Eucalypts are the world's most widely planted hardwood trees. Their outstanding diversity, adaptability and growth have made them a global renewable resource of fibre and energy. We sequenced and assembled .94% of the 640-megabase genome of Eucalyptus grandis. Of 36,376 predicted protein-coding genes, 34% occur in tandem duplications, the largest proportion thus far in plant genomes. Eucalyptus also shows the highest diversity of genes for specialized metabolites such as terpenes that act as chemical defence and provide unique pharmaceutical oils. Genome sequencing of the E. grandis sister species E. globulus and a set of inbred E. grandis tree genomes reveals dynamic genome evolution and hotspots of inbreeding depression. The E. grandis genome is the first reference for the eudicot order Myrtales and is placed here sister to the eurosids. This resource expands our understanding of the unique biology of large woody perennials and provides a powerful tool to accelerate comparative biology, breeding and biotechnology.A major opportunity for a sustainable energy and biomaterials economy in many parts of the world lies in a better understanding of the molecular basis of superior growth and adaptation in woody plants. Part of this opportunity involves species of Eucalyptus L'Hér, a genus of woody perennials native to Australia 1 . The remarkable adaptability of eucalypts coupled with their fast growth and superior wood properties has driven their rapid adoption for plantation forestry in more than 100 countries across six continents (.20 million ha) 2 , making eucalypts the most widely planted hardwood forest trees in the world. The subtropical E. grandis and the temperate E. globulus stand out as targets of breeding programmes worldwide. Planted eucalypts provide key renewable resources for the production of pulp, paper, biomaterials and bioenergy, while mitigating human pressures on native forests 3 . Eucalypts also have a large diversity and high concentration of essential oils (mixtures of mono-and sesquiterpenes), many of which have ecological functions as well as medicinal and industrial uses. Predominantly outcrossers 1 with hermaphroditic animal-pollinated flowers, eucalypts are highly heterozygous and display pre-and postzygotic barriers to selfing to reduce inbreeding depression for fitness and survival 4 .To mitigate the challenge of assembling a highly heterozygous genome, we sequenced the genome of 'BRASUZ1', a 17-year-old E. grandis genotype derived from one generation of selfing. The availability of annotated forest tree genomes from two separately evolving rosid lineages, Eucalyptus (order Myrtales) and Populus (order Malpighiales 5 ), in combination with genomes from domesticated woody plants (for example, Vitis, Prunus, Citrus), provides a comparative foundation for addressing

SummaryWe used whole genome resequencing of pooled individuals to develop a high-density single-nucleotide polymorphism (SNP) chip for Eucalyptus. Genomes of 240 trees of 12 species were sequenced at 3.59 each, and 46 997 586 raw SNP variants were subject to multivariable filtering metrics toward a multispecies, genome-wide distributed chip content.Of the 60 904 SNPs on the chip, 59 222 were genotyped and 51 204 were polymorphic across 14 Eucalyptus species, providing a 96% genome-wide coverage with 1 SNP/12-20 kb, and 47 069 SNPs at ≤ 10 kb from 30 444 of the 33 917 genes in the Eucalyptus genome.Given the EUChip60K multi-species genotyping flexibility, we show that both the sample size and taxonomic composition of cluster files impact heterozygous call specificity and sensitivity by benchmarking against 'gold standard' genotypes derived from deeply sequenced individual tree genomes. Thousands of SNPs were shared across species, likely representing ancient variants arisen before the split of these taxa, hinting to a recent eucalypt radiation. We show that the variable SNP filtering constraints allowed coverage of the entire site frequency spectrum, mitigating SNP ascertainment bias.The EUChip60K represents an outstanding tool with which to address population genomics questions in Eucalyptus and to empower genomic selection, GWAS and the broader study of complex trait variation in eucalypts.

Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.

Although genome-wide association studies (GWAS) have provided valuable insights into the decoding of the relationships between sequence variation and complex phenotypes, they have explained little heritability. Regional heritability mapping (RHM) provides heritability estimates for genomic segments containing both common and rare allelic effects that individually contribute too little variance to be detected by GWAS. We carried out GWAS and RHM for seven growth, wood and disease resistance traits in a breeding population of 768 Eucalyptus hybrid trees using EuCHIP60K. Total genomic heritabilities accounted for large proportions (64-89%) of pedigree-based trait heritabilities, providing additional evidence that complex traits in eucalypts are controlled by many sequence variants across the frequency spectrum, each with small contributions to the phenotypic variance. RHM detected 26 quantitative trait loci (QTLs) encompassing 2191 single nucleotide polymorphisms (SNPs), whereas GWAS detected 13 single SNP-trait associations. RHM and GWAS QTLs individually explained 5-15% and 4-6% of the genomic heritability, respectively. RHM was superior to GWAS in capturing larger proportions of genomic heritability. Equated to previously mapped QTLs, our results highlighted genomic regions for further examination towards gene discovery. RHM-QTLs bearing a combination of common and rare variants could be useful enhancements to incorporate prior knowledge of the underlying genetic architecture in genomic prediction models.

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BackgroundHigh-throughput SNP genotyping has become an essential requirement for molecular breeding and population genomics studies in plant species. Large scale SNP developments have been reported for several mainstream crops. A growing interest now exists to expand the speed and resolution of genetic analysis to outbred species with highly heterozygous genomes. When nucleotide diversity is high, a refined diagnosis of the target SNP sequence context is needed to convert queried SNPs into high-quality genotypes using the Golden Gate Genotyping Technology (GGGT). This issue becomes exacerbated when attempting to transfer SNPs across species, a scarcely explored topic in plants, and likely to become significant for population genomics and inter specific breeding applications in less domesticated and less funded plant genera.ResultsWe have successfully developed the first set of 768 SNPs assayed by the GGGT for the highly heterozygous genome of Eucalyptus from a mixed Sanger/454 database with 1,164,695 ESTs and the preliminary 4.5X draft genome sequence for E. grandis. A systematic assessment of in silico SNP filtering requirements showed that stringent constraints on the SNP surrounding sequences have a significant impact on SNP genotyping performance and polymorphism. SNP assay success was high for the 288 SNPs selected with more rigorous in silico constraints; 93% of them provided high quality genotype calls and 71% of them were polymorphic in a diverse panel of 96 individuals of five different species.SNP reliability was high across nine Eucalyptus species belonging to three sections within subgenus Symphomyrtus and still satisfactory across species of two additional subgenera, although polymorphism declined as phylogenetic distance increased.ConclusionsThis study indicates that the GGGT performs well both within and across species of Eucalyptus notwithstanding its nucleotide diversity ≥2%. The development of a much larger array of informative SNPs across multiple Eucalyptus species is feasible, although strongly dependent on having a representative and sufficiently deep collection of sequences from many individuals of each target species. A higher density SNP platform will be instrumental to undertake genome-wide phylogenetic and population genomics studies and to implement molecular breeding by Genomic Selection in Eucalyptus.

SummaryWe used high-density single nucleotide polymorphism (SNP) data and whole-genome pooled resequencing to examine the landscape of population recombination (q) and nucleotide diversity (ϴ w ), assess the extent of linkage disequilibrium (r 2 ) and build the highest density linkage maps for Eucalyptus.At the genome-wide level, linkage disequilibrium (LD) decayed within c. 4-6 kb, slower than previously reported from candidate gene studies, but showing considerable variation from absence to complete LD up to 50 kb. A sharp decrease in the estimate of q was seen when going from short to genome-wide inter-SNP distances, highlighting the dependence of this parameter on the scale of observation adopted. Recombination was correlated with nucleotide diversity, gene density and distance from the centromere, with hotspots of recombination enriched for genes involved in chemical reactions and pathways of the normal metabolic processes.The high nucleotide diversity (ϴ w = 0.022) of E. grandis revealed that mutation is more important than recombination in shaping its genomic diversity (q/ϴ w = 0.645). Chromosomewide ancestral recombination graphs allowed us to date the split of E. grandis (1.7-4.8 million yr ago) and identify a scenario for the recent demographic history of the species.Our results have considerable practical importance to Genome Wide Association Studies (GWAS), while indicating bright prospects for genomic prediction of complex phenotypes in eucalypt breeding.

We report a genomic selection (GS) study of growth and wood quality traits in an outbred F hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40-0.57 for individual selection and 0.56-0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25 kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.