scholarly journals Multi-Trait and Trait-Assisted Genomic Prediction of Winter Wheat Quality Traits Using Advanced Lines from Four Breeding Cycles

2019 ◽  
Keyword(s):  
Winter Wheat ◽  
Genomic Prediction ◽  
Wheat Quality ◽  
Quality Traits ◽  
Breeding Cycles

scholarly journals Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat

2021 ◽  
Author(s):  
Meriem Aoun ◽  
Arron Carter ◽  
Yvonne A. Thompson ◽  
Brian Ward ◽  
Craig F. Morris
Keyword(s):  
Winter Wheat ◽  
Genomic Prediction ◽  
Quality Traits ◽  
End Use

scholarly journals Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 669 ◽  
Author(s):  
Peter S. Kristensen ◽  
Just Jensen ◽  
Jeppe R. Andersen ◽  
Carlos Guzmán ◽  
Jihad Orabi ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Prediction ◽  
Association Studies ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Breeding Programs ◽  
Genome Wide ◽  
Flour Yield

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype–environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.

scholarly journals Genomic prediction for malting quality traits in practical barley breeding programs

2020 ◽  
Author(s):  
Pernille Sarup ◽  
Vahid Edriss ◽  
Nanna Hellum Kristensen ◽  
Jens Due Jensen ◽  
Jihad Orabi ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Spring Barley ◽  
Malting Quality ◽  
Breeding Cycle ◽  
Quality Traits ◽  
Training Population ◽  
Barley Breeding ◽  
Breeding Cycles

AbstractGenomic prediction can be advantageous in barley breeding for traits such as yield and malting quality to increase selection accuracy and minimize expensive phenotyping. In this paper, we investigate the possibilities of genomic selection for malting quality traits using a limited training population. The size of the training population is an important factor in determining the prediction accuracy of a trait. We investigated the potential for genomic prediction of malting quality within breeding cycles with leave one out (LOO) cross-validation, and across breeding cycles with leave set out (LSO) cross-validation. In addition, we investigated the effect of training population size on prediction accuracy by random two, four, and ten-fold cross-validation. The material used in this study was a population of 1329 spring barley lines from four breeding cycles. We found medium to high narrow sense heritabilities of the malting traits (0.31 to 0.65). Accuracies of predicting breeding values from LOO tests ranged from 0.6 to 0.9 making it worth the effort to use genomic prediction within breeding cycles. Accuracies from LSO tests ranged from 0.39 to 0.70 showing that genomic prediction across the breeding cycles were possible as well. Accuracy of prediction increased when the size of the training population increased. Therefore, prediction accuracy might be increased both within and across breeding cycle by increasing size of the training population

scholarly journals Effect of genotype and environment on wheat quality

Genetika ◽  
2009 ◽  
Vol 41 (3) ◽  
pp. 247-253 ◽  
Author(s):  
Veselinka Zecevic ◽  
Desimir Knezevic ◽  
Jelena Boskovic ◽  
Milomirka Madic
Keyword(s):  
Winter Wheat ◽  
Field Trial ◽  
Research Centre ◽  
Wheat Quality ◽  
Quality Traits ◽  
Wheat Cultivars ◽  
Gluten Content ◽  
Winter Wheat Cultivars ◽  
Small Grains ◽  
Sedimentation Value

Five winter wheat cultivars created in Small Grains Research Centre of Kragujevac (Ana Morava, Toplica, Vizija, Takovcanka and Lazarica) were grown at the macro field trial in three locations (Kragujevac, Sombor and Backa Topola) during three years (2004-2006). Influence of genetic and agro-ecological conditions of locations on wheat quality components (sedimentation value and wet gluten content) was investigated. The analysis of variance suggested there were highly significant differences among genotypes (G), investigated years (Y) and locations (L) for sedimentation value and wet gluten content. Apart from individual influence of the factors, their interactions (G x Y, G x L, Y x L, G x Y x L) were also high significant for both investigated traits. In average the highest sedimentation value (40.6 ml) and wet gluten content (31.85 %) established at Backa Topola locality. The highest value of all investigated cultivars and localities established at cultivar Vizija (45.3 ml) in Backa Topola, while the lowest at Lazarica (31.7 ml) in Sombor. The highest wet gluten content was measured at Backa Topola locality by cultivar Toplica (38.53 %). In this investigation Backa Topola locality was favourable for both investigated quality traits.

Probability of Wheat Quality Traits Falling within Acceptable Limits

Crop Science ◽  
1994 ◽  
Vol 34 (4) ◽  
pp. 866-869 ◽  
Author(s):  
K. M. Eskridge ◽  
C. J. Peterson ◽  
A. W. Grombacher
Keyword(s):  
Wheat Quality ◽  
Quality Traits

Cultivar-by-Environment Interactions for Wheat Quality Traits in Semiarid Conditions

2001 ◽  
Vol 78 (3) ◽  
pp. 363-367 ◽  
Author(s):  
Novica Mladenov ◽  
Novo Przulj ◽  
Nikola Hristov ◽  
Veselinka Djuric ◽  
Milivoje Milovanovic
Keyword(s):  
Wheat Quality ◽  
Quality Traits ◽  
Semiarid Conditions
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