Implementation of the Realized Genomic Relationship Matrix to Open-Pollinated White Spruce Family Testing for Disentangling Additive from Nonadditive Genetic Effects

Genomic selection in western redcedar: from proof of concept to operational application

Forests face many threats. While traditional breeding may be too slow to deliver well-adapted trees, genomic selection (GS) can accelerate the process. We describe a comprehensive study of GS from proof of concept to operational application in western redcedar (WRC, Thuja plicata). Using genomic data, we developed models on a training population (TrP) of trees to predict breeding values (BVs) in a target seedling population (TaP) for growth, heartwood chemistry, and foliar chemistry traits. We used cross-validation to assess prediction accuracy (PACC) in the TrP; we also validated models for early-expressed foliar traits in the TaP. Prediction accuracy was high across generations, environments, and ages. PACC was not reduced to zero among unrelated individuals in TrP and was only slightly reduced in the TaP, confirming strong linkage disequilibrium and the ability of the model to generate accurate predictions across breeding generations. Genomic BV predictions were correlated with those from pedigree but displayed a wider range of within-family variation due to the ability of GS to capture the Mendelian sampling term. Using predicted TaP BVs in multi-trait selection, we functionally implemented and integrated GS into an operational tree-breeding program.

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.

Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden)

Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.

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

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 contingencies 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.

Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch

Genomic dissection of genetic effects on desirable traits and the subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additive and non-additive effects (dominance, imprinting and epistasis) were evaluated for nine valuable traits related to growth, wood physical and chemical properties, and competitive ability using three pedigree-based and four Genomics-based Best Linear Unbiased Predictions (GBLUP) models and used to determine the genetic model. The predictive ability (PA) of two genomic prediction methods, GBLUP and Reproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classified into two types based on different quantitative genetic architectures: for type I, including wood chemical properties and Pilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive ability and acoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominance and imprinting showed low to moderate contributions (< 36.26%). GBLUP was more suitable for traits of type I (PAs = 0.37-0.39 vs. 0.14-0.25), and RKHS was more suitable for traits of type II (PAs = 0.23-0.37 vs. 0.07-0.23). Non-additive effects make no meaningful contribution to the enhancement of PA of GBLUP method for all traits. These findings enhance our current understanding of the architecture of quantitative traits and lay the foundation for the development of genomic selection strategies in Japanese larch.

Impact of inclusion non-additive effects on genome-wide association and variance's components in Scottish black sheep

CONTEXT

It's well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection.

AIM

This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep.

METHODS

This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used.

KEY RESULTS

The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model.

CONCLUSIONS

The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16-24 weeks in Scottish Blackface lambs.

IMPLICATIONS

It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.

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.

Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce

Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait loci (QTLs) in a population by estimating the realized genomic relationships between the individuals and by capturing the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method to capture Mendelian segregation effects. Using QTLs detected in a genome-wide association study (GWAS) may improve GP. Here, we performed GWAS and GP in a population with 904 clones from 32 full-sib families using a newly developed 50 k SNP Norway spruce array. Through GWAS we identified 41 SNPs associated with budburst stage (BB) and the largest effect association explained 5.1% of the phenotypic variation (PVE). For the other five traits such as growth and wood quality traits, only 2 - 13 associations were observed and the PVE of the strongest effects ranged from 1.2% to 2.0%. GP using approximately 100 preselected SNPs, based on the smallest p-values from GWAS showed the greatest predictive ability (PA) for the trait BB. For the other traits, a preselection of 2000-4000 SNPs, was found to offer the best model fit according to the Akaike information criterion being minimized. But PA-magnitudes from GP using such selections were still similar to that of GP using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP provided that the PVE of the QTL was ≥ 2.5%.

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.

Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.

Genomic selection reveals hidden relatedness and increased breeding efficiency in western redcedar polycross breeding

Western redcedar (WRC) is an ecologically and economically important forest tree species characterized by low genetic diversity with high self-compatibility and high heartwood durability. Using sequence capture genotyping of target genic and non-genic regions, we genotyped 44 parent trees and 1520 offspring trees representing 26 polycross (PX) families collected from three progeny test sites using 45,378 SNPs. Trees were phenotyped for eight traits related to growth, heartwood and foliar chemistry associated with wood durability and deer browse resistance. We used the genomic realized relationship matrix for paternity assignment, maternal pedigree correction, and to estimate genetic parameters. We compared genomics-based (GBLUP) and two pedigree-based (ABLUP: polycross and reconstructed full-sib [FS] pedigrees) models. Models were extended to estimate dominance genetic effects. Pedigree reconstruction revealed significant unequal male contribution and separated the 26 PX families into 438 FS families. Traditional maternal PX pedigree analysis resulted in up to 51% overestimation in genetic gain and 44% in diversity. Genomic analysis resulted in up to 22% improvement in offspring breeding value (BV) theoretical accuracy, 35% increase in expected genetic gain for forward selection, and doubled selection intensity for backward selection. Overall, all traits showed low to moderate heritability (0.09-0.28), moderate genotype by environment interaction (type-B genetic correlation: 0.51-0.80), low to high expected genetic gain (6.01%-55%), and no significant negative genetic correlation reflecting no large trade-offs for multi-trait selection. Only three traits showed a significant dominance effect. GBLUP resulted in smaller but more accurate heritability estimates for five traits, but larger estimates for the wood traits. Comparison between all, genic-coding, genic-non-coding and intergenic SNPs showed little difference in genetic estimates. In summary, we show that GBLUP overcomes the PX limitations, successfully captures expected historical and hidden relatedness as well as linkage disequilibrium (LD), and results in increased breeding efficiency in WRC.

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.

Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP

Modeling environmental spatial heterogeneity can improve the efficiency of forest tree genomic evaluation. Furthermore, genotyping costs can be lowered by reducing the number of markers needed. We investigated the impact on variance components, breeding value accuracy, and bias of two phenotypic data adjustments (experimental design and autoregressive spatial models), and a relationship matrix calculated from a subset of markers selected for their ability to infer ancestry. Using a multiple-trait multiple-site single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, four scenarios (2 phenotype adjustments × 2 marker sets) were applied to diameter at breast height (DBH), height (HT), and resistance to western gall rust (WGR) in four open-pollinated progeny trials of lodgepole pine, with 1490 (out of 11,188) trees genotyped with 25,099 SNPs. As a control, we fitted the conventional ABLUP model using pedigree information. The highest heritability estimates were achieved for the ABLUP followed closely by the ssGBLUP with the full marker set and using the spatial phenotype adjustments. The highest predictive ability was obtained by using a reduced marker subset (8000 SNPs) when either the spatial (DBH: 0.429, and WGR: 0.513) or design (HT: 0.467) phenotype corrections were used. No significant difference was detected in prediction bias among the six fitted models, and all values were close to 1 (0.918-1.014). Results demonstrated that selecting informative markers, such as those capturing ancestry, can improve the predictive ability. The use of spatial correlation structure increased traits' heritability and reduced prediction bias, while increases in predictive ability were trait-dependent.

Genomic studies with preselected markers reveal dominance effects influencing growth traits in Eucalyptus nitens

Genomic selection (GS) is being increasingly adopted by the tree breeding community. Most of the GS studies in trees are focused on estimating additive genetic effects. Exploiting the dominance effects offers additional opportunities to improve genetic gain. To detect dominance effects, trait-relevant markers may be important compared to nonselected markers. Here, we used preselected markers to study the dominance effects in a Eucalyptus nitens (E. nitens) breeding population consisting of open-pollinated (OP) and controlled-pollinated (CP) families. We used 8221 trees from six progeny trials in this study. Of these, 868 progeny and 255 parents were genotyped with the E. nitens marker panel. Three traits; diameter at breast height (DBH), wood basic density (DEN), and kraft pulp yield (KPY) were analyzed. Two types of genomic relationship matrices based on identity-by-state (IBS) and identity-by-descent (IBD) were tested. Performance of the genomic best linear unbiased prediction (GBLUP) models with IBS and IBD matrices were compared with pedigree-based additive best linear unbiased prediction (ABLUP) models with and without the pedigree reconstruction. Similarly, the performance of the single-step GBLUP (ssGBLUP) with IBS and IBD matrices were compared with ABLUP models using all 8221 trees. Significant dominance effects were observed with the GBLUP-AD model for DBH. The predictive ability of DBH is higher with the GBLUP-AD model compared to other models. Similarly, the prediction accuracy of genotypic values is higher with GBLUP-AD compared to the GBLUP-A model. Among the two GBLUP models (IBS and IBD), no differences were observed in predictive abilities and prediction accuracies. While the estimates of predictive ability with additive effects were similar among all four models, prediction accuracies of ABLUP were lower than the GBLUP models. The prediction accuracy of ssGBLUP-IBD is higher than the other three models while the theoretical accuracy of ssGBLUP-IBS is consistently higher than the other three models across all three groups tested (parents, genotyped, and nongenotyped). Significant inbreeding depression was observed for DBH and KPY. While there is a linear relationship between inbreeding and DBH, the relationship between inbreeding and KPY is nonlinear and quadratic. These results indicate that the inbreeding depression of DBH is mainly due to directional dominance while in KPY it may be due to epistasis. Inbreeding depression may be the main source of the observed dominance effects in DBH. The significant dominance effect observed for DBH may be used to select complementary parents to improve the genetic merit of the progeny in E. nitens.

Genetic Variation in Drought-Tolerance Traits and Their Relationships to Growth in Pinus radiata D. Don Under Water Stress

The selection of drought-tolerant genotypes is globally recognized as an effective strategy to maintain the growth and survival of commercial tree species exposed to future drought periods. New genomic selection tools that reduce the time of progeny trials are required to substitute traditional tree breeding programs. We investigated the genetic variation of water stress tolerance in New Zealand-grown Pinus radiata D. Don using 622 commercially-used genotypes from 63 families. We used quantitative pedigree-based (Genomic Best Linear Unbiased Prediction or ABLUP) and genomic-based (Genomic Best Linear Unbiased Prediction or GBLUP) approaches to examine the heritability estimates associated with water stress tolerance in P. radiata. Tree seedling growth traits, foliar carbon isotope composition (δ13C), and dark-adapted chlorophyll fluorescence (Y) were monitored before, during and after 10 months of water stress. Height growth showed a constant and moderate heritability level, while the heritability estimate for diameter growth and δ13C decreased with water stress. In contrast, chlorophyll fluorescence exhibited low heritability after 5 and 10 months of water stress. The GBLUP approach provided less breeding value accuracy than ABLUP, however, the relative selection efficiency of GBLUP was greater compared with ABLUP selection techniques. Although there was no significant relationship directly between δ13C and Y, the genetic correlations were significant and stronger for GBLUP. The positive genetic correlations between δ13C and tree biomass traits under water stress indicated that intraspecific variation in δ13C was likely driven by differences in the genotype's photosynthetic capacity. The results show that foliar δ13C can predict P. radiata genotype tolerance to water stress using ABLUP and GBLUP approaches and that such approaches can provide a faster screening and selection of drought-tolerant genotypes for forestry breeding programs.

Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill

Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multigenerational Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved the prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker-derived relationships into prediction models.

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.

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

The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.

Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar

Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.

Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita

Most of the genomic studies in plants and animals have used additive models for studying genetic parameters and prediction accuracies. In this study, we used genomic models with additive and nonadditive effects to analyze the genetic architecture of growth and wood traits in an open-pollinated (OP) population of Eucalyptus pellita. We used two progeny trials consisting of 5742 trees from 244 OP families to estimate genetic parameters and to test genomic prediction accuracies of three growth traits (diameter at breast height - DBH, total height - Ht and tree volume - Vol) and kraft pulp yield (KPY). From 5742 trees, 468 trees from 28 families were genotyped with 2023 pre-selected markers from candidate genes. We used the pedigree-based additive best linear unbiased prediction (ABLUP) model and two marker-based models (single-step genomic BLUP – ssGBLUP and genomic BLUP – GBLUP) to estimate the genetic parameters and compare the prediction accuracies. Analyses with the two genomic models revealed large dominant effects influencing the growth traits but not KPY. Theoretical breeding value accuracies were higher with the dominance effect in ssGBLUP model for the three growth traits. Accuracies of cross-validation with random folding in the genotyped trees have ranged from 0.60 to 0.82 in different models. Accuracies of ABLUP were lower than the genomic models. Accuracies ranging from 0.50 to 0.76 were observed for within family cross-validation predictions with low relationships between training and validation populations indicating part of the functional variation is captured by the markers through short-range linkage disequilibrium (LD). Within-family phenotype predictive abilities and prediction accuracies of genetic values with dominance effects are higher than the additive models for growth traits indicating the importance of dominance effects in predicting phenotypes and genetic values. This study demonstrates the importance of genomic approaches in OP families to study nonadditive effects. To capture the LD between markers and the quantitative trait loci (QTL) it may be important to use informative markers from candidate genes.

Increased Prediction Ability in Norway Spruce Trials Using a Marker X Environment Interaction and Non-Additive Genomic Selection Model

A genomic selection study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in 2 Northern Swedish trials at 2 locations using exome capture as a genotyping platform. Nonadditive effects including dominance and first-order epistatic interactions (including additive-by-additive, dominance-by-dominance, and additive-by-dominance) and marker-by-environment interaction (M×E) effects were dissected in genomic and phenotypic selection models. Genomic selection models partitioned additive and nonadditive genetic variances more precisely than pedigree-based models. In addition, predictive ability in GS was substantially increased by including dominance and slightly increased by including M×E effects when these effects are significant. For velocity, response to genomic selection per year increased up to 78.9/80.8%, 86.9/82.9%, and 91.3/88.2% compared with response to phenotypic selection per year when genomic selection was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for sites 1 and 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also when they are significant may improve the genetic gain. For tree height, Pilodyn, and modulus of elasticity (MOE), response to genomic selection per year improved up to 68.9%, 91.3%, and 92.6% compared with response to phenotypic selection per year, respectively.Subject Area: Quantitative genetics and Mendelian inheritance

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs

With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data. This study undertook an extensive analysis of 907 elite winter barley (Hordeum vulgare L.) lines across multiple environments from two breeding companies. Six genomic prediction models were evaluated to demonstrate the effect of using pedigree and marker information individually and in combination, as well their interactions with the environment. Each model was evaluated using three cross-validation schemes that allows the prediction of newly developed lines (lines that have not been evaluated in any environment), prediction of new or unobserved years, and prediction of newly developed lines in unobserved years. The results showed that the best prediction model depends on the cross-validation scheme employed. In predicting newly developed lines in known environments, marker information had no advantage to pedigree information. Predictions in this scenario also benefited from including genotype-by-environment interaction in the models. However, when predicting lines and years not observed previously, marker information was superior to pedigree data. Nonetheless, such scenarios did not benefit from the addition of genotype-by-environment interaction. A combination of pedigree-based and marker-based information produced a similar or only marginal improvement in prediction ability. It was also discovered that combining populations from the different breeding programs to increase training population size had no advantage in prediction.

Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce

Background

Genomic selection (GS) or genomic prediction is considered as a promising approach to accelerate tree breeding and increase genetic gain by shortening breeding cycle, but the efforts to develop routines for operational breeding are so far limited. We investigated the predictive ability (PA) of GS based on 484 progeny trees from 62 half-sib families in Norway spruce (Picea abies (L.) Karst.) for wood density, modulus of elasticity (MOE) and microfibril angle (MFA) measured with SilviScan, as well as for measurements on standing trees by Pilodyn and Hitman instruments.

Results

GS predictive abilities were comparable with those based on pedigree-based prediction. Marker-based PAs were generally 25–30% higher for traits density, MFA and MOE measured with SilviScan than for their respective standing tree-based method which measured with Pilodyn and Hitman. Prediction accuracy (PC) of the standing tree-based methods were similar or even higher than increment core-based method. 78–95% of the maximal PAs of density, MFA and MOE obtained from coring to the pith at high age were reached by using data possible to obtain by drilling 3–5 rings towards the pith at tree age 10–12.

Conclusions

This study indicates standing tree-based measurements is a cost-effective alternative method for GS. PA of GS methods were comparable with those pedigree-based prediction. The highest PAs were reached with at least 80–90% of the dataset used as training set. Selection for trait density could be conducted at an earlier age than for MFA and MOE. Operational breeding can also be optimized by training the model at an earlier age or using 3 to 5 outermost rings at tree age 10 to 12 years, thereby shortening the cycle and reducing the impact on the tree.

In Situ Genetic Evaluation of European Larch Across Climatic Regions Using Marker-Based Pedigree Reconstruction

Sustainable and efficient forestry in a rapidly changing climate is a daunting task. The sessile nature of trees makes adaptation to climate change challenging; thereby, ecological services and economic potential are under risk. Current long-term and costly gene resources management practices have been primarily directed at a few economically important species and are confined to defined ecological boundaries. Here, we present a novel in situ gene-resource management approach that conserves forest biodiversity and improves productivity and adaptation through utilizing basic forest regeneration installations located across a wide range of environments without reliance on structured tree breeding/conservation methods. We utilized 4,267 25- to 35-year-old European larch trees growing in 21 reforestation installations across four distinct climatic regions in Austria. With the aid of marker-based pedigree reconstruction, we applied multi-trait, multi-site quantitative genetic analyses that enabled the identification of broadly adapted and productive individuals. Height and wood density, proxies to fitness and productivity, yielded in situ heritability estimates of 0.23 ± 0.07 and 0.30 ± 0.07, values similar to those from traditional “structured” pedigrees methods. In addition, individual trees selected with this approach are expected to yield genetic response of 1.1 and 0.7 standard deviations for fitness and productivity attributes, respectively, and be broadly adapted to a range of climatic conditions. Genetic evaluation across broad climatic gradients permitted the delineation of suitable reforestation areas under current and future climates. This simple and resource-efficient management of gene resources is applicable to most tree species.

Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce

Genomic selection (GS) has a large potential for improving the prediction accuracy of breeding values and significantly reducing the length of breeding cycles. In this context, the choice of mating designs becomes critical to improve the efficiency of breeding operations and to obtain the largest genetic gains per time unit. Polycross mating designs have been traditionally used in tree and plant breeding to perform backward selection of the female parents. The possibility to use genetic markers for paternity identification and for building genomic prediction models should allow for a broader use of polycross tests in forward selection schemes. We compared the accuracies of genomic predictions of offspring's breeding values from a polycross and a full-sib (partial diallel) mating design with similar genetic background in white spruce (Picea glauca). Trees were phenotyped for growth and wood quality traits, and genotyped for 4092 SNPs representing as many gene loci distributed across the 12 spruce chromosomes. For the polycross progeny test, heritability estimates were smaller, but more precise using the genomic BLUP (GBLUP) model as compared with pedigree-based models accounting for the maternal pedigree or for the reconstructed full pedigree. Cross-validations showed that GBLUP predictions were 22-52% more accurate than predictions based on the maternal pedigree, and 5-7% more accurate than predictions using the reconstructed full pedigree. The accuracies of GBLUP predictions were high and in the same range for most traits between the polycross (0.61-0.70) and full-sib progeny tests (0.61-0.74). However, higher genetic gains per time unit were expected from the polycross mating design given the shorter time needed to conduct crosses. Considering the operational advantages of the polycross design in terms of easier handling of crosses and lower associated costs for test establishment, we believe that this mating scheme offers great opportunities for the development and operational application of forward GS.

Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement

Prediction performance for winter wheat grain yield and end-use quality traits. Prediction accuracies evaluated by cross-validations are significantly overestimated. Nonparametric algorithms outperform the parametric alternatives in cross-year predictions. Strategically designing training population improves response to selection. Response to selection varies across growing seasons and environments. Considering the practicality of applying genomic selection (GS) in the line development stage of a hard red winter (HRW) wheat (Triticum aestivum L.) variety development program (VDP), the effectiveness of GS was evaluated by prediction accuracy and by the response to selection across field seasons that demonstrated challenges for crop improvement under significant climate variability. Important breeding targets for wheat improvement in the southern Great Plains of the United States, including grain yield, kernel weight, wheat protein content, and sodium dodecyl sulfate (SDS) sedimentation volume as a rapid test for predicting bread-making quality, were used to estimate the effectiveness of GS across harvest years from 2014 (drought) to 2016 (normal). In general, nonparametric algorithms reproducing kernel Hilbert space (RKHS) and random forest (RF) produced higher accuracies in both same-year cross-validations (CVs) and cross-year predictions for the purpose of line selection. Further, the stability of GS performance was greatest for SDS sedimentation volume but least for wheat protein content. To ensure long-term genetic gain, our study on selection response suggested that across this sample of environmental variability, and though there are cases where phenotypic selection (PS) might be still preferred, training conducted under drought or in suboptimal conditions could provide an encouraging prediction outcome when selection decisions were made in normal conditions. However, it is not advisable to use training information collected from a normal season to predict trait performance under drought conditions. Finally, the superiority of response to selection was most evident if the training population (TP) can be optimized.

Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees

The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive-dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits.

Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP

Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach (ssGBLUP) allows genomic prediction to take into account both genotyped and non-genotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and bias, of including phenotypic observation from non-genotyped trees in a standard tree GBLUP evaluation. We compared the efficiency of the conventional pedigree-based (ABLUP), GBLUP and ssGBLUP approaches to evaluate eight growth and wood quality traits in a Eucalyptus hybrid population, genotyped with 33,398 single nucleotide polymorphisms (SNPs) using the EucHIP60k. Theoretical accuracies, predictive ability and bias were calculated by ten-fold cross validation on all traits. The use of additional phenotypic information from non-genotyped trees by means of ssGBLUP provided higher predictive ability (from 37% to 75%) and lower prediction bias (from 21% to 73%) for the genetic component of non-phenotyped but genotyped trees when compared to GBLUP. The increase (decrease) in the prediction accuracy (bias) became stronger as trait heritability decreased. We concluded that ssGBLUP is a promising breeding tool to improve accuracies and bias over classical GBLUP for genomic evaluation in Eucalyptus breeding practice.

Quantitative genetic parameters for growth and wood properties in Eucalyptus "urograndis" hybrid using near-infrared phenotyping and genome-wide SNP-based relationships

A thorough understanding of the heritability, genetic correlations and additive and non-additive variance components of tree growth and wood properties is a requisite for effective tree breeding. This knowledge is essential to maximize genetic gain, that is, the amount of increase in trait performance achieved annually through directional selection. Understanding the genetic attributes of traits targeted by breeding is also important to sustain decade-long genetic progress, that is, the progress made by increasing the average genetic value of the offspring as compared to that of the parental generation. In this study, we report quantitative genetic parameters for fifteen growth, wood chemical and physical traits for the world-famous Eucalyptus urograndis hybrid (E. grandis × E. urophylla). These traits directly impact the optimal use of wood for cellulose pulp, paper, and energy production. A population of 1,000 trees sampled in a progeny trial was phenotyped directly or following the development and use of near-infrared spectroscopy calibration models. Trees were genotyped with 33,398 SNPs and 24,001 DArT-seq genome-wide markers and genomic realized relationship matrices (GRM) were used for parameter estimation with an individual-tree additive-dominant mixed model. Wood chemical properties and wood density showed stronger genetic control than growth, cellulose and fiber traits. Additive effects are the main drivers of genetic variation for all traits, but dominance plays an equally or more important role for growth, singularly in this hybrid. GRM´s with >10,000 markers provided stable relationships estimates and more accurate parameters than pedigrees by capturing the full genetic relationships among individuals and disentangling the non-additive from the additive genetic component. Low correlations between growth and wood properties indicate that simultaneous selection for wood traits can be applied with minor effects on genetic gain for growth. Conversely, moderate to strong correlations between wood density and chemical traits exist, likely due to their interdependency on cell wall structure such that responses to selection will be connected for these traits. Our results illustrate the advantage of using genome-wide marker data to inform tree breeding in general and have important consequences for operational breeding of eucalypt urograndis hybrids.

Conservation of genetic diversity hotspots of the high-valued relic yellowhorn (Xanthoceras sorbifolium) considering climate change predictions

Genetic structure and major climate factors may contribute to the distribution of genetic diversity of a highly valued oil tree species Xanthoceras sorbifolium (yellowhorn). Long-term over utilization along with climate change is affecting the viability of yellowhorn wild populations. To preserve the species known and unknown valuable gene pools, the identification of genetic diversity "hotspots" is a prerequisite for their consideration as in situ conservation high priority. Chloroplast DNA (cpDNA) diversity was high among 38 natural populations (H d = 0.717, K = 4.616, Tajmas' D = -0.22) and characterized by high genetic divergence (F ST = 0.765) and relatively low gene flow (N m = 0.03), indicating populations isolation reflecting the species' habitat fragmentation and inbreeding depression. Six out of the studied 38 populations are defined as genetic diversity "hotspots." The number and geographic direction of cpDNA mutation steps supported the species southwest to northeast migration history. Climatic factors such as extreme minimum temperature over 30 years indicated that the identified genetic "hotspots" are expected to experience 5°C temperature increase in next following 50 years. The results identified vulnerable genetic diversity "hotspots" and provided fundamental information for the species' future conservation and breeding activities under the anticipated climate change. More specifically, the role of breeding as a component of a gene resource management strategy aimed at fulfilling both utilization and conservation goals.

Genomic Selection in Cereal Breeding

Genomic Selection (GS) is a method in plant breeding to predict the genetic value of untested lines based on genome-wide marker data. The method has been widely explored with simulated data and also in real plant breeding programs. However, the optimal strategy and stage for implementation of GS in a plant-breeding program is still uncertain. The accuracy of GS has proven to be affected by the data used in the GS model, including size of the training population, relationships between individuals, marker density, and use of pedigree information. GS is commonly used to predict the additive genetic value of a line, whereas non-additive genetics are often disregarded. In this review, we provide a background knowledge on genomic prediction models used for GS and a view on important considerations concerning data used in these models. We compare within- and across-breeding cycle strategies for implementation of GS in cereal breeding and possibilities for using GS to select untested lines as parents. We further discuss the difference of estimating additive and non-additive genetic values and its usefulness to either select new parents, or new candidate varieties.

Expected benefit of genomic selection over forward selection in conifer breeding and deployment

Genomic selection is a proven technology in animal and plant breeding to accelerate genetic gain, but as yet is to be fully realised in forest tree breeding. This paper examines, through stochastic simulation, the potential benefits of genomic selection (GS) over forward selection (FS) in a typical conifer breeding program. Methods of speeding the deployment of selected material were also considered, including top-grafting onto mature seed orchard ortets, using additional replicates of clones in archives for crossing, and embryogenesis and clonal propagation. Genetic gain per generation was found to increase considerably when the size of the training population was larger (800 c.f. 3000 clones), or when the heritability was higher (0.2 c.f. 0.5). The largest genetic gain, of 24% was achieved where large training populations (3000 clones) and high heritability traits (0.5) were combined. The accuracy of genomic breeding values (GEBVs) increased with the increase in the number of clones in the training population, the heritability of the trait and the density of the SNP markers. Calculated accuracies of simulated GEBVs and genetic gain per unit of time suggested that 2000 clones in the training population is the minimum size for effective genomic selection for conifers. Compared with forward selection, genomic selection with 2000 clones in the training population, and a 60K SNP panel, an increase of 1.58 mm per year in diameter-at-breast-height (DBH) and 2.44 kg/m³ per year for wood density can be expected. After one generation (9-years), this would be equivalent to 14.23 mm and 21.97 kg/m³ for DBH and wood density respectively. Deploying clones of the selected individuals always resulted in higher additional genetic gain than deploying progeny/seedlings. Deploying genetic material selected from genomic selection with top-grafting for early coning appeared to be the best option. Application of genomic selection to conifer breeding programs, combined with deployment tools such as top-grafting and embryogenesis are powerful tools to speed the delivery of genetic gain to the forest.

Effect of Hidden Relatedness on Single-Step Genetic Evaluation in an Advanced Open-Pollinated Breeding Program

Open-pollinated (OP) mating is frequently used in forest tree breeding due to the relative temporal and financial efficiency of the approach. The trade-off is the lower precision of the estimated genetic parameters. Pedigree/sib-ship reconstruction has been proven as a tool to correct and complete pedigree information and to improve the precision of genetic parameter estimates. Our study analyzed an advanced generation Eucalyptus population from an OP breeding program using single-step genetic evaluation. The relationship matrix inferred from sib-ship reconstruction was used to rescale the marker-based relationship matrix (G matrix). This was compared with a second scenario that used rescaling based on the documented pedigree. The proposed single-step model performed better with respect to both model fit and the theoretical accuracy of breeding values. We found that the prediction accuracy was superior when using the pedigree information only when compared with using a combination of the pedigree and genomic information. This pattern appeared to be mainly a result of accumulated unrecognized relatedness over several breeding cycles, resulting in breeding values being shrunk toward the population mean. Using biased, pedigree-based breeding values as the base with which to correlate predicted GEBVs, resulted in the underestimation of prediction accuracies. Using breeding values estimated on the basis of sib-ship reconstruction resulted in increased prediction accuracies of the genotyped individuals. Therefore, selection of the correct base for estimation of prediction accuracy is critical. The beneficial impact of sib-ship reconstruction using G matrix rescaling was profound, especially in traits with inbreeding depression, such as stem diameter.

Phenotyping Whole Forests Will Help to Track Genetic Performance

Phenotyping is the accurate and precise physical description of organisms. Accurate and quantitative phenotyping underpins the delivery of benefits from genetic improvement programs in agriculture. In forest trees, phenotyping at an equivalent precision has been impossible because trees and forests are large, long-lived, and highly variable. These facts have restricted the delivery of genetic gains in forestry compared to other agricultural sectors. We describe a landscape-scale phenotyping platform that integrates remote sensing, spatial information systems, and genomics to facilitate the delivery of greater gains enabling forestry to catch up with other sectors. Combining remote sensing at a range of spatial and temporal scales with genomics will ultimately impact on tree breeding globally.

Genomic-based multiple-trait evaluation in Eucalyptus grandis using dominant DArT markers

We investigated the impact of combining the pedigree- and genomic-based relationship matrices in a multiple-trait individual-tree mixed model (a.k.a., multiple-trait combined approach) on the estimates of heritability and on the genomic correlations between growth and stem straightness in an open-pollinated Eucalyptus grandis population. Additionally, the added advantage of incorporating genomic information on the theoretical accuracies of parents and offspring breeding values was evaluated. Our results suggested that the use of the combined approach for estimating heritabilities and additive genetic correlations in multiple-trait evaluations is advantageous and including genomic information increases the expected accuracy of breeding values. Furthermore, the multiple-trait combined approach was proven to be superior to the single-trait combined approach in predicting breeding values, in particular for low-heritability traits. Finally, our results advocate the use of the combined approach in forest tree progeny testing trials, specifically when a multiple-trait individual-tree mixed model is considered.

Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits

BACKGROUND

Sweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years.

RESULTS

High broad-sense heritabilities of 0.83, 0.77, and 0.76 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic model resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance.

CONCLUSIONS

Predicted breeding values and genetic values revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.

Multienvironment genomic variance decomposition analysis of open-pollinated Interior spruce (Picea glauca x engelmannii)

The advantages of open-pollinated (OP) family testing over controlled crossing (i.e., structured pedigree) are the potential to screen and rank a large number of parents and offspring with minimal cost and efforts; however, the method produces inflated genetic parameters as the actual sibling relatedness within OP families rarely meets the half-sib relatedness assumption. Here, we demonstrate the unsurpassed utility of OP testing after shifting the analytical mode from pedigree- (ABLUP) to genomic-based (GBLUP) relationship using phenotypic tree height (HT) and wood density (WD) and genotypic (30k SNPs) data for 1126 38-year-old Interior spruce (Picea glauca (Moench) Voss x P. engelmannii Parry ex Engelm.) trees, representing 25 OP families, growing on three sites in Interior British Columbia, Canada. The use of the genomic realized relationship permitted genetic variance decomposition to additive, dominance, and epistatic genetic variances, and their interactions with the environment, producing more accurate narrow-sense heritability and breeding value estimates as compared to the pedigree-based counterpart. The impact of retaining (random folding) vs. removing (family folding) genetic similarity between the training and validation populations on the predictive accuracy of genomic selection was illustrated and highlighted the former caveats and latter advantages. Moreover, GBLUP models allowed breeding value prediction for individuals from families that were not included in the developed models, which was not possible with the ABLUP. Response to selection differences between the ABLUP and GBLUP models indicated the presence of systematic genetic gain overestimation of 35 and 63% for HT and WD, respectively, mainly caused by the inflated estimates of additive genetic variance and individuals' breeding values given by the ABLUP models. Extending the OP genomic-based models from single to multisite made the analysis applicable to existing OP testing programs.

Genomic relationships reveal significant dominance effects for growth in hybrid Eucalyptus

Non-additive genetic effects can be effectively exploited in control-pollinated families with the availability of genome-wide markers. We used 41,304 SNP markers and compared pedigree vs. marker-based genetic models by analysing height, diameter, basic density and pulp yield for Eucalyptus urophylla × E.grandis control-pollinated families represented by 949 informative individuals. We evaluated models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance). We showed that the models can capture a large proportion of the genetic variance from dominance and epistasis for growth traits as those components are typically not independent. We also showed that we could partition genetic variances more precisely when using relationship matrices derived from markers compared to using only pedigree information. In addition, phenotypic prediction accuracies were only slightly increased by including dominance effects for growth traits since estimates of non-additive variances yielded rather high standard errors. This novel result improves our current understanding of the architecture of quantitative traits and recommends accounting for dominance variance when developing genomic selection strategies in hybrid Eucalyptus.

Dynamic Gene-Resource Landscape Management of Norway Spruce: Combining Utilization and Conservation

Traditional gene-resource management programs for forest trees are long-term endeavors requiring sustained organizational commitment covering extensive landscapes. While successful in maintaining adaptation, genetic diversity and capturing traditional growth attributes gains, these programs are dependent on rigid methods requiring elaborate mating schemes, thus making them slow in coping with climate change challenges. Here, we review the significance of Norway spruce in the boreal region and its current management practices. Next, we discuss opportunities offered by novel technologies and, with the use of computer simulations, we propose and evaluate a dynamic landscape gene-resource management in Norway. Our suggested long-term management approach capitalizes on: (1) existing afforestation activities, natural crosses, and DNA-based pedigree assembly to create structured pedigree for evaluation, thus traditional laborious control crosses are avoided and (2) landscape level genetic evaluation, rather than localized traditional progeny trials, allowing for screening of adapted individuals across multiple environmental gradients under changing climate. These advantages lead to greater genetic response to selection in adaptive traits without the traditional breeding and testing scheme, facilitating conservation of genetic resources within the breeding population of the most important forest tree species in Norway. The use of in situ selection from proven material exposed to realistic conditions over vast territories has not been conducted in forestry before. Our proposed approach is in contrast to worldwide current programs, where genetic evaluation is constrained by the range of environments where testing is conducted, which may be insufficient to capture the broad environmental variation necessary to tackle adaptation under changing climate.

Genomic Quantitative Genetics to Study Evolution in the Wild

Quantitative genetic theory provides a means of estimating the evolutionary potential of natural populations. However, this approach was previously only feasible in systems where the genetic relatedness between individuals could be inferred from pedigrees or experimental crosses. The genomic revolution opened up the possibility of obtaining the realized proportion of genome shared among individuals in natural populations of virtually any species, which could promise (more) accurate estimates of quantitative genetic parameters in virtually any species. Such a 'genomic' quantitative genetics approach relies on fewer assumptions, offers a greater methodological flexibility, and is thus expected to greatly enhance our understanding of evolution in natural populations, for example, in the context of adaptation to environmental change, eco-evolutionary dynamics, and biodiversity conservation.

Exploration of genetic architecture through sib-ship reconstruction in advanced breeding population of Eucalyptus nitens

Accurate inference of relatedness between individuals in breeding population contributes to the precision of genetic parameter estimates, effectiveness of inbreeding management and the amount of genetic progress delivered from breeding programs. Pedigree reconstruction has been proven to be an efficient tool to correct pedigree errors and recover hidden relatedness in open pollinated progeny tests but the method can be limited by the lack of parental genotypes and the high proportion of alien pollen from outside the breeding population. Our study investigates the efficiency of sib-ship reconstruction in an advanced breeding population of Eucalyptus nitens with only partially tracked pedigree. The sib-ship reconstruction allowed the identification of selfs (4% of the sample) and the exploration of their potential effect on inbreeding depression in the traits studied. We detected signs of inbreeding depression in diameter at breast height and growth strain while no indications were observed in wood density, wood stiffness and tangential air-dry shrinkage. After the application of a corrected sib-ship relationship matrix, additive genetic variance and heritability were observed to increase where signs of inbreeding depression were initially detected. Conversely, the same genetic parameters for traits that appeared to be free of inbreeding depression decreased in size. It therefore appeared that greater genetic variance may be due, at least in part, to contributions from inbreeding in these studied populations rather than a removal of inbreeding as is traditionally thought.

Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model

We report a genomic selection (GS) study of growth and wood quality traits in an outbred F₂ hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40-0.57 for individual selection and 0.56-0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25 kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.

Single-Step BLUP with Varying Genotyping Effort in Open-Pollinated Picea glauca

Maximization of genetic gain in forest tree breeding programs is contingent on the accuracy of the predicted breeding values and precision of the estimated genetic parameters. We investigated the effect of the combined use of contemporary pedigree information and genomic relatedness estimates on the accuracy of predicted breeding values and precision of estimated genetic parameters, as well as rankings of selection candidates, using single-step genomic evaluation (HBLUP). In this study, two traits with diverse heritabilities [tree height (HT) and wood density (WD)] were assessed at various levels of family genotyping efforts (0, 25, 50, 75, and 100%) from a population of white spruce (Picea glauca) consisting of 1694 trees from 214 open-pollinated families, representing 43 provenances in Québec, Canada. The results revealed that HBLUP bivariate analysis is effective in reducing the known bias in heritability estimates of open-pollinated populations, as it exposes hidden relatedness, potential pedigree errors, and inbreeding. The addition of genomic information in the analysis considerably improved the accuracy in breeding value estimates by accounting for both Mendelian sampling and historical coancestry that were not captured by the contemporary pedigree alone. Increasing family genotyping efforts were associated with continuous improvement in model fit, precision of genetic parameters, and breeding value accuracy. Yet, improvements were observed even at minimal genotyping effort, indicating that even modest genotyping effort is effective in improving genetic evaluation. The combined utilization of both pedigree and genomic information may be a cost-effective approach to increase the accuracy of breeding values in forest tree breeding programs where shallow pedigrees and large testing populations are the norm.

De Novo Transcriptome Assembly and Characterization for the Widespread and Stress-Tolerant Conifer Platycladus orientalis

Platycladus orientalis, of the family Cupressaceae, is a widespread conifer throughout China and is extensively used for ecological reforestation, horticulture, and in medicine. Transcriptome assemblies are required for this ecologically important conifer for understanding genes underpinning adaptation and complex traits for breeding programs. To enrich the species' genomic resources, a de novo transcriptome sequencing was performed using Illumina paired-end sequencing. In total, 104,073,506 high quality sequence reads (approximately 10.3 Gbp) were obtained, which were assembled into 228,948 transcripts and 148,867 unigenes that were longer than 200 nt. Quality assessment using CEGMA showed that the transcriptomes obtained were mostly complete for highly conserved core eukaryotic genes. Based on similarity searches with known proteins, 62,938 (42.28% of all unigenes), 42,158 (28.32%), and 23,179 (15.57%) had homologs in the Nr, GO, and KOG databases, 25,625 (17.21%) unigenes were mapped to 322 pathways by BLASTX comparison against the KEGG database and 1,941 unigenes involved in environmental signaling and stress response were identified. We also identified 43 putative terpene synthase (TPS) functional genes loci and compared them with TPSs from other species. Additionally, 5,296 simple sequence repeats (SSRs) were identified in 4,715 unigenes, which were assigned to 142 motif types. This is the first report of a complete transcriptome analysis of P. orientalis. These resources provide a foundation for further studies of adaptation mechanisms and molecular-based breeding programs.