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Genomic-based multiple-trait evaluation in Eucalyptus grandis using dominant DArT markers
Abstract
We investigated the impact of combining the pedigree- and genomic-based relationship matrices in a multiple-trait individual-tree mixed model (a.k.a., multiple-trait combined approach) on the estimates of heritability and on the genomic correlations between growth and stem straightness in an open-pollinated Eucalyptus grandis population. Additionally, the added advantage of incorporating genomic information on the theoretical accuracies of parents and
[ver mas...]
We investigated the impact of combining the pedigree- and genomic-based relationship matrices in a multiple-trait individual-tree mixed model (a.k.a., multiple-trait combined approach) on the estimates of heritability and on the genomic correlations between growth and stem straightness in an open-pollinated Eucalyptus grandis population. Additionally, the added advantage of incorporating genomic information on the theoretical accuracies of parents and offspring breeding values was evaluated. Our results suggested that the use of the combined approach for estimating heritabilities and additive genetic correlations in multiple-trait evaluations is advantageous and including genomic information increases the expected accuracy of breeding values. Furthermore, the multiple-trait combined approach was proven to be superior to the single-trait combined approach in predicting breeding values, in particular for low-heritability traits. Finally, our results advocate the use of the combined approach in forest tree progeny testing trials, specifically when a multiple-trait individual-tree mixed model is considered.
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Author
Cappa, Eduardo Pablo;
El-Kassaby, Yousry A.;
Muñoz, Facundo;
Garcia, Martin Nahuel;
Villalba, Pamela Victoria;
Klapste, Jaroslav;
Marcucci Poltri, Susana Noemi;
Fuente
Plant science 271 : 27-33. (June 2018)
Date
2018-06
ISSN
0168-9452
Formato
pdf
Tipo de documento
artículo
Palabras Claves
Derechos de acceso
Restringido
Excepto donde se diga explicitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)