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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.

The identification of genotypes more tolerant to water deficit is a challenge to breeding programs. In this research, our objectives were to identify and validate traits for tolerance to water deficit in eucalypts. The estimation of genotypic parameters and early selection are proposed based on mixed models, selection indexes and validation schemes. Seedlings with 110 days were grown in a greenhouse for 12 weeks, and two water deficit treatments were conducted (polyethylene glycol and water limitation). A total of 26 biomarkers were evaluated, and 15 of them were significant, exhibited adequate heritability, and used for screening: final plant height, increment in height, increment in diameter, area of mature and fully expanded leaf, nutrient contents of N, K, Ca, Mg, S, Cu, Zn, Mn and B, photosynthesis (A) and stomatal conductance (gs). Both treatments were adequate to discriminate water deficit-tolerant clones. The ranking of tolerant clones according to their phenotype in the field demonstrates the potential for early selection and is consistent with the maintenance of water-deficit-tolerance mechanisms until adulthood. There is evidence that the choice of biomarker depends on the species involved and different strategies contributing to the tolerance trait.

Key messageWe propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an “omics” scale of environmental attributes drives the prediction of unobserved genotype performances.Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of “envirotypes” at multiple “enviromic” markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the “GIS–GEI”) which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.

Apomictic forage species using hybridization involves a large number of hybrids in the initial breeding stages, requiring modern evaluation strategies. The objective of this study was to develop a strategy called \"Within-microsite Checks\" to evaluate hybrids from the early phases of the Guineagrass breeding program. The strategy employs common checks in each experimental microsite to assess the quality of the microenvironment in the experiment using plant-based indices linked to spatial (rows and columns) arrangements. The scheme was tested at Embrapa Beef Cattle in Campo Grande, Brazil, where 2,100 hybrids were evaluated in the initial selection stage. Each microsite had 32 plants, two checks, and thirty hybrids, with evaluations done individually and at various times of the year for Canopy Height, Regrowth Density, and Regrowth Speed traits. The plant-based index for each microsite corresponds to the check average for each trait. The mixed model methodology was used to test various random and fixed effects. The plant-based index used as a fixed effect had the greatest impact on the model fitness. Regardless of the trait considered, the association of the plant-based index with the spatial random effects showed the best performance among the models. Estimating the spatial variance improves the accuracy of the variance components. The narrow and broad sense heritability coefficients were high (> 0.68) for all traits, indicating high prediction accuracy. The \"Within-microsite Checks\" strategy uses a plant-based index to characterize microsite environmental quality, potentially improving prediction accuracy and selection efficiency in the early stages of forage breeding programs.

The availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G × E interaction (c2int) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to ≈100%) of trait heritability. At the same probability threshold, three marker–trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (∼42% to ∼64%), probably due to LD between markers and genes and/or rare allele variants not sampled. RHM in autogamous species had the potential to identify larger-effect QTL combining allelic variants that could be effectively incorporated into whole-genome prediction models and tracked through breeding generations using marker-assisted selection.

Abstract We present an R-based function for defining TPE as GIS-polygons, intended for use in enviromics studies. It offers customizable parameters, such as pixel size, buffer boundaries, and concavity, providing enhanced flexibility for G×E analysis. This tool optimizes genotypic, envirotypic, and spatial assessments, serving as a powerful resource for breeding research.

Evaluates provenances and progenies of Eucalyptus viminalis and compares them to other Eucalyptus spp. to inform the selection of superior materials with high productive potential and resistance to typical climatic conditions in the subtropical region of Brazil. Outlines future strategies for the genetic improvement of this potential eucalypt species for such region. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

The identification of genotypes more tolerant to water deficit is a challenge to breeding programs. In this research, our objectives were to identify and validate traits for tolerance to water deficit in eucalypts. The estimation of genotypic parameters and early selection are proposed based on mixed models, selection indexes and validation schemes. Seedlings with 110 days were grown in a greenhouse for 12 weeks, and two water deficit treatments were conducted (polyethylene glycol and water limitation). A total of 26 biomarkers were evaluated, and 15 of them were significant, exhibited adequate heritability, and used for screening: final plant height, increment in height, increment in diameter, area of mature and fully expanded leaf, nutrient contents of N, K, Ca, Mg, S, Cu, Zn, Mn and B, photosynthesis (A) and stomatal conductance (gs). Both treatments were adequate to discriminate water deficit-tolerant clones. The ranking of tolerant clones according to their phenotype in the field demonstrates the potential for early selection and is consistent with the maintenance of water-deficit-tolerance mechanisms until adulthood. There is evidence that the choice of biomarker depends on the species involved and different strategies contributing to the tolerance trait.

We propose an R-based function that facilitates the definition of TPE (Target Population of Environments) as GIS polygons for enviromics studies in plant breeding. By adjusting parameters such as pixel size, buffers, and concavity, this function enhances envirotypic-based G×E interaction analysis and provides a flexible tool to optimize environmental and spatial assessments.

Enviromics is an omics approach that investigates a phenomenon using all available environmental information. This study explores the use of enviromic covariates in studies of genotype × environment (G×E) interactions in upland rice in Brazil, utilizing a field trial dataset from 143 locations over 27 years, covering diverse environmental conditions. The platforms WorldClim, NASA POWER, and SoilGrids were used to extract data, resulting in 383 environmental covariates. The objective of this study was to evaluate the use of enviromic kernels to integrate GIS and genetic data for predicting upland rice productivity across Brazil and to determine the optimal number of environmental covariates required to ensure model accuracy and stability. The predictive abilities of the enviromic model peaked with around 81 covariates, stabilizing when all 383 were included, suggesting the importance of a comprehensive dataset for accurate predictions. Analysis reveals that environmental dissimilarities are more critical than geographical distance for genotypic variability, reinforcing the need to consider multiple covariates in predictive models. Heritability mapping revealed spatial variations, with regions of high heritability concentrated in southern Brazil, where genetic selection may be more efficient. The clustering of mega-environments was not efficient, highlighting the complexity of G×E interactions, and confirming that pixel-by-pixel enviromic models are a safer approach for recommending breeding actions for upland rice. This study suggests strategies to improve genotype selection for specific conditions, guiding the expansion of rice cultivation into new agricultural areas in Brazil. The findings also contribute to rice-growing regions worldwide, especially in countries cultivating upland rice under diverse conditions.