Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Hardner, C. M., Bally, I. S. E. and Wright, C. L. (2012) Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model. Euphytica, 186 2: 463-477. doi:10.1007/s10681-012-0639-7


Author Hardner, C. M.
Bally, I. S. E.
Wright, C. L.
Title Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model
Journal name Euphytica   Check publisher's open access policy
ISSN 0014-2336
1573-5060
Publication date 2012-07-01
Sub-type Article (original research)
DOI 10.1007/s10681-012-0639-7
Volume 186
Issue 2
Start page 463
End page 477
Total pages 15
Place of publication Dordrecht, Netherlands
Publisher Springer
Collection year 2013
Language eng
Abstract Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0. 69 to 0. 88 with correlations among harvest seasons within trials greater than 0. 96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G × E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.
Keyword Blup
Linear mixed model
Heritability
Genetic correlation
Genotype-by-environment interaction
Phenotypic Correlations
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 29 February 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Agriculture and Food Sciences
Queensland Alliance for Agriculture and Food Innovation
Official 2013 Collection
 
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