Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

From sequences to sustainability: Exploring dipterocarp genomes for oleoresin production, timber quality, and conservation

Dipterocarp species dominate tropical forest ecosystems and provide key ecological and economic value through their use of aromatic resins, medicinal chemicals, and high-quality timber. However, habitat loss and unsustainable logging have endangered many Dipterocarpaceae species. Genomic strategies provide new opportunities for both elucidating the molecular pathways underlying these desirable traits and informing conservation efforts for at-risk taxa. This review summarizes the progress in dipterocarp genomics analysis and applications. We describe 16 recently published Dipterocarpaceae genome sequences, representing crucial genetic blueprints. Phylogenetic comparisons delineate evolutionary relationships among species and provide frameworks for pinpointing functional changes underlying specialized metabolism and wood development patterns. We also discuss connections revealed thus far between specific gene families and both oleoresin biosynthesis and wood quality traits-including the identification of key terpenoid synthases and cellulose synthases likely governing pathway flux. Moreover, the characterization of adaptive genomic markers offers vital resources for supporting conservation practices prioritizing resilient genotypes displaying valuable oleoresin and timber traits. Overall, progress in dipterocarp functional and comparative genomics provides key tools for addressing the intertwined challenges of preserving biodiversity in endangered tropical forest ecosystems while sustainably deriving aromatic chemicals and quality lumber that support diverse human activities.

Integrating environmental gradients into breeding: application of genomic reactions norms in a perennial species

Global warming threatens the productivity of forest plantations. We propose here the integration of environmental information into a genomic evaluation scheme using individual reaction norms, to enable the quantification of resilience in forest tree improvement and conservation strategies in the coming decades. Random regression models were used to fit wood ring series, reflecting the longitudinal phenotypic plasticity of tree growth, according to various environmental gradients. The predictive ability of the models was considered to select the most relevant environmental gradient, namely a gradient derived from an ecophysiological model and combining trunk water potential and temperature. Even if the individual ranking was preserved over most of the environmental gradient, strong genotype x environment interactions were detected in the extreme unfavorable part of the gradient, which includes environmental conditions that are very likely to be more frequent in the future. Combining genomic information and longitudinal data allowed to predict the growth of individuals in environments where they have not been observed. Phenotyping of 50% of the individuals in all the environments studied allowed to predict the growth of the remaining 50% of individuals in all these environments with a predictive ability of 0.25. Without changing the total number of observations, adding observations in a reduced number of environments for the individuals to be predicted, while decreasing the number of individuals phenotyped in all environments, increased the predictive ability to 0.59, highlighting the importance of phenotypic data allocation. We found that genomic reaction norms are useful for the characterization and prediction of the function of genetic parameters and facilitate breeding in a climate change context.

De Novo Transcriptome Assembly of Cedar (Cedrela odorata L.) and Differential Gene Expression Involved in Herbivore Resistance

Timber trees are targets of herbivorous attacks. The identification of genes associated with pest resistance can be accomplished through differential expression analysis using transcriptomes. We reported the de novo assembly of cedar (Cedrela odorata L.) transcriptome and the differential expression of genes involved in herbivore resistance. The assembly and annotation of the transcriptome were obtained using RNAseq from healthy cedar plants and those infested with Chrysobothris yucatanensis. A total of 325.6 million reads were obtained, and 127,031 (97.47%) sequences were successfully assembled. A total of 220 herbivory-related genes were detected, of which 170 genes were annotated using GO terms, and 161 genes with 245 functions were identified-165, 75, and 5 were molecular functions, biological processes, and cellular components, respectively. To protect against herbivorous infestation, trees produce toxins and volatile compounds which are modulated by signaling pathways and gene expression related to molecular functions and biological processes. The limited number of genes identified as cellular components suggests that there are minimal alterations in cellular structure in response to borer attack. The chitin recognition protein, jasmonate ZIM-domain (JAZ) motifs, and response regulator receiver domain were found to be overexpressed, whereas the terpene synthase, cytochrome P450, and protein kinase domain gene families were underexpressed. This is the first report of a cedar transcriptome focusing on genes that are overexpressed in healthy plants and underexpressed in infested plants. This method may be a viable option for identifying genes associated with herbivore resistance.

Genetic parameter changes and age-age correlations in Pinus koraiensis growth over 40-year progeny testing

BACKGROUND

Early selection in tree breeding could be achieved by addressing the longevity of tree improvement activities. Genetic parameter changes and age-age correlations are essential for determining the optimal timing of early selection. Practical tracking of genetic parameters of Pinus koraiensis, a major timber species with economic and ecological value, has become feasible as its progeny testing has entered the mid-term age in Korea. However, research on the age-age correlation of P. koraiensis as progeny trials approach rotation age is limited. This study aimed to investigate genetic parameter trends and age-age correlations in P. koraiensis progeny. P. koraiensis progeny were assessed at two sites using a linear mixed-effects model with two-dimensional spatial autoregressive structure. Height, diameter, and volume growth were measured in 11 assessments over 40 years.

RESULTS

Genetic parameters, such as height and diameter, showed different patterns of change. The heritability ranged for the three growth traits in 0.083-0.710, 0.288-0.781, and 0.299-0.755 across the sites and age. Height heritability and its coefficient of variance decreased, whereas the diameter and volume estimates remained relatively constant. Correlations with Age 40 for phenotypic, genetic, and rank of breeding values ranged between 0.16 and 0.92, 0.594 and 0.988, and 0.412 and 0.965, respectively. These correlations generally increased as the age approached Age 40, with particularly high levels observed at Age 26 and Age 30.

CONCLUSION

The observed genetic trends in P. koraiensis progeny testing offer valuable insights for early and precise selection. Notably, selecting superior genotypes at Ages 26-30 is supported by discernible genetic gains and robust correlations. Future research should integrate unbalanced data for selecting mother trees or families and conduct a comprehensive economic analysis of early selection to validate its practical benefits.

Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.)

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164-0.498 and a predictive ability of 0.018-0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86-5.10, and against the square root of heritability was 0.19-0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.

An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

Comparing modeling methods of genomic prediction for growth traits of a tropical timber species, Shorea macrophylla

Introduction

Shorea macrophylla is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to S. macrophylla.

Methods

To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of S. macrophylla in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for diameter at breast height and total height in the 7th year from planting (D7 and H7).

Results and discussion

These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 6 methods and their derivatives with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability for D7 and H7 with the sets of selected SNPs and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high predictive abilities for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. Predictive abilities were higher in H7 when smaller number of SNP subsets selected by GWAS p-value was used, However, D7 showed the contrary tendency, which might have originated from the difference in genetic architecture between primary and secondary growth of the species. This study suggests that GS with GWAS-based SNP selection can be used in breeding for non-cultivated tree species to improve initial growth and reduce genotyping costs for next-generation seedlings.

Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids

Introduction

Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus.

Methods

Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts.

Results

Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees.

Discussion

We demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.

Genome-wide association studies: an intuitive solution for SNP identification and gene mapping in trees

Analysis of natural diversity in wild/cultivated plants can be used to understand the genetic basis for plant breeding programs. Recent advancements in DNA sequencing have expanded the possibilities for genetically altering essential features. There have been several recently disclosed statistical genetic methods for discovering the genes impacting target qualities. One of these useful methods is the genome-wide association study (GWAS), which effectively identifies candidate genes for a variety of plant properties by examining the relationship between a molecular marker (such as SNP) and a target trait. Conventional QTL mapping with highly structured populations has major limitations. The limited number of recombination events results in poor resolution for quantitative traits. Only two alleles at any given locus can be studied simultaneously. Conventional mapping approach fails to work in perennial plants and vegetatively propagated crops. These limitations are sidestepped by association mapping or GWAS. The flexibility of GWAS comes from the fact that the individuals being examined need not be linked to one another, allowing for the use of all meiotic and recombination events to increase resolution. Phenotyping, genotyping, population structure analysis, kinship analysis, and marker-trait association analysis are the fundamental phases of GWAS. With the rapid development of sequencing technologies and computational methods, GWAS is becoming a potent tool for identifying the natural variations that underlie complex characteristics in crops. The use of high-throughput sequencing technologies along with genotyping approaches like genotyping-by-sequencing (GBS) and restriction site associated DNA (RAD) sequencing may be highly useful in fast-forward mapping approach like GWAS. Breeders may use GWAS to quickly unravel the genomes through QTL and association mapping by taking advantage of natural variances. The drawbacks of conventional linkage mapping can be successfully overcome with the use of high-resolution mapping and the inclusion of multiple alleles in GWAS.

Tolerant Epitypes of Elicited Holm Oak Somatic Embryos Could Be Revealed by Challenges in Dual Culture with Phytophthora cinnamomi Rands

Holm oaks (Quercus ilex L.) can suffer severe infection by the oomycete Phytophthora cinnamomi Rands; the production of more tolerant plants is, therefore, required. Embryo formation is a key period in the establishment of epigenetic memory. Somatic embryos from three holm oak genotypes were elicited, either over 3 days or 60 days, with methyl-jasmonate, salicylic acid (SA), β-aminobutyric acid (BABA), or benzothiadiazole (all at 50 μM and 100 μM), or 10% and 30% of a filtered oomycete extract (FILT10 and FILT30) to activate plant immune responses. The number of embryos produced and conversion rate under all conditions were recorded. Some elicited embryos were then exposed to P. cinnamomi in dual culture, and differential mycelial growth and the progression of necrosis were measured. The same was performed with the roots of germinated embryos. Within genotypes, significant differences were seen among the elicitation treatments in terms of both variables. Embryos and roots of 60-day BABA, SA, or FILT10 treatments inhibited mycelium growth. The 3-day BABA (either concentration) and 60-day FILT10 induced the greatest inhibition of necrosis. Mycelium and necrosis inhibition were compared with those of tolerant trees. Both inhibitions might be a defense response maintained after primed embryo germination, thus increasing the likelihood of tolerance to infection.

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

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.

Identifying Pathogen and Allele Type Simultaneously in a Single Well Using Droplet Digital PCR

In the transmission control of chronic and untreatable livestock diseases such as bovine leukemia virus (BLV) infection, the removal of viral superspreaders is a fundamental approach. On the other hand, selective breeding of cattle with BLV-resistant capacity is also critical for reducing the viral damage to productivity by keeping infected cattle. To provide a way of measuring BLV proviral load (PVL) and identifying susceptible/resistant cattle simply and rapidly, we developed a fourplex droplet digital PCR method targeting the BLV pol gene, BLV-susceptible bovine major histocompatibility complex (BoLA)-DRB3*016:01 allele, resistant DRB3*009:02 allele, and housekeeping RPP30 gene (IPATS-BLV). IPATS-BLV successfully measured the percentage of BLV-infected cells and determined allele types precisely. Furthermore, it discriminated homozygous from heterozygous carriers. Using this method to determine the impact of carrying these alleles on the BLV PVL, we found DRB3*009:02-carrying cattle could suppress the PVL to a low or undetectable level, even with the presence of a susceptible heterozygous allele. Although the population of DRB3*016:01-carrying cattle showed significantly higher PVLs compared with cattle carrying other alleles, their individual PVLs were highly variable. Because of the simplicity and speed of this single-well assay, our method has the potential of being a suitable platform for the combined diagnosis of pathogen level and host biomarkers in other infectious diseases satisfying the two following characteristics of disease outcomes: (i) pathogen level acts as a critical maker of disease progression; and (ii) impactful disease-related host genetic biomarkers are already identified. IMPORTANCE While pathogen-level quantification is an important diagnostic of disease severity and transmissibility, disease-related host biomarkers are also useful in predicting outcomes in infectious diseases. In this study, we demonstrate that combined proviral load (PVL) and host biomarker diagnostics can be used to detect bovine leukemia virus (BLV) infection, which has a negative economic impact on the cattle industry. We developed a fourplex droplet digital PCR assay for PVL of BLV and susceptible and resistant host genes named IPATS-BLV. IPATS-BLV has inherent merits in measuring PVL and identifying susceptible and resistant cattle with superior simplicity and speed because of a single-well assay. Our new laboratory technique contributes to strengthening risk-based herd management used to control within-herd BLV transmission. Furthermore, this assay design potentially improves the diagnostics of other infectious diseases by combining the pathogen level and disease-related host genetic biomarker to predict disease outcomes.

Crop genomic selection with deep learning and environmental data: A survey

Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.

Lignin engineering in forest trees: From gene discovery to field trials

Wood is an abundant and renewable feedstock for the production of pulp, fuels, and biobased materials. However, wood is recalcitrant toward deconstruction into cellulose and simple sugars, mainly because of the presence of lignin, an aromatic polymer that shields cell-wall polysaccharides. Hence, numerous research efforts have focused on engineering lignin amount and composition to improve wood processability. Here, we focus on results that have been obtained by engineering the lignin biosynthesis and branching pathways in forest trees to reduce cell-wall recalcitrance, including the introduction of exotic lignin monomers. In addition, we draw general conclusions from over 20 years of field trial research with trees engineered to produce less or altered lignin. We discuss possible causes and solutions for the yield penalty that is often associated with lignin engineering in trees. Finally, we discuss how conventional and new breeding strategies can be combined to develop elite clones with desired lignin properties. We conclude this review with priorities for the development of commercially relevant lignin-engineered trees.

Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection

The ecological and economic importance of forest trees is evident and their survival is necessary to provide the raw materials needed for wood and paper industries, to preserve the diversity of associated animal and plant species, to protect water and soil, and to regulate climate. Forest trees are threatened by anthropogenic factors and biotic and abiotic stresses. Various diseases, including those caused by fungal pathogens, are one of the main threats to forest trees that lead to their dieback. Genomics and transcriptomics studies using next-generation sequencing (NGS) methods can help reveal the architecture of resistance to various diseases and exploit natural genetic diversity to select elite genotypes with high resistance to diseases. In the last two decades, QTL mapping studies led to the identification of QTLs related to disease resistance traits and gene families and transcription factors involved in them, including NB-LRR, WRKY, bZIP and MYB. On the other hand, due to the limitation of recombination events in traditional QTL mapping in families derived from bi-parental crosses, genome-wide association studies (GWAS) that are based on linkage disequilibrium (LD) in unstructured populations overcame these limitations and were able to narrow down QTLs to single genes through genotyping of many individuals using high-throughput markers. Association and QTL mapping studies, by identifying markers closely linked to the target trait, are the prerequisite for marker-assisted selection (MAS) and reduce the breeding period in perennial forest trees. The genomic selection (GS) method uses the information on all markers across the whole genome, regardless of their significance for development of a predictive model for the performance of individuals in relation to a specific trait. GS studies also increase gain per unit of time and dramatically increase the speed of breeding programs. This review article is focused on the progress achieved in the field of dissecting forest tree disease resistance architecture through GWAS and QTL mapping studies. Finally, the merit of methods such as GS in accelerating forest tree breeding programs is also discussed.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

Genomic selection in tropical perennial crops and plantation trees: a review

To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01326-4.

Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.)

Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.

Generating Douglas-fir Breeding Value Estimates Using Airborne Laser Scanning Derived Height and Crown Metrics

Progeny test trials in British Columbia are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of parent trees and their offspring. Accurate and timely collection of phenotypic data is critical for estimating BVs with confidence. Airborne Laser Scanning (ALS) data have been used to measure tree height and structure across a wide range of species, ages and environments globally. Here, we analyzed a Coastal Douglas-fir [Pseudotsuga menziesii var. menziesii (Mirb.)] progeny test trial located in British Columbia, Canada, using individual tree high-density Airborne Laser Scanning (ALS) metrics and traditional ground-based phenotypic observations. Narrow-sense heritability, genetic correlations, and BVs were estimated using pedigree-based single and multi-trait linear models for 43 traits. Comparisons of genetic parameter estimates between ALS metrics and traditional ground-based measures and single- and multi-trait models were conducted based on the accuracy and precision of the estimates. BVs were estimated for two ALS models (ALS_CAN and ALS_ACC) representing two model-building approaches and compared to a baseline model using field-measured traits. The ALS_CAN model used metrics reflecting aspects of vertical distribution of biomass within trees, while ALS_ACC represented the most statistically accurate model. We report that the accuracy of both the ALS_CAN (0.8239) and ALS_ACC (0.8254) model-derived BVs for mature tree height is a suitable proxy for ground-based mature tree height BVs (0.8316). Given the cost efficiency of ALS, forest geneticists should explore this technology as a viable tool to increase breeding programs' overall efficiency and cost savings.

Genome properties of key oil palm (Elaeis guineensis Jacq.) breeding populations

A good knowledge of the genome properties of the populations makes it possible to optimize breeding methods, in particular genomic selection (GS). In oil palm (Elaeis guineensis Jacq), the world's main source of vegetable oil, this would provide insight into the promising GS results obtained so far. The present study considered two complex breeding populations, Deli and La Mé, with 943 individuals and 7324 single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing. Linkage disequilibrium (LD), haplotype sharing, effective size (N_e), and fixation index (F_st) were investigated. A genetic linkage map spanning 1778.52 cM and with a recombination rate of 2.85 cM/Mbp was constructed. The LD at r²=0.3, considered the minimum to get reliable GS results, spanned over 1.05 cM/0.22 Mbp in Deli and 0.9 cM/0.21 Mbp in La Mé. The significant degree of differentiation existing between Deli and La Mé was confirmed by the high F_st value (0.53), the pattern of correlation of SNP heterozygosity and allele frequency among populations, and the decrease of persistence of LD and of haplotype sharing among populations with increasing SNP distance. However, the level of resemblance between the two populations over short genomic distances (correlation of r values between populations >0.6 for SNPs separated by <0.5 cM/1 kbp and percentage of common haplotypes >40% for haplotypes <3600 bp/0.20 cM) likely explains the superiority of GS models ignoring the parental origin of marker alleles over models taking this information into account. The two populations had low N_e (<5). Population-specific genetic maps and reference genomes are recommended for future studies.

Full-Length Transcriptome Sequencing and Comparative Transcriptomic Analyses Provide Comprehensive Insight Into Molecular Mechanisms of Cellulose and Lignin Biosynthesis in Cunninghamia lanceolata

Cunninghamia lanceolata is an essential timber species that provide 20%-30% raw materials for China's timber industry. Although a few transcriptomes have been published in C. lanceolata, full-length mRNA transcripts and regulatory mechanisms behind the cellulose and lignin biosynthesis have not been thoroughly investigated. Here, PacBio Iso-seq and RNA-seq analyses were adapted to identify the full-length and differentially expressed transcripts along a developmental gradient from apex to base of C. lanceolata shoots. A total of 48,846 high-quality full-length transcripts were obtained, of which 88.0% are completed transcriptome based on benchmarking universal single-copy orthologs (BUSCO) assessment. Along stem developmental gradient, 18,714 differentially expressed genes (DEGs) were detected. Further, 28 and 125 DEGs were identified as enzyme-coding genes of cellulose and lignin biosynthesis, respectively. Moreover, 57 transcription factors (TFs), including MYB and NAC, were identified to be involved in the regulatory network of cellulose and lignin biosynthesis through weighted gene co-expression network analysis (WGCNA). These TFs are composed of a comparable regulatory network of secondary cell wall formation in angiosperms, revealing a similar mechanism may exist in gymnosperms. Further, through qRT-PCR, we also investigated eight specific TFs involved in compression wood formation. Our findings provide a comprehensive and valuable source for molecular genetics breeding of C. lanceolata and will be beneficial for molecular-assisted selection.

Plant Genotype to Phenotype Prediction Using Machine Learning

Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( h g 2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( h g 2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( h g 2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.

Metadata analysis indicates biased estimation of genetic parameters and gains using conventional pedigree information instead of genomic-based approaches in tree breeding

Forest tree improvement helps provide adapted planting stock to ensure growth productivity, fibre quality and carbon sequestration through reforestation and afforestation activities. However, there is increasing doubt that conventional pedigree provides the most accurate estimates for selection and prediction of performance of improved planting stock. When the additive genetic relationships among relatives is estimated using pedigree information, it is not possible to take account of Mendelian sampling due to the random segregation of parental alleles. The use of DNA markers distributed genome-wide (multi-locus genotypes) makes it possible to estimate the realized additive genomic relationships, which takes account of the Mendelian sampling and possible pedigree errors. We reviewed a series of papers on conifer and broadleaf tree species in which both pedigree-based and marker-based estimates of genetic parameters have been reported. Using metadata analyses, we show that for heritability and genetic gains, the estimates obtained using only the pedigree information are generally biased upward compared to those obtained using DNA markers distributed genome-wide, and that genotype-by-environment (GxE) interaction can be underestimated for low to moderate heritability traits. As high-throughput genotyping becomes economically affordable, we recommend expanding the use of genomic selection to obtain more accurate estimates of genetic parameters and gains.

Changing Temperature Conditions during Somatic Embryo Maturation Result in Pinus pinaster Plants with Altered Response to Heat Stress

Under the global warming scenario, obtaining plant material with improved tolerance to abiotic stresses is a challenge for afforestation programs. In this work, maritime pine (Pinus pinaster Aiton) plants were produced from somatic embryos matured at different temperatures (18, 23, or 28 °C, named after M18, M23, and M28, respectively) and after 2 years in the greenhouse a heat stress treatment (45 °C for 3 h/day for 10 days) was applied. Temperature variation during embryo development resulted in altered phenotypes (leaf histology, proline content, photosynthetic rates, and hormone profile) before and after stress. The thickness of chlorenchyma was initially larger in M28 plants, but was significantly reduced after heat stress, while increased in M18 plants. Irrespective of their origin, when these plants were subjected to a heat treatment, relative water content (RWC) and photosynthetic carbon assimilation rates were not significantly affected, although M18 plants increased net photosynthesis rate after 10 days recovery (tR). M18 plants showed proline contents that increased dramatically (2.4-fold) when subjected to heat stress, while proline contents remained unaffected in M23 and M28 plants. Heat stress significantly increased abscisic acid (ABA) content in the needles of maritime pine plants (1.4-, 3.6- and 1.9-fold in M18, M23, and M28 plants, respectively), while indole-3-acetic acid content only increased in needles from M23 plants. After the heat treatment, the total cytokinin contents of needles decreased significantly, particularly in M18 and M28 plants, although levels of active forms (cytokinin bases) did not change in M18 plants. In conclusion, our results suggest that maturation of maritime pine somatic embryos at lower temperature resulted in plants with better performance when subjected to subsequent high temperature stress, as demonstrated by faster and higher proline increase, lower increases in ABA levels, no reduction in active cytokinin, and a better net photosynthesis rate recovery.

Utilization of the Hollies (Ilex L. spp.): A Review

The hollies (Ilex L., Aquifoliaceae) form a large (>669 spp.) genus of forest trees and shrubs, which is almost cosmopolitan in mesic environments but most diverse in subtropical China and montane South America. Throughout the range of the genus, Ilex species have been utilized as beverages, medicines, ornamentals, honey plants, timber, and for various other minor uses. Recent studies on the genomics, evolution, and biogeography of Ilex now make it possible to take a systematic approach to understanding and expanding the economic importance of the genus, but information on existing uses is scattered among numerous published and unpublished sources. We therefore review the existing literature on utilization of Ilex species, supplementing this with information from the grey literature and product websites. We show that, despite the number and diversity of known uses, most Ilex species are not known to be utilized at present, suggesting considerable unrealized potential. We highlight gaps in our knowledge and opportunities for expanded usage. Finally, we discuss how the availability of a new phylogeny and whole genome can assist screening of additional wild species for economic potential and facilitate breeding programs for species already under cultivation.

Genetic Engineering and Genome Editing for Improving Nitrogen Use Efficiency in Plants

Low nitrogen availability is one of the main limiting factors for plant growth and development, and high doses of N fertilizers are necessary to achieve high yields in agriculture. However, most N is not used by plants and pollutes the environment. This situation can be improved by enhancing the nitrogen use efficiency (NUE) in plants. NUE is a complex trait driven by multiple interactions between genetic and environmental factors, and its improvement requires a fundamental understanding of the key steps in plant N metabolism—uptake, assimilation, and remobilization. This review summarizes two decades of research into bioengineering modification of N metabolism to increase the biomass accumulation and yield in crops. The expression of structural and regulatory genes was most often altered using overexpression strategies, although RNAi and genome editing techniques were also used. Particular attention was paid to woody plants, which have great economic importance, play a crucial role in the ecosystems and have fundamental differences from herbaceous species. The review also considers the issue of unintended effects of transgenic plants with modified N metabolism, e.g., early flowering—a research topic which is currently receiving little attention. The future prospects of improving NUE in crops, essential for the development of sustainable agriculture, using various approaches and in the context of global climate change, are discussed.

A genome variation map provides insights into the genetics of walnut adaptation and agronomic traits

BACKGROUND

Common walnut (Juglans regia L.) is one of the top four most consumed nuts in the world due to its health benefits and pleasant taste. Despite its economic importance, the evolutionary history and genetic control of its adaptation and agronomic traits remain largely unexplored.

RESULTS

We report a comprehensive walnut genomic variation map based on whole-genome resequencing of 815 walnut accessions. Evolutionary analyses suggest that Chinese J. regia diverged from J. sigillata with extensive hybridizations after the split of the two species. In contrast to annual crops, the genetic diversity and heterozygous deleterious mutations of Chinese common walnut trees have continued to increase during the improvement process. Selective sweep analyses identify 902 genes uniquely selected in the improved common walnut compared to its progenitor population. Five major-effect loci are identified to be involved in walnut adaptations to temperature, precipitation, and altitude. Genome-wide association studies reveal 27 genomic loci responsible for 18 important agronomic traits, among which JrFAD2 and JrANR are the potentially major-effect causative genes controlling linoleic acid content and color of the endopleura of the nut, respectively.

CONCLUSIONS

The largest genomic resource for walnuts to date has been generated and explored in this study, unveiling their evolutionary history and cracking the genetic code for agronomic traits and environmental adaptation of this economically crucial crop tree.

Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.

Metabolic engineering in woody plants: challenges, advances, and opportunities

Woody plant species represent an invaluable reserve of biochemical diversity to which metabolic engineering can be applied to satisfy the need for commodity and specialty chemicals, pharmaceuticals, and renewable energy. Woody plants are particularly promising for this application due to their low input needs, high biomass, and immeasurable ecosystem services. However, existing challenges have hindered their widespread adoption in metabolic engineering efforts, such as long generation times, large and highly heterozygous genomes, and difficulties in transformation and regeneration. Recent advances in omics approaches, systems biology modeling, and plant transformation and regeneration methods provide effective approaches in overcoming these outstanding challenges. Promises brought by developments in this space are steadily opening the door to widespread metabolic engineering of woody plants to meet the global need for a wide range of sustainably sourced chemicals and materials.

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.

Induced Genetic Variations in Fruit Trees Using New Breeding Tools: Food Security and Climate Resilience

Fruit trees provide essential nutrients to humans by contributing to major agricultural outputs and economic growth globally. However, major constraints to sustainable agricultural productivity are the uncontrolled proliferation of the population, and biotic and abiotic stresses. Tree mutation breeding has been substantially improved using different physical and chemical mutagens. Nonetheless, tree plant breeding has certain crucial bottlenecks including a long life cycle, ploidy level, occurrence of sequence polymorphisms, nature of parthenocarpic fruit development and linkage. Genetic engineering of trees has focused on boosting quality traits such as productivity, wood quality, and resistance to biotic and abiotic stresses. Recent technological advances in genome editing provide a unique opportunity for the genetic improvement of woody plants. This review examines application of the CRISPR-Cas system to reduce disease susceptibility, alter plant architecture, enhance fruit quality, and improve yields. Examples are discussed of the contemporary CRISPR-Cas system to engineer easily scorable PDS genes, modify lignin, and to alter the flowering onset, fertility, tree architecture and certain biotic stresses.

Breeding for Climate Change Resilience: A Case Study of Loblolly Pine (Pinus taeda L.) in North America

Earth's atmosphere is warming and the effects of climate change are becoming evident. A key observation is that both the average levels and the variability of temperature and precipitation are changing. Information and data from new technologies are developing in parallel to provide multidisciplinary opportunities to address and overcome the consequences of these changes in forest ecosystems. Changes in temperature and water availability impose multidimensional environmental constraints that trigger changes from the molecular to the forest stand level. These can represent a threat for the normal development of the tree from early seedling recruitment to adulthood both through direct mortality, and by increasing susceptibility to pathogens, insect attack, and fire damage. This review summarizes the strengths and shortcomings of previous work in the areas of genetic variation related to cold and drought stress in forest species with particular emphasis on loblolly pine (Pinus taeda L.), the most-planted tree species in North America. We describe and discuss the implementation of management and breeding strategies to increase resilience and adaptation, and discuss how new technologies in the areas of engineering and genomics are shaping the future of phenotype-genotype studies. Lessons learned from the study of species important in intensively-managed forest ecosystems may also prove to be of value in helping less-intensively managed forest ecosystems adapt to climate change, thereby increasing the sustainability and resilience of forestlands for the future.

Priming Maritime Pine Megagametophytes during Somatic Embryogenesis Improved Plant Adaptation to Heat Stress

In the context of global climate change, forest tree research should be addressed to provide genotypes with increased resilience to high temperature events. These improved plants can be obtained by heat priming during somatic embryogenesis (SE), which would produce an epigenetic-mediated transgenerational memory. Thereby, we applied 37 °C or 50 °C to maritime pine (Pinus pinaster) megagametophytes and the obtained embryogenic masses went through the subsequent SE phases to produce plants that were further subjected to heat stress conditions. A putative transcription factor WRKY11 was upregulated in priming-derived embryonal masses, and also in the regenerated P37 and P50 plants, suggesting its role in establishing an epigenetic memory in this plant species. In vitro-grown P50 plants also showed higher cytokinin content and SOD upregulation, which points to a better responsiveness to heat stress. Heat exposure of two-year-old maritime pine plants induced upregulation of HSP70 in those derived from primed embryogenic masses, that also showed better osmotic adjustment and higher increases in chlorophyll, soluble sugars and starch contents. Moreover, ϕPSII of P50 plants was less affected by heat exposure. Thus, our results suggest that priming at 50 °C at the SE induction phase is a promising strategy to improve heat resilience in maritime pine.

Root Rot Resistance Locus PaLAR3 Is Delivered by Somatic Embryogenesis (SE) Pipeline in Norway Spruce (Picea abies (L.) Karst.)

Research Highlights: The Norway spruce somatic embryogenesis (SE) pipeline is suitable for multiplication of material with root rot resistance traits. Background and Objectives: Heterobasidion root rot is the economically most severe forest pathogen in Europe, reducing the benefit of planting elite forest material. In this study, the SE-propagation ability of elite Norway spruce material carrying root rot resistance traits was studied. Materials and Methods: We analyzed the presence of the root rot resistance locus PaLAR3B among 80 Finnish progeny-tested Norway spruce plus-trees used for SE-plant production as well as in 241 SE lines (genotypes) derived from them. Seven full-sib families with lines having either AA, AB, or BB genotype for PaLAR3 locus were further studied for their SE-plant propagation ability. Results: The results indicate that 47.5% of the studied elite trees carry the PaLAR3B allele (45% are heterozygous and 2.5% homozygous). The resistance allele was present among the SE lines as expected based on Mendelian segregation and did not interfere with somatic embryo production capacity. All embryos from PaLAR3 genotypes germinated well and emblings were viable in the end of first growing season. However, in three families, PaLAR3B homo- or heterozygotes had 23.2% to 32.1% lower viability compared to their respective hetero- or PaLAR3A homozygotes. Conclusions: There is no trade-off between root rot resistance locus PaLAR3B and somatic embryo production ability, but the allele may interfere with Norway spruce embling establishment.

Genetics and Improvement of Forest Trees

Forest tree improvement has mainly been implemented to enhance the productivity of artificial forests [...]