Breeding for adaptation to climate change: genomic selection for drought response in a white spruce multi-site polycross test

Optimizing Genomic Parental Selection for Categorical and Continuous-Categorical Multi-Trait Mixtures

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

A Bayesian optimization R package for multitrait parental selection

Selecting and mating parents in conventional phenotypic and genomic selection are crucial. Plant breeding programs aim to improve the economic value of crops, considering multiple traits simultaneously. When traits are negatively correlated and/or when there are missing records in some traits, selection becomes more complex. To address this problem, we propose a multitrait selection approach using the Multitrait Parental Selection (MPS) R package-an efficient tool for genetic improvement, precision breeding, and conservation genetics. The package employs Bayesian optimization algorithms and three loss functions (Kullback-Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. The software's functionality includes three main functions-EvalMPS, FastMPS, and ApproxMPS-catering to different data availability scenarios. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits.

Genomic prediction of resistance to Hymenoscyphus fraxineus in common ash (Fraxinus excelsior L.) populations

The increase in introduced insect pests and pathogens due to anthropogenic environmental changes has become a major concern for tree species worldwide. Common ash (Fraxinus excelsior L.) is one of such species facing a significant threat from the invasive fungal pathogen Hymenoscyphus fraxineus. Some studies have indicated that the susceptibility of ash to the pathogen is genetically determined, providing some hope for accelerated breeding programs that are aimed at increasing the resistance of ash populations. To address this challenge, we used a genomic selection strategy to identify potential genetic markers that are associated with resistance to the pathogen causing ash dieback. Through genome-wide association studies (GWAS) of 300 common ash individuals from 30 populations across Poland (ddRAD, dataset A), we identified six significant SNP loci with a p-value ≤1 × 10-4 associated with health status. To further evaluate the effectiveness of GWAS markers in predicting health status, we considered two genomic prediction scenarios. Firstly, we conducted cross-validation on dataset A. Secondly, we trained markers on dataset A and tested them on dataset B, which involved whole-genome sequencing of 20 individuals from two populations. Genomic prediction analysis revealed that the top SNPs identified via GWAS exhibited notably higher prediction accuracies compared to randomly selected SNPs, particularly with a larger number of SNPs. Cross-validation analyses using dataset A showcased high genomic prediction accuracy, predicting tree health status with over 90% accuracy across the top SNP sets ranging from 500 to 10,000 SNPs from the GWAS datasets. However, no significant results emerged for health status when the model trained on dataset A was tested on dataset B. Our findings illuminate potential genetic markers associated with resistance to ash dieback, offering support for future breeding programs in Poland aimed at combating ash dieback and bolstering conservation efforts for this invaluable tree species.

Contrasting physiological strategies explain heterogeneous responses to severe drought conditions within local populations of a widespread conifer

Understanding how trees prioritize carbon gain at the cost of drought vulnerability under severe drought conditions is crucial for predicting which genetic groups and individuals will be resilient to future climate conditions. In this study, we investigated variations in growth, tree-ring anatomy as well as carbon and oxygen isotope ratios to assess the sensitivity and the xylem formation process in response to an episode of severe drought in 29 mature white spruce (Picea glauca [Moench] Voss) families grown in a common garden trial. During the drought episode, the majority of families displayed decreased growth and exhibited either sustained or increased intrinsic water-use efficiency (iWUE), which was largely influenced by reduced stomatal conductance as revealed by the dual carbon‑oxygen isotope approach. Different water-use strategies were detected within white spruce populations in response to drought conditions. Our results revealed intraspecific variation in the prevailing physiological mechanisms underlying drought response within and among populations of Picea glauca. The presence of different genetic groups reflecting diverse water-use strategies within this largely-distributed conifer is likely to lessen the negative effects of drought and decrease the overall forest ecosystems' sensitivity to it.

Assembly and annotation of the black spruce genome provide insights on spruce phylogeny and evolution of stress response

Black spruce (Picea mariana [Mill.] B.S.P.) is a dominant conifer species in the North American boreal forest that plays important ecological and economic roles. Here, we present the first genome assembly of P. mariana with a reconstructed genome size of 18.3 Gbp and NG50 scaffold length of 36.0 kbp. A total of 66,332 protein-coding sequences were predicted in silico and annotated based on sequence homology. We analyzed the evolutionary relationships between P. mariana and 5 other spruces for which complete nuclear and organelle genome sequences were available. The phylogenetic tree estimated from mitochondrial genome sequences agrees with biogeography; specifically, P. mariana was strongly supported as a sister lineage to P. glauca and 3 other taxa found in western North America, followed by the European Picea abies. We obtained mixed topologies with weaker statistical support in phylogenetic trees estimated from nuclear and chloroplast genome sequences, indicative of ancient reticulate evolution affecting these 2 genomes. Clustering of protein-coding sequences from the 6 Picea taxa and 2 Pinus species resulted in 34,776 orthogroups, 560 of which appeared to be specific to P. mariana. Analysis of these specific orthogroups and dN/dS analysis of positive selection signatures for 497 single-copy orthogroups identified gene functions mostly related to plant development and stress response. The P. mariana genome assembly and annotation provides a valuable resource for forest genetics research and applications in this broadly distributed species, especially in relation to climate adaptation.

The phenotypic and genetic effects of drought-induced stress on wood specific conductivity and anatomical properties in white spruce seedlings, and relationships with growth and wood density

Drought frequency and intensity are projected to increase with climate change, thus amplifying stress on forest trees. Resilience to drought implicates physiological traits such as xylem conductivity and wood anatomical traits, which are related to growth and wood density. Integrating drought-stress response traits at the juvenile stage into breeding criteria could help promote the survival of planted seedlings under current and future climate and thus, improve plantation success. We assessed in greenhouse the influence of drought-induced stress on 600 two-year-old white spruce (Picea glauca) seedlings from 25 clonal lines after two consecutive growing seasons. Three levels of drought-induced stress were applied: control, moderate and severe. Seedlings were also planted at a 45° angle to clearly separate compression from normal wood. We looked at the phenotypic and genetic effects of drought stress on xylem specific hydraulic conductivity, lumen diameter, tracheid diameter and length, and the number of pits per tracheid in the normal wood. We detected no significant effects of drought stress except for tracheid length, which decreased with increasing drought stress. We found low to high estimates of trait heritability, which generally decreased with increasing drought stress. Genetic correlations were higher than phenotypic correlations for all treatments. Specific conductivity was genetically highly correlated positively with lumen diameter and tracheid length under all treatments. Tracheid length and diameter were always negatively correlated genetically, indicating a trade-off in resource allocation. Moderate to high genetic correlations sometimes in opposite direction were observed between physico-anatomical and productivity traits, also indicating trade-offs. A large variation was observed among clones for all physico-anatomical traits, but clonal ranks were generally stable between control and drought-induced treatments. Our results indicate the possibility of early screening of genetic material for desirable wood anatomical attributes under normal growing conditions, thus allowing to improve the drought resilience of young trees.

Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce

Introduction

Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains.

Methods

Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models.

Results

While wood quality traits were mostly additively inherited, considerable non-additive effects and lower heritabilities were detected for growth traits. For growth, GBLUP-AD better partitioned the additive and dominance effects into roughly equal variances, while ABLUP-AD strongly overestimated dominance. The predictive abilities of breeding and total genetic value estimates were similar between ABLUP-AD and GBLUP-AD when predicting individuals from the same families as those included in the training dataset. However, GBLUP-AD outperformed ABLUP-AD when predicting for new unphenotyped families that were not represented in the training dataset, with, on average, 22% and 53% higher predictive ability of breeding and genetic values, respectively. Resampling simulations showed that GBLUP-AD required smaller sample sizes than ABLUP-AD to produce precise estimates of genetic variances and accurate predictions of genetic values. Still, regardless of the method used, large training datasets were needed to estimate additive and non-additive genetic variances precisely.

Discussion

This study highlights the different quantitative genetic architectures between growth and wood traits. Furthermore, the usefulness of genomic additive-dominance models for predicting new families should allow practicing mating allocation to maximize the total genetic values for the propagation of elite material.

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought-two major threats to banana production-used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.

Genetic basis of growth reaction to drought stress differs in contrasting high-latitude treeline ecotones of a widespread conifer

Climate change is increasing the frequency and intensity of drought events in many boreal forests. Trees are sessile organisms with a long generation time, which makes them vulnerable to fast climate change and hinders fast adaptations. Therefore, it is important to know how forests cope with drought stress and to explore the genetic basis of these reactions. We investigated three natural populations of white spruce (Picea glauca) in Alaska, located at one drought-limited and two cold-limited treelines with a paired plot design of one forest and one treeline plot. We obtained individual increment cores from 458 trees and climate data to assess dendrophenotypes, in particular the growth reaction to drought stress. To explore the genetic basis of these dendrophenotypes, we genotyped the individual trees at 3000 single nucleotide polymorphisms in candidate genes and performed genotype-phenotype association analysis using linear mixed models and Bayesian sparse linear mixed models. Growth reaction to drought stress differed in contrasting treeline populations. Therefore, the populations are likely to be unevenly affected by climate change. We identified 40 genes associated with dendrophenotypic traits that differed among the treeline populations. Most genes were identified in the drought-limited site, indicating comparatively strong selection pressure of drought-tolerant phenotypes. Contrasting patterns of drought-associated genes among sampled sites and in comparison to Canadian populations in a previous study suggest that drought adaptation acts on a local scale. Our results highlight genes that are associated with wood traits which in turn are critical for the establishment and persistence of future forests under climate change.

Tree Maladaptation Under Mid-Latitude Early Spring Warming and Late Cold Spell: Implications for Assisted Migration

Global warming is predicted to extend the growing season of trees and plants, and advance spring phenology. However, intensification of extreme climate events in mid-latitude forests, from weakening of the jet stream and atmospheric blockings, may expose trees to increased risk associated with more frequent late-spring frosts. Still, little is known regarding the intraspecific variation in frost tolerance and how it may be shaped by local adaptation to the climate of seed origin. As part of an assisted migration trial located in different bioclimatic zones in the province of Quebec, Canada, and following an extensive late-spring frost that occurred at the end of May 2021, we evaluated the frost damages on various white spruce (Picea glauca) seed sources tested on three sites (south, central, and north). The severity of frost damages was assessed on 5,376 trees after the cold spell and an early spring warming which advanced bud flush by approximately 10 days on average. The frost damage rate was similar among sites and seed sources and averaged 99.8%. Frost damage severity was unrelated to the latitude of seed origin but was variable among sites. The proportion of severely damaged trees was higher in the northern site, followed by central and southern sites. The proportion of severely damaged trees was linearly and inversely related to tree height before the frost event. Apical growth cancelation was not significantly different among seed sources including local ones, and averaged 74, 46, and 22%, respectively, in central, northern, and southern plantation sites. This study provides recommendations to limit the loss of plantation productivity associated with such a succession of spring climate anomalies. Implications for seed transfer models in the context of climate change and productivity of spruce plantations are discussed in the light of lack of local adaptation to such pronounced climate instability and ensuing large-scale maladaptation.