Using Genomic Selection to Leverage Resources among Breeding Programs: Consortium-Based Breeding

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.

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Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

Agronomy ◽

10.3390/agronomy9090479 ◽

2019 ◽

Vol 9 (9) ◽

pp. 479 ◽

Cited By ~ 7

Author(s):

Larkin ◽

Lozada ◽

Mason

Keyword(s):

Genomic Selection ◽

Wheat Yield ◽

Wheat Breeding ◽

Breeding Value ◽

Selection Marker ◽

Successful Implementation ◽

Head Blight ◽

Phenotypic Data ◽

Breeding Programs ◽

A Genome

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.

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Progress in Ohio Soft Red Winter Wheat Breeding: Grain Yield and Agronomic Traits of Cultivars Released from 1871 to 1987

Crop Science ◽

10.2135/cropsci1993.0011183x003300060050x ◽

1993 ◽

Vol 33 (6) ◽

pp. 1382-1386 ◽

Cited By ~ 7

Author(s):

W. A. Berzonsky ◽

H. N. Lafever

Keyword(s):

Winter Wheat ◽

Grain Yield ◽

Agronomic Traits ◽

Wheat Breeding ◽

Soft Red Winter Wheat ◽

Red Winter Wheat

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Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Plants ◽

10.3390/plants9091236 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1236

Author(s):

Elisa Cappetta ◽

Giuseppe Andolfo ◽

Antonio Di Matteo ◽

Amalia Barone ◽

Luigi Frusciante ◽

...

Keyword(s):

Genomic Selection ◽

Complex Traits ◽

Breeding Value ◽

Critical Parameters ◽

Breeding Programs ◽

Selection Strategies ◽

Genome Wide ◽

Line Selection ◽

Tomato Breeding ◽

Accuracy Of Prediction

Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.

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Genomic Selection in Winter Wheat Breeding Using a Recommender Approach

Genes ◽

10.3390/genes11070779 ◽

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.

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Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program

10.1101/2021.04.12.439532 ◽

2021 ◽

Cited By ~ 1

Author(s):

Karansher S. Sandhu ◽

Shruti S. Patil ◽

Michael O. Pumphrey ◽

Arron H. Carter

Keyword(s):

Deep Learning ◽

Protein Content ◽

Genomic Selection ◽

Grain Protein Content ◽

Wheat Breeding ◽

Spectral Information ◽

Grain Protein ◽

Learning Models ◽

Breeding Programs ◽

Wheat Breeding Program

AbstractPrediction of breeding values and phenotypes is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine and deep learning algorithms applied to complex traits in plants can improve prediction accuracies in the context of GS. Spectral reflectance indices further provide information about various physiological parameters previously undetectable in plants. This research explores the potential of multi-trait (MT) machine and deep learning models for predicting grain yield and grain protein content in wheat using spectral information in GS models. This study compares the performance of four machine and deep learning-based uni-trait (UT) and MT models with traditional GBLUP and Bayesian models. The dataset consisted of 650 recombinant inbred lines from a spring wheat breeding program, grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. MT-GS models performed 0-28.5% and −0.04-15% superior to the UT-GS models for predicting grain yield and grain protein content. Random forest and multilayer perceptron were the best performing machine and deep learning models to predict both traits. These two models performed similarly under UT and MT-GS models. Four explored Bayesian models gave similar accuracies, which were less than machine and deep learning-based models, and required increased computational time. Green normalized difference vegetation index best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine and deep learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.Core IdeasPotential for combining high throughput phenotyping, machine and deep learning in breeding.Multi-trait models exploit information from secondary correlated traits efficiently.Spectral information improves genomic selection models.Deep learning can aid plant breeders owing to increased data generated in breeding programs

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Optical Sorter-Based Selection Effectively Identifies Soft Red Winter Wheat Breeding Lines With Fhb1 and Enhances FHB Resistance in Lines With and Without Fhb1

Frontiers in Plant Science ◽

10.3389/fpls.2020.01318 ◽

2020 ◽

Vol 11 ◽

Author(s):

W. Jesse Carmack ◽

Anthony Clark ◽

Yanhong Dong ◽

Gina Brown-Guedira ◽

David Van Sanford

Keyword(s):

Winter Wheat ◽

Wheat Breeding ◽

Breeding Lines ◽

Soft Red Winter Wheat ◽

Fhb Resistance ◽

Red Winter Wheat

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Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing

Theoretical and Applied Genetics ◽

10.1007/s00122-020-03638-5 ◽

2020 ◽

Vol 133 (10) ◽

pp. 2869-2879

Author(s):

Nan Wang ◽

Hui Wang ◽

Ao Zhang ◽

Yubo Liu ◽

Diansi Yu ◽

...

Keyword(s):

Genomic Selection ◽

Doubled Haploid ◽

Early Stage ◽

Low Cost ◽

Phenotypic Selection ◽

Breeding Value ◽

Main Task ◽

Training Population ◽

Dh Lines ◽

Stage Yield

Abstract Key message Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. Abstract With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year’s data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

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Physiologic Specialization of Puccinia triticina on Wheat in the United States in 2008

Plant Disease ◽

10.1094/pdis-94-6-0775 ◽

2010 ◽

Vol 94 (6) ◽

pp. 775-780 ◽

Cited By ~ 13

Author(s):

J. A. Kolmer ◽

D. L. Long ◽

M. E. Hughes

Keyword(s):

United States ◽

Winter Wheat ◽

Leaf Rust ◽

Great Plains ◽

Puccinia Triticina ◽

The United States ◽

Wheat Breeding ◽

Eastern United States ◽

Soft Red Winter Wheat ◽

Red Winter Wheat

Collections of Puccinia triticina were obtained from rust-infected wheat (Triticum aestivum) leaves provided by cooperators throughout the United States and from surveys of wheat fields and wheat breeding plots by USDA-ARS personnel in the Great Plains, Ohio River Valley, Southeast, and Washington State in order to determine the virulence of the wheat leaf rust population in 2008. Single uredinial isolates (730 in total) were derived from the collections and tested for virulence phenotype on lines of Thatcher wheat that are near-isogenic for leaf rust resistance genes Lr1, Lr2a, Lr2c, Lr3, Lr9, Lr16, Lr24, Lr26, Lr3ka, Lr11, Lr17, Lr30, LrB, Lr10, Lr14a, Lr18, Lr21, Lr28, and a winter wheat line with Lr41. Forty-eight virulence phenotypes were described. Virulence phenotypes TDBGG, TCRKG, and MLDSD were the three most common phenotypes. TDBGG is virulent to Lr24 and was found in both the soft red winter wheat and hard red winter wheat regions. Phenotype TCRKG is virulent to Lr11, Lr18, and Lr26 and is found mostly in the soft red winter wheat region in the eastern United States. Phenotype MLDSD is virulent to Lr17 and Lr41 and was widely distributed in the Great Plains. Virulence to Lr21 was not found in any of the tested isolates. Virulence to Lr11 and Lr18 increased in 2008 in the soft red winter wheat regions. Two separate epidemiological zones of P. triticina in the soft red winter wheat region of the southern and eastern states and in the hard red wheat region of the Great Plains were described.

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

BMC Genetics ◽

10.1186/s12863-019-0785-1 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 14

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.

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