Bayesian analysis of growth, stem straightness and branching quality in full-sib families of Eucalyptus globulus

Mora, Freddy; Ballesta, Paulina; Serra, Nicolle

doi:10.1590/1678-4499.20180317

Cited by 15 publications

(8 citation statements)

References 46 publications

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“…The prediction methods (genomic and pedigree-based) were also compared by the values of heritability and genetic gains. In the case of the pedigree-based method [80], the heritability (hfalse^a2) in a narrow sense was calculated as follows:hfalse^a2=σfalse^a2σfalse^a2+σfalse^ε2 where σfalse^a2 and σfalse^ε2 correspond to the additive genetic and residual variances, respectively. In the case of Bayesian genomic prediction models (SNP/haplotypes), genomic heritability (hfalse^g2), genomic variance (σfalse^g2) and the residual variance (σfalse^ε2) were estimated using the marginal posterior distributions of each estimated parameter [81,82,83].…”

Section: Methodsmentioning

confidence: 99%

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Ballesta

Maldonado

Pérez‐Rodríguez

et al. 2019

Plants

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Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.

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Section: Methodsmentioning

confidence: 99%

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Ballesta

Maldonado

Pérez‐Rodríguez

et al. 2019

Plants

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“…ST was evaluated in the first 2/3 of the total height of the tree, considering an ordinal scale of seven levels, in which 0 represents trees with curvature in the first third of the total height of the tree, and 6 represents trees that could present a slight curvature in the upper third of the tree without loss of productivity (Ballesta et al, 2019). BQ was evaluated according to different criteria of quality (i.e., diameter, angle, and distribution of branches in the tree), considering an ordinal scale of six levels, in which 1 represents a tree with serious limitations and 6 represents a tree with all branching variables in good condition (Mora et al, 2019). Total tree height was measured using a Suunto R hypsometer, while DBH was measured with a diameter tape at 1.3 m above ground level.…”

Section: Eucalypt Experimentsmentioning

confidence: 99%

Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network

et al. 2020

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Genomic selection models were investigated to predict several complex traits in breeding populations of Zea mays L. and Eucalyptus globulus Labill. For this, the following methods of Machine Learning (ML) were implemented: (i) Deep Learning (DL) and (ii) Bayesian Regularized Neural Network (BRNN) both in combination with different hyperparameters. These ML methods were also compared with Genomic Best Linear Unbiased Prediction (GBLUP) and different Bayesian regression models [Bayes A, Bayes B, Bayes Cπ, Bayesian Ridge Regression, Bayesian LASSO, and Reproducing Kernel Hilbert Space (RKHS)]. DL models, using Rectified Linear Units (as the activation function), had higher predictive ability values, which varied from 0.27 (pilodyn penetration of 6 years old eucalypt trees) to 0.78 (flowering-related traits of maize). Moreover, the larger mini-batch size (100%) had a significantly higher predictive ability for wood-related traits than the smaller mini-batch size (10%). On the other hand, in the BRNN method, the architectures of one and two layers that used only the pureline function showed better results of prediction, with values ranging from 0.21 (pilodyn penetration) to 0.71 (flowering traits). A significant increase in the prediction ability was observed for DL in comparison with other methods of genomic prediction (Bayesian alphabet models, GBLUP, RKHS, and BRNN). Another important finding was the usefulness of DL models (through an iterative algorithm) as an SNP detection strategy for genome-wide association studies. The results of this study confirm the importance of DL for genome-wide analyses and crop/tree improvement strategies, which holds promise for accelerating breeding progress.

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“…The Adjusted-Entry Means of the general linear model (experimental design) were used as the adjusted phenotypes according to Contreras-Soto et al [26] and Arriagada et al [27]. Correlations between each pair of traits were calculated using a Bayesian bi-trait model [28][29][30]. The statistical analysis was performed using the R package MCMCglmm (version 3.6.1; https://www.r-project.org) [31].…”

Section: Snp-and Haplotype-based Gwasmentioning

confidence: 99%

SNP- and Haplotype-Based GWAS of Flowering-Related Traits in Maize with Network-Assisted Gene Prioritization

et al. 2019

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Maize (Zea mays L.) is one of the most crucial crops for global food security worldwide. For this reason, many efforts have been undertaken to address the efficient utilization of germplasm collections. In this study, 322 inbred lines were used to link genotypic variations (53,403 haplotype blocks (HBs) and 290,973 single nucleotide polymorphisms (SNPs)) to corresponding differences in flowering-related traits in two locations in Southern Brazil. Additionally, network-assisted gene prioritization (NAGP) was applied in order to better understand the genetic basis of flowering-related traits in tropical maize. According to the results, the linkage disequilibrium (LD) decayed rapidly within 3 kb, with a cut-off value of r2 = 0.11. Total values of 45 and 44 marker-trait associations (SNPs and HBs, respectively) were identified. Another important finding was the identification of HBs, explaining more than 10% of the total variation. NAGP identified 44, 22, and 34 genes that are related to female/male flowering time and anthesis-silking interval, respectively. The co-functional network approach identified four genes directly related to female flowering time (p < 0.0001): GRMZM2G013398, GRMZM2G021614, GRMZM2G152689, and GRMZM2G117057. NAGP provided new insights into the genetic architecture and mechanisms underlying flowering-related traits in tropical maize.

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Bayesian analysis of growth, stem straightness and branching quality in full-sib families of Eucalyptus globulus

Cited by 15 publications

References 46 publications

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network

SNP- and Haplotype-Based GWAS of Flowering-Related Traits in Maize with Network-Assisted Gene Prioritization

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