The status of genomics and genetics research in the Myrtaceae, a large family of dicotyledonous woody plants, is reviewed withEucalyptusas the focal genus. The family contains over 5,650 species in 130 to 150 genera, predominantly of neo-tropical and Southern Hemisphere distribution. Several genera are well known for their economic importance worldwide. Myrtaceae are typically diploids with small to intermediate genome size. Microsatellites have been developed for several genera while higher throughput marker systems such as diversity arrays technology and single nucleotide polymorphism are available forEucalyptus. Molecular data have been fundamental to current perspectives on the phylogeny, phylogeography and taxonomy of the Myrtaceae, while numerous studies of genetic diversity have been carried out particularly as it relates to endangered, rare, fragmented, overharvested or economically important species. Large expressed sequence tag collections for species ofEucalyptushave recently become public to support the annotation of theEucalyptus grandisgenome. Transcriptomics inEucalyptushas advanced by microarrays and next-generation sequencing focusing on wood development. Linkage maps forEucalyptusdisplay high synteny across species and have been extensively used to map quantitative trait loci for a number of traits including growth, wood quality, disease and insect resistance. Candidate gene-based association genetics have successfully found marker–trait associations for wood and fiber traits. Genomic selection experiments have demonstrated clear potential to improve the efficiency of breeding programs while freeze-tolerant transgenicEucalyptustrials have recently been initiated. The recently releasedE. grandisgenome, sequenced to an average coverage of 8×, will open up exceptional opportunities to advance Myrtaceae genetics and genomics research.

Linkage disequilibrium (LD) mapping using natural populations results in higher resolution of marker-trait associations compared to family-based quantitative trait locus (QTL) studies. Depending on the extent of LD, it is possible to identify alleles within candidate genes associated with a trait. Analysis of a natural mutant in Arabidopsis has shown that mutations in cinnamoyl CoA reductase (CCR), a key lignin gene, affect physical properties of the secondary cell wall such as stiffness and strength. Using this gene, we tested whether LD mapping could identify alleles associated with microfibril angle (MFA), a wood quality trait affecting stiffness and strength of wood. We identified 25 common single-nucleotide polymorphism (SNP) markers in the CCR gene in Eucalyptus nitens. Using single-marker and haplotype analyses in 290 trees from a E. nitens natural population, two haplotypes significantly associated with MFA were found. These results were confirmed in two full-sib families of E. nitens and Eucalyptus globulus. In an effort to understand the functional significance of the SNP markers, we sequenced the cDNA clones and identified an alternatively spliced variant from the significant haplotype region. This study demonstrates that LD mapping can be used to identify alleles associated with wood quality traits in natural populations of trees.

Water stress limits plant survival and production in many parts of the world. Identification of genes and alleles responding to water stress conditions is important in breeding plants better adapted to drought. Currently there are no studies examining the transcriptome wide gene and allelic expression patterns under water stress conditions. We used RNA sequencing (RNA-seq) to identify the candidate genes and alleles and to explore the evolutionary signatures of selection.

Populations with low linkage disequilibrium (LD) offer unique opportunities to study functional variants influencing quantitative traits. We exploited the low LD in forest trees to identify functional polymorphisms in a Eucalyptus nitens COBRA-like gene (EniCOBL4A), whose Arabidopsis homolog has been implicated in cellulose deposition. Linkage analysis in a full-sib family revealed that EniCOBL4A is the most strongly associated marker in a quantitative trait locus (QTL) region for cellulose content. Analysis of LD by genotyping 11 common single-nucleotide polymorphisms (SNPs) and a simple sequence repeat (SSR) in an association population revealed that LD declines within the length of the gene. Using association studies we fine mapped the effect of the gene to SNP7, a synonymous SNP in exon 5, which occurs between two small haplotype blocks. We observed patterns of allelic expression imbalance (AEI) and differential binding of nuclear proteins to the SNP7 region that indicate that SNP7 is a cis-acting regulatory polymorphism affecting allelic expression. We also observed AEI in SNP7 heterozygotes in a full-sib family that is linked to heritable allele-specific methylation near SNP7. This study demonstrates the potential to reveal functional polymorphisms underlying quantitative traits in low LD populations.

Genomic selection (GS) is becoming an important selection tool in crop breeding. In this study, we compared the ability of different GS models to predict time to young microspore (TYM), a flowering time-related trait, spike grain number under control conditions (SGNC) and spike grain number under osmotic stress conditions (SGNO) in two wheat biparental doubled haploid populations with unrelated parents. Prediction accuracies were compared using BayesB, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO / BL), ridge regression best linear unbiased prediction (RR-BLUP), partial least square regression (PLS), and sparse partial least square regression (SPLS) models. Prediction accuracy was tested with 10-fold cross-validation within a population and with independent validation in which marker effects from one population were used to predict traits in the other population. High prediction accuracies were obtained for TYM (0.51–0.84), whereas moderate to low accuracies were observed for SGNC (0.10–0.42) and SGNO (0.27–0.46) using cross-validation. Prediction accuracies based on independent validation are generally lower than those based on cross-validation. BayesB and SPLS outperformed all other models in predicting TYM with both cross-validation and independent validation. Although the accuracies of all models are similar in predicting SGNC and SGNO with cross-validation, BayesB and SPLS had the highest accuracy in predicting SGNC with independent validation. In independent validation, accuracies of all the models increased by using only the QTL-linked markers. Results from this study indicate that BayesB and SPLS capture the linkage disequilibrium between markers and traits effectively leading to higher accuracies. Excluding markers from QTL studies reduces prediction accuracies.