Genomic-based multiple-trait evaluation in Eucalyptus grandis using dominant DArT markers
2018
Cappa, Eduardo | El Kassaby, Yousry A | Munoz, Facundo | Garcia, Martin N. | Villalba, Pamela V. | Klápště, Jaroslav | Marcucci Poltri, Susana N. | Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET) | Instituto Nacional de Tecnología Agropecuaria (INTA) | IPIMAR - Instituto Nacional de Recursos Biológicos ; IPIMAR - Instituto Nacional de Recursos Biológicos | Department of Forest and Conservation Sciences, Faculty of Forestry ; University of British Columbia (UBC) | Biologie intégrée pour la valorisation de la diversité des arbres et de la forêt (BioForA) ; Institut National de la Recherche Agronomique (INRA)-Office national des forêts (ONF) | Czech University of Life Sciences Prague (CZU) | BIOTECSUR platform [UE127118]; Instituto Nacional de Tecnologia Agropecuaria [PNFOR-1104064]; Spanish Ministry of Economy and Competitiveness [MTM2016-77501-P]
International audience
显示更多 [+] 显示较少 [-]英语. 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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