scholarly journals Deciphering the Genetics of Major End-Use Quality Traits in Wheat

2019 ◽  
Author(s):  
Sepehr Mohajeri Naraghi ◽  
Senay Simsek ◽  
Ajay Kumar ◽  
S.M. Hisam Al Rabbi ◽  
Mohammed S. Alamri ◽  
...  
Keyword(s):  
Recombinant Inbred Lines ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Peak Time ◽  
Quality Traits ◽  
Gene Encoding ◽  
Breeding Programs ◽  
B1 Gene ◽  
A Subunit ◽  
End Use

AbstractImproving the end-use quality traits is one of the primary objectives in wheat breeding programs. In the current study, a population of 127 recombinant inbred lines (RILs) derived from a cross between Glenn (PI-639273) and Traverse (PI-642780) was developed and used to identify quantitative trait loci (QTL) for 16 end-use quality traits in wheat. The phenotyping of these 16 traits was performed in nine environments in North Dakota, USA. The genotyping for the RIL population was conducted using the wheat Illumina iSelect 90K SNP assay. A high-density genetic linkage map consisting of 7,963 SNP markers identified a total of 76 additive QTL (A-QTL) and 73 digenic epistatic QTL (DE-QTL) associated with these traits. Overall, 12 stable major A-QTL and three stable DE-QTL were identified for these traits, suggesting that both A-QTL and DE-QTL played an important role in controlling end-use quality traits in wheat. The most significant A-QTL (AQ.MMLPT.ndsu.1B) was detected on chromosome 1B for mixograph middle line peak time. The AQ.MMLPT.ndsu.1B A-QTL was located very close to the position of the Glu-B1 gene encoding for a subunit of high molecular weight glutenin and explained up to 24.43% of phenotypic variation for mixograph MID line peak time. A total of 23 co-localized QTL loci were detected, suggesting the possibility of the simultaneous improvement of the end-use quality traits through selection procedures in wheat breeding programs. Overall, the information provided in this study could be used in marker-assisted selection to increase selection efficiency and to improve the end-use quality in wheat.

scholarly journals Genetic Architecture of End-use Quality Traits in Soft White Winter Wheat

2021 ◽  
Author(s):  
Meriem Aoun ◽  
Arron H. Carter ◽  
Craig F. Morris ◽  
Alecia M. Kiszonas
Keyword(s):  
Winter Wheat ◽  
Genetic Architecture ◽  
The United States ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Complex Nature ◽  
Quality Trait ◽  
Quality Traits ◽  
Breeding Lines ◽  
End Use

Abstract Background:Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States.Results:This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end‑use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection.Conclusions:In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.

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 A Combined Linkage and GWAS Analysis Identifies QTLs Linked to Soybean Seed Protein and Oil Content

2019 ◽  
Vol 20 (23) ◽  
pp. 5915 ◽  
Author(s):  
Tengfei Zhang ◽  
Tingting Wu ◽  
Liwei Wang ◽  
Bingjun Jiang ◽  
Caixin Zhen ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Oil Content ◽  
Genome Wide Association Study ◽  
Recombinant Inbred Lines ◽  
Soybean Seed ◽  
Snp Markers ◽  
Growth Stages ◽  
Breeding Programs ◽  
Genome Wide ◽  
Excellent Source

Soybean is an excellent source of vegetable protein and edible oil. Understanding the genetic basis of protein and oil content will improve the breeding programs for soybean. Linkage analysis and genome-wide association study (GWAS) tools were combined to detect quantitative trait loci (QTL) that are associated with protein and oil content in soybean. Three hundred and eight recombinant inbred lines (RILs) containing 3454 single nucleotide polymorphism (SNP) markers and 200 soybean accessions, including 94,462 SNPs and indels, were applied to identify QTL intervals and significant SNP loci. Intervals on chromosomes 1, 15, and 20 were correlated with both traits, and QTL qPro15-1, qPro20-1, and qOil5-1 reproducibly correlated with large phenotypic variations. SNP loci on chromosome 20 that overlapped with qPro20-1 were reproducibly connected to both traits by GWAS (p < 10−4). Twenty-five candidate genes with putative roles in protein and/or oil metabolisms within two regions (qPro15-1, qPro20-1) were identified, and eight of these genes showed differential expressions in parent lines during late reproductive growth stages, consistent with a role in controlling protein and oil content. The new well-defined QTL should significantly improve molecular breeding programs, and the identified candidate genes may help elucidate the mechanisms of protein and oil biosynthesis.

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 Selection in Winter Wheat Breeding Using a Recommender Approach

Genes ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 779
Author(s):  
Dennis N. Lozada ◽  
Arron H. Carter
Keyword(s):  
Winter Wheat ◽  
Genomic Selection ◽  
Prediction Models ◽  
Heading Date ◽  
Predictive Ability ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Bayesian Regression ◽  
Phenotypic Trait ◽  
Breeding Programs

Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs.

scholarly journals Late-maturity α-amylase (LMA): exploring the underlying mechanisms and end-use quality effects in wheat

Planta ◽  
2021 ◽  
Vol 255 (1) ◽  
Author(s):  
Ashley E. Cannon ◽  
Elliott J. Marston ◽  
Alecia M. Kiszonas ◽  
Amber L. Hauvermale ◽  
Deven R. See
Keyword(s):  
Wheat Breeding ◽  
Protein Accumulation ◽  
Comprehensive Overview ◽  
Breeding Programs ◽  
Future Directions ◽  
Open Questions ◽  
Late Maturity ◽  
End Use ◽  
Molecular Aspects ◽  
Underlying Mechanisms

Abstract Main conclusion A comprehensive understanding of LMA from the underlying molecular aspects to the end-use quality effects will greatly benefit the global wheat industry and those whose livelihoods depend upon it. Abstract Late-maturity α-amylase (LMA) leads to the expression and protein accumulation of high pI α-amylases during late grain development. This α-amylase is maintained through harvest and leads to an unacceptable low falling number (FN), the wheat industry’s standard measure for predicting end-use quality. Unfortunately, low FN leads to significant financial losses for growers. As a result, wheat researchers are working to understand and eliminate LMA from wheat breeding programs, with research aims that include unraveling the genetic, biochemical, and physiological mechanisms that lead to LMA expression. In addition, cereal chemists and quality scientists are working to determine if and how LMA-affected grain impacts end-use quality. This review is a comprehensive overview of studies focused on LMA and includes open questions and future directions.

scholarly journals High molecular weight glutenin gene diversity in Aegilops tauschii demonstrates unique origin of superior wheat quality

2021 ◽  
Author(s):  
Emily Delorean ◽  
LiangLiang Gao ◽  
Jose Fausto Cervantes Lopez ◽  
The Open Wild Wheat Consortium ◽  
Brande Wulff ◽  
...  
Keyword(s):  
Gene Diversity ◽  
Aegilops Tauschii ◽  
Tetraploid Wheat ◽  
Wheat Breeding ◽  
D Genome ◽  
Breeding Programs ◽  
Lineage 2 ◽  
End Use ◽  
Genomic Resource ◽  
New Alleles

Abstract Central to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome of Aegilops tauschii to tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity of Ae. tauschii. We discovered 45 haplotypes in Glu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele 2+12 was found in Ae. tauschii Lineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele 5+10 allele originated in Lineage 3, a recently characterized lineage of Ae. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity in Ae. tauschii at Glu-D1. Ae. tauschii is thus a reservoir for unique Glu-D1 alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs.

scholarly journals Genetic control of seed dormancy and pre-harvest sprouting in wheat

2006 ◽  
Vol 63 (6) ◽  
pp. 564-566 ◽  
Author(s):  
Claudinei Andreoli ◽  
Manoel Carlos Bassoi ◽  
Dionisio Brunetta
Keyword(s):  
Seed Dormancy ◽  
Bimodal Distribution ◽  
Wheat Breeding ◽  
Breeding Programs ◽  
Control Seed ◽  
Grain Color ◽  
The Mean ◽  
The Cross ◽  
End Use ◽  
Financial Losses

Pre-harvest sprouting (PHS) damage leads to occasional massive losses in all wheat producing areas, causing downgrading of grain quality, that severely limits end-use applications and results in substantial financial losses to farmers and food processors. Red grain color is a traditional marker for resistance to sprouting in wheat breeding programs, however red-grained genotype alone does not always guarantee effective resistance. The objective of this work was to find genes for resistance to PHS and investigate its inheritance in Brazilian wheat cultivars. Genetic variation for dormancy was investigated in the parents, F1 and 300 F2 lines derived from the cross Frontana × OR1 and its reciprocal. The germination/dormancy sprouted grains was evaluated on fifty seeds per replication, germinated in paper towel rolls at 20ºC for 5 days. A bimodal distribution for dormancy occurred in the Frontana/OR1 and OR1/Frontana derived F2 populations. The mean ratio of dormant and non-dormant seeds of the cross and its reciprocal was 85:1115, fitting a digenic model of 1:15 (P < 0.05). In fact, all non after-ripened F1 seeds germinated. The F2 distribution indicates that two major genes, here called A,a and B,b, control seed dormancy, which it appears to be recessive to dormancy. Only the homozygous aabb is dormant. As expected, there was no effect of maternal tissues.

scholarly journals Genomic Selection for Processing and End‐Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program

2016 ◽  
Vol 9 (2) ◽  
Author(s):  
Sarah D. Battenfield ◽  
Carlos Guzmán ◽  
R. Chris Gaynor ◽  
Ravi P. Singh ◽  
Roberto J. Peña ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Bread Wheat ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Spring Bread Wheat ◽  
Wheat Breeding Program ◽  
Selection For ◽  
End Use
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