scholarly journals The effects on grain quality traits of a grain serpin protein and the VPM1 segment in southern Australian wheat breeding

2008 ◽  
Vol 59 (10) ◽  
pp. 883 ◽  
Author(s):  
Karen Cane ◽  
P. J. Sharp ◽  
H. A. Eagles ◽  
R. F. Eastwood ◽  
G. J. Hollamby ◽  
...  
Keyword(s):  
Grain Quality ◽  
Protein A ◽  
Quality Parameters ◽  
Wheat Breeding ◽  
Relationship Matrix ◽  
Quality Traits ◽  
Breeding Programs ◽  
Active Selection ◽  
Australian Wheat ◽  
Milling Yield

Production of wheat of sufficient quality to meet market demands is an ongoing agricultural challenge. Identification and evaluation of alleles of genes affecting quality parameters enables breeders to improve their germplasm by active selection towards specific allele combinations. Using a large dataset obtained from southern Australian wheat breeding programs, and including a relationship matrix in the analysis to minimise bias, we re-evaluated the effects of high- and low-molecular-weight glutenin alleles and puroindoline alleles on the grain quality parameters Rmax, dough extensibility, dough development time, flour water absorption, and milling yield and found that estimated effects were in close agreement with those from earlier analyses without a relationship matrix. We also evaluated, for the first time, the effects on the same quality parameters of 2 alleles (wild-type and null) of a defence grain protein, a serpin located on chromosome 5B. In addition, we assessed the effect of the VPM1 alien segment. The serpin null allele significantly reduced milling yield by ~0.4 g of flour per 100 g of grain milled across different germplasm sources and flour protein levels. In Australian germplasm, the origin of this allele was traced to a 19th Century introduction from India by William Farrer; however other sources, of significance in international breeding programs, were also identified. Our analysis of the effect of the VPM1 segment on quality traits revealed no detrimental effects of its presence on the traits we measured.

The storage protein activator gene Spa-B1 and grain quality traits in southern Australian wheat breeding programs

2012 ◽  
Vol 63 (4) ◽  
pp. 311 ◽  
Author(s):  
H. A. Eagles ◽  
Karen Cane ◽  
Marie Appelbee ◽  
Haydn Kuchel ◽  
R. F. Eastwood ◽  
...  
Keyword(s):  
Storage Protein ◽  
Grain Quality ◽  
Mixed Model ◽  
Important Determinant ◽  
Market Value ◽  
Wheat Breeding ◽  
Quality Traits ◽  
Breeding Programs ◽  
Protein Activator ◽  
Australian Wheat

Grain quality is an important determinant of market value of wheat in southern Australia and in many other parts of the world. Identification of the genes that influence grain quality traits and estimation of effects of alleles of these genes can improve the effectiveness of wheat breeding. An efficient method for estimating the effects of alleles of recently discovered genes is to use mixed-model analyses in large plant breeding datasets that have already been characterised for previously known genes. We used this method to estimate the effects of two alleles of Spa-B1, a storage protein activator gene that is linked to Glu-B1, on grain quality traits. Alleles of the two genes tracked together as haplotypes for generations, but recombination events were identified. These recombination events were used to enhance confidence in identification of the alleles. The effects of the alleles of Spa-B1 were small and statistically not significant for all of the grain quality traits in our population.

Genetic and environmental variation for grain quality traits routinely evaluated in southern Australian wheat breeding programs

2002 ◽  
Vol 53 (9) ◽  
pp. 1047 ◽  
Author(s):  
H. A. Eagles ◽  
G. J. Hollamby ◽  
R. F. Eastwood
Keyword(s):  
Water Absorption ◽  
Development Time ◽  
Grain Quality ◽  
Wheat Breeding ◽  
Breeding Programs ◽  
Environmental Correlations ◽  
Flour Protein ◽  
Australian Wheat ◽  
Dough Development Time ◽  
Milling Yield

Milling yield, maximum dough resistance (Rmax), dough extensibility, flour protein concentration (flour protein), particle size index (PSI), water absorption, and dough development time are important determinants of grain quality and are routinely evaluated in Australian wheat breeding programs. Information on allelic variation at the 6 loci determining glutenin proteins is also regularly obtained and used to predict Rmax and extensibility. For each character, except dough development time, 4029 observations on 2377 lines and 94 environments were analysed to estimate genotypic and environmental variances, heritabilities, genotypic and environmental correlations, and the effects of glutenin genes. A subset was analysed for dough development time. Milling yield, Rmax, extensibility, PSI, water absorption, and dough development time had intra-class correlation coefficients, or broad-sense heritabilities, between 0.66 and 0.76, and extensibility had a value of 0.52, with flour protein at 0.36. Genotypic and environmental correlations between extensibility and flour protein were high at +0.78 and +0.85, respectively. Rmax had a genotypic correlation with dough development time of +0.67, which was substantially due to pleiotropic effects of glutenin genes. Rmax, extensibility, PSI, and dough development time were influenced by glutenin genes. For Rmax about 50% of the genotypic variance could be explained by glutenin genes. For extensibility about 50% could be explained by flour protein, with 50% of the remainder by the inclusion of glutenin genes. For dough development time about 15% could be explained by flour protein, with a further 30% by glutenin genes. For PSI, about 40% of the genotypic variation could be accounted for by glutenin genes after the removal of the effects of flour protein and milling yield. We concluded that dough development time could be added to Rmax and extensibility as a trait that can be usefully predicted by the glutenin genes, but more work is required for PSI.

Puroindoline genes and their effects on grain quality traits in southern Australian wheat cultivars

2004 ◽  
Vol 55 (1) ◽  
pp. 89 ◽  
Author(s):  
Karen Cane ◽  
Merrin Spackman ◽  
H. A. Eagles
Keyword(s):  
Water Absorption ◽  
Grain Quality ◽  
High Water ◽  
Major Effect ◽  
Wheat Breeding ◽  
Quality Data ◽  
Breeding Programs ◽  
Australian Wheat ◽  
End Use ◽  
Milling Yield

Grain hardness is a major determinant of the classification and end-use of wheat. Two genes, Pina-D1 and Pinb-D1, have a major effect on this trait, so for wheat breeding programs it is important to identify the alleles of these genes present in elite germplasm. This study was conducted to identify the alleles present in southern Australian germplasm, and to determine if they affected quality characteristics other than grain hardness.Only 3 genotypes were identified. These were Pina-D1a/Pinb-D1a producing soft grain, Pina-D1a/Pinb-D1b producing moderately hard grain, and Pina-D1b/Pinb-D1a producing very hard grain. WW15 was the probable source of Pina-D1a/Pinb-D1b in most cultivars; however, Halberd represented another source. An important source of Pina-D1b/Pinb-D1a was the CIMMYT line Pavon, with sources from the old Australian cultivars Gabo and Falcon probably still present in modern germplasm.In an analysis of grain quality data from the Victorian Institute for Dryland Agriculture breeding program, the Pina-D1b/Pinb-D1a genotype had a significantly higher water absorption and significantly lower milling yield than the Pina-D1a/Pinb-D1b genotype, which indicates that these genes will impede the development of hard-grained cultivars that combine high water absorption and high milling yield.

Contributions of glutenin and puroindoline genes to grain quality traits in southern Australian wheat breeding programs

2006 ◽  
Vol 57 (2) ◽  
pp. 179 ◽  
Author(s):  
H. A. Eagles ◽  
Karen Cane ◽  
R. F. Eastwood ◽  
G. J. Hollamby ◽  
Haydn Kuchel ◽  
...  
Keyword(s):  
Water Absorption ◽  
Development Time ◽  
Grain Quality ◽  
Genotypic Variation ◽  
Wheat Breeding ◽  
Wagga Wagga ◽  
Breeding Programs ◽  
Dough Development Time ◽  
Milling Yield

Glutenin genes were known to influence maximum dough resistance (Rmax), dough extensibility (extensibility), and dough development time, whereas puroindoline genes were known to influence grain hardness, flour water absorption (water absorption), and milling yield. These are important determinants of grain quality of wheat in Australia. This study was conducted to investigate the combined effect of these genes on Rmax, extensibility, dough development time, water absorption, and milling yield in a large dataset assembled from the breeding programs based at Horsham, Victoria; Roseworthy, South Australia; and Wagga Wagga, New South Wales; for at least 10 seasons. The effect of the glutenin genes on Rmax, extensibility, and dough development time was confirmed, as was the effect of the puroindoline genes on water absorption and milling yield. In addition, puroindoline genes were shown to significantly affect extensibility and dough development time. The Pina-D1a/Pinb-D1b genotype increased extensibility, dough development time, and milling yield relative to the Pina-D1b/Pinb-D1a genotype. Both of these genotypes are present in cultivars classified as hard-grained in southern Australia. Therefore, the allelic composition of both glutenin and puroindoline genes is required to predict the grain quality of hard wheat in southern Australian breeding programs. The glutenin and puroindoline genes in combination accounted for more than 50% of the genotypic variance for these traits, except for milling yield, but a substantial proportion of the genotypic variation could not be attributed to these genes, indicating that other genes affecting the traits were present in the populations of these wheat-breeding programs.

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 Grain Quality and Multivariate Analysis of Various Quality Parameters in Rice Genotypes

2019 ◽  
Vol 39 (04) ◽  
Author(s):  
Kasturi Majumder ◽  
Disharee Nath ◽  
Rambilash Mallick ◽  
Tapash Dasgupta
Keyword(s):  
Significant Variation ◽  
Grain Quality ◽  
Quality Parameters ◽  
Consumer Acceptance ◽  
High Yield ◽  
Rice Breeding ◽  
Quality Traits ◽  
Rice Varieties ◽  
Rice Genotypes ◽  
Selection Of

Thirty-six rice genotypes were evaluated for thirteen different quality parameters along with yield/plant to assess genetic estimates of the traits and the extent of genetic diversity among the varieties. Analysis of variance was conducted to determine GCV, PCV, heritability and GA of the genotypes with respect to all characters. Significant variation was observed in many traits among the genotypes offering scope for selection. Correlation analysis determined the nature of relationship among these characters. UPGMA studies revealed six major clusters and cluster I and II were the largest with maximum number of genotypes. The study identified that the varieties namely, Black Gora, Kalinga-2, Dudheswar, ARC 10086, IR-36, IR-64 and Nipponbare possessed good quality traits and high yield performance. The current study indicated that developing rice varieties for consumer acceptance with good grain quality traits along with high yield will be very useful in rice breeding and in selection of parents for hybridization to combine both high yield and improved quality traits.

scholarly journals Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

2021 ◽  
Author(s):  
Karansher S Sandhu ◽  
Meriem Aoun ◽  
Craig Morris ◽  
Arron H Carter
Keyword(s):  
Deep Learning ◽  
Grain Yield ◽  
Cross Validation ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Learning Models ◽  
Breeding Programs ◽  
Wheat Breeding Program ◽  
End Use

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Hence, testing is delayed until later stages in the breeding program. Delayed phenotyping results in advancement of inferior end-use quality lines into the program. Genomic selection provides an alternative to predict performance using genome-wide markers. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron), were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models performed superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trailing. Furthermore, the superior performance of machine and deep learning models strengthen the idea to include them in large scale breeding programs for predicting complex traits.

scholarly journals Grain quality traits important in feed barley

2012 ◽  
Vol 66 (1-2) ◽  
pp. 1-9 ◽  
Author(s):  
Māra Bleidere ◽  
Zinta Gaile
Keyword(s):  
Grain Quality ◽  
Animal Feed ◽  
Spring Barley ◽  
Age Groups ◽  
Quality Parameters ◽  
Grain Weight ◽  
Quality Traits ◽  
Hordeum Vulgare L ◽  
Volume Weight ◽  
Northern Areas

Grain quality traits important in feed barley Spring barley (Hordeum vulgare L.) traditionally has been a major cereal crop for animal feed especially in Northern areas and also in Latvia. It is complicated to define what the ideal feed barley should be, as the requirements widely differ not only for different species, but even for different age groups of the same species of animals. Therefore, the breeding of feed barley has been developing very slowly and building on the basis of agronomic and beer barley quality parameters. Targeted breeding of barley varieties for a definite application purpose of the grain is connected with selection according to different criteria. The present article shows that the feed quality of barley is influenced both by physical grain quality indicators (colour, grain weight and size, hull content, 1000 grain weight, volume weight and grain hardness) and by the chemical composition (carbohydrates, non-starch polysaccharides, amino acids, fibre, protein, fat, minerals and vitamins). On the basis of the information collected, a profile of a high quality feed barley variety for different groups of animals is defined.

scholarly journals Genotype X Environment Interaction for Quality Traits in Bread Wheat Genotype Tested At Six Environments

2019 ◽  
Vol 4 (2) ◽  
Keyword(s):  
Bread Wheat ◽  
Grain Quality ◽  
Wheat Breeding ◽  
Environment Interaction ◽  
Quality Traits ◽  
Wheat Genotypes ◽  
Significant Difference ◽  
Wide Adaptation ◽  
New Varieties

The study was conducted to evaluate the effect of GEI and its magnitude on the grain quality of bread wheat genotypes in Ethiopia. 15 bread wheat genotypes were evaluated using RCBD with four replications at six different locations in Ethiopia during 2017/18 cropping season. Combine Analysis of variance showed highly significant (P<0.001) differences among genotype, environment and GEI for investigated quality traits except GEI shows non-significant difference in dry gluten and gluten index. The environment contributed more than 50% only in PC (83.6%) and HLW (56.1%). The three components, G, E and GxE made almost similar contribution to most of the quality traits (WG, DG and GI), although the contribution of the environment was a little higher. Hardness index was determined mainly by the genotype (69.3%). The contribution of GxE was higher than that of genotype in all quality traits except in HDI and GI, again indicating the important role of GxE in the determination of wheat quality traits. Genotype ETBW9045 and ETBW8065 gave the best value of protein in the favorable means (15.05% and 14.75%) respectively. The Hidase had the highest value of wet gluten (58.2%) and dry gluten (24.38%) in average for all investigated locations (58.2%). GGE biplot declared ETBW9045 (#10) and ETBW8065 (#6) genotypes as stable in all quality. These two genotypes ETBW9045 (#10) and ETBW8065 (#6) are recommended for wide adaptation and for crossing. This study demonstrates success in wheat breeding for improved quality in bread wheat. The study also provides information on the combined stability of improved quality of the nationally important bread wheat genotypes. Therefore, the results of this study could be valuable for national bread wheat breeding programs to develop new varieties with high stable grain quality.

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