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Genomic Prediction in Family Bulks Using Different Traits and Cross-Validations in Pine

View ORCID ProfileEsteban F. Rios, Mario H. M. L. Andrade, Marcio F.R. Resende Jr, Matias Kirst, View ORCID ProfileMarcos D.V. de Resende, View ORCID ProfileJaneo E. de Almeida Filho, Salvador A. Gezan, View ORCID ProfilePatricio Munoz
doi: https://doi.org/10.1101/2021.03.10.434809
Esteban F. Rios
*Agronomy Department, University of Florida, Gainesville, FL, 32611
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  • For correspondence: estebanrios@ufl.edu
Mario H. M. L. Andrade
*Agronomy Department, University of Florida, Gainesville, FL, 32611
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Marcio F.R. Resende Jr
†Horticultural Sciences Department, University of Florida, Gainesville, FL 32611
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Matias Kirst
‡School of Forest Resources and Conservation, University of Florida, Gainesville, FL
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Marcos D.V. de Resende
§EMBRAPA Café/Department of Statistics, Federal University of Viçosa, Avenida PH Rolfs S/N, Viçosa, Brazil
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Janeo E. de Almeida Filho
**Bayer Crop Science, Estrada da Invernadinha, 2000, Coxilha-RS, Brazil
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Salvador A. Gezan
††VSN International Ltd, Hemel Hempstead, United Kingdom
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Patricio Munoz
†Horticultural Sciences Department, University of Florida, Gainesville, FL 32611
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Abstract

Genomic prediction (GP) integrates statistical, genomic and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in biology, breeding scheme, propagation method, and unit of selection, no universal GP approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family bulk is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5-20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values at the individual (GEBV) and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family bulks, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of GP in these situations.

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Posted March 12, 2021.
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Genomic Prediction in Family Bulks Using Different Traits and Cross-Validations in Pine
Esteban F. Rios, Mario H. M. L. Andrade, Marcio F.R. Resende Jr, Matias Kirst, Marcos D.V. de Resende, Janeo E. de Almeida Filho, Salvador A. Gezan, Patricio Munoz
bioRxiv 2021.03.10.434809; doi: https://doi.org/10.1101/2021.03.10.434809
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Genomic Prediction in Family Bulks Using Different Traits and Cross-Validations in Pine
Esteban F. Rios, Mario H. M. L. Andrade, Marcio F.R. Resende Jr, Matias Kirst, Marcos D.V. de Resende, Janeo E. de Almeida Filho, Salvador A. Gezan, Patricio Munoz
bioRxiv 2021.03.10.434809; doi: https://doi.org/10.1101/2021.03.10.434809

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