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- Calleja-Rodriguez, Ainhoa, et al.
(författare)
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Analysis of phenotypic- and Estimated Breeding Values (EBV) to dissect the genetic architecture of complex traits in a Scots pine three-generation pedigree design
- 2019
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Ingår i: Journal of Theoretical Biology. - : Elsevier BV. - 0022-5193 .- 1095-8541. ; 462, s. 283-292
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Tidskriftsartikel (refereegranskat)abstract
- In forest tree breeding, family-based Quantitative Trait Loci (QTL) studies are valuable as methods to dissect the complexity of a trait and as a source of candidate genes. In the field of conifer research, our study contributes to the evaluation of phenotypic and predicted breeding values for the identification of QTL linked to complex traits in a three-generation pedigree population in Scots pine (Pinus sylvestris L.). A total of 11 470 open pollinated F-2-progeny trees established at three different locations, were measured for growth and adaptive traits. Breeding values were predicted for their 360 mothers, originating from a single cross of two grand-parents. A multilevel LASSO association analysis was conducted to detect QTL using genotypes of the mothers with the corresponding phenotypes and Estimated Breeding Values (EBV). Different levels of genotype-by-environment (G x E) effects among sites at different years, were detected for survival and height. Moderate-to-low narrow sense heritabilities and EBV accuracies were found for all traits and all sites. We identified 18 AFLPs and 12 SNPs to be associated with QTL for one or more traits. 62 QTL were significant with percentages of variance explained ranging from 1.7 to 18.9%. In those cases where the same marker was associated to a phenotypic or an ebvQTL, the ebvQTL always explained higher proportion of the variance, maybe due to the more accurate nature of Estimated Breeding Values (EBV). Two SNP-QTL showed pleiotropic effects for traits related with hardiness, seed, cone and flower production. Furthermore, we detected several QTL with significant effects across multiple ages, which could be considered as strong candidate loci for early selection. The lack of reproducibility of some QTL detected across sites may be due to environmental heterogeneity reflected by the genotype- and QTL-by-environment effects. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
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| 2. |
- Calleja-Rodriguez, Ainhoa, et al.
(författare)
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Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine
- 2020
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 21
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Tidskriftsartikel (refereegranskat)abstract
- Background Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. Results Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection. Conclusions Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.
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| 3. |
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| 4. |
- Calleja-Rodriguez, Ainhoa, et al.
(författare)
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Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris
- 2021
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Ingår i: Frontiers in Plant Science. - : Frontiers Media SA. - 1664-462X. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive x additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.
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| 5. |
- Calleja-Rodriguez, Ainhoa, et al.
(författare)
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Genotype-by-environment interactions and the dynamic relationship between tree vitality and height in northern Pinus sylvestris
- 2019
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Ingår i: Tree Genetics and Genomes. - : Springer Science and Business Media LLC. - 1614-2942 .- 1614-2950. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Tree health and growth rate must both be considered in Scots pine breeding for harsh areas such as northern Sweden. Univariate (UV) and multivariate (MV) multi-environment trial (MET) analyses of tree vitality (a measure of tree health) and height (a measure of growth rate) were conducted for four series of open-pollinated Scots pine progeny trials (20 trials total), to evaluate age trends, patterns, and drivers of genotype-by-environment interaction (G x E). The lowest standard errors were obtained for the MV MET analyses, indicating that MV analyses are preferable to UV analyses. By incorporating factor-analytic structures, the most complex data sets could be handled, suggesting that factor-analytic analyses are preferred for evaluation of forest progeny trials. We detected strong patterns of G x E for both tree vitality and height, and the driver of G x E was found mainly to be differences in degree day temperature sum, such that G x E was higher between trials with more contrasting temperature sums. The genetic correlations, between vitality and height within sites, were generally positive and were driven by the harshness of the trial; mild trials had lower genetic correlations than did harsh trials. The sign of the across-site genetic correlations between vitality and height changed from positive to negative in some cases, as the differences between the temperature sum of the trials increased. These findings support the hypothesis that tree height assessed in harsh environments with low survival is likely to reflect health and survival ability to a greater extent than growth capacity.
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| 6. |
- Calleja-Rodriguez, Ainhoa
(författare)
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Quantitative Genetics and Genomic Selection of Scots pine
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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%).
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