Calleja-Rodriguez, Ainhoa
(2019).
Quantitative genetics and genomic selection of Scots pine.
Diss. (sammanfattning/summary)
Umeå :
Sveriges lantbruksuniv.,
Acta Universitatis Agriculturae Sueciae, 1652-6880
; 2019:36
ISBN 978-91-7760-390-0
eISBN 978-91-7760-391-7
[Doctoral thesis]
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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%).
Authors/Creators: | Calleja-Rodriguez, Ainhoa | ||||||||||
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Title: | Quantitative genetics and genomic selection of Scots pine | ||||||||||
Series Name/Journal: | Acta Universitatis Agriculturae Sueciae | ||||||||||
Year of publishing : | 2019 | ||||||||||
Depositing date: | 29 April 2019 | ||||||||||
Number: | 2019:36 | ||||||||||
Number of Pages: | 67 | ||||||||||
Papers/manuscripts: |
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Place of Publication: | Umeå | ||||||||||
Publisher: | Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences | ||||||||||
ISBN for printed version: | 978-91-7760-390-0 | ||||||||||
ISBN for electronic version: | 978-91-7760-391-7 | ||||||||||
ISSN: | 1652-6880 | ||||||||||
Language: | English | ||||||||||
Publication Type: | Doctoral thesis | ||||||||||
Article category: | Other scientific | ||||||||||
Full Text Status: | Public | ||||||||||
Agris subject categories.: | F Plant production > F30 Plant genetics and breeding K Forestry > K10 Forestry production | ||||||||||
Subjects: | (A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science (A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Genetics and Breeding (A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Plant Biotechnology | ||||||||||
Keywords: | Scots pine, genotype-by-environment, multiple variables, factor analysis, quantitative trait locus, genomic predictions, non-additive effects, Bayesian LASSO, Bayesian ridge regression, GBLUP | ||||||||||
URN:NBN: | urn:nbn:se:slu:epsilon-p-99801 | ||||||||||
Permanent URL: | http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-99801 | ||||||||||
ID Code: | 16094 | ||||||||||
Faculty: | S - Faculty of Forest Sciences | ||||||||||
Department: | (S) > Dept. of Forest Genetics and Plant Physiology | ||||||||||
Deposited By: | SLUpub Connector | ||||||||||
Deposited On: | 29 Apr 2019 13:50 | ||||||||||
Metadata Last Modified: | 09 Sep 2020 00:25 |
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