Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience

Fugeray-Scarbel, Aline; Bastien, Catherine; Dupont‐Nivet, Mathilde; Lemarié, Stéphane

doi:10.3389/fgene.2021.629737

Cited by 28 publications

(16 citation statements)

References 59 publications

(65 reference statements)

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“…Ultimately, genomic selection can be implemented to improve the choice of parents to either preserve genetic diversity or optimize crossbreeding among lines [44]. Caution should be exercised when using GEBVs solely in selection as some lines having high estimated breeding values can have low observed phenotypic values, as indicated by low prediction accuracies for some traits.…”

Section: Discussionmentioning

confidence: 99%

Ridge regression and deep learning models for genomewide selection of complex traits in New Mexican chile peppers

Lozada

Sandhu

Bhatta

2023

Preprint

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Background. Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA. Results. Accuracy of prediction differed across different models under five-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection. Conclusions. Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.

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Section: Discussionmentioning

confidence: 99%

Ridge regression and deep learning models for genomewide selection of complex traits in New Mexican chile peppers

Lozada

Sandhu

Bhatta

2023

Preprint

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“…Overall, our results provided a strong justification for wheat breeders for integrating GS in developing stripe rust resistance spring wheat germplasm using the CV2 and/or CV0 scenarios regardless of the models and genetic background, which would reduce the cost, resources, and logistics associated with field evaluation by at least 25%. In addition, GS could significantly reduce the time required for developing and registering/releasing improved varieties [27,41].…”

Section: Discussionmentioning

confidence: 99%

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

Semagn

Iqbal

Jarquín

et al. 2022

Plants

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Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.

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“…As genotypic data, a fully imputed SNP matrix with 2,029 samples and about three million homozygous SNPs was used (Arouisse et al, 2020). On that data, we applied a linkagedisequilibrium (LD) pruning using PLINK v1.9 to exclude highly correlated markers (Purcell et al, 2007;Chang et al, 2015). Phenotypic data was downloaded from the publicly available AraPheno database (Seren et al, 2016;Togninalli et al, 2020).…”

Section: Arabidopsis Thalianamentioning

confidence: 99%

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

et al. 2022

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Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.

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Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience

Cited by 28 publications

References 59 publications

Ridge regression and deep learning models for genomewide selection of complex traits in New Mexican chile peppers

Ridge regression and deep learning models for genomewide selection of complex traits in New Mexican chile peppers

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

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