scholarly journals Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 479 ◽  
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.

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

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1555
Author(s):  
Clay Sneller ◽  
Carlos Ignacio ◽  
Brian Ward ◽  
Jessica Rutkoski ◽  
Mohsen Mohammadi
Keyword(s):  
Genomic Selection ◽  
Early Stage ◽  
Family Relationship ◽  
Wheat Breeding ◽  
Breeding Value ◽  
Breeding Programs ◽  
Soft Red Winter Wheat ◽  
Sharing Scheme ◽  
Using Data ◽  
Red Winter Wheat

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.

scholarly journals Identification of a Novel Genomic Region Associated With Resistance to Fusarium Head Blight in Chinese Winter Wheat

2021 ◽  
Author(s):  
Yunzhe Zhao ◽  
Xinying Zhao ◽  
Mengqi Ji ◽  
Wenqi Fang ◽  
Hong Guo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Genome Wide Association Study ◽  
Wheat Breeding ◽  
Scab Resistance ◽  
Yield Reduction ◽  
Transferase Activity ◽  
Head Blight ◽  
A Genome ◽  
Fhb Resistance

Abstract Background: Fusarium head blight (FHB) is a disease affecting wheat spikes caused by Fusarium species, which leads to cases of severe yield reduction and seed contamination. Therefore, identifying resistance genes from various sources is always of importance to wheat breeders. In this study, a genome-wide association study (GWAS) focusing on FHB using a high-density genetic map constructed with 90K single nucleotide polymorphism (SNP) arrays in a panel of 205 elite winter wheat accessions, was conducted in 3 environments. Results: Sixty-six significant marker–trait associations (MTAs) were identified (P<0.001) on fifteen chromosomes explaining 5.4–11.2% of the phenotypic variation therein. Some important new genomic regions involving FHB resistance were found on chromosomes 2A, 3B, 5B, 6A, and 7B. On chromosome 7B, 6 MTAs at 92 genetic positions were found in 2 environments. Moreover, there were 11 MTAs consistently associated with diseased spikelet rate and diseased rachis rate as pleiotropic effect loci. Eight new candidate genes of FHB resistance were predicated in wheat. Of which, three genes: TraesCS5D01G006700, TraesCS6A02G013600, and TraesCS7B02G370700 on chromosome 5DS, 6AS, and 7BL, respectively, were important in defending against FHB by regulating chitinase activity, calcium ion binding, intramolecular transferase activity, and UDP-glycosyltransferase activity in wheat. In addition, a total of six excellent alleles associated with wheat scab resistance were discovered. Conclusion: These results provide important genes/loci for enhancing FHB resistance in wheat breeding populations by marker-assisted selection.

scholarly journals Molecular Characterization and Expression of PFT, an FHB Resistance Gene at the Fhb1 QTL in Wheat

2018 ◽  
Vol 108 (6) ◽  
pp. 730-736 ◽  
Author(s):  
Yi He ◽  
Xu Zhang ◽  
Yu Zhang ◽  
Dawood Ahmad ◽  
Lei Wu ◽  
...  
Keyword(s):  
Defense Responses ◽  
Wheat Breeding ◽  
Flanking Sequence ◽  
Head Blight ◽  
Breeding Programs ◽  
Diverse Range ◽  
Fhb Resistance ◽  
Trait Locus ◽  
Sumai 3

Fusarium head blight (FHB) is a destructive fungal disease in wheat worldwide. Efforts have been carried out to combat this disease, and the pore-forming toxin-like (PFT) gene at the quantitative trait locus (QTL) Fhb1 was isolated and found to confer resistance to FHB in Sumai 3. In this study, we characterized PFT in 348 wheat accessions. Four haplotypes of PFT were identified. The wild haplotype of PFT had higher resistance than other haplotypes and explained 13.8% of phenotypic variation in FHB resistance by association analysis. PFT was highly expressed during early flowering and increased after Fusarium graminearum treatment in Sumai 3. Analysis of the 5′ flanking sequence of PFT predicted that the cis elements of the PFT promoter were related to hormones and biological defense responses. However, PFT existed not only in the FHB-resistant accessions but also in some susceptible accessions. These results suggested that FHB resistance in a diverse range of wheat genotypes is partially conditioned by PFT. The profiling of FHB resistance and the PFT locus in this large collection of wheat germplasm may prove helpful for incorporating FHB resistance into wheat breeding programs, although more work is needed to reveal the exact role of the QTL Fhb1 in conferring resistance to fungal spread.

scholarly journals Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program

2020 ◽  
Author(s):  
Sikiru Adeniyi Atanda ◽  
Michael Olsen ◽  
Juan Burgueño ◽  
Jose Crossa ◽  
Daniel Dzidzienyo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Primary Objective ◽  
Breeding Program ◽  
Breeding Cycle ◽  
Training Set ◽  
Maize Breeding ◽  
Phenotypic Data ◽  
Breeding Programs

Abstract Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. Abstract The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

scholarly journals Major QTL with pleiotropic effects controlling time of leaf budburst and flowering-related traits in walnut (Juglans regia L.)

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Şakir Burak Bükücü ◽  
Mehmet Sütyemez ◽  
Sina Kefayati ◽  
Aibibula Paizila ◽  
Abdulqader Jighly ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Juglans Regia ◽  
Genotypic Variation ◽  
Phenotypic Traits ◽  
Juvenile Period ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
Genome Wide ◽  
A Genome ◽  
Major Qtl

Abstract Breeding studies in walnut (Juglans regia L.) are usually time consuming due to the long juvenile period and therefore, this study aimed to determine markers associated with time of leaf budburst and flowering-related traits by performing a genome-wide association study (GWAS). We investigated genotypic variation and its association with time of leaf budburst and flowering-related traits in 188 walnut accessions. Phenotypic data was obtained from 13 different traits during 3 consecutive years. We used DArT-seq for genotyping with a total of 33,519 (14,761 SNP and 18,758 DArT) markers for genome-wide associations to identify marker underlying these traits. Significant correlations were determined among the 13 different traits. Linkage disequilibrium decayed very quickly in walnut in comparison with other plants. Sixteen quantitative trait loci (QTL) with major effects (R2 between 0.08 and 0.23) were found to be associated with a minimum of two phenotypic traits each. Of these QTL, QTL05 had the maximum number of associated traits (seven). Our study is GWAS for time of leaf budburst and flowering-related traits in Juglans regia L. and has a strong potential to efficiently implement the identified QTL in walnut breeding programs.

scholarly journals Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Plants ◽  
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.

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 Application of genomic selection in agriculture

2018 ◽  
Vol 33 (4) ◽  
Author(s):  
Neeraj Budhlakoti ◽  
D. C. Mishra ◽  
Devendra Arora ◽  
Rajeev Ranjan Kumar
Keyword(s):  
Genomic Selection ◽  
Marker Assisted Selection ◽  
Genetic Improvement ◽  
Breeding Value ◽  
Slow Time ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Sequencing Technologies ◽  
Breeding Technique ◽  
Density Marker

Traditional breeding technique for genetic improvement of crops are based on, information on phenotypes and pedigrees to predict breeding values, has been found very successful. But, genetic gain through this technique is found to be very slow, time consuming. However, Due to availability of latest DNA sequencing technologies, now it is possible to estimate breeding values more accurately by using information on variation in DNA sequence. Lots of research has been done in direction of marker assisted selection, still it has some limitation on its implementation. Genomic selection (GS) is proposed to overcome such limitation. GS is a form of marker-assisted selection in which genetic markers covering the whole genome are used. GS predicts breeding value using information available on phenotype and high density marker. Several techniques has been developed for selection and prediction of genotype, these techniques are based on analysis of genotypic and phenotypic data.

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

2021 ◽  
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

Sign in / Sign up

Export Citation Format

Share Document