Background: Genomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E. grandis and 78 E. urophylla parents and their 949 F-1 hybrids to develop genomic prediction models for eight phenotypic traits-basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA). Results: Heritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, the GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the true proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in F-1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore either GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions. Conclusions: The size and composition of the training set and number of SNPs used are the two most important factors for model prediction, compared to the statistical methods and the genomic location of SNPs. Furthermore, training the prediction model based on pure parental species only provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in Eucalyptus breeding programs by the selection of interspecific hybrids.

Non-additive genetic effects can be effectively exploited in control-pollinated families with the availability of genome-wide markers. We used 41,304 SNP markers and compared pedigree vs. marker-based genetic models by analysing height, diameter, basic density and pulp yield for Eucalyptus urophylla x E.grandis control-pollinated families represented by 949 informative individuals. We evaluated models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance). We showed that the models can capture a large proportion of the genetic variance from dominance and epistasis for growth traits as those components are typically not independent. We also showed that we could partition genetic variances more precisely when using relationship matrices derived from markers compared to using only pedigree information. In addition, phenotypic prediction accuracies were only slightly increased by including dominance effects for growth traits since estimates of non-additive variances yielded rather high standard errors. This novel result improves our current understanding of the architecture of quantitative traits and recommends accounting for dominance variance when developing genomic selection strategies in hybrid Eucalyptus.

Genome-wide association studies are a powerful and widely used approach to decipher the genetic control of quantitative traits. One of the major challenges for traits in hybrid forest trees, such as hybrid Eucalyptus, is dissecting also non-additive effects for complex traits using a traditional linear mixed model. These non-additive effects, especially over-dominance effects, are one of most important hypotheses for the genetic basis of heterosis. In this study, we used a population including 949 F1 hybrids and their 174 parents, that were phenotyped for circumference at breast height and height at age of three years and six years, and also genotyped at 37,832 informative SNPs. Here we use and compare single-trait and multi-trait association models by accounting for additive and over-dominance effects, to evaluate genomic regions associated with the growth traits. For additive effect-based association model, nine significant SNPs were observed in multi-trait analyses, whereas only two unique SNPs were detected in single-trait analyses. These two SNPs were also identified in the multi-trait model. When evaluating over-dominance effects, 17 and 13 SNPs were identified from multi-trait and single-trait models, respectively. Moreover, more phenotypic variation can be explained by SNPs identified from multi-trait GWAS when including over-dominance effects. Overall, this study shows the added values of including over-dominance and considering multiple traits for identifying genomic regions that control traits of interest and that could contribute to heterosis in hybrids.

Background: The initiation of growth cessation and dormancy represent critical life history trade offs between survival and growth and have important fitness effects in perennial plants Such adaptive life history traits often show strong local adaptation along environmental gradients but, despite then importance, the genetic architecture of these traits remains poorly understood.

Results: We integrate whole genome re sequencing with environmental and phenotypic data from common garden experiments to investigate the genomic basis of local adaptation across a latitudinal gradient in European aspen (Populus tremula). A single genomic region containing the PtFT2 gene mediates local adaptation in the timing of bud set and explains 65% of the observed genetic variation in bud set This locus is the likely target of a recent selective sweep that originated right before or during colonization of northern Scandinavia following the last glaciation Field and greenhouse experiments confirm that variation in PtFT2 gene expression affects the phenotypic variation in bud set that we observe in wild natural populations.

Conclusions: Our results reveal a major effect locus that determines the timing of bud set and that has facilitated rapid adaptation to shorter growing seasons and colder climates in European aspen. The discovery of a single locus explaining a substantial fraction of the variation in a key life-history trait is remarkable, given that such traits are generally considered to be highly polygenic. These findings provide a dramatic illustration of how loci of large effect for adaptive traits can arise and be maintained over large geographical scales in natural populations.