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34 results for “wheat (Triticum aestivum)”
ECOBREED WP2 T2.1 Winter common wheat (Triticum aestivum) - Late maturity group
<p>Description of the winter common wheat (Triticum aestivum) late maturity group nursery. Tested within T2.1 in Germany (by Secobra), Czech Republic (by Selgen) and Slovakia (by NPPC) in 2019/2020.</p>
ECOBREED WP2 T2.3 Wheat (Triticum aestivum) - Allelopathic activity nursery
<p>Description of the common wheat (Triticum aestivum) allelopathic activity nursery. Tested within T2.3 in Spain (by UVIGO).</p>
Fig. 4 in Some Changes In Oxidative Processes In The Organs Of Wheat Seedlings (Triticum Aestivum L.) In The Presence Of Antimycin A
Fig. 4. The electrophoretic activity of catalase in different parts of the first leaves (2, 4, 6 - the apical parts, 1, 3, 5 – basal parts).
Fig. 1 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 1. The rate of superoxide (O2˙ˉ) production (µmol/h) in the etiolated first leaves at the early (from 4th to 5th days) and late (from 7th to 8th days) stages of seedlings development.
Fig. 2 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 2. Catalase activity in the etiolated first leaves at the early (from 4th to 5th days) and late (from 7th to 8th days) stages of seedling development.
Fig. 4 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 4. Catalase activity in the etiolated first leaves at the (A) early (from 4th to 5th days) and (B) late (from 7th to 8th days) stages of seedling development (C ‒ control 26oC; E ‒ 26oC → 32oC; E1 ‒ 32o→42oC; E2 ‒ 26oC → 42oC).
Fig. 3 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 3. The rate of superoxide (O2˙ˉ) production (µmol/h) in the etiolated first leaves at the (A) early (from 4th to 5th days) and (B) late (from 7th to 8th days) stages of seedlings development (C ‒ control 26oC; E ‒ 26oC → 32oC; E1 ‒ 32o→42oC; E2 ‒ 26oC → 42oC).
Fig. 1 in Rice leaf folder Cnaphalocrocis medinalis (Lepidoptera: Crambidae) on wheat (Triticum aestivum; Poales: Poaceae) in India
Fig. 1. Infestation of wheat by larvae and pupae of Cnaphalocrocis medinalis, and genitalic and morphological characters of adults. A: Damaged leaves with larvae; B: larva in rolled leaf; C: close-up of larva; D: pupa on leaf; E: close-up of pupa; F: adult male; G: male aedeagus; H: female genitalia; I: male genitalia, dorsal. J: male genitalia, ventral. a, androconial hairs; aa, anterior apophysis; bc, bursa copulatrix; c, cornuti; db, ductus bursae; pa, posterior apophysis; s, signum.
Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
<p>The mapping population consists of 166 recombinant inbred lines (RILs) derived from a cross between HD3086 and HI1500.</p> <p><strong>Phenotypic data</strong><br>The RILs population along with parents were evaluated under four conditions namely timely sown irrigation (TSIR) taken as control, timely sown restricted irrigation (TSRI), late sown irrigation (LSIR), and late sown restricted irrigation (LSRI) conditions at Delhi, and under restricted irrigation condition at Indore. From each plot, 20 random spikes were harvested and spikes from each plot were threshed separately. While cleaning, care was taken to prevent metal and dust contamination. The grain iron concentration (GFeC) and grain zinc concentration (GZnC) were measured using Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000 M/s Oxford Inc, USA). The thousand kernel weight (TKW) was recorded by counting 1000 grains manually and weighted with an electronic balance.</p> <p><strong>Genotypic data</strong><br>DNA was extracted from 21 days old seedlings using CTAB method (Murray and Thompson, 1980). Genomic DNA quality was determined using 0.8% agarose gel electrophoresis with λ DNA as the standard and quantified using nanodrop. The 35K SNP Axiom breeders' array was used for genotyping of parents and the RILs population.</p>
Genome-wide investigation and transcriptional profiling of the oxidosqualene cyclase (OSC) genes in wheat (Triticum aestivum L.)
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Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
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Data from: Mining the stable quantitative trait loci for agronomic traits in wheat (Triticum aestivum L.) based on an introgression line population
<p><span><span><b>Background</b>: Human demand for wheat will continue to increase together with the continuous global population growth. Agronomic traits in wheat are susceptible to environmental conditions. Therefore, in breeding practice, priority is given to QTLs of agronomic traits that can be stably detected across multiple environments and over many years.</span></span></p> <p><span><span><b>Results: </b>In this study, QTL analysis was conducted for eight agronomic traits using an introgression line population across eight environments (drought stressed and well-watered) for five years. In total, 44 additive QTLs for the above agronomic traits were detected on 15 chromosomes. Among these, <i>qPH-6A</i>, <i>qHD-1A</i>, <i>qSL-2A</i>, <i>qHD-2D</i> and<i> qSL-6A</i> were detected across seven, six, five, five and four environments, respectively. The means in the phenotypic variation explained by these five QTLs were 12.26%, 9.51%, 7.77%, 7.23%, and 8.49%, respectively. </span></span></p> <p><b>Conclusions: </b>We identified five stable QTLs, which includes <i>qPH-6A</i>, <i>qHD-1A</i>, <i>qSL-2A</i>, <i>qHD-2D</i> and<i> qSL-6A</i>. They play a critical role in wheat agronomic traits. One of the dwarf genes<i> Rht14</i>, <i>Rht16</i>, <i>Rht18</i> and <i>Rht25</i> on chromosome 6A might be the candidate gene for <i>qPH-6A</i>. The <i>qHD-1A</i> and <i>qHD-2D</i> were novel stable QTLs for heading date and they differed from known vernalization genes, photoperiod genes and earliness per se genes.</p>
Data from: Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (Triticum aestivum L.) using genome-wide association study
<p>The study material in GWAS panel with 280 common bread wheat genotypes was selected from All India Coordinated Research Project on Wheat and Barley to map the genomic regions responsible for enhanced Grain Zinc Content (GZnC), Grain Iron Content (GZnC) and Thousand Kernel weight (TKW).</p> <p><strong>Phenotypic data:</strong></p> <p>The GWAS panel was evaluated at five different environments: E1-University of Agricultural Sciences, research farm, Dharwad (15°29'20.71"N, 74°59'3.35"E, 750m AMSL), E2-ICAR- Indian Agricultural Research Institute, New Delhi (28°38′30.5″N, 77°09′58.2″E, 228 m AMSL), E3-Indian Agricultural Research Institute, Jharkhand (24°16'58.4"N, 85°21'16.1"E, 651m AMSL), E4-ICAR-Indian Institute of Wheat and Barley, Karnal (29°41'8.2644''N, 76°59'25.9692''E, 250m AMSL), and E5-Punjab Agricultural University, Ludhiana (30o54' N, 75o48'E, 247m AMSL). Around 20 g of grain sample from each genotype were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000; Oxford Instruments plc, Abingdon, United Kingdom) calibrated with glass beads-based values. To record TKW, the Numigral grain counter was used to count the grain number, the reading was set at 1000 grains and the weight of the grains was recorded in grams with an electronic balance. The GFeC, GZnC were expressed as milligram per kilogram (mg/kg), GPC in percentage (%), TKW in grams (gms).</p> <p><strong>Genotypic data:</strong></p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of 21 days-old seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 14,790 high-quality SNPs was used in GWAS analysis. The detailed information of the methods and software used, data analysis and GWAS is available at DOI: 10.1038/s41598-022-15992-z.</p>
Data from: Genome-wide association study for grain yield and component traits in wheat (Triticum aestivum L.)
<p>The study material in GWAS panel with 280 common bread wheat genotypes was selected from All India Coordinated Research Project on Wheat and Barley to map the genomic regions governing days to heading (DH), grain filling duration (GFD), grain number per spike (GNPS), grain weight per spike (GWPS), plant height (PH), and grain yield (GY).</p> <p><strong>Phenotypic data:</strong></p> <p>The GWAS panel was evaluated at five different environments during the 2020-21 <em>Rabi</em> (winter) season: E1-University of Agricultural Sciences, research farm, Dharwad (15°29'20.71"N, 74°59'3.35"E, 750m AMSL), E2-ICAR-Indian Agricultural Research Institute, New Delhi (28°38′30.5″N, 77°09′58.2″E, 228 m AMSL), E3-Indian Agricultural Research Institute, Jharkhand (24°16'58.4"N, 85°21'16.1"E, 651m AMSL), E4-ICAR-Indian Institute of Wheat and Barley, Karnal (29°41'8.2644''N, 76°59'25.9692''E, 250m AMSL), and E5-Punjab Agricultural University, Ludhiana (30o54' N, 75o48'E, 247m AMSL). The genotypes were planted in an augmented block design along with repeated checks (DBW187, MACS6222, WH1124, and WH1142). All the genotypes of a GWAS panel were phenotyped for six quantitative traits i.e. GWPS (gm), GY (gm), PH (cm) at five locations, GFD (days), DH (days) at four locations and GNPS (number) at two locations. Phenotypic data were analyzed using the R package 'augmentedRCBD'</p> <p><strong>Genotypic data:</strong></p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of 21 days-old seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 14,790 high-quality SNPs was used in GWAS analysis. </p>
Genomic Selection Paves Way for the Identification of Rust Disease Resistant Genotypes in Bread Wheat (Triticum aestivum).
<p><span>In the last two decades, genomic prediction (GP) or Genomic Selection (GS) methods have been widely adopted in various plant and animal breeding programs globally. GP/GS is a promising method that employs genomic markers to calculate genomic-estimated breeding values (GEBVs) to select best individuals. To evaluate the performance of different genomic selection (GS) models, we examined six different models namely, ridge regression (RR), least absolute shrinkage and selection operator (LASSO), genomic best linear unbiased prediction (GBLUP), elastic net (EN), reproducing kernel Hilbert spacing (RKHS), and random forest (RF) models, for seedling and adult plant resistance to leaf, stem and stripe rust of wheat using a panel of 347 wheat germplasm accessions. The GBLUP and RF models performed noticeably better than the other GS models, with mean predictive abilities of 0.5 and 0.4 for seedling resistance and 0.4 and 0.3 for adult plant resistance (APR) for leaf and stem rust, respectively. Unfortunately, except for a few environments, the performance of GP models in the current study is quite low for stripe rust for both seedling and APR. The outcomes of this study revealed the capability of GP to be applied for breeding initiatives aimed at developing wheat varieties resistant to rust diseases. </span><span>Moreover, based on favorable allele analysis we also identified a total of 2 lines (CRP-165/42, HGP1-470) that showed resistance to most of the pathotypes at seedling and adult plant stage to all three rusts.<strong><span> </span></strong>These lines can serve as valuable resources for future breeding programs focused on rust resistance.</span></p> <p><strong><span>Keywords: </span></strong><span>GS;</span><strong><span> </span></strong><span>GEBVs; leaf rust; stem rust; stripe rust; seedling resistance; APR</span></p>
Data from: Genome-wide association study for grain yield and component traits in wheat (Triticum aestivum L.)
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Genetic variation, genotypic and phenotypic correlation, heritability, and path coefficient analysis of yield and yield related traits in bread wheat (Triticum aestivum L.) varieties at Gitilo Dale, western Ethiopia
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Data from: Mining the stable quantitative trait loci for agronomic traits in wheat (Triticum aestivum L.) based on an introgression line population
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Data from: Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (Triticum aestivum L.) using genome-wide association study
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Optical maps refine the bread wheat Triticum aestivum cv Chinese Spring genome assembly
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