Genomic prediction reveals unexplored variation in grain protein and lysine content across a vast winter wheat genebank collection
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ABSTRACT:
SUBMITTER: Berkner M
PROVIDER: S-EPMC10808176 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Subsampling a reduced number of accessions from ex situ genebank collections, known as core collections, is a widely applied method for the investigation of stored genetic diversity and for an exploitation by breeding and research. Optimizing core collections for genome-wide association studies could potentially maximize opportunities to discover relevant and rare variation. In the present study, eight strategies to sample core collections were implemented separately for two traits, namely susceptibility to yellow rust and stem lodging, on about 6,300 accessions of winter wheat (Triticum aestivum L.). Each strategy maximized different parameters or emphasized another aspect of the collection; the strategies relied on genomic data, phenotypic data or a combination thereof. The resulting trait-customized core collections of eight different sizes, covering the range between 100 and 800 accession samples, were analyzed based on characteristics such as population stratification, number of duplicate genotypes and genetic diversity. Furthermore, the statistical power for an association study was investigated as a key criterion for comparisons. While sampling extreme phenotypes boosts the power especially for smaller core collections of up to 500 accession samples, maximization of genetic diversity within the core collection minimizes population stratification and avoids the accumulation of less informative duplicate genotypes when increasing the size of a core collection. Advantages and limitations of different strategies to create trait-customized core collections are discussed for different scenarios of the availability of resources and data.
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Project description:Wheat (Triticum aestivum L.) is a staple food crop for the global human population, and thus wheat breeders are consistently working to enhance its yield worldwide. In this study, we utilized a sub-set of Indian wheat mini core germplasm to underpin the genetic architecture for seed shape-associated traits. The wheat mini core subset (125 accessions) was genotyped using 35K SNP array and evaluated for grain shape traits such as grain length (GL), grain width (GW), grain length, width ratio (GLWR), and thousand grain weight (TGW) across the seven different environments (E1, E2, E3, E4, E5, E5, E6, and E7). Marker-trait associations were determined using a multi-locus random-SNP-effect Mixed Linear Model (mrMLM) program. A total of 160 non-redundant quantitative trait nucleotides (QTNs) were identified for four grain shape traits using two or more GWAS models. Among these 160 QTNs, 27, 36, 38, and 35 QTNs were associated for GL, GW, GLWR, and TGW respectively while 24 QTNs were associated with more than one trait. Of these 160 QTNs, 73 were detected in two or more environments and were considered reliable QTLs for the respective traits. A total of 135 associated QTNs were annotated and located within the genes, including ABC transporter, Cytochrome450, Thioredoxin_M-type, and hypothetical proteins. Furthermore, the expression pattern of annotated QTNs demonstrated that only 122 were differentially expressed, suggesting these could potentially be related to seed development. The genomic regions/candidate genes for grain size traits identified in the present study represent valuable genomic resources that can potentially be utilized in the markers-assisted breeding programs to develop high-yielding varieties.
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Project description:Grain protein content (GPC), is one of the most important trait in wheat and its characterized by a very complex genetic control. The identification of wheat varieties with high GPC (HGPC), as well as the characterization of central enzymes involved in these processes, are important for more sustainable agricultural practices. In this study, we focused on Glutamine synthetase (GS) as a candidate to study GPC in wheat. We analyzed GS expression and its enzymatic activity in different tissues and phenological stages in 10 durum wheat genotypes with different GPC. Although each genotype performed quite differently from the others, both because their genetic variability and their adaptability to specific environmental conditions, the highest GS activity and expression were found in genotypes with HGPC and vice versa the lowest ones in genotypes with low GPC (LGPC). Moreover, in genotypes contrasting in GPC bred at different nitrogen regimes (0, 60, 140 N Unit/ha) GS behaved differently in diverse organs. Nitrogen supplement increased GS expression and activity in roots of all genotypes, highlighting the key role of this enzyme in nitrogen assimilation and ammonium detoxification in roots. Otherwise, nitrogen treatments decreased GS expression and activity in the leaves of HGPC genotypes and did not affect GS in the leaves of LGPC genotypes. Finally, no changes in GS and soluble protein content occurred at the filling stage in the caryopses of all analyzed genotypes.
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Project description:A total of 323 paired grain and grain dust samples (particle size <1650 μm) were collected from combines at harvest (56%), on-farm bins (28%), and experimental minibins seeded with an ochratoxin A (OTA)/Penicillium verrucosum hot spots (15%) of which >98% were soft red winter wheat. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to detect 21 mycotoxins, including deoxynivalenol (DON) and its plant-conjugated form, deoxynivalenol 3-β-d-glucoside (DON 3-Glc). Except for DON 3-Glc, all mycotoxin concentrations found in grain dust were higher than in grain (p < 0.0030). Pearson correlation coefficients and two-variable regression show a significant (p < 0.0001) linear relationship between the mycotoxin content in grain and that in grain dust with 19 toxins. In only five mycotoxins (DON, OTA, ochratoxin B, citrinin, and enniatin A1), more than 82% of the variation in the data is explained by the two-variable regression model. Because of its higher mean concentration and detection frequency, only DON produced a strong relationship (p < 0.0001, r 2 = 0.949) with low root-mean-square error (RMSE) (293.41 ng/g). The results suggest that modeling levels in grain based upon levels in grain dust can be used to estimate DON in grain bulk.
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Project description:Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model's prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.
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Project description:BackgroundGenebanks worldwide are transforming into biodigital resource centers, providing access not only to the plant material itself but also to its phenotypic and genotypic information. Adding information for relevant traits will help boost plant genetic resources' usage in breeding and research. Resistance traits are vital for adapting our agricultural systems to future challenges.FindingsHere we provide phenotypic data for the resistance against Blumeria graminis f. sp. tritici, the causal agent of wheat powdery mildew-a substantial risk to our agricultural production. Using a modern high-throughput phenotyping system, we infected and photographed a total of 113,638 wheat leaves of 7,320 winter wheat (Triticum aestivum L.) plant genetic resources of the German Federal ex situ Genebank for Agricultural and Horticultural Crops and 154 commercial genotypes. We quantified the resistance reaction captured by images and provide them here, along with the raw images.ConclusionThis massive amount of phenotypic data, combined with already published genotypic data, also provides a valuable and unique training dataset for the development of novel genotype-based predictions as well as mapping methods.
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Project description:Grain quality traits determine the classification of registered wheat (Triticum aestivum L.) varieties. Although environmental factors and crop management practices exert a considerable influence on wheat quality traits, a significant proportion of the variance is attributed to the genetic factors. To identify the underlying genetic factors of wheat quality parameters viz., grain protein content (GPC), grain starch content (GSC), and grain hardness (GH), we evaluated 372 diverse European wheat varieties in replicated field trials in up to eight environments. We observed that all of the investigated traits hold a wide and significant genetic variation, and a significant negative correlation exists between GPC and GSC plus grain yield. Our association analyses based on 26,694 high-quality single nucleotide polymorphic markers revealed a strong quantitative genetic nature of GPC and GSC with associations on groups 2, 3, and 6 chromosomes. The identification of known Puroindoline-b gene for GH provided a positive analytic proof for our studies. We report that a locus QGpc.ipk-6A controls both GPC and GSC with opposite allelic effects. Based on wheat's reference and pan-genome sequences, the physical characterization of two loci viz., QGpc.ipk-2B and QGpc.ipk-6A facilitated the identification of the candidate genes for GPC. Furthermore, by exploiting additive and epistatic interactions of loci, we evaluated the prospects of predictive breeding for the investigated traits that suggested its efficient use in the breeding programs.
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Project description: Not available
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