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Key message This review highlights some of the discoveries and applications made possible by “omics” technologies over the last 10 years and provides perspectives for pioneering research to increase our understanding of tree biology. Context A decade after the first forest tree genome sequence was released into the public domain, the rapidly evolving genomics and bioinformatics toolbox has advanced our understanding of the structure, functioning, and evolution of forest tree genomes. Aims and methods This review highlights some of the discoveries and applications that “omics” technologies have made possible for forest trees over the past 10 years. Results In this review, we start by our current understanding of genome evolution and intricacies of gene regulation for reproduction, development, and responses to biotic and abiotic stresses. We then skim over advances in interactome analysis and epigenomics, the knowledge of the extent of genetic variation within and between species, revealing micro- and macro-evolutionary processes and species history, together with the complex architecture of quantitative traits. We finally end with applications in genetic resource conservation and breeding. Conclusion The knowledge gained through the use of these technologies has a huge potential impact for adapting forests to the main challenges they will have to face: changing demand from ecosystem services with potentially conflicting strategies in terms of conservation and use, as well as climate changes and associated threats. Genomics will undoubtedly play a major role over the next decade and beyond, not only to further understand the mechanisms underlying adaptation and evolution but also to develop and implement innovative management and policy actions to preserve the adaptability of natural forests and intensively managed plantations.

International audience; In a context of climate change and forest decline, a better understanding of the sources of tree flexibility involved in phenotypic plasticity and adaptation is needed. These last years, the role of epigenetics in the response to environmental variations has been established in several model plants at the genotype level but little is known at the level of natural populations grown in pedoclimatic sites. Here, we focused on three French natural populations of black poplar, a key pioneer tree from watersheds, planted in common garden and subjected to controlled variations of water availability. We estimated common genetic parameters such as narrow-sense heritability (h(2)), phenotypic differentiation index (P-ST), and the overall genetic differentiation index (F-ST) from genome-wide SNPs to evaluate the extent of epigenetic variations. Indeed, global DNA methylation levels from individuals exposed to drought or irrigated in a common garden were used. We found that the three populations were not distinguished by their levels of DNA methylation. However, a moderate drought was associated to a significant decrease in DNA methylation in the populations. Narrow-sense heritability and P-ST estimates of DNA methylation were similar to those found for biomass productivity. Heritability and P-ST were higher when trees were subjected to drought than in control condition. Negative genetic correlations between global DNA methylation and height or biomass were detected in drought condition only. Altogether, our data highlight that global DNA methylation acts as a genetic marker of natural population differentiation under drought stress in a pedoclimatic context.

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.

UMR DAP équipe AFEF Publication Inra prise en compte dans l'analyse bibliométrique des publications scientifiques mondiales sur les Fruits, les Légumes et la Pomme de terre. Période 2000-2012. http://prodinra.inra.fr/record/256699; International audience; The present study aimed to dissect tree architectural plasticity into genetic, ontogenetic and environmental effects over the first 4 years of growth of an apple F1 progeny by means of quantitative traits loci (QTL) mapping. Both growth and branching processes were phenotyped on the consecutive annual shoots of different axes within a tree. For each studied trait, predicted values (best linear unbiased predictors, BLUPs) of the genotypic (G) effect or its interaction with tree age (G×A) and climatic year (G×Y) were extracted from mixed linear models of repeated data. These BLUPs, which are independent from autocorrelations between repeated measurements, were used for QTL mapping. QTL detection power was improved by this two-step approach. For each architectural process, numerous QTLs were detected and some particularly interesting co-localised in common genomic regions, for internode lengthening, top diameter, and number and percentage of axillary shoots. When several QTLs were detected for a given trait, global models were estimated, which explained a maximum of 40% of the total variance for both internode length and top diameter and 28% for branching. QTLs detected for BLUPs of G×Y effects were interpreted as resulting from the interaction between genetic maximal potential of growth and climatic factors, while those for G×A effects were interpreted in relation to tree ontogeny. Most of the latter ones were found to be concomitant with key development stages during which the trait average started to decrease, but with different magnitudes depending on genotype

In fruit trees, understanding genetic determinisms of architectural traits is considered as a promising manner to control vegetative development and yield regularity. In this context, our study aimed to classify 2-year-old apple hybrids on the basis of their architectural traits. From a fine phenotyping, trees were described as tree graphs, including topological and geometric information. To evaluate the similarity between trees, comparison methods based on edit operations (substitution, insertion and deletion) were carried out. Distance between two tree graphs was computed by minimising the sum of the costs of the edit operations applied to transform one tree into another. Two algorithms for the comparison of unordered and partially ordered tree graphs were applied to a sub-sample of the population, taking into account several geometric attributes. For each comparison, a dissimilarity matrix was computed, and subsequently trees were clustered. A local interpretation of the matched entities was proposed through schematic representations of the trees, and similarities between trees were analysed within and between clusters. The tree graphs, both unordered or partially ordered and whether the attributes were considered or not, were grouped, by clustering, according to the number of entities per tree. The robustness of the unordered comparison was demonstrated by its application to the whole population, since it provided results similar to those obtained on the sub-sample. Further developments towards a higher relative weight of geometric versus topological information are discussed in the perspective to define an architectural ideotype in apple.

Iron, cobalt and iron–cobalt nanoparticle properties, such as diameter, saturation magnetization (Ms), crystal structure, surface composition and stability in physiological solutions, were investigated according to the annealing temperature used prior to their encapsulation into poly(d, l-lactic-co-glycolic acid) (PLGA) microcarriers. These new 60-μm microparticles should exhibit an Ms around 70 emu g−1 to be guided in real time from their intravascular injection site to a tumor with a magnetic resonance imaging scanner. The challenge in the preparation of the nanoparticles consisted in limiting Ms loss by oxidation and the release of metallic ions. It was found that when the annealing temperature reached 650 °C, Fe nanoparticles coalesced, the mean diameter reached (O) 361 ± 138 nm and Ms increased to 171 emu g−1. These nanoparticles exhibited a core of α-Fe and a shell of Fe3O4. On the opposite, Co nanoparticle properties were not affected by the annealing temperature: O and Ms were around 120 nm and 140 emu g−1, respectively. FeCo (60:40, atomic percent) nanoparticles coalesced at an annealing temperature >550 °C, O and Ms reached 217 nm and 213 emu g−1, respectively. Co and FeCo nanoparticles with a Co atomic proportion >15 % were coated with a graphite shell when the temperature was set to 550 °C. In physiological solution, Fe and Co nanoparticles significantly released more ions than FeCo nanoparticles. After the preparation steps prior to their encapsulation, the Ms of Fe and FeCo nanoparticles decreased by 25 and 3 %, respectively. FeCo–PLGA microparticles possessed a relatively high Ms (73 emu g−1) while that of Fe–PLGA microparticle (20 emu g−1) was too low for efficient targeting. The graphite shell was efficient to preserve Ms during the encapsulation.