Wheat Breeding: Procedures and Strategies – Series Ⅱ

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Crop Breeding and Genetics".

Deadline for manuscript submissions: closed (9 January 2022) | Viewed by 28726

Special Issue Editors

Plant and Environmental Sciences, Clemson University, Clemson, SC 29634, USA
Interests: development of genetic resources for trait discovery and cultivar development; genomic prediction; genetic mapping; breeding for improved disease and pest resistance
Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY 40506, USA
Interests: inheritance of resistance to Fusarium head blight; breeding for adaptation to climate change; genetic factors associated with nitrogen use efficiency; genomic selection; locally sourcing bread wheat
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Special Issue Information

Dear Colleagues,

Series II of the Special Issue of Agronomy, “Wheat Breeding: Procedures and Strategies”, provides another excellent opportunity to publish research articles (original and review) that focus on accelerating wheat improvement through the utilization of advanced genomic and phenomic technologies.  More specifically, Series II will highlight recent discoveries in the following areas:

  • Genomic prediction implementation: While wheat breeding programs continue efforts to validate and improve genomic prediction, we have reached the implementation stage of this technology. It is imperative to develop empirical evidence to support the utilization of genomic prediction at various stages of breeding pipelines.
  • Capturing G x E interaction: Correct measurement of genotype x environment interactions for complex traits (e.g., yield) is still a challenge, especially as weather patterns become more violent and unpredictable. Modeling, interpreting, and subsequently accounting for G x E in genomic prediction is critical.
  • Advanced phenomics: As genomic sequencing has improved and become more cost-effective, field phenotyping (accuracy and throughput) is now a limiting factor in genetic mapping and cultivar development. Development of advanced phenotyping technology is essential to increase trait discovery and breeding efficiency.
  • Organic wheat breeding: The demand for organic wheat has increased dramatically, but research has been limited on understanding and developing the optimal crop ideotype for organic production systems.

Dr. Richard Boyles
Prof. Dr. David Van Sanford
Guest Editors

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Keywords

  • Genomic resources for trait discovery and prediction
  • Implementation of genomic prediction for cultivar development
  • Genetic marker identification, development, and utilization
  • Genotype x environment interaction modeling and capture
  • Promising advances in gene editing
  • Advanced phenomics for increased phenotyping accuracy and throughput
  • Organic wheat cultivar development strategies

Published Papers (8 papers)

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Research

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20 pages, 3180 KiB  
Article
Evaluation of Methods for Measuring Fusarium-Damaged Kernels of Wheat
by Arlyn J. Ackerman, Ryan Holmes, Ezekiel Gaskins, Kathleen E. Jordan, Dawn S. Hicks, Joshua Fitzgerald, Carl A. Griffey, Richard Esten Mason, Stephen A. Harrison, Joseph Paul Murphy, Christina Cowger and Richard E. Boyles
Agronomy 2022, 12(2), 532; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12020532 - 21 Feb 2022
Cited by 6 | Viewed by 3723
Abstract
Fusarium head blight (FHB) is one of the most economically destructive diseases of wheat (Triticum aestivum L.), causing substantial yield and quality loss worldwide. Fusarium graminearum is the predominant causal pathogen of FHB in the U.S., and produces deoxynivalenol (DON), a mycotoxin [...] Read more.
Fusarium head blight (FHB) is one of the most economically destructive diseases of wheat (Triticum aestivum L.), causing substantial yield and quality loss worldwide. Fusarium graminearum is the predominant causal pathogen of FHB in the U.S., and produces deoxynivalenol (DON), a mycotoxin that accumulates in the grain throughout infection. FHB results in kernel damage, a visual symptom that is quantified by a human observer enumerating or estimating the percentage of Fusarium-damaged kernels (FDK) in a sample of grain. To date, FDK estimation is the most efficient and accurate method of predicting DON content without measuring presence in a laboratory. For this experiment, 1266 entries collectively representing elite varieties and SunGrains advanced breeding lines encompassing four inoculated FHB nurseries were represented in the analysis. All plots were subjected to a manual FDK count, both exact and estimated, near-infrared spectroscopy (NIR) analysis, DON laboratory analysis, and digital imaging seed phenotyping using the Vibe QM3 instrument developed by Vibe imaging analytics. Among the FDK analytical platforms used to establish percentage FDK within grain samples, Vibe QM3 showed the strongest prediction capabilities of DON content in experimental samples, R2 = 0.63, and higher yet when deployed as FDK GEBVs, R2 = 0.76. Additionally, Vibe QM3 was shown to detect a significant SNP association at locus S3B_9439629 within major FHB resistance quantitative trait locus (QTL) Fhb1. Visual estimates of FDK showed higher prediction capabilities of DON content in grain subsamples than previously expected when deployed as genomic estimated breeding values (GEBVs) (R2 = 0.71), and the highest accuracy in genomic prediction, followed by Vibe QM3 digital imaging, with average Pearson’s correlations of r = 0.594 and r = 0.588 between observed and predicted values, respectively. These results demonstrate that seed phenotyping using traditional or automated platforms to determine FDK boast various throughput and efficacy that must be weighed appropriately when determining application in breeding programs to screen for and develop resistance to FHB and DON accumulation in wheat germplasms. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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18 pages, 1881 KiB  
Article
Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat
by Karansher S. Sandhu, Paul D. Mihalyov, Megan J. Lewien, Michael O. Pumphrey and Arron H. Carter
Agronomy 2021, 11(12), 2528; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11122528 - 13 Dec 2021
Cited by 23 | Viewed by 4710
Abstract
Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by [...] Read more.
Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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15 pages, 1315 KiB  
Communication
Using Genomic Selection to Leverage Resources among Breeding Programs: Consortium-Based Breeding
by Clay Sneller, Carlos Ignacio, Brian Ward, Jessica Rutkoski and Mohsen Mohammadi
Agronomy 2021, 11(8), 1555; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11081555 - 04 Aug 2021
Cited by 5 | Viewed by 1978
Abstract
Genomic selection has many applications within individual programs. Here, we discuss the benefits of forming a GS-based breeding consortium (GSC) among programs within the context of a recently formed a GSC of soft red winter wheat breeding programs. The GSC will genotype lines [...] Read more.
Genomic selection has many applications within individual programs. Here, we discuss the benefits of forming a GS-based breeding consortium (GSC) among programs within the context of a recently formed a GSC of soft red winter wheat breeding programs. The GSC will genotype lines from each member breeding program (MBP) and conduct cooperative phenotyping. The primary GSC benefit is that each MBP can use GS to predict the local and broad value of all germplasm from all MBPs including lines in the early stages of testing, thus increasing the effective size of each MBP without significant new investment. We identified eight breeding aspects that are essential to GSC success and analyzed how our GSC fits those criteria. We identified a core of >5700 related lines from the MBPs that can serve in training populations. Germplasm from each MBP provided breeding value to other MBPs and program-specific adaption was low. GS accuracy was acceptable within programs but was low between programs when using training populations with little testing connectivity, but increased when using data from trials with high testing connectivity between MBPs. In response we initiated sparse-testing with a germplasm sharing scheme utilizing family relationship to connect our phenotyping of early-stage lines. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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12 pages, 436 KiB  
Article
Development of a Five-Parameter Model to Facilitate the Estimation of Additive, Dominance, and Epistatic Effects with a Mediating Using Bootstrapping in Advanced Generations of Wheat (Triticum aestivum L.)
by Ahmed E. A. Khalaf, Mohamed A. M. Eid, Kamal H. Ghallab, Sherif R. M. El-Areed, Ahmed A. M. Yassein, Mostafa M. Rady, Esmat F. Ali and Ali Majrashi
Agronomy 2021, 11(7), 1325; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11071325 - 29 Jun 2021
Cited by 4 | Viewed by 1794
Abstract
As a result of two crosses among three local varieties of wheat, five populations (P1, P2, F5, F6 and F7) were used as parents and grown during two successive seasons; 2016/2017 and 2017/2018. To [...] Read more.
As a result of two crosses among three local varieties of wheat, five populations (P1, P2, F5, F6 and F7) were used as parents and grown during two successive seasons; 2016/2017 and 2017/2018. To estimate five types of gene action (e.g., mean effects, additive, dominance, additive × additive, and dominance × dominance), five formulas were developed from with algebraic solution, algebraic proof, and mathematical proof. Besides, to test adequate of a simple additive-dominance model, three formulas A, B, and C scaling test were developed. The path analysis method by PROCESS Macro, AMOS, and Bootstrapping was employed to assess the relationships between grain yield/plant (GYP) as the dependent variable and each one of the number of spikes (NS) and 1000-grain weight (TW) as the independent variables. The results show that there are eight validated equations used to estimate the scaling test (A, B and C) and five types of gene effects (m, a, D, I and L), respectively. Confidence interval using Bootstrapping results indicate that TW was played as the partial mediator between NS as an exogenous variable and GYP as an endogenous variable. Generation means analysis is a relatively simple and statistically reliable tool suitable for the fundamental estimation of different genetic influences. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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19 pages, 1814 KiB  
Article
Does Leaf Waxiness Confound the Use of NDVI in the Assessment of Chlorophyll When Evaluating Genetic Diversity Panels of Wheat?
by Kamal Khadka, Andrew J. Burt, Hugh J. Earl, Manish N. Raizada and Alireza Navabi
Agronomy 2021, 11(3), 486; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11030486 - 05 Mar 2021
Cited by 1 | Viewed by 2021
Abstract
Ground and aerial-based high throughput phenotyping platforms (HTPPs) to evaluate chlorophyll-related traits have been utilized to predict grain yield in crops including wheat (Triticum aestivum L.). This study evaluated chlorophyll-related and other physiological and yield traits in a panel of 318 Nepali [...] Read more.
Ground and aerial-based high throughput phenotyping platforms (HTPPs) to evaluate chlorophyll-related traits have been utilized to predict grain yield in crops including wheat (Triticum aestivum L.). This study evaluated chlorophyll-related and other physiological and yield traits in a panel of 318 Nepali spring wheat genotypes, termed the Nepali Wheat Diversity Panel (NWDP). Field experiments were conducted using an alpha-lattice design in Nepal and Canada. Chlorophyll-related traits were evaluated with a Soil Plant Analysis Development (SPAD) meter and the normalized difference vegetation index (NDVI) using a handheld GreenSeeker and an Unmanned Aerial Vehicle (UAV). Relative leaf epicuticular waxiness was recorded using visual assessments. There was a significant positive association (p < 0.001) between waxiness and SPAD-based chlorophyll estimates, and both of these traits displayed a significant positive relationship with grain yield. However, unexpectedly, NDVI derived from both GreenSeeker and UAV was negatively associated with waxiness and grain yield. The results obtained after segregating the trait means into groups based on waxiness scores and breeding history of genotypes indicated that waxiness along with precipitation could be affecting the multispectral reflectance. These results suggest that caution should be taken when evaluating a large and diverse wheat population for leaf chlorophyll using high-throughput NDVI methods. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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18 pages, 4288 KiB  
Article
Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program
by Sebastian Michel, Franziska Löschenberger, Ellen Sparry, Christian Ametz and Hermann Bürstmayr
Agronomy 2020, 10(10), 1591; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10101591 - 17 Oct 2020
Cited by 5 | Viewed by 2434
Abstract
The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring [...] Read more.
The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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Review

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19 pages, 1278 KiB  
Review
Optimizing Plant Breeding Programs for Genomic Selection
by Lance F. Merrick, Andrew W. Herr, Karansher S. Sandhu, Dennis N. Lozada and Arron H. Carter
Agronomy 2022, 12(3), 714; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12030714 - 16 Mar 2022
Cited by 19 | Viewed by 6685
Abstract
Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) [...] Read more.
Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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17 pages, 1357 KiB  
Review
Utilizing Genomic Selection for Wheat Population Development and Improvement
by Lance F. Merrick, Andrew W. Herr, Karansher S. Sandhu, Dennis N. Lozada and Arron H. Carter
Agronomy 2022, 12(2), 522; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12020522 - 19 Feb 2022
Cited by 13 | Viewed by 3967
Abstract
Wheat (Triticum aestivum L.) breeding programs can take over a decade to release a new variety. However, new methods of selection, such as genomic selection (GS), must be integrated to decrease the time it takes to release new varieties to meet the [...] Read more.
Wheat (Triticum aestivum L.) breeding programs can take over a decade to release a new variety. However, new methods of selection, such as genomic selection (GS), must be integrated to decrease the time it takes to release new varieties to meet the demand of a growing population. The implementation of GS into breeding programs is still being explored, with many studies showing its potential to change wheat breeding through achieving higher genetic gain. In this review, we explore the integration of GS for a wheat breeding program by redesigning the traditional breeding pipeline to implement GS. We propose implementing a two-part breeding strategy by differentiating between population improvement and product development. The implementation of GS in the product development pipeline can be integrated into most stages and can predict within and across breeding cycles. Additionally, we explore optimizing the population improvement strategy through GS recurrent selection schemes to reduce crossing cycle time and significantly increase genetic gain. The recurrent selection schemes can be optimized for parental selection, maintenance of genetic variation, and optimal cross-prediction. Overall, we outline the ability to increase the genetic gain of a breeding program by implementing GS and a two-part breeding strategy. Full article
(This article belongs to the Special Issue Wheat Breeding: Procedures and Strategies – Series Ⅱ)
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