Evaluation of genomic selection and marker-assisted selection in Miscanthus and energycane

Olatoye, Marcus O.; Clark, Lindsay V.; Wang, Jianping; Yang, Xiping; Yamada, Toshihiko; Sacks, Erik J.; Lipka, Alexander E.

doi:10.1007/s11032-019-1081-5

Cited by 24 publications

(16 citation statements)

References 73 publications

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“…As expected, IM methods performed poorly to predict accurate genotypic values when QTL number was large (Bernardo and Yu 2007; Lorenzana and Bernardo 2009; Mayor and Bernardo 2009; Olatoye et al 2019) (Figures 2 and S6). Therefore, for complex traits, genomic prediction should not be based only on QTLs detected by IM methods.…”

Section: Discussionsupporting

confidence: 68%

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Brault

Doligez

et al. 2020

Preprint

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Viticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a potential key to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs, multivariate methods being potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. We applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental grapevine progeny, in order to compare their ability to predict genotypic values and detect QTLs. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. Using simulations, we recommend the penalized regression method Elastic Net (EN) as a default for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. These methods can be applied to other traits and species.

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Section: Discussionsupporting

confidence: 68%

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Brault

Doligez

et al. 2020

Preprint

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“…For each set of parents, 50 F 1 individuals were simulated and then intermated to simulate 216 F 2 individuals. The protocol implemented to simulate these individuals has been previously described (Olatoye et al 2019). In brief, normal meiotic segregation was assumed and a custom Haldane mapping function was used to simulate crossovers based on the aforementioned genetic map of 356 markers in the 09F2 population.…”

Section: Gs Model and Quantification Of Prediction Accuracymentioning

confidence: 99%

“…A custom script in the R programming language similar to the one described in Olatoye et al (2019) was used to simulate traits in each diversity panel and F 2 population. This script randomly selected a subset of the 356 discrete markers to be quantitative trait nucleotides (QTNs) underlying these traits.…”

Section: Gs Model and Quantification Of Prediction Accuracymentioning

confidence: 99%

Training Population Optimization for Genomic Selection in Miscanthus

Olatoye

Clark

LaBonte

et al. 2020

G3 Genes|Genomes|Genetics

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Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.

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“…It sped up the process of introgression of a gene while increasing genetic gain compared to the classical selection, especially for disease resistance [129]. Integration of GS with genome-wide association studies (GWAS) can prevent the loss of target genes and sustain increased genetic gain through an appropriate capture of large-and small-effect QTL underlying a trait of interest [130].…”

Section: Recurrent Genomic Selection and Reciprocal Recurrent Genomicmentioning

confidence: 99%

Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

et al. 2020

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Sugarcane is a major industrial crop cultivated in tropical and subtropical regions of the world. It is the primary source of sugar worldwide, accounting for more than 70% of world sugar consumption. Additionally, sugarcane is emerging as a source of sustainable bioenergy. However, the increase in productivity from sugarcane has been small compared to other major crops, and the rate of genetic gains from current breeding programs tends to be plateauing. In this review, some of the main contributors for the relatively slow rates of genetic gain are discussed, including (i) breeding cycle length and (ii) low narrow-sense heritability for major commercial traits, possibly reflecting strong non-additive genetic effects involved in quantitative trait expression. A general overview of genomic selection (GS), a modern breeding tool that has been very successfully applied in animal and plant breeding, is given. This review discusses key elements of GS and its potential to significantly increase the rate of genetic gain in sugarcane, mainly by (i) reducing the breeding cycle length, (ii) increasing the prediction accuracy for clonal performance, and (iii) increasing the accuracy of breeding values for parent selection. GS approaches that can accurately capture non-additive genetic effects and potentially improve the accuracy of genomic estimated breeding values are particularly promising for the adoption of GS in sugarcane breeding. Finally, different strategies for the efficient incorporation of GS in a practical sugarcane breeding context are presented. These proposed strategies hold the potential to substantially increase the rate of genetic gain in future sugarcane breeding.

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Evaluation of genomic selection and marker-assisted selection in Miscanthus and energycane

Cited by 24 publications

References 73 publications

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Training Population Optimization for Genomic Selection in Miscanthus

Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

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