High Throughput Testing of Key Wheat Quality Traits in Hard Red Spring Wheat Breeding Programs

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.

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

10.1101/2021.05.24.445513 ◽

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.

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Inheritance of vernalization response in three populations of spring wheat

Canadian Journal of Plant Science ◽

10.4141/cjps94-134 ◽

1994 ◽

Vol 74 (4) ◽

pp. 753-757 ◽

Cited By ~ 3

Author(s):

P. E. Jedel

Keyword(s):

Spring Wheat ◽

Triticum Aestivum L ◽

Wheat Breeding ◽

Vernalization Response ◽

Breeding Programs ◽

Reciprocal Crosses ◽

Vrn Genes ◽

Selection For ◽

Early Late ◽

Made In

Vernalization responses are known to differ among spring wheat (Triticum aestivum L.) genotypes. Three crosses were made to determine the inheritance of vernalization response in the spring wheat cultivars Cajeme 71, Yecora 70, Glenlea, Pitic 62 and Neepawa. Segregation analyses of days to anthesis were made of the F2 generation in a growth room (25/15 °C, 16/8 h). Segregation analysis of the F3 generation was made in a summer greenhouse. Reciprocal crosses between Neepawa and Pitic 62 indicated an early/late/transgressively late ratio of 12:3:1 in the F2 generation. The F3 generation results fitted an early/late/transgressively late/segregating ratio of 4:1:1:10. Based on the segregation of transgressively late types from both crosses, it was concluded that the genes for spring habit in Pitic 62 and Neepawa were different and not maternally inherited. The Glenlea/Pitic 62 cross produced one transgressively late segregant in an F2 population of 97 plants. The data fitted an early/late/transgressively late ratio of 60:3:1, indicating that Glenlea may differ from Pitic at three Vrn loci. Therefore, either Glenlea or Pitic 62 may carry two dominant Vrn alleles. The reciprocal crosses between Yecora 70 and Cajeme 71 did not segregate transgressively late types in the F2 generation. Therefore, those cultivars had a Vrn allele in common. Selection for vernalization response might be useful when introducing exotic germplasm into spring wheat breeding programs and in manipulating maturity responses. Key words: Vernalization, spring wheat, Vrn genes

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THE INFLUENCE OF ORANGE WHEAT BLOSSOM MIDGE (Sitodiplosis mosellana Géhin) DAMAGE ON HARD RED SPRING WHEAT QUALITY AND THE EFFECTIVENESS OF INSECTICIDE TREATMENTS

Canadian Journal of Plant Science ◽

10.4141/cjps87-097 ◽

1987 ◽

Vol 67 (3) ◽

pp. 697-712 ◽

Cited By ~ 26

Author(s):

J. E. DEXTER ◽

K. R. PRESTON ◽

L. A. COOKE ◽

B. C. MORGAN ◽

J. E. KRUGER ◽

...

Keyword(s):

Protein Content ◽

Spring Wheat ◽

Protein Quality ◽

Proteolytic Enzymes ◽

Sodium Dodecyl ◽

Wheat Quality ◽

Hard Red Spring Wheat ◽

Sedimentation Volume ◽

Flour Color ◽

Wheat Blossom Midge

Orange wheat blossom midge damage can impart serious loss of quality to Canadian hard red spring wheat. The extent of wheat quality deterioration is highly variable and not well related to degree of visual midge damage. Midge-damaged hard red spring wheat exhibits very high protein content, reduced flour yield, dark flour color, increased flour ash, weak sticky dough properties, low baking absorption and poor bread quality. Midge-damaged wheat contains normal levels of α-amylase and proteolytic enzymes. Severely midge-damaged wheat exhibits inferior gluten protein quality, but the cause remains obscure. The poor baking quality of severely midge-damaged wheat is associated with an unusually low sodium dodecyl sulfate (SDS) sedimentation volume. Samples with visual evidence of midge damage can be rapidly screened for quality defects on the basis of protein content and SDS-sedimentation volume. Aerial applications of Cygon and Lorsban to fields of midge-infested hard red spring wheat significantly reduced visual midge damage, and significantly reduced the extent of wheat quality deterioration.Key words: Midge (orange wheat blossom), wheat (spring), insecticide treatment

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IMPLICATIONS CONCERNING THE FREQUENCY OF CONTROL PLOTS IN WHEAT BREEDING NURSERIES

Canadian Journal of Plant Science ◽

10.4141/cjps68-027 ◽

1968 ◽

Vol 48 (2) ◽

pp. 149-153 ◽

Cited By ~ 15

Author(s):

K. G. Briggs ◽

L. H. Shebeski

Keyword(s):

Triticum Aestivum ◽

Soil Fertility ◽

Spring Wheat ◽

Wheat Breeding ◽

Control Plot ◽

Good Measure ◽

Hard Red Spring Wheat ◽

Yielding Ability ◽

University Of Manitoba ◽

The University

Control plots of Triticum aestivum var. Manitou were grown adjacent to every plot of breeding material in three hard red spring wheat nurseries at the University of Manitoba. Simple correlations between yields were high (r =.88,.87 and.63) and significant (P.01) for control plots at 2.7-m (9-ft) centers but decreased rapidly to nonsignificance with increasing distance between plot centers. The data indicate that for the particular type of plot used, the yield of a control plot provides a good measure of the soil fertility in terms of the yielding ability of an adjacent plot.

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Genetic Analysis of Adult Plant Resistance to Yellow Rust and Leaf Rust in Common Spring Wheat Quaiu 3

Plant Disease ◽

10.1094/pdis-02-12-0141-re ◽

2013 ◽

Vol 97 (6) ◽

pp. 728-736 ◽

Cited By ~ 19

Author(s):

B. R. Basnet ◽

R. P. Singh ◽

S. A. Herrera-Foessel ◽

A. M. H. Ibrahim ◽

J. Huerta-Espino ◽

...

Keyword(s):

Spring Wheat ◽

Resistance Genes ◽

Plant Resistance ◽

Adult Plant Resistance ◽

Molecular Mapping ◽

Puccinia Striiformis ◽

Wheat Breeding ◽

Adult Plant ◽

Breeding Programs ◽

Qualitative Approaches

Identifying and utilizing rust resistance genes in wheat has been hampered by the continuous and rapid emergence of new pathogen races. A major focus of many wheat breeding programs is achieving durable adult plant resistance (APR) to yellow (stripe) rust (YR) and leaf (brown) rust (LR), caused by Puccinia striiformis and P. triticina, respectively. This study aimed to determine the genetic basis of resistance to YR and LR in the common spring wheat ‘Quaiu 3’. To that end, we evaluated 198 F5 recombinant inbred lines (RILs), derived from a cross of susceptible ‘Avocet-YrA’ with Quaiu 3, for APR to LR and YR in artificially inoculated field trials conducted in Mexico during the 2009 and 2010 growing seasons. High narrow-sense heritability (h2) estimates, ranging between 0.91 and 0.95, were obtained for both LR and YR disease severities for both years. The quantitative and qualitative approaches used to estimate gene numbers showed that, in addition to known resistance genes, there are at least two to three APR genes associated with LR and YR resistance in the RIL population. The moderately effective race-specific resistance gene Lr42 and the pleiotropic slow-rusting APR gene Lr46/Yr29 were found to interact with additional unidentified APR genes. The unidentified APR genes should be of particular interest for further characterization through molecular mapping, and for utilization by wheat breeding programs.

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

Crop and Pasture Science ◽

10.1071/cp12055 ◽

2012 ◽

Vol 63 (4) ◽

pp. 311 ◽

Cited By ~ 3

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.

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Marker assisted selection in BMQ related breeding programs

Genetika ◽

10.2298/gensr0801039o ◽

2008 ◽

Vol 40 (1) ◽

pp. 39-49 ◽

Cited By ~ 1

Author(s):

Dragana Obreht ◽

Borislav Kobiljski ◽

Mihajla Djan ◽

Ljiljana Vapa

Keyword(s):

Dna Sequences ◽

Dna Markers ◽

Marker Assisted Selection ◽

Wheat Breeding ◽

Quality Traits ◽

Vegetable Crops ◽

Bread Making ◽

Technological Parameters ◽

Breeding Programs ◽

Wide Range

Implementation of marker assisted selection (MAS) in bread making quality (BMQ) oriented breeding programs could allow genetic potential assessment of genotypes prior to their phenotypic evaluation. The mechanisms underlying some quality traits in wheat are now understood. This knowledge, coupled with the availability of the DNA sequences of the genes encoding gluten proteins and the wide application of the PCR, has enabled the design of diagnostic DNA markers for these quality traits. Bread wheat breeding programs developed in Institute of Field and Vegetable Crops, Novi Sad have originated a wide range of quality cultivars with strong flours and hard grain texture. During twenty years, in the process of bread-making quality prediction, composition of HMW glutenin subunits were analyzed beside standard technological parameters. However, in order to improve our breeding strategies new generations of PCR-based BMQ related markers were included in selection programs. This paper provides an overview of diagnostic DNA markers that are currently in use in foreign and domestic wheat selection programs. .

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