Epsilon Open Archive

Abstract

The final objective of tree improvement programs is to increase the frequency of favourable alleles in a population, for the traits of interest within the breeding programs. To achieve this, it is crucial to decompose the phenotypic variance accurately into its genetic and environmental components in order to obtain a precise estimation of genetic parameters and to increase genetic gains. The overall aim of this thesis was to increase the accuracy of genetic parameter estimation by incorporating new quantitative genetics models to the analysis of multiple traits in multiple trials of Scots pine, and to develop a genomic selection protocol to accelerate genetic gain. Factor analysis was incorporated to multivariate multi-environment analyses and it allowed to evaluate up to 19 traits simultaneously. As a result, precise patterns of genotype-by-environment interactions (G  E) were observed for tree vitality and height; moreover, it was possible to detect the main driver of the G  E: differences in temperature sum among sites. Traditional quantitative trait loci (QTL) analysis of phenotypic data was compared with the detection of QTL with estimated breeding values (EBV) for the first time in a three generation pedigree and, as outcome, it was noticed that if a QTL was associated to a EBV and to a phenotypic trait, the proportion of variance explained by the QTLEBV was higher than the QTL-phenotype. Additionally, several QTL were detected across several ages, which may make them suitable as candidates for early selection. Genomic selection (GS) could aid to reduce the breeding cycle by shortening the periods of progeny field testing, and consequently increasing genetic gains per year. Genomic predictions, including additive and non-additive effects through different prediction models were compared with traditional pedigree-based models; it was seen an overestimation of genetic parameters for pedigree-based models, even larger when nonadditive effects could not be discerned from additive and residual effects. Prediction accuracies and abilities of the genomic models were sufficient to achieve higher selection efficiencies and responses per year varying between 50-90% by shortening 50% the breeding cycle. For the selection of the top 50 individuals, higher gains were estimated if non-additive effects are incorporated to the models (7 – 117%).