Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments

Authors

  • M. F. R. Resende Jr,

    1. Genetics and Genomics Graduate Program, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
    2. School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
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  • P. Muñoz,

    1. Plant Molecular and Cellular Biology Graduate Program, University of Florida, PO Box 110690, Gainesville, FL 32611, USA
    2. School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
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  • J. J. Acosta,

    1. School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
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  • G. F. Peter,

    1. School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
    2. University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
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  • J. M. Davis,

    1. School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
    2. University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
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  • D. Grattapaglia,

    1. Plant Genetics Laboratory, Embrapa – Recursos Genéticos e Biotecnologia, Parque Estação Biológica, Brasília, DF 70770-970, Brazil
    2. Graduate Program in Genomic Sciences and Biotechnology, Universidade Católica de Brasília–SGAN 916 modulo B, Brasília, DF 70790-160, Brazil
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  • M. D. V. Resende,

    1. EMBRAPA Forestry, Estrada da Ribeira, km 111 Caixa Postal 319, Colombo, PR 83411-000 Brazil
    2. Department of Forest Engineering, Universidade Federal de Viçosa, Viçosa, MG 36571-000 Brazil
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  • M. Kirst

    1. School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
    2. University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL 32611, USA
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Errata

This article is corrected by:

  1. Errata: Corrigendum Volume 193, Issue 4, 1099, Article first published online: 9 January 2012

Author for correspondence:
Matias Kirst
Tel: +1 352 846 0900
Email: mkirst@ufl.edu

Summary

  • 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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