Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model
Rafael Tassinari Resende,Marcos Deon Vilela de Resende,Marcos Deon Vilela de Resende,F. F. e Silva,Camila Ferreira Azevedo,E.K. Takahashi,Orzenil B. Silva-Junior,Orzenil B. Silva-Junior,Dario Grattapaglia,Dario Grattapaglia +9 more
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TLDR
Responses to genomic selection on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities, contributing further experimental data supporting the positive prospects of GS in forest trees.Abstract:
We report a genomic selection (GS) study of growth and wood quality traits in an outbred F2 hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40–0.57 for individual selection and 0.56–0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25 kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.read more
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Journal ArticleDOI
Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding
Dario Grattapaglia,Orzenil B. Silva-Junior,Orzenil B. Silva-Junior,Rafael Tassinari Resende,Eduardo P. Cappa,Bárbara S. F. Müller,Bárbara S. F. Müller,Biyue Tan,Fikret Isik,Blaise Ratcliffe,Yousry A. El-Kassaby +10 more
TL;DR: Genomic selection has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values, and areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios.
Journal ArticleDOI
Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives
TL;DR: This new method of genomic selection is based on the analysis of all effects of quantitative trait loci using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual.
Journal ArticleDOI
Genomic relationships reveal significant dominance effects for growth in hybrid Eucalyptus.
TL;DR: This work evaluated models accounting for additive, dominance, and first-order epistatic interactions and showed that the models can capture a large proportion of the genetic variance from dominance and epistasis for growth traits as those components are typically not independent.
Journal ArticleDOI
Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform
Frances R. Thistlethwaite,Blaise Ratcliffe,Jaroslav Klápště,Jaroslav Klápště,Jaroslav Klápště,Ilga Porth,Charles Chen,Michael Stoehr,Yousry A. El-Kassaby +8 more
TL;DR: While GS models’ prediction accuracies were high, the main driving force was the pedigree tracking rather than LD, and it is likely that many more markers are needed to increase the chance of capturing the LD between causal genes and markers.
Journal ArticleDOI
Genomic Prediction of Autotetraploids; Influence of Relationship Matrices, Allele Dosage, and Continuous Genotyping Calls in Phenotype Prediction.
Ivone de Bem Oliveira,Marcio F. R. Resende,Luís Felipe V. Ferrão,Rodrigo R. Amadeu,Jeffrey B. Endelman,Matias Kirst,Alexandre Siqueira Guedes Coelho,Patricio R. Munoz +7 more
TL;DR: Comparing the use of read depth as continuous parameterization with ploidy parameterizations in the context of genomic selection (GS) and the genotypic and phenotypic data used in this study are made available for comparative analysis of dosage calling and genomic selection prediction models in the contexts of autopolyploids.
References
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Book
Introduction to quantitative genetics
TL;DR: The genetic constitution of a population: Hardy-Weinberg equilibrium and changes in gene frequency: migration mutation, changes of variance, and heritability are studied.
Journal ArticleDOI
The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values
TL;DR: This study shows that markers can capture genetic relationships among genotyped animals, thereby affecting accuracies of GEBVs, and the method of choice was Bayes-B; FR–LS should be investigated further, whereas RR–BLUP cannot be recommended.
Journal ArticleDOI
Genomic selection: genome-wide prediction in plant improvement
TL;DR: Several genomic selection (GS) models are reviewed with respect to both the prediction accuracy and genetic gain from selection.
Book ChapterDOI
Genomic Selection in Plant Breeding. Knowledge and Prospects.
Aaron J. Lorenz,Shiaoman Chao,Franco G. Asoro,Elliot Lee Heffner,Takeshi Hayashi,Hiroyoshi Iwata,Kevin P. Smith,Mark E. Sorrells,Jean-Luc Jannink +8 more
TL;DR: The results reviewed here suggest that GS will play a large role in the plant breeding of the future and should prove useful to breeders as they assess the value of GS in the context of their populations and resources.
Journal ArticleDOI
Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer.
TL;DR: Sommer as discussed by the authors is an open-source R package to facilitate the use of mixed models for genomic selection and hybrid prediction purposes using more than one variance component and allowing specification of covariance structures.
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