Heritability studies have been progressively relayed by quantitative trait loci (QTLs) analyses that have allowed the identification of many genomic regions involved in the control of architectural traits. Using simple growth traits, QTLs have been identified controlling the tree shape as, for instance, on
Populus (
Bradshaw & Stettler, 1995;
Wu, 1998),
Eucalyptus (
Verhaegen et al., 1997) and apple tree (
Segura et al., 2007;
Segura, Durel, & Costes, 2009), with several studies dedicated to QTL identification for columnar phenotypes (
Kenis & Keulemans, 2007;
Tian, Wang, Zhang, James, & Dai, 2005) and dwarfing traits induced by rootstock (see
Foster et al. 2015 and references within). Some transgenic experiments in poplar have also reported that particular genes could affect the crown architecture (as reviewed by
Dubouzet, Strabala, and Wagner (2013)).
The accessibility to genomics resources has increased rapidly during the last decade thanks to next generation sequencing (NGS) and high-throughput genotyping technologies. In fruit trees, several reference genomes have been published in the past 5years, for 3 main species in the Rosaceae family, apple (
Velasco et al., 2010), peach (
The IPGI et al., 2013) and pear (
Chagné et al., 2014), but also on tropical or subtropical fruit species such as
Theobroma cacao (
Argout et al., 2011) or
Citrus sinensis (
Xu et al., 2013). Other reference genomes are under construction and should be able soon, in particular for olive or avocado tree. In forest trees, few reference genomes are available in
Populus (
Tuskan et al., 2006), and more recently loblolly pine (
Zimin et al., 2014) and
Eucalyptus grandis (
Myburg et al., 2014). In all species, such resource allows a fine description of both the genome structure (genome features, number and nature of genes) and the associated nucleotidic variability (SNP, insertions/deletions) within genes and between genes, along each chromosome.
Moreover, the annotation of thousands of genes in fruit and forest trees allowed their analysis under different environmental conditions, developmental stages and for different levels of plant organization (tissues, organs…) (
Table 1). Several gene families potentially involved in tree growth and tree shape variability have been identified. For instance, exhaustive inventories have been performed for genes related to auxin pathways (ARF, AUX/IAA and TIR) in the ‘Golden Delicious’ genome (
Devoghalaere et al., 2012) or for the genes of the GRAS family in
Prunus mume (
Lu, Wang, Xu, Sun, & Zhang, 2015). The expression profiles of many major genes for tree growth have also been described. As an example, expression profiles of secondary cell wall-related genes implicated in cellulose and xylan biosynthesis have been studied in shoot tips, young and mature leaves, floral buds, roots and wood forming tissues of
Eucalyptus grandis (
Myburg et al., 2014). Gene atlas dedicated to forest trees are also available allowing transcriptional and proteomic profiling in several contexts. The molecular plasticity of shoot apices of eucalyptus was studied in response to water deficit using NGS which provided extensive transcriptome coverage (
Villar et al., 2011). In oak, a pyrosequencing strategy to study the bud dormancy induction and release generated 6471 contigs of differentially expressed genes (
Ueno et al., 2013) and allowed the detection of several expressional candidate genes. Candidate gene approaches have also been conducted in maritime pine to reveal expression variation of cuticular-related genes in needles submitted to drought stress, one of the major environmental stresses influencing tree growth (
Le Provost et al., 2013). Finally available gene sequences were also useful to identify the nature of protein showing changes in abundance.
Bedon et al. (2012) reported the proteomic plasticity of two clones of eucalyptus in different water regimes. Similar examples of gene or miRNA atlas can be found in fruit trees, for their response to water stress (
Bassett et al., 2014;
Eldem et al., 2012) or during winter dormancy (
Falavigna et al., 2014).
