Current status and trends in forest genomics

Forests are not only the most predominant of the Earth\u27s terrestrial ecosystems, but are also the core supply for essential products for human use. However, global climate change and ongoing population explosion severely threatens the health of the forest ecosystem and aggravtes the deforestation and forest degradation. Forest genomics has great potential of increasing forest productivity and adaptation to the changing climate. In the last two decades, the field of forest genomics has advanced quickly owing to the advent of multiple high-throughput sequencing technologies, single cell RNA-seq, clustered regularly interspaced short palindromic repeats (CRISPR)-mediated genome editing, and spatial transcriptomes, as well as bioinformatics analysis technologies, which have led to the generation of multidimensional, multilayered, and spatiotemporal gene expression data. These technologies, together with basic technologies routinely used in plant biotechnology, enable us to tackle many important or unique issues in forest biology, and provide a panoramic view and an integrative elucidation of molecular regulatory mechanisms underlying phenotypic changes and variations. In this review, we recapitulated the advancement and current status of 12 research branches of forest genomics, and then provided future research directions and focuses for each area. Evidently, a shift from simple biotechnology-based research to advanced and integrative genomics research, and a setup for investigation and interpretation of many spatiotemporal development and differentiation issues in forest genomics have just begun to emerge

Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

Background Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. Results Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection. Conclusions Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%

Expected benefits of genomic selection for growth and wood quality traits in Eucalyptus grandis

Genomic selection (GS) can substantially reduce breeding cycle times in forest trees compared to traditional breeding cycles. Practical implementation of GS in tree breeding requires an assessment of significant drivers of genetic gains over time, which may differ among species and breeding objectives. We present results of a GS study of growth and wood quality traits in an operational Eucalyptus grandis breeding program in South Africa. The training population consisted of 1575 full and half-sib individuals, genotyped with the Eucalyptus (EUChip60K) SNP chip resulting in 15,040 informative SNP markers. The accuracy of the GS models ranged from 0.47 (diameter) to 0.67 (fibre width). We compared a 4-year GS breeding cycle equivalent to half of a traditional 8-year E. grandis breeding cycle and obtained GS efficiencies ranging from 1.20 (wood density) to 1.62 (fibre length). Simulated over 17 years, the ratio of the accumulated genetic gains between three GS cycles and two traditional breeding cycles ranged from 1.53 (diameter) to 3.35 (wood density). To realise these genetic gains per unit time in E. grandis breeding, we show that significant adjustments have to be made to integrate GS into operational breeding steps.Mondi South Africa (Pty) Ltd: Forests: Research and Development, National Research Foundation (NRF), Department of Science and Technology: Bioinformatics and Functional Genomics Programme (BFG) and Department Trade and Industry South Africa: Technology and Human Resource Industry Programme (THRIP).http://link.springer.com/journal/112952021-06-14hj2021BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant Patholog

A genome-wide SNP genotyping resource for tropical pine tree species

We performed gene and genome targeted SNP discovery towards the development of a genome-wide, multispecies genotyping array for tropical pines. Pooled RNA-seq data from shoots of seedlings from five tropical pine species was used to identify transcript-based SNPs resulting in 1.3 million candidate Affymetrix SNP probe sets. In addition, we used a custom 40 K probe set to perform capture-seq in pooled DNA from 81 provenances representing the natural ranges of six tropical pine species in Mexico and Central America resulting in 563 K candidate SNP probe sets. Altogether, 300 K RNA-seq (72%) and 120 K capture-seq (28%) derived SNP probe sets were tiled on a 420 K screening array that was used to genotype 576 trees representing the 81 provenances and commercial breeding material. Based on the screening array results, 50 K SNPs were selected for commercial SNP array production including 20 K polymorphic SNPs for P. patula, P. tecunumanii, P. oocarpa and P. caribaea, 15 K for P. greggii and P. maximinoi, 13 K for P. elliottii and 8K for P. pseudostrobus. We included 9.7 K ancestry informative SNPs that will be valuable for species and hybrid discrimination. Of the 50 K SNP markers, 25% are polymorphic in only one species, while 75% are shared by two or more species. The Pitro50K SNP chip will be useful for population genomics and molecular breeding in this group of pine species that, together with their hybrids, represent the majority of fast-growing tropical and subtropical pine plantations globally.DATA AVAILABILITY STATEMENT : The pooled targeted capture sequencing data have been made available via NCBI SRA BioProject accession PRJNA742386. RNA-seq data are available via NCBI SRA BioProject accessions PRJNA416697 (P. tecunumanii), PRJNA416698 (P. patula), PRJNA685280 (P. oocarpa), PRJNA685281 (P. greggii) and PRJNA685282 (P. maximinoi). Metadata and probe set sequences used for markers selected for the 50 K commercial array are available as Supporting Information (Table S5). Genotype data set used for PCA and STRUCTURE analysis is available in Supporting Information (Table S6).http://www.wileyonlinelibrary.com/journal/men2022-08-12hj2022BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant Patholog

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding

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

Sparsity-Promoting Spatiotemporal Regularization for Data Mining in Functional Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) has opened unprecedented avenues to observe the human brain non-invasively. In particular, for about two decades, functional MRI (fMRI) has enabled to monitor brain function using the blood-oxygen-level-dependent (BOLD) contrast as a proxy for neuronal activity. The impact of fMRI on neurosciences, medicine, and psychology is ever increasing and has been mainly focussing on (1) understanding brain organization in terms of segregation (i.e., localized processing) and integration (i.e., distributed processing), specifically, related to sensory processing and cognition; (2) exploring temporal characteristics of brain processes. FMRI provides large spatiotemporal datasets, typically one whole-brain volume with spatial resolution of 1–3 mm in each dimension, every 1–4 seconds during several minutes. The structure of neurophysiological contributions in these data is complex and therefore requires advanced data processing. Conventional fMRI analysis is exploiting timing properties of a stimulation or task paradigm designed by the experimenter; i.e., evidence is sought for the presence of a hypothetical BOLD response. More recently, the community has shown increasing interest in spontaneous brain activity acquired during resting-state fMRI (RS-fMRI). In the absence of any task, data-driven or exploratory methods have found great use. In particular, blind source separation such as independent component analysis (ICA) has been widely applied to RS-fMRI data. One limitation of current data-driven methods is the lack of incorporating knowledge about the hemodynamic system, which governs any activity-related signal component in the fMRI measurements. In this dissertation, we build upon the latest advances in convex optimization and propose a novel framework that can reveal activity-inducing signals at the fMRI timescale. In particular, our regularization strategy, termed “total activation” (TA), allows deconvolving the fMRI signal to remove hemodynamic blur and to improve spatial contrast of activation patterns by incorporating knowledge about meaningful brain regions. The contribution of our method lies in adapting and tailoring state-of-the-art signal processing techniques with specific domain knowledge from fMRI and neurosciences. First, we extend “total variation” (TV), which is a well-recognized method in image processing for edge-preserving regularization. TV favors signals that are piecewise constant, and, therefore, whose derivatives are sparse. We generalize this concept for signals of which the derivative of an additional linear differential operator is sparse, and build a variational formulation for the denoising problem. The recovered signal can be also studied after applying the regularizing operators; e.g., applying the differential operator will lead to the piecewise constant driving signal, while applying an additional derivative reveals a sparse “innovation” signal. Fast and efficient schemes from convex optimization are deployed to solve the variational problem at hand. Second, we apply TA for fMRI data analysis to explore the underlying “deblurred” activity-inducing signals. The temporal regularization is based on generalization of TV where the differential operator is chosen to invert the (linearized) hemodynamic system. Consequently, this will favor block-type activity-inducing signals without restrictions on timing nor duration. The spatial regularization uses a mixed-norm regularization to favor coherent activity-inducing signals in brain regions chosen from an anatomical brain atlas. After demonstrating the feasibility of the proposed method using simulated data, we show results on experimental fMRI data consisting of long resting-state periods and a few unanticipated visual stimuli. The method is able to readily recover plausible activation patterns for the visual stimuli without any prior knowledge about their timing. More interestingly, we also recovered complex spatiotemporal patterns of spontaneous activity that were organized in “resting-state networks”, as such providing a new approach to study non-stationary dynamics of RS-fMRI, a research direction that will be of major interest in the coming years. Finally, we include convincing results for localizing epileptogenic brain regions in patients using simultaneous EEG-fMRI recordings