scholarly journals Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

2021 ◽  
Author(s):  
Lance F. Merrick ◽  
Arron H. Carter
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Predictive Ability ◽  
Breeding Programs ◽  
Selection Models

scholarly journals Comparison of Genomic Selection Models for Exploring Predictive Ability of Complex Traits in Breeding Programs

2021 ◽  
Author(s):  
Lance F. Merrick ◽  
Arron H. Carter
Keyword(s):  
Complex Traits ◽  
Seedling Emergence ◽  
Predictive Ability ◽  
Complex Trait ◽  
Parametric Models ◽  
Support Vector ◽  
Breeding Programs ◽  
Breeding Lines ◽  
Mapping Panel ◽  
Increase In Accuracy

AbstractTraits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait’s genetic basis. One such trait, seedling emergence of wheat (Triticum aestivumL.) from deep planting, presents a unique opportunity to explore the best method to use and implement GS models to predict a complex trait. 17 GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel (DP) phenotyped from 2015-2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross-validations. However, the consistent moderate accuracy of cBLUP and other parametric models indicates no need to implement computationally demanding non-parametric models for complex traits. There was an increase in accuracy using cross-validations from 0.40 to 0.41 and independent validations from 0.10 to 0.17 using diversity panels lines to breeding lines. The environmental effects of complex traits can be overcome by combining years of the same populations. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using parametric models within their own breeding programs with increased accuracy as they combine training populations over the years.

scholarly journals Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

2021 ◽  
Author(s):  
Xabi Cazenave ◽  
Bernard Petit ◽  
Marc Lateur ◽  
Hilde Nybom ◽  
Jiri Sedlak ◽  
...  
Keyword(s):  
Genetic Resources ◽  
Genomic Selection ◽  
Predictive Ability ◽  
Practical Implementation ◽  
Specific Marker ◽  
Training Set ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Breeding Cycles ◽  
Two Populations

Abstract Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

scholarly journals Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

2021 ◽  
Author(s):  
Xabi Cazenave ◽  
Bernard Petit ◽  
Francois Laurens ◽  
Charles-Eric Durel ◽  
Helene Muranty
Keyword(s):  
Genetic Resources ◽  
Genomic Selection ◽  
Predictive Ability ◽  
Practical Implementation ◽  
Specific Marker ◽  
Training Set ◽  
High Genetic Diversity ◽  
Breeding Programs ◽  
Breeding Cycles ◽  
Two Populations

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and were always highest when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

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 Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing

2017 ◽  
Vol 131 (3) ◽  
pp. 703-720 ◽  
Author(s):  
Marty J. Faville ◽  
Siva Ganesh ◽  
Mingshu Cao ◽  
M. Z. Zulfi Jahufer ◽  
Timothy P. Bilton ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Predictive Ability ◽  
Genotyping By Sequencing ◽  
Training Set ◽  
Selection Models

scholarly journals Non-Parametric Genetic Prediction of Complex Traits with Latent Dirichlet Process Regression Models

2017 ◽  
Author(s):  
Ping Zeng ◽  
Xiang Zhou
Keyword(s):  
Genomic Selection ◽  
Dirichlet Process ◽  
Complex Traits ◽  
Gene Expressions ◽  
Breeding Programs ◽  
Genetic Prediction ◽  
Robust Prediction ◽  
Selection Of ◽  
Effect Size Distribution ◽  
Non Parametric

AbstractUsing genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model (DPR). DPR is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare DPR with several commonly used prediction methods with simulations. We further apply DPR to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.

scholarly journals Phantom Epistasis in Genomic Selection: On the Predictive Ability of Epistatic Models

2020 ◽  
Vol 10 (9) ◽  
pp. 3137-3145 ◽  
Author(s):  
Matías F Schrauf ◽  
Johannes W R Martini ◽  
Henner Simianer ◽  
Gustavo de los Campos ◽  
Rodolfo Cantet ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Association Studies ◽  
Predictive Ability ◽  
Additive Models ◽  
Marker Density ◽  
Interaction Terms ◽  
Biological Interpretation ◽  
Genetic Value

Abstract Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density (“Phantom Epistasis”). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.

scholarly journals Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

2020 ◽  
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 ◽  
Superior Genotypes ◽  
Tomato Breeding ◽  
Accuracy Of Prediction ◽  
Selection Of

Genomic selection (GS) is a predictive approach that was build up to increase the rate of genetic gain per unit of time in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effect. GS enables the prediction of breeding value of candidate genotypes for selection. In this work we address important issues related to GS and its implementation in tomato breeding context. Genomic constrains and critical parameters affecting the accuracy of prediction in such crop such as phenotyping, genotyping training population composition and size and statistical method should be carefully evaluated. Comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding program are also discussed. GS applied to tomato breeding has already shown to be feasible. We illustrated how GS can improve the rate of gain in elite lines selection, descendent and in backcross schemes. The GS schemes begin to be delineated and computer science can provide support for future selection strategies. A new breeding framework is beginning to emerge for optimizing tomato improvement procedures.

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