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Azevedo CF, Resende MDV, Silva FF, Viana JMS, Valente MSF, Resende MFR, Oliveira EJ. New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program. Genet Mol Res 2016; 15:gmr8838. [PMID: 27808382 DOI: 10.4238/gmr.15048838] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Genomic selection is the main force driving applied breeding programs and accuracy is the main measure for evaluating its efficiency. The traditional estimator (TE) of experimental accuracy is not fully adequate. This study proposes and evaluates the performance and efficiency of two new accuracy estimators, called regularized estimator (RE) and hybrid estimator (HE), which were applied to a practical cassava breeding program and also to simulated data. The simulation study considered two individual narrow sense heritability levels and two genetic architectures for traits. TE, RE, and HE were compared under four validation procedures: without validation (WV), independent validation, ten-fold validation through jacknife allowing different markers, and with the same markers selected in each cycle. RE presented accuracies closer to the parametric ones and less biased and more precise ones than TE. HE proved to be very effective in the WV procedure. The estimators were applied to five traits evaluated in a cassava experiment, including 358 clones genotyped for 390 SNPs. Accuracies ranged from 0.67 to 1.12 with TE and from 0.22 to 0.51 with RE. These results indicated that TE overestimated the accuracy and led to one accuracy estimate (1.12) higher than one, which is outside of the parameter space. Use of RE turned the accuracy into the parameter space. Cassava breeding programs can be more realistically implemented using the new estimators proposed in this study, providing less risky practical inferences.
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Affiliation(s)
- C F Azevedo
- Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, MG, Brasil
| | - M D V Resende
- Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, MG, Brasil.,Embrapa Floresta, Colombo, PR, Brasil
| | - F F Silva
- Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, MG, Brasil
| | - J M S Viana
- Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, MG, Brasil
| | - M S F Valente
- Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, MG, Brasil
| | - M F R Resende
- RAPiD Genomics, Florida Innovation Hub, Gainesville, FL, USA
| | - E J Oliveira
- Embrapa Mandioca e Fruticultura, Cruz das Almas, BA, Brasil
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de Almeida Filho JE, Guimarães JFR, E Silva FF, de Resende MDV, Muñoz P, Kirst M, Resende MFR. The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity (Edinb) 2016; 117:33-41. [PMID: 27118156 PMCID: PMC4901355 DOI: 10.1038/hdy.2016.23] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 12/07/2015] [Accepted: 03/04/2016] [Indexed: 02/01/2023] Open
Abstract
Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive-dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.
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Affiliation(s)
- J E de Almeida Filho
- School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA.,Graduate Program in Genetics and Improvement, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.,Department of Zootecnia, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil
| | - J F R Guimarães
- School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA.,Graduate Program in Genetics and Improvement, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil.,Department of Zootecnia, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil
| | - F F E Silva
- Department of Zootecnia, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil
| | - M D V de Resende
- EMBRAPA Florestas/Department of Statistics, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil
| | - P Muñoz
- Agronomy Department, University of Florida, Gainesville, FL, USA
| | - M Kirst
- University of Florida Genetics Institute, University of Florida, Gainesville, FL, USA.,School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
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Resende MFR, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 2012; 193:1099. [PMID: 21973055 DOI: 10.1111/j.1469-8137.2011.04048.x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
• Genomic selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate genomic selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. • Here, we evaluated the utility of genomic selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single-nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using genomic random regression best linear unbiased predictor (BLUP). • Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The selection efficiency per unit time was estimated as 53-112% higher using genomic selection compared with phenotypic selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. • These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.
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Affiliation(s)
- M F R Resende
- Genetics and Genomics Graduate Program, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
| | - P Muñoz
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, PO Box 110690, Gainesville, FL 32611, USA
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
| | - J J Acosta
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
| | - G F Peter
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
| | - J M Davis
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
| | - D Grattapaglia
- Plant Genetics Laboratory, Embrapa - Recursos Genéticos e Biotecnologia, Parque Estação Biológica, Brasília, DF 70770-970, Brazil
- Graduate Program in Genomic Sciences and Biotechnology, Universidade Católica de Brasília-SGAN 916 modulo B, Brasília, DF 70790-160, Brazil
| | - M D V Resende
- EMBRAPA Forestry, Estrada da Ribeira, km 111 Caixa Postal 319, Colombo, PR 83411-000 Brazil
- Department of Forest Engineering, Universidade Federal de Viçosa, Viçosa, MG 36571-000 Brazil
| | - M Kirst
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
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Resende MFR, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 2012; 193:617-624. [PMID: 21973055 DOI: 10.1111/j.1469-8137.2011.03895.x] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
• Genomic selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate genomic selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. • Here, we evaluated the utility of genomic selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single-nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using genomic random regression best linear unbiased predictor (BLUP). • Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The selection efficiency per unit time was estimated as 53-112% higher using genomic selection compared with phenotypic selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. • These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.
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Affiliation(s)
- M F R Resende
- Genetics and Genomics Graduate Program, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
| | - P Muñoz
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, PO Box 110690, Gainesville, FL 32611, USA
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
| | - J J Acosta
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
| | - G F Peter
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
| | - J M Davis
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
| | - D Grattapaglia
- Plant Genetics Laboratory, Embrapa - Recursos Genéticos e Biotecnologia, Parque Estação Biológica, Brasília, DF 70770-970, Brazil
- Graduate Program in Genomic Sciences and Biotechnology, Universidade Católica de Brasília-SGAN 916 modulo B, Brasília, DF 70790-160, Brazil
| | - M D V Resende
- EMBRAPA Forestry, Estrada da Ribeira, km 111 Caixa Postal 319, Colombo, PR 83411-000 Brazil
- Department of Forest Engineering, Universidade Federal de Viçosa, Viçosa, MG 36571-000 Brazil
| | - M Kirst
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
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