Editorial

Pre-Breeding Climate-Resilient Tree Crops

Andrés J Cortés^1,2*

¹Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, CI La Selva, Km 7 vía Rionegro - Las Palmas, Rionegro, Colombia

²Facultad de Ciencias Agrarias - Departamento de Ciencias Forestales, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia

Submission: September 19, 2023; Published: October 03, 2023

*Corresponding author: Andrés J Cortés, Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, CI La Selva, Km 7 vía Rionegro - Las Palmas, Rionegro, Facultad de Ciencias Agrarias and Departamento de Ciencias Forestales, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia

How to cite this article: Andrés J C. Pre-Breeding Climate-Resilient Tree Crops. Agri Res & Tech: Open Access J. 2023; 27 (4): 556389. DOI: 10.19080/ARTOAJ.2023.27.556389

Abstract

The prevalence of tree crops vulnerable to drought and heat stress is on the rise as a consequence of climate change. Therefore, there is an urgent need for the development of innovative analytical approaches to identify the origins of natural tree adaptation and integrate them into the improvement of sustainable populations, with the ultimate goal of ensuring their resilience. In this invited comment to Agricultural Research & Technology, I envision and exemplify how modeling analytical methods via genome-environment associations might be utilized to forecast tree adaptive responses in their natural distribution ranges to better face ongoing climate change. This theoretical exercise will contribute to the advancement of sustainable tree production in the face of extreme weather patterns.

Keywords: Polygenic adaptation; Genome-Environment Associations; Plant genomics; Tree breeding

Editorial

Rethinking Genome-Environment Associations for Tree Species

Tree populations contain natural distinct responses to abiotic stressors, which are seldom found in more advanced breeding generations [1]. Nevertheless, the realization of this potential has proven to be difficult, primarily due to the limitations of phenotyping in natural environments, the highly admixed nature of pedigrees in the wild, and the intricate inheritance patterns associated with key trait variation. For instance, adaptation would often involve multiple loci with infinitesimal effects hard to detect [2]. In order to bridge these limitations, Genome-Environment Association (GEA) inferences inspired in classical Genome-Wide Association Study (GWAS) models [3] could be employed as a way to disclose and leverage naturally existent Linkage Disequilibrium (LD) with bioclimatic variables across tree populations.

Aiming for the integration of GEA models with more empirical estimations of the adaptive potential may also boost predictive improvement for adaptation in tree crops [4]. The utilization of modern genomic evolutionary analysis could further assist interpreting the phenomena of evolutionary conservatism, parallelism, and convergence as part of the genetic foundations governing adaptation to diverse forms of abiotic stresses across a broad spectrum of environments. These factors include drivers summarized by Ellegren and Galtier [5] like ecological disruptive and background selection, gene flow, shared ancestral polymorphism, and variation in mutation and recombination rates. Such processes have been explored in more detailed in other works [6-9], and specifically within the context of GEA predictions [10-16]. After all, uncovering the ultimate molecular and evolutionary mechanisms underlying ecological divergence and its plethora of genomic determinants requires contrasting selection forces with those from demography and randomness.

In this regard, research on tree crops that utilize the GEA paradigm can provide significant confirmations of classical molecular genetics’ hypotheses in long-lived species by shedding light on the causals of naturally convergent adaptations. For example, is probable that molecular markers located near significant ecological-associated Quantitative Trait Loci (QTLs) may experience hitchhiking effects [7], as result of a low recombination rate and considerable linkage disequilibrium [17,18], which in turn are expected to be more pronounced towards centromeric regions. It is important to acknowledge these factors in the study of extensive LD and hitchhiking effects, the ultimate proxies for GEA estimations, even if they are assumed to be minimal in perennial tree crop species with long-distance dispersal and overlapping generation times [19]. Last but not least, advancing GEA studies into the molecular mechanisms of tree evolutionary ecological divergence will require the functional genetic dissection of putative adaptive loci, including the validation of candidate genes, their segmentation from intrinsic genomic features such as adaptive haplotype blocks, and the transgenesis and genetic edition of regulatory mutations that contribute to the genomic basis of adaptation.

Shifting from Estimation to Prediction of the Adaptive Potential

The implementation of predictive models from the GEA results can be impaired by disregarding the choice of using the complete set of available Single Nucleotide Polymorphism (SNP) genetic markers, in favor of selecting the most predictive SNP dataset according to the GWAS-type analysis [20]. The very same property that makes the polygenic adaptation infinitesimal model unique may be compromised when sub-setting the SNP pool. Ergo, relying solely on SNP markers derived from the GEA inferences will overlook SNPs with minor effects, which are typically undetected by GWAS-type models. Yet, these SNPs may still contribute to the overall unexplained heritability of the adaptive traits.

More effective strategies to utilize alleles responsible for adaptation in tree species include: (1) employing weighted Genomic Prediction (GP) models [21] that incorporate GWASbased GEA estimates [22], and (2) optimizing the selection of markers by generating saturation curves of the predictive ability using different marker sets ranked according to their beta effects derived from the GP model suggested in (1), rather than relying on estimations obtained only from GWAS-inspired models [23,24]. In the context of tree pre-breeding targets focused on abiotic tolerance, the optimization of marker sets for environmental genotyping might prove advantageous [25]. Such optimization is expected to merge the GEA thinking with speed breeding tactics, as suggested by Migicovsky and Myles [26] and Watson et al. [27]. Marker optimization has historically been used to enhance the efficiency of high-throughput screenings. In addition, there is a current shift in the field of GP from utilizing Best Linear Unbiased Prediction (BLUP)-type models to adopting machine learning (ML) methodologies [28-30].

Finally, climate-based ecophysiological modeling is also promising to categorize tree genotypes based on the stress gradient of the geographic regions from which they originate. For that, climatic data is gathered by utilizing the georeferencing at the time of sample collection, as original highlighted by Hancock et al. [31], but latter on transformed into more biologically meaningful scores [32]. Furthermore, not all tree samples possess the necessary characteristics to effectively calibrate climatebased ecophysiological models from raw bioclimatic variables. Therefore, it is recommended to place emphasis on specific abiotic stress types and resample genotypes accordingly in a bulksegregant manner.

While it is acknowledged that GEA, GP, ML and speed breeding have proven to be valuable methods for the improvement and deployment of customized adaptive variants [33,34], specially in tree crops [35], it is yet important to note that these techniques are innovations in predictive modeling that may not directly align with methodological advancements in the field [36]. To compensate, in silico pre- and breeding models can be advantageous in the optimization of these varietal development tactics. In silico breeding simulates and modifies the selection intensity in order to minimize genetic erosion and cycle length, while ensuring that overall genetic gains are not compromised [37].

Concluding Remarks

In essence, GEA enhances our comprehension of the intricate dynamics between tree genomes and their surrounding environment, hence influencing the long-term selection and genetic gain of adaptive phenotypic traits [35]. The mechanistic understanding of how the genomes of perennial tree species function in natural environments holds great potential for utilizing existing standing adaptation to support pre-breeding initiatives targeting drought [38-40] and heat stress [41,42]. This can be achieved through the use of multi-dimensional adaptive scores, which provide a comprehensive assessment of tree adaptability [43], even for biotic antagonistic agents [44,45], while balancing more classical improvement targets in perennials [46,47]. Additionally, GEA helps identifying the factors that either enable or limit future adaptive responses to evolving environmental conditions.

Acknowledgment

A.J.C. visualized this commentary with theoretical and computational reflections from M.W. Blair and F. López-Hernández, his long-term mentor and pupil. Appreciations are also offered to M.J. Torres for support during the drafting of this manuscript. Finally, the Editorial Board is very much thanked for cheering submissions to this nascent journal.

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