Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species

Background The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with similar to 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesiiMirb. (Franco)) and Interior spruce (Picea glauca(Moench) Voss xPicea engelmanniiParry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200-50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F(1)families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. Results As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000-15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. Conclusions Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits

Integrating genomic information and productivity and climate-adaptability traits into a regional white spruce breeding program

Tree improvement programs often focus on improving productivity-related traits; however, under present climate change scenarios, climate change-related (adaptive) traits should also be incorporated into such programs. Therefore, quantifying the genetic variation and correlations among productivity and adaptability traits, and the importance of genotype by environment interactions, including defense compounds involved in biotic and abiotic resistance, is essential for selecting parents for the production of resilient and sustainable forests. Here, we estimated quantitative genetic parameters for 15 growth, wood quality, drought resilience, and monoterpene traits for Picea glauca (Moench) Voss (white spruce). We sampled 1,540 trees from three open-pollinated progeny trials, genotyped with 467,224 SNP markers using genotyping-by-sequencing (GBS). We used the pedigree and SNP information to calculate, respectively, the average numerator and genomic relationship matrices, and univariate and multivariate individual-tree models to obtain estimates of (co)variance components. With few site-specific exceptions, all traits examined were under genetic control. Overall, higher heritability estimates were derived from the genomic- than their counterpart pedigree-based relationship matrix. Selection for height, generally, improved diameter and water use efficiency, but decreased wood density, microfibril angle, and drought resistance. Genome-based correlations between traits reaffirmed the pedigree-based correlations for most trait pairs. High and positive genetic correlations between sites were observed (average 0.68), except for those pairs involving the highest elevation, warmer, and moister site, specifically for growth and microfibril angle. These results illustrate the advantage of using genomic information jointly with productivity and adaptability traits, and defense compounds to enhance tree breeding selection for changing climate.Instituto de Recursos BiológicosFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Cappa, Eduardo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Klutsch, Jenifer G. University of Alberta; Department of Renewable Resources; CanadaFil: Sebastian-Azcona, Jaime. University of Alberta; Department of Renewable Resources; CanadaFil: Ratchiffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáFil: Xiaojing, Wei. University of Alberta; Department of Renewable Resources; CanadaFil: Da Ros, Letitia. University of British Columbia. Faculty of Forestry. Department of Wood Science; CanadáFil: Yang, Liu. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáFil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados UnidosFil: Benowicz, Andy. Alberta Agriculture and Forestry. Forest Stewardship and Trade Branch; CanadáFil: Sadoway, Shane. Blue Ridge Lumber Inc.; CanadáFil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; CanadáFil: Erbilgin, Nadir. University of Alberta; Department of Renewable Resources; CanadaFil: Thomas, Barb R. University of Alberta; Department of Renewable Resources; CanadaFil: El-Kassaby, Yousry A. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canad

Decoupling of height growth and drought or pest resistance tradeoffs is revealed through multiple common-garden experiments of lodgepole pine

14 páginas.- 4 figuras.- 1 tabla.- 101 referencias.- Supplementary material is available online at Evolution (https://academic.oup.com/evolut/qpad004) . A correction has been published: Evolution, Volume 77, Issue 4, 1 April 2023, Page 1174, https://doi.org/10.1093/evolut/qpad030 Issue Section: Correction This is a correction to: Yang Liu, Nadir Erbilgin, Eduardo Pablo Cappa, Charles Chen, Blaise Ratcliffe, Xiaojing Wei, Jennifer G Klutsch, Aziz Ullah, Jaime Sebastian Azcona, Barb R Thomas, Yousry A El-Kassaby, Decoupling of height growth and drought or pest resistance tradeoffs is revealed through multiple common-garden experiments of lodgepole pine, Evolution, 2023; qpad004, https://doi.org/10.1093/evolut/qpad004 In the originally published version of this manuscript, the images for Figures 3 and 4 were erroneously transposed. The images are now in their correct position to align with their respective legends. The Funding section was erroneously removed, and its details situated at the end of the Acknowledgements section. Funding details are now situated within the replaced Funding section. The publisher would like to apologise for the errors introduced here. The errors have been corrected in the article online.The environment could alter growth and resistance tradeoffs in plants by affecting the ratio of resource allocation to various competing traits. Yet, how and why functional tradeoffs change over time and space is poorly understood particularly in long-lived conifer species. By establishing four common-garden test sites for five lodgepole pine populations in western Canada, combined with genomic sequencing, we revealed the decoupling pattern and genetic underpinnings of tradeoffs between height growth, drought resistance based on δ13C and dendrochronology, and metrics of pest resistance based on pest suitability ratings. Height and δ13C correlation displayed a gradient change in magnitude and/or direction along warm-to-cold test sites. All cold test sites across populations showed a positive height and δ13C relationship. However, we did not observe such a clinal correlation pattern between height or δ13C and pest suitability. Further, we found that the study populations exhibiting functional tradeoffs or synergies to various degrees in test sites were driven by non-adaptive evolutionary processes rather than adaptive evolution or plasticity. Finally, we found positive genetic relationships between height and drought or pest resistance metrics and probed five loci showing potential genetic tradeoffs between northernmost and the other populations. Our findings have implications for deciphering the ecological, evolutionary, and genetic bases of the decoupling of functional tradeoffs due to environmental change.This work was funded by Genome Canada, Genome Alberta through Alberta Economic Trade and evelopment, Genome British Columbia, the University of Alberta, the University of Calgary, the University of Cambridge, and the Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture (CE200100015).Peer reviewe

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

Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

BACKGROUND: Metabolic phenotyping has become an important 'bird's-eye-view' technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of 'top-down' chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems. METHODOLOGY/PRINCIPAL FINDINGS: The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 (1)H and (13)C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each (13)C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient (13)C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts. CONCLUSIONS/SIGNIFICANCE: Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of (13)C atoms of given metabolites on development-dependent changes in the 56 identified (13)C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism

Exploring genomic selection in conifers

Breeding conifer species for phenotypic improvement is challenging due to delayed expression of important phenotypes related to productivity and their late sexual maturity, causing long recurrent selection cycles. Genomic selection (GS) can address such shortcomings through early prediction of phenotypes based on large numbers of jointly considered genomic markers, typically, single nucleotide polymorphisms (SNPs). Additionally, current conifer breeding genetic evaluations are based on pedigree-based predictions. However, the maximization of genetic gain in breeding programs is contingent on the accuracy of the predicted breeding values and precision of the estimated genetic parameters, which can also be improved using GS. While GS has become a new paradigm in animal breeding, it is still in its infancy for tree improvement. Thus, GS requires validation before it can be operationally implemented. Collectively, this dissertation explores some of the challenges associated with the application of GS in forest tree improvement programs. Namely, the efficiency of GS compared to traditional phenotypic selection, methods to implement GS in a cost-efficient manner, and the prediction accuracy (PA) of phenotypes across generations, life-stages, and environments. To address these challenges I structured this dissertation into three analyses which use several GS methodologies, three genotyping platforms, and three conifer species. The first study explores the temporal decay and relative efficiency of GS PA for interior spruce (Picea engelmannii × glauca). The second study investigates the use of single-step GS (ssGBLUP) to improve the precision and accuracy of genetic parameter estimates for white spruce (Picea glauca). The third study focuses on the combined use of ssGBLUP and climate data to improve intra- and inter-generation PA in unobserved environments for Douglas-fir (Pseudotsuga menziesii). The results from these three studies demonstrated that: i) updating GS models requires iv phenotypic data at least mid-rotation age to accurately reflect mature growth traits, ii) the relative efficiency of GS is greater than traditional selection assuming a 25% reduction in breeding cycle length, iii) ssGBLUP is an effective tool for improvement in the genetic evaluation of openpollinated mating designs, and iv) inclusion of climate variables as environmental covariates in the GS models yields improvement in PA for unobserved environments.Forestry, Faculty ofGraduat

Efficient genomics based 'end-to-end' selective tree breeding framework

<p>Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingences and concerns. Here, we introduce an "end-to-end" selective tree breeding framework that: 1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, 2) generates unprecedented resolution of genealogical relationships among tested individuals, and 3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals' breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits' correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.</p><p>Funding provided by: Natural Sciences and Engineering Research Council<br>Crossref Funder Registry ID: https://ror.org/01h531d29<br>Award Number: Discovery Grant</p><p>Funding provided by: Johnson's Family Forest Biotechnology Endowment*<br>Crossref Funder Registry ID: <br>Award Number: </p><p>See Readme file</p&gt

Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS

Coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is one of the most commercially important softwood species in North America. In British Columbia, Canada, breeding has increased volume gains between 20 and 30%, while 97% of seedlings come from improved seed sources. Branching traits in particular, have a strong influence on strength and stiffness of Douglas-fir wood; however, they are rarely measured. Remotely Piloted Aerial Systems and Airborne Laser Scanning Systems (RPAS-LS) produce high-density three-dimensional point clouds that can be used for the creation of internal geometric features describing individual tree branching structures. We analyzed a Coastal Douglas-fir progeny test trial located in British Columbia, Canada, and developed a new method to estimate branch attributes from RPAS-LS data for inclusion as selection criteria in tree improvement programs. Branch length, angle, width, and volume were estimated for each tree. Narrow-sense heritability (the proportion of variation due to genetics) and genetic correlations were also estimated. The method extracted branch length with a correlation (r) of 0.93 compared to manual measurements. Using these branch attributes, results then show that branch angle had the highest heritability (0.277), while tree height and branch length had the highest genetic correlation (0.668). These findings are encouraging for forest managers as they indicate that branch level metrics should be considered when selecting trees in breeding programs

Data from: Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing

Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions: The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima