scholarly journals Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement

2019 ◽  
Author(s):  
Xiaowei Hu ◽  
Brett F. Carver ◽  
Carol Powers ◽  
Liuling Yan ◽  
Lan Zhu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Protein Content ◽  
Genomic Selection ◽  
Prediction Accuracy ◽  
Wheat Variety ◽  
Wheat Protein ◽  
Response To Selection ◽  
Training Population ◽  
Sedimentation Volume

AbstractThe genomic revolution opened up the possibility for predicting un-tested phenotypes in schemes commonly referred as genomic selection (GS). Considering the practicality of applying GS in the line development stage of a hard red winter (HRW) wheat variety development program (VDP), effectiveness of GS was evaluated by prediction accuracy, as well as by the response to selection across field seasons that demonstrated challenges for crop improvement under significant climate variability. Important breeding targets for HRW wheat improvement in the southern Great Plains of USA, including Grain Yield, Kernel Weight, Wheat Protein content, and Sodium Dodecyl Sulfate (SDS) Sedimentation Volume as a rapid test for predicting bread-making quality, were used to estimate GS’s effectiveness across harvest years from 2014 (drought) to 2016 (normal). In general, nonparametric algorithms RKHS and RF produced higher accuracies in both same-year/environment cross validations and cross-year/environment predictions, for the purpose of line selection in this bi-parental doubled haploid (DH) population. Further, the stability of GS performance was greatest for SDS Sedimentation Volume but least for Wheat Protein content. To ensure long-term genetic gain, our study on selection response suggested that across this sample of environmental variability, and though there are cases where phenotypic selection (PS) might be still preferential, training conducted under drought stress or in suboptimal conditions could still provide an encouraging prediction outcome, when selection decisions were made in normal conditions. However, it is not advisable to use training information collected from a normal field season to predict trait performance under drought conditions. Further, the superiority of response to selection was most evident if the training population can be optimized.Core IdeasPrediction performance for winter wheat grain yield and end-use quality traits.Prediction accuracy evaluated by cross validations significantly overestimated.Non-parametric algorithms outperform, when considering cross-year predictions.Strategically designing training population improves response to selection.Response to selection varied across growing seasons/environments.

scholarly journals Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Dennis N. Lozada ◽  
R. Esten Mason ◽  
Jose Martin Sarinelli ◽  
Gina Brown-Guedira
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Population Size ◽  
Genomic Selection ◽  
Agronomic Traits ◽  
Response To Selection ◽  
Training Population ◽  
Soft Red Winter Wheat ◽  
Selection For ◽  
Red Winter Wheat

Abstract Background Genomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections. Results Genomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64–70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between − 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was “superior” to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone. Conclusions Our results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

scholarly journals Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement

2019 ◽  
Vol 12 (3) ◽  
pp. 180090 ◽  
Author(s):  
Xiaowei Hu ◽  
Brett F. Carver ◽  
Carol Powers ◽  
Liuling Yan ◽  
Lan Zhu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Wheat Variety ◽  
Response To Selection ◽  
Winter Wheat Variety ◽  
Selection For

scholarly journals Some correlations between parameters of winter wheat technological quality

2006 ◽  
Vol 54 (1) ◽  
pp. 23-30
Author(s):  
Jindřiška Kučerová
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Protein Content ◽  
Negative Correlation ◽  
Weather Conditions ◽  
Loaf Volume ◽  
Bread Making ◽  
Wheat Varieties ◽  
Baking Quality ◽  
Technological Quality

The results of three-year trials (1999 to 2001) conducted with six winter wheat varieties in which was studied the grain yield and parameters of technological quality. Varieties of wheat come from four different localities of the Czech Republic. The most favourable weather conditions, a lot of precipitation and high temperature in the course of ripening from three years were proved in the year 2000. The best grain yield were in 2001 (average of sites 8.84 t/ha) and variety Semper, worst quality, had the highest grain yield of 9.17 t/ha, the least grain yield had Sulamit, best quality (7.94 t/ha). The laboratory analysis revealed negative correlation between grain yield and baking quality. The number of statistically highly significant correlations among bread-making quality parameters too.The negative correlation was of grain yield and grain volume mass (P < 0.05), Zeleny test and protein content taken as a whole for three years (P < 0.01). The correlation of loaf volume, which is the traits of baking quality and Zeleny test (r = 0.6016**), protein content (r = 0.5932**), dough stability (r = 0.2898**) and flour water absorption (r = 0.3632**) was positive (P < 0.01).

Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy

Crop Science ◽  
2017 ◽  
Vol 57 (3) ◽  
pp. 1325-1337 ◽  
Author(s):  
Alexandra Duhnen ◽  
Amandine Gras ◽  
Simon Teyssèdre ◽  
Michel Romestant ◽  
Bruno Claustres ◽  
...  
Keyword(s):  
Protein Content ◽  
Genomic Selection ◽  
Prediction Accuracy ◽  
Seed Protein ◽  
Breeding Program ◽  
Seed Protein Content ◽  
Selection For ◽  
Program Data

scholarly journals The content of Fusarium mycotoxins, grain yield and quality of winter wheat cultivars under organic and conventional cropping systems

2008 ◽  
Vol 54 (No. 9) ◽  
pp. 395-402 ◽  
Author(s):  
M. Váňová ◽  
K. Klem ◽  
P. Míša ◽  
P. Matušinsky ◽  
J. Hajšlová ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Protein Content ◽  
Crop Rotation ◽  
Cropping System ◽  
Kernel Weight ◽  
National Standard ◽  
Bread Making ◽  
Volume Weight ◽  
Significant Difference

Nine cultivars of winter wheat were compared in organic and conventional crop rotation systems. Bread-making quality was evaluated using three parameters [thousand-kernel weight (TKW) in g, volume weight in g/l, protein content in %]. Grain yield, TKW and protein content of winter wheat in organic cropping system were significantly lower as compared to any intensity in conventional cropping system. However, clover as a preceding crop to winter wheat in organic crop rotation ensured a sufficient amount of nitrogen for grain yield, which was 6.72 t/ha on average of the three years. The requirement of the Czech national standard for bread wheat minimum value of protein content (11.5%) was met in conventional crop rotation in all cases. Average value of protein content in organic crop rotation met this limit too, but it was below the required value in two cases. The required value (760 g/l) of volume weight was met in majority of cases in organic crop rotation. The following species of the genus <I>Fusarium were</I> found: <I>F. culmorum, F. graminearum, F. poae</I> and <I>F. avenaceum</I>. All samples were screened for the content of deoxynivalenol (DON). There was no significant difference in the DON content between winter wheat grain from organic crop rotation and conventional crop rotation at high intensity.

scholarly journals Technology of nutriating winter wheat varieties in variety-soil-fertilizer system

2021 ◽  
Vol 244 ◽  
pp. 02040
Author(s):  
Bakhtiyor Atoev ◽  
Jandos Kaypnazorov ◽  
Mukhayyo Egamberdieva ◽  
Samad Makhammadiev ◽  
Murod Karimov ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Grain Quality ◽  
Wheat Variety ◽  
Dry Mass ◽  
Climatic Conditions ◽  
Wheat Varieties ◽  
Fertilizer System ◽  
Soil Fertilizer ◽  
Winter Wheat Varieties

In this article, the reaction of winter wheat varieties to fertilizers in irrigated soils in the varietal-soil-fertilizer system was studied and a feeding system was developed and recommended for each soil-climatic conditions and varieties. Appropriate fertilizer standards have been developed for each wheat variety, which have increased the germination, weeding, accumulation, tuberization, spike formation, dry mass accumulation, grain quality, and yield structure and yield of winter wheat. N250P200K200 kg/ha was obtained from Polovchanka variety of winter wheat at the rate of N250P200K200 kg/ha used in irrigated brown meadow soils, while in typical irrigated gray soils the yield of winter wheat was higher than N250P200K200 kg/ha of pure wheat with N250P200K200 kg/ha. Grain yield was 80.18 tons/ha from Tanya variety, 76.38 tons/ha from Krasnodar-99 variety and 82.32 tons/ha from Polovchanka variety under N200P150K150 kg/ha. Under the influence of the same optimal fertilizer standards, the growth and development of winter wheat, nutrient accumulation, and grain yield and grain quality are improved, and the efficiency of fertilizers is increased.

scholarly journals Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

2020 ◽  
Vol 10 (7) ◽  
pp. 2265-2273 ◽  
Author(s):  
Ahmad H. Sallam ◽  
Emily Conley ◽  
Dzianis Prakapenka ◽  
Yang Da ◽  
James A. Anderson
Keyword(s):  
Population Structure ◽  
Protein Content ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Predictive Ability ◽  
Training Population ◽  
Percentage Points ◽  
And Training ◽  
Fold Cross Validation ◽  
Single Snps

The use of haplotypes may improve the accuracy of genomic prediction over single SNPs because haplotypes can better capture linkage disequilibrium and genomic similarity in different lines and may capture local high-order allelic interactions. Additionally, prediction accuracy could be improved by portraying population structure in the calibration set. A set of 383 advanced lines and cultivars that represent the diversity of the University of Minnesota wheat breeding program was phenotyped for yield, test weight, and protein content and genotyped using the Illumina 90K SNP Assay. Population structure was confirmed using single SNPs. Haplotype blocks of 5, 10, 15, and 20 adjacent markers were constructed for all chromosomes. A multi-allelic haplotype prediction algorithm was implemented and compared with single SNPs using both k-fold cross validation and stratified sampling optimization. After confirming population structure, the stratified sampling improved the predictive ability compared with k-fold cross validation for yield and protein content, but reduced the predictive ability for test weight. In all cases, haplotype predictions outperformed single SNPs. Haplotypes of 15 adjacent markers showed the best improvement in accuracy for all traits; however, this was more pronounced in yield and protein content. The combined use of haplotypes of 15 adjacent markers and training population optimization significantly improved the predictive ability for yield and protein content by 14.3 (four percentage points) and 16.8% (seven percentage points), respectively, compared with using single SNPs and k-fold cross validation. These results emphasize the effectiveness of using haplotypes in genomic selection to increase genetic gain in self-fertilized crops.

PRODUCTIVITY AND QUALITY OF WINTER WHEAT VARIETY GRAIN IN CONDITIONSOF MIDDLE VOLGA REGION STEPPE

2016 ◽  
Vol 1 (4) ◽  
pp. 25-28
Author(s):  
Маслова ◽  
Galina Maslova ◽  
Лавренникова ◽  
Olga Lavrennikova
Keyword(s):  
Winter Wheat ◽  
Protein Content ◽  
Wheat Variety ◽  
Weather Conditions ◽  
High Rate ◽  
Volga Region ◽  
Middle Volga Region ◽  
Winter Wheat Variety ◽  
Middle Volga

The purpose of research is to increase the productivity and quality of winter wheat variety trials competitive grain varieties, depending on weather conditions. Studied varieties: Povolzhskaya 86, Kinel’skaya 8, Povolzhskaya niva, Konstantinovskaya. Varieties cultivated by traditional technology, the fresh pair. The data for the 2012-2015 biennium. Grain quality was assessed a number of indicators that characterize its physico-chemical and technological properties: nature grain, vitreous, the strength of flour, protein content, adhesive wine. The maximum value in terms of nature is characterized by grain corn all classes in 2013 and 2014 (782-816 g/l). The high rate of vitreous grains observed in 2012, 2014, 2015 (72-92%). Good data on the same data obtained for the protein content, wet gluten flour strength. It was found that the environmental conditions during the formation and ripening of grain in years of research have a significant impact on productivity and ka-honors winter wheat. The study group of varieties set up in the laboratory breeding and seed, has a rapid rate of accumulation of dry matter. They are adapted to the formation of us, full grain in the conditions of unstable arid climate of the Middle Volga region.

Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression

2002 ◽  
Vol 139 (3) ◽  
pp. 307-318 ◽  
Author(s):  
P. M. HANSEN ◽  
J. R. JØRGENSEN ◽  
A. THOMSEN
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Least Squares ◽  
Protein Content ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Spring Barley ◽  
Grain Protein Content ◽  
Least Squares Regression ◽  
Reflectance Measurements

By providing both spatial and temporal information remote sensing may function as an important source of data for site-specific crop management. This technology has been used for nitrogen application strategies to obtain optimum yield and grain quality. Here, the objective was to use early repeated remotely sensed multi-spectral data to predict grain yield and quality for winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). The crops were sown with two different seeding rates and a wide range of nitrogen strategies were applied. Multi-way partial least squares regression (N-PLS) was used to predict grain yield and protein content. The results were compared with unfold-PLS1 and PLS1 using reflectance data from the last measurement day. Both single reflectance wavelengths and selected vegetation indices were used simultaneously. The results reveal that all models can make a good prediction of yield in both crops with unfold-PLS1 and N-PLS as the best. However, estimation of grain protein content at harvest was very poorly determined in barley, as no relation between the reflectance measurements and barley protein content was obtained. The relation between reflectance measurements and protein content was slightly better in wheat, where especially N-PLS improved the prediction of grain protein content. The overall conclusion of the present experiments is that data from repeated measurements of reflectance used in multi-way partial least squares regression before heading improved the prediction of grain yield and protein content in wheat and barley.

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