Effects of allelic variation at Rht-B1 and Rht-D1 on grain yield and agronomic traits of southern US soft red winter wheat

Euphytica ◽  
2019 ◽  
Vol 215 (10) ◽  
Author(s):  
Habibullah Hayat ◽  
R. Esten Mason ◽  
Dennis N. Lozada ◽  
Andrea Acuna ◽  
Amanda Holder ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Agronomic Traits ◽  
Allelic Variation ◽  
Soft Red Winter Wheat ◽  
Southern Us ◽  
Red Winter Wheat

scholarly journals Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Dennis N. Lozada ◽  
R. Esten Mason ◽  
Jose Martin Sarinelli ◽  
Gina Brown-Guedira
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Population Size ◽  
Genomic Selection ◽  
Agronomic Traits ◽  
Response To Selection ◽  
Training Population ◽  
Soft Red Winter Wheat ◽  
Selection For ◽  
Red Winter Wheat

Abstract Background Genomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections. Results Genomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64–70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between − 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was “superior” to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone. Conclusions Our results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

scholarly journals Quantifying the Effects of Fusarium Head Blight on Grain Yield and Test Weight in Soft Red Winter Wheat

2015 ◽  
Vol 105 (3) ◽  
pp. 295-306 ◽  
Author(s):  
Jorge David Salgado ◽  
Laurence V. Madden ◽  
Pierce A. Paul
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Fusarium Head Blight ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Test Weight ◽  
Head Blight ◽  
Soft Red Winter Wheat ◽  
Red Winter Wheat

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum, is known to negatively affect wheat grain yield (YLD) and test weight (TW). However, very little emphasis has been placed on formally quantifying FHB–YLD and FHB–TW relationships. Field plots of three soft red winter wheat cultivars—‘Cooper’ (susceptible to FHB), ‘Hopewell’ (susceptible), and ‘Truman’ (moderately resistant)—were grown during the 2009, 2010, 2011, and 2012 seasons, and spray inoculated with spore suspensions of F. graminearum and Parastagonospora nodorum to generate a range of FHB and Stagonospora leaf blotch (SLB) levels. FHB index (IND) and SLB were quantified as percent diseased spike and flag leaf area, respectively, and YLD (kg ha−1) and TW (kg m−3) data were collected. Using IND as a continuous covariate and cultivar (CV) and SLB as categorical fixed effects, linear mixed-model regression analyses (LMMR) were used to model the IND–YLD and IND–TW relationship and to determine whether these relationships were influenced by CV and SLB. The final models fitted to the data were of the generic form y = a + b (IND), where a (intercept) or b (slope) could also depend on other factors. LMMR analyses were also used to estimate a and b by combining the studies from these 4 years with an additional 16 experiments conducted from 2003 to 2013, and bivariate random-effects meta-analysis was used to estimate population mean b ([Formula: see text]) and a (ā) for the IND–YLD relationship. YLD and TW decreased as IND increased, with b ranging from −3.2 to −2.3 kg m−3 %−1 for TW. For the IND–YLD relationship, [Formula: see text] was −51.7 kg ha−1 %IND−1 and ā was 4,426.7 kg ha−1. Neither cultivar nor SLB affected the IND–YLD relationship but SLB affected a of the IND–TW regression lines, whereas cultivar affected b. Plots with the highest levels of SLB (based on ordinal categories for SLB) had the lowest a and Hopewell had the highest b. The level of IND at which a 50-kg m−3 reduction in TW was predicted to occur was 19, 16, and 22% for Cooper, Hopewell, and Truman, respectively. A yield loss of 1 MT ha−1 was predicted to occur at 19% IND. The rate of reduction in relative TW or YLD per unit increase in IND was between −0.39 and −0.32%−1 for TW and −1.17%−1 for YLD. Results from this study could be integrated into more general models to evaluate the economics of FHB management strategies.

scholarly journals Identification of regions under selection and loci controlling agronomic traits in a soft red winter wheat population

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Rupesh Gaire ◽  
Herbert Ohm ◽  
Gina Brown‐Guedira ◽  
Mohsen Mohammadi
Keyword(s):  
Winter Wheat ◽  
Agronomic Traits ◽  
Soft Red Winter Wheat ◽  
Wheat Population ◽  
Red Winter Wheat

Soft Red Winter Wheat (Triticum aestivum) Response to Dicamba and Dicamba Plus 2,4-D

1989 ◽  
Vol 3 (1) ◽  
pp. 67-71 ◽  
Author(s):  
Jill Schroeder ◽  
Philip A. Banks
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Grain Yield ◽  
Field Experiments ◽  
Wheat Cultivars ◽  
Soft Red Winter Wheat ◽  
Winter Wheat Cultivars ◽  
Red Winter Wheat

Soft red winter wheat cultivars were evaluated in field experiments in Georgia for tolerance to dicamba alone and mixed with 2,4-D. Treatments reduced ‘Florida 302’ yield more than ‘Florida 301’ or ‘Coker 983’ at Tifton in 1986. Mid-tillering Florida 302 wheat was more sensitive to treatment than fully tillered wheat. In 1987, dicamba plus 2,4-D applied at mid-tillering reduced yields of all cultivars in Watkinsville. Injury and yield reductions occurred primarily when mid-tiller treatments were applied to wheat that was planted 10 or 21 days later than recommended at Tifton or Watkinsville, respectively. When applied according to labeling, dicamba or dicamba plus 2,4-D use in Georgia soft red winter wheat can reduce grain yield.

scholarly journals Performance of soft red winter wheat subjected to field soil waterlogging: Grain yield and yield components

2016 ◽  
Vol 194 ◽  
pp. 57-64 ◽  
Author(s):  
M. Nelly Arguello ◽  
R. Esten Mason ◽  
Trenton L. Roberts ◽  
Nithya Subramanian ◽  
Andrea Acuña ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Yield Components ◽  
Field Soil ◽  
Soil Waterlogging ◽  
Soft Red Winter Wheat ◽  
Yield And Yield Components ◽  
Red Winter Wheat

scholarly journals UGRC Ring, soft red winter wheat

2015 ◽  
Vol 95 (5) ◽  
pp. 1033-1035
Author(s):  
Lily Tamburic-Ilincic ◽  
Arend Smid
Keyword(s):  
Triticum Aestivum ◽  
Powdery Mildew ◽  
Winter Wheat ◽  
Grain Yield ◽  
Triticum Aestivum L ◽  
Falling Number ◽  
Soft Red Winter Wheat ◽  
Flour Yield ◽  
Moderately Resistant ◽  
Red Winter Wheat

Tamburic-Ilincic, L. and Smid, A. 2015. UGRC Ring, soft red winter wheat. Can. J. Plant Sci. 95: 1033–1035. UGRC Ring is a soft red winter wheat (Triticum aestivum L.) cultivar registered for Ontario, Canada. It has high grain yield, with good pastry quality (high flour yield, high falling number) and is moderately resistant to powdery mildew. UGRC Ring has good winter hardiness and is well adapted for the winter wheat growing areas of Ontario.

scholarly journals Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia

2018 ◽  
Author(s):  
Brian P. Ward ◽  
Gina Brown-Guedira ◽  
Frederic L. Kolb ◽  
David A. Van Sanford ◽  
Priyanka Tyagi ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Reference Genome ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Soft Red Winter Wheat ◽  
Locus Model ◽  
Genome Wide ◽  
Red Winter Wheat

AbstractGrain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 324 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate promising avenues for increasing grain yield by exploiting variation in traits relating to the number of grains per unit area, as well as phenological traits influencing grain-filling duration of genotypes.

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