because recombination rates are very low. Once identified, a broadly informative marker would reliably predict the male-sterile phenotype. A set of 429 hybrids incorporating <i>Ms3</i> were constructed, both within US hard winter wheats, and between these winter wheats and Asian spring wheats. Association of the male-sterile phenotype with those polymorphic DNA sequence tags that localized to chromosome 5A was tested using case-control association analysis. Two highly significant (logarithm of odds [LOD] > 30) single nucleotide polymorphism (SNP)–trait associations were obtained. One SNP was developed into a highly sensitive, reliable molecular marker for the <i>Ms3</i>-associated male-sterile. Previously described breeding strategies using DMS were updated for trait-targeted marker-assisted backcrossing and gene pyramiding, S<sub>1</sub> recurrent selection, and early-generation genomic selection. Application of DMS to association mapping, with the particular use case of the multiparent advanced generation intercross population, is also described.</p>
Data from: Introgression of chromosome segments from multiple alien species in wheat breeding lines with wheat streak mosaic virus resistance
Pyramiding of alien-derived Wheat streak mosaic virus (WSMV) resistance and resistance enhancing genes in wheat is a cost-effective and environmentally safe strategy for disease control. PCR-based markers and cytogenetic analysis with genomic in situ hybridisation were applied to identify alien chromatin in four genetically diverse populations of wheat (Triticum aestivum) lines incorporating chromosome segments from Thinopyrum intermedium and Secale cereale (rye). Out of 20 experimental lines, 10 carried Th. intermedium chromatin as T4DL*4Ai#2S translocations, while, unexpectedly, 7 lines were positive for alien chromatin (Th. intermedium or rye) on chromosome 1B. The newly described rye 1RS chromatin, transmitted from early in the pedigree, was associated with enhanced WSMV resistance. Under field conditions, the 1RS chromatin alone showed some resistance, while together with the Th. intermedium 4Ai#2S offered superior resistance to that demonstrated by the known resistant cultivar Mace. Most alien wheat lines carry whole chromosome arms, and it is notable that these lines showed intra-arm recombination within the 1BS arm. The translocation breakpoints between 1BS and alien chromatin fell in three categories: (i) at or near to the centromere, (ii) intercalary between markers UL-Thin5 and Xgwm1130 and (iii) towards the telomere between Xgwm0911 and Xbarc194. Labelled genomic Th. intermedium DNA hybridised to the rye 1RS chromatin under high stringency conditions, indicating the presence of shared tandem repeats among the cereals. The novel small alien fragments may explain the difficulty in developing well-adapted lines carrying Wsm1 despite improved tolerance to the virus. The results will facilitate directed chromosome engineering producing agronomically desirable WSMV-resistant germplasm.
Wheat Breeding Multimodal Dataset
<p>This dataset is a wheat breeding multimodal dataset, including wheat germplasm data, wheat phenotypic data, wheat cultivation technique data, wheat plant protection technique data, wheat seed price data, UAV remote sensing data, and experimental site weather data. The data sources are field acquisition and online public data.</p> <p>The wheat germplasm data comes from the Chinese Crop Germplasm Information Network (https://www.cgris.net/). The data on wheat cultivation technique and wheat plant protection technique come from search engines (Google, Bing, Baidu), and the search terms include "wheat cultivation technique, wheat plant protection technique, 小麦栽培技术, 小麦植保技术". The wheat seed historical price data comes from the National Seed Market Monitoring Information Release Platform (http://202.127.45.18/) - China. UAV remote sensing data is the result of further processing after being obtained from field experiments. Weather data comes from meteorological equipment at various agricultural experimental bases and meteorological observation stations of the China Meteorological Administration.</p> <p>The acquisition and processing of data are described in the relevant part of the manuscript.</p> <p>This dataset will be continuously updated in the future to help breeding work be carried out efficiently and accelerate the breeding process of excellent varieties.</p>
Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction
<p>In hard winter wheat breeding, the evaluation of end-use quality is expensive and time-consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome-wide association mapping (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard winter wheat. Advanced breeding lines (n=462) from 2015-2017 were genotyped using genotyping-by-sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time; most of which were within or in tight linkage to glutenin and gliadin loci, and could be suitable for marker-assisted breeding. Candidate genes for newly associated loci are phosphate-dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits; thus we evaluated various GS models for end-use quality traits. As a baseline, univariate GS models had 0.25 to 0.55 prediction accuracy in cross-validation and from 0 to 0.41 in forward-prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end-use quality.</p>
Data from: An assessment of wheat yield sensitivity and breeding gains in hot environments
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Data from: Introgression of chromosome segments from multiple alien species in wheat breeding lines with wheat streak mosaic virus resistance
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Ms3 dominant genetic male sterility for wheat improvement with molecular breeding
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Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction
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