Fig 1.
Summary flowchart of the experimental steps employed to estimate genetic parameters for growth and wood traits in Eucalyptus hybrid urograndis.
Growth data and wood sample NIRS spectra were collected from the sampled subset of 1,000 trees across full-sib families. A subset of between 200 and 350 trees selected based on maximizing NIRS spectra distance was phenotyped (wood chemical and physical traits) and data used to develop acceptable NIRS calibration models used to predicted lignin and wood density for the remaining 650 trees. Pedigree, genotypes (SNPs and DArT-seq), growth and wood trait data, either directly measured for 200 trees (cellulose, hemicellulose, microfibril angle, fibers, coarseness) or predicted for 1000 trees (lignin, wood density) were employed for genetic parameter estimation. Block arrows indicate step processes, thin dashed arrows indicate data or sample use.
Table 1.
Number of families and trees sampled for the different mating types and their respective species and hybrids involved.
Table 2.
Range variation for the 15 phenotypic traits assessed in the Eucalyptus urograndis hybrid population.
Number of trees for which trait values were ultimately used in the quantitative parameters analyses (n), and statistics: mean, median, standard deviation (SD), phenotypic coefficient of variation (CV), minimum (Min.), and maximum (Max.) values observed.
Fig 2.
Pedigree and genomic relationships.
Distribution of the number of pairwise additive relationships (excluding the diagonal elements) (left) and heat maps of the pairwise relationship matrices (right) among the 970 trees of the Eucalyptus population, estimated using the expected pedigrees, 33,398 SNPs and 24,001 DArT-seq markers (top to bottom). The heat map scales show the continuum of the realized genetic relationships between pairs of individuals, from no relationship (dark blue areas corresponding to values below and up to zero), increasing to half-sib relationships (light blue shades around 0.25) up to full-sib relationships (yellow areas corresponding to values around 0.5).
Table 3.
Narrow-sense () and broad-sense (
) heritabilities and their approximate standard error (SE), and the ratio of dominance to additive variance
for each growth, chemical and physical wood trait.
Heritabilities were estimated using the additive relationship matrix based on the pedigree (A) and genomic relationship matrix using for the dominance component the parametrization of Vitezica et al. (2013, GDVitezica) and Su et al. (2012, GDSu) constructed from all available SNPs (~ 33K).
Table 4.
Pearson correlations between different growth, chemical and physical wood traits from the phenotype and breeding values of the univariate analysis of the Eucalyptus grandis × E. urophylla breeding population.
In each cell from top to bottom: genotypic correlation based on SNP-based realized relationship matrix (~33K); genotypic correlation based on DArTseq-based realized relationship matrix (~24K); genotypic correlation based on pedigree-based relationship matrix; phenotypic correlations.