Bridging genomics and quantitative genetics of Eucalyptus: genome-wide prediction and genetic parameter estimation for growth and wood properties using high-density SNP data - Lima, Bruno Marco de (2014)
Convergence of quantitative genetics and genomics is becoming the way that fundamental genetics and applied breeding will be carried out in the next decades. This study bridges the quantitative genetics of complex growth and wood properties traits with genomic technologies towards a more innovative approach to tree breeding. Planted forests play a major role to fulfill the growing world demand for wood products and energy. Eucalypts stand out for their high productivity and versatile wood resulting from the advanced breeding programs associated to clonal propagation and modern silviculture. Despite their fast growth, breeding cycles still take several years and wood properties assessment is limited to a sample of trees in the late stages of selection due to the costs involved in wood phenotyping, not exploitingthe range of genetic variation in wood properties. In this study, we examined fifteen traits including growth and wood chemical and physical properties in 1,000 individuals sampled from an elite Eucalyptus breeding population. Near-infrared spectroscopy (NIRS) models were developed and used for high-throughput phenotyping of wood traits.Highdensity data for 29,090 SNPs was used to obtain accurate pedigree-record-free estimates of trait variance components, heritabilities, genetic and phenotypic correlations, based on a realized relationship matrix, comparing them to pedigree-based estimates. To the best of our knowledge, this is the first study to do this in plants. NIRS predictions were accurate for wood chemical traits and wood density, and variably successful for physical traits. Heritabilities were medium for growth (0.34 to 0.44), high for wood chemical traits (0.56 to 0.85) and variable for wood physical traits (0.11 to 0.63). High positive correlations among growth traits and negative between cellulose and lignin content were observed, while correlations between wood chemical and physical traits and between growth and wood quality traits were low although significant. Phenotypes and SNP markers were then used to build genomic predictive models using a marker density higher than any previous genomic selection study in trees (1 SNP/21 kbp). Two models (RR-BLUP and Bayesian LASSO) that differ regarding the assumed distribution of marker effects were used for genomic predictions. Predictions were compared to those obtained by phenotypic BLUP. Predictive abilities very similar by the two models and strongly correlated to the heritabilities. Accurate genomic-enabled predictions were obtained for wood chemical traits related to lignin, wood density and growth, although generally 15 to 25% lower than those achieved by phenotypic BLUP prediction. Nevertheless, genomic predictions yielded a coincidence above 70% in selecting the top 30 trees ranked by phenotypic selection for growth, wood density and S:G ratio, and 60% when tandem selection was applied. The results of this study open opportunities for an increased use of highthroughput NIRS phenotyping and genome-wide SNP genotyping in Eucalyptus breeding, allowing accurate pedigree-record-free estimation of genetic parameters and prediction of genomic breeding values for yet to be phenotyped trees. These applications should become routine in tree breeding programs for the years to come, significantly reducing the length of breeding cycles while optimizing resource allocation and sustainability of the breeding endeavor.