Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding.

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study's objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone's reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone's reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.

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.

Tall Pinus luzmariae trees with genes from P. herrerae.

CONTEXT: Pinus herrerae and P. luzmariae are endemic to western Mexico, where they cover an area of more than 1 million hectares. Pinus herrerae is also cultivated in field trials in South Africa and South America, because of its considerable economic importance as a source of timber and resin. Seed quality, afforestation success and desirable traits may all be influenced by the presence of hybrid trees in seed stands. AIMS: We aimed to determine the degree of hybridization between P. herrerae and P. luzmariae in seed stands of each species located in the Sierra Madre Occidental, Durango, Mexico. METHODS: AFLP molecular markers from samples of 171 trees across five populations were analyzed with STRUCTURE and NewHybrids software to determine the degree of introgressive hybridization. The accuracy of STRUCTURE and NewHybrids in detecting hybrids was quantified using the software Hybridlab 1.0. Morphological analysis of 131 samples from two populations of P. herrerae and two populations of P. luzmariae was also conducted by Random Forest classification. The data were compared by Principal Coordinate Analysis (PCoA) in GenAlex 6.501. RESULTS: Hybridization between Pinus herrerae and P. luzmariae was observed in all seed stands under study and resulted in enhancement of desirable silvicultural traits in the latter species. In P. luzmariae, only about 16% molecularly detected hybrids correspond to those identified on a morphological basis. However, the morphology of P. herrerae is not consistent with the molecularly identified hybrids from one population and is only consistent with 3.3 of those from the other population. CONCLUSIONS: This is the first report of hybrid vigour (heterosis) in Mexican pines. Information about hybridization and introgression is essential for developing effective future breeding programs, successful establishment of plantations and management of natural forest stands. Understanding how natural hybridization may influence the evolution and adaptation of pines to climate change is a cornerstone to sustainable forest management including adaptive silviculture.

Estimates of genetic parameters, genetic variability, and selection in the S1 generation of peach / Estimativas de parâmetros genéticos, variabilidade genética e seleção em geração S1 de pessegueiros

The growing of peach in mild winter regions is made viable through the use of genotypes that have low need for cold conditions, and this is one of the main aims of breeding for these regions. Thus, the aims of this study were to estimate genetic parameters, evaluate genetic variability, and select families adapted to mild winter regions in the S1 generation of peach through mixed model methodology (REML/BLUP). For that purpose, 22 populations, 84 families, and 2090 individuals were evaluated for the following traits: bud burst rate (BR), node density (ND), plant height (PH), and trunk diameter (TD). Genetic variability was found for all the traits. Individual heritability in the broad sense was of low and medium magnitudes. The PH trait had positive genotypic correlation of high magnitude with TD. The ND trait had moderate negative genotypic correlation with PH and TD. Clustering by the Tocher method resulted in the formation of six mutually exclusive groups. Considering selection intensity of 25%, simultaneous selection for BR, ND, and TD led to predicted gains of 11.3% for BR, 9.7% for ND, -14.2% for PH, and -14.3% for TD, showing the great potential of the germplasm evaluated.(AU)

O cultivo do pessegueiro em regiões de inverno ameno é viabilizado pela utilização de genótipos que apresentam baixa necessidade de frio, sendo este um dos principais objetivos do melhoramento para essas regiões. Assim, os objetivos deste estudo foram estimar parâmetros genéticos, avaliar a variabilidade genética e selecionar famílias adaptadas a regiões de inverno ameno em geração S1 de pessegueiros via metodologia de modelos mistos (REML/BLUP). Para isso, 22 populações, 84 famílias e 2090 indivíduos foram avaliados quanto as características: taxa de brotação (TB), densidade de nós (DN), altura da planta (AP) e diâmetro do tronco (DT). Verificou-se variabilidade genética para todas as características. As herdabilidades individuais no sentido amplo foram de baixa e média magnitudes. A característica AP apresentou correlação genética positiva de magnitude elevada com DT. A característica DN apresentou correlação genética negativa moderada com AP e DT. O agrupamento pelo método de Tocher resultou na formação de seis grupos mutuamente excludentes. Considerando intensidade de seleção de 25%, a seleção simultânea para TB, DN e DT propiciou ganhos preditos de 11.3% para TB, 9.7% para DN, -14.2% para AP e -14.3% para DT, evidenciando o grande potencial do germoplasma avaliado.(AU)

Estimates of genetic parameters, genetic variability, and selection in the S1 generation of peach / Estimativas de parâmetros genéticos, variabilidade genética e seleção em geração S 1 de pessegueiros

ABSTRACT: The growing of peach in mild winter regions is made viable through the use of genotypes that have low need for cold conditions, and this is one of the main aims of breeding for these regions. Thus, the aims of this study were to estimate genetic parameters, evaluate genetic variability, and select families adapted to mild winter regions in the S1 generation of peach through mixed model methodology (REML/BLUP). For that purpose, 22 populations, 84 families, and 2090 individuals were evaluated for the following traits: bud burst rate (BR), node density (ND), plant height (PH), and trunk diameter (TD). Genetic variability was found for all the traits. Individual heritability in the broad sense was of low and medium magnitudes. The PH trait had positive genotypic correlation of high magnitude with TD. The ND trait had moderate negative genotypic correlation with PH and TD. Clustering by the Tocher method resulted in the formation of six mutually exclusive groups. Considering selection intensity of 25%, simultaneous selection for BR, ND, and TD led to predicted gains of 11.3% for BR, 9.7% for ND, -14.2% for PH, and -14.3% for TD, showing the great potential of the germplasm evaluated.

RESUMO: O cultivo do pessegueiro em regiões de inverno ameno é viabilizado pela utilização de genótipos que apresentam baixa necessidade de frio, sendo este um dos principais objetivos do melhoramento para essas regiões. Assim, os objetivos deste estudo foram estimar parâmetros genéticos, avaliar a variabilidade genética e selecionar famílias adaptadas a regiões de inverno ameno em geração S1 de pessegueiros via metodologia de modelos mistos (REML/BLUP). Para isso, 22 populações, 84 famílias e 2090 indivíduos foram avaliados quanto as características: taxa de brotação (TB), densidade de nós (DN), altura da planta (AP) e diâmetro do tronco (DT). Verificou-se variabilidade genética para todas as características. As herdabilidades individuais no sentido amplo foram de baixa e média magnitudes. A característica AP apresentou correlação genética positiva de magnitude elevada com DT. A característica DN apresentou correlação genética negativa moderada com AP e DT. O agrupamento pelo método de Tocher resultou na formação de seis grupos mutuamente excludentes. Considerando intensidade de seleção de 25%, a seleção simultânea para TB, DN e DT propiciou ganhos preditos de 11.3% para TB, 9.7% para DN, -14.2% para AP e -14.3% para DT, evidenciando o grande potencial do germoplasma avaliado.

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding.

Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.

BACKGROUND: The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. RESULTS: Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000-10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. CONCLUSIONS: This study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.