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
2018 ◽  
Vol 9 ◽  
Author(s):  
Kendra L. Jernigan ◽  
Jayfred V. Godoy ◽  
Meng Huang ◽  
Yao Zhou ◽  
Craig F. Morris ◽  
...  

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 669 ◽  
Author(s):  
Peter S. Kristensen ◽  
Just Jensen ◽  
Jeppe R. Andersen ◽  
Carlos Guzmán ◽  
Jihad Orabi ◽  
...  

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.


2021 ◽  
Author(s):  
Meriem Aoun ◽  
Arron H. Carter ◽  
Craig F. Morris ◽  
Alecia M. Kiszonas

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.


Crop Science ◽  
2013 ◽  
Vol 53 (3) ◽  
pp. 793-801 ◽  
Author(s):  
Ali Bakhsh ◽  
Neway Mengistu ◽  
P. S. Baenziger ◽  
I. Dweikat ◽  
S. N. Wegulo ◽  
...  

Crop Science ◽  
2013 ◽  
Vol 53 (5) ◽  
pp. 1953-1967 ◽  
Author(s):  
Walid M. El-Feki ◽  
Patrick F. Byrne ◽  
Scott D. Reid ◽  
Nora L.V. Lapitan ◽  
Scott D. Haley

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