Predicting Thermal Adaptation by Looking Into Populations' Genomic Past

Abiotic Stress Tolerance Boosted by Genetic Diversity in Plants

Plant breeding [...].

Genomic evidence for climate-linked diversity loss and increased vulnerability of wild barley spanning 28 years of climate warming

The information on how plant populations respond genetically to climate warming is scarce. Here, landscape genomic and machine learning approaches were integrated to assess genetic response of 10 wild barley (Hordeum vulgare ssp. spontaneum; WB) populations in the past and future, using whole genomic sequencing (WGS) data. The WB populations were sampled in 1980 and again in 2008. Phylogeny of accessions was roughly in conformity with sampling sites, which accompanied by admixture/introgressions. The 28-y climate warming resulted in decreased genetic diversity, increased selection pressure, and an increase in deleterious single nucleotide polymorphism (dSNP) numbers, heterozygous deleterious and total deleterious burdens for WB. Genome-environment associations identified some candidate genes belonging to peroxidase family (HORVU2Hr1G057450, HORVU4Hr1G052060 and HORVU4Hr1G057210) and heat shock protein 70 family (HORVU2Hr1G112630). The gene HORVU2Hr1G120170 identified by selective sweep analysis was under strong selection during the climate warming of the 28-y, and its derived haplotypes were fixed by WB when faced with the 28-y increasingly severe environment. Temperature variables were found to be more important than precipitation variables in influencing genomic variation, with an eco-physiological index gdd5 (growing degree-days at the baseline threshold temperature of 5 °C) being the most important determinant. Gradient forest modelling revealed higher predicted genomic vulnerability in Sede Boqer under future climate scenarios at 2041-2070 and 2071-2100. Additionally, estimates of effective population size (Ne) tracing back to 250 years indicated a forward decline in all populations over time. Our assessment about past genetic response and future vulnerability of WB under climate warming is crucial for informing conservation efforts for wild cereals and rational use strategies.

Comprehensive Time-Course Transcriptome Reveals the Crucial Biological Pathways Involved in the Seasonal Branch Growth in Siberian Elm (Ulmus pumila)

Timber, the most prevalent organic material on this planet, is the result of a secondary xylem emerging from vascular cambium. Yet, the intricate processes governing its seasonal generation are largely a mystery. To better understand the cyclic growth of vascular tissues in elm, we undertook an extensive study examining the anatomy, physiology, and genetic expressions in Ulmus pumila. We chose three robust 15-year-old elm trees for our study. The cultivars used in this study were collected from the Inner Mongolia Autonomous Region in China and nurtured in the tree farm of Shandong Normal University. Monthly samples of 2-year-old elm branches were taken from the tree from February to September. Marked seasonal shifts in elm branch vascular tissues were observed by phenotypic observation: In February, the cambium of the branch emerged from dormancy, spurring growth. By May, elms began generating secondary xylem, or latewood, recognized by its tiny pores and dense cell structure. From June to August, there was a marked increase in the thickness of the secondary xylem. Transcriptome sequencing provides a potential molecular mechanism for the thickening of elm branches and their response to stress. In February, the tree enhanced its genetic responses to cold and drought stress. The amplified expression of CDKB, CYCB, WOX4, and ARF5 in the months of February and March reinforced their essential role in the development of the vascular cambium in elm. Starting in May, the elm deployed carbohydrates as a carbon resource to synthesize the abundant cellulose and lignin necessary for the formation of the secondary wall. Major genes participating in cellulose (SUC and CESA homologs), xylan (UGD, UXS, IRX9, IRX10, and IRX14), and lignin (PAL, C4H, 4CL, HCT, C3H, COMT, and CAD) biosynthetic pathways for secondary wall formation were up-regulated by May or/and June. In conclusion, our findings provided a foundation for an in-depth exploration of the molecular processes dictating the seasonal growth of elm timber.

Identification of Two Flip-Over Genes in Grass Family as Potential Signature of C4 Photosynthesis Evolution

In flowering plants, C4 photosynthesis is superior to C3 type in carbon fixation efficiency and adaptation to extreme environmental conditions, but the mechanisms behind the assembly of C4 machinery remain elusive. This study attempts to dissect the evolutionary divergence from C3 to C4 photosynthesis in five photosynthetic model plants from the grass family, using a combined comparative transcriptomics and deep learning technology. By examining and comparing gene expression levels in bundle sheath and mesophyll cells of five model plants, we identified 16 differentially expressed signature genes showing cell-specific expression patterns in C3 and C4 plants. Among them, two showed distinctively opposite cell-specific expression patterns in C3 vs. C4 plants (named as FOGs). The in silico physicochemical analysis of the two FOGs illustrated that C3 homologous proteins of LHCA6 had low and stable pI values of ~6, while the pI values of LHCA6 homologs increased drastically in C4 plants Setaria viridis (7), Zea mays (8), and Sorghum bicolor (over 9), suggesting this protein may have different functions in C3 and C4 plants. Interestingly, based on pairwise protein sequence/structure similarities between each homologous FOG protein, one FOG PGRL1A showed local inconsistency between sequence similarity and structure similarity. To find more examples of the evolutionary characteristics of FOG proteins, we investigated the protein sequence/structure similarities of other FOGs (transcription factors) and found that FOG proteins have diversified incompatibility between sequence and structure similarities during grass family evolution. This raised an interesting question as to whether the sequence similarity is related to structure similarity during C4 photosynthesis evolution.

Ocean current patterns drive the worldwide colonization of eelgrass (Zostera marina)

Currents are unique drivers of oceanic phylogeography and thus determine the distribution of marine coastal species, along with past glaciations and sea-level changes. Here we reconstruct the worldwide colonization history of eelgrass (Zostera marina L.), the most widely distributed marine flowering plant or seagrass from its origin in the Northwest Pacific, based on nuclear and chloroplast genomes. We identified two divergent Pacific clades with evidence for admixture along the East Pacific coast. Two west-to-east (trans-Pacific) colonization events support the key role of the North Pacific Current. Time-calibrated nuclear and chloroplast phylogenies yielded concordant estimates of the arrival of Z. marina in the Atlantic through the Canadian Arctic, suggesting that eelgrass-based ecosystems, hotspots of biodiversity and carbon sequestration, have only been present there for ~243 ky (thousand years). Mediterranean populations were founded ~44 kya, while extant distributions along western and eastern Atlantic shores were founded at the end of the Last Glacial Maximum (~19 kya), with at least one major refuge being the North Carolina region. The recent colonization and five- to sevenfold lower genomic diversity of the Atlantic compared to the Pacific populations raises concern and opportunity about how Atlantic eelgrass might respond to rapidly warming coastal oceans.

A Study of GUS Expression in Arabidopsis as a Tool for the Evaluation of Gene Evolution, Function and the Role of Expression Derived from Gene Duplication

Gene duplication played a fundamental role in eukaryote evolution and different copies of a given gene can be present in extant species, often with expressions and functions differentiated during evolution. We assume that, when such differentiation occurs in a gene copy, this may be indicated by its maintenance in all the derived species. To verify this hypothesis, we compared the histological expression domains of the three β-glucuronidase genes (AtGUS) present in Arabidopsis thaliana with the GUS evolutionary tree in angiosperms. We found that AtGUS gene expression overlaps in the shoot apex, the floral bud and the root hairs. In the root apex, AtGUS3 expression differs completely from AtGUS1 and AtGUS2, whose transcripts are present in the root cap meristem and columella, in the staminal cell niche, in the epidermis and in the proximal cortex. Conversely, AtGUS3 transcripts are limited to the old border-like cells of calyptra and those found along the protodermal cell line. The GUS evolutionary tree reveals that the two main clusters (named GUS1 and GUS3) originate from a duplication event predating angiosperm radiation. AtGUS3 belongs to the GUS3 cluster, while AtGUS1 and AtGUS2, which originate from a duplication event that occurred in an ancestor of the Brassicaceae family, are found together in the GUS1 cluster. There is another, previously undescribed cluster, called GUS4, originating from a very ancient duplication event. While the copy of GUS4 has been lost in many species, copies of GUS3 and GUS1 have been conserved in all species examined.

An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens

It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats.

Identification of QTLs and Putative Candidate Genes for Plant Architecture of Lotus Revealed by Regional Association Mapping

The lotus (Nelumbo Adans.) is one of the most economically relevant ornamental aquatic plants. Plant architecture (PA) is an important trait for lotus classification, cultivation, breeding, and applications. However, the underlying genetic and molecular basis controlling PA remains poorly understood. In this study, an association study for PA-related traits was performed with 93 genome-wide microsatellite markers (simple sequence repeat, SSR) and 51 insertion-deletion (InDel) markers derived from the candidate regions using a panel of 293 lotus accessions. Phenotypic data analysis of the five PA-related traits revealed a wide normal distribution and high heritability from 2013 to 2016, which indicated that lotus PA-related traits are highly polygenic traits. The population structure (Q-matrix) and the relative kinships (K-matrix) of the association panels were analyzed using 93 SSR markers. The mixed linear model (MLM) taking Q-matrix and K-matrix into account was used to estimate the association between markers and the traits. A total of 26 markers and 65 marker-trait associations were identified by considering associations with p < 0.001 and Q < 0.05. Based on the significant markers, two QTLs on Chromosome 1 were identified, and two candidate genes were preliminarily determined. The results of our study provided useful information for the lotus breeding aiming at different PA phenotypes using a molecular-assisted selection (MAS) method and also laid the foundation for the illustration of the molecular mechanism underlying the major QTL and key markers associated with lotus PA.

Investigation of mutation load and rate in androgenic mutant lines of rapeseed in early generations evaluated by high-density SNP genotyping

Oilseed rape (Brassica napus) is an important oil crop distributed worldwide with a broad adaptation to different climate zones. The cultivation of rapeseed is one of the most commercially viable areas in crop production. Altogether 269,093 ha of rapeseed are cultivated in Kazakhstan. However, all rapeseed cultivars and lines cultivated in Kazakhstan on an industrial scale predominantly belong to the foreign breeding system. Therefore, the formation of a diverse genetic pool for breeding new, highly productive cultivars adopted to the environmental conditions of Kazakhstan is the most important goal in country selection programs. In this work, we have developed ethyl methanesulfonate (EMS) doubled haploid mutant lines from plant material of cultivars 'Galant' and 'Kris' to broad diversity of rapeseed in Kazakhstan. The development of mutant lines was performed via embryo callusogenesis or embryo secondary callusogenesis. Mutants were investigated by Brassica90k SNP array, and we were able to locate 24,657 SNPs from 26,256 SNPs filtered by quality control on the genome assembly (Bra_napus_v2.0). Only 18,831 SNPs were assigned to the available annotated genomic features. The most frequent combination of mutations according to reference controls was adenine with guanine (70%), followed by adenine with cytosine (28.8%), and only minor fractions were cytosine with guanine (0.54%) and adenine with thymine (0.59%). We revealed 5606.27 markers for 'Kris' and 4893.01 markers for 'Galant' by mutation occurrence. Most mutation occurrences were occupied by double mutations where progenitors and offspring were homozygous by different alleles, enabling the selection of appropriate genotypes in a short period of time. Regarding the biological impact of mutations, 861 variants were reported as having a low predicted impact, with 1042 as moderate and 121 as high; all others were reported as belonging to non-coding sequences, intergenic regions, and other features with the effect of modifiers. Protein encoding genes, such as wall-associated receptor kinase-like protein 5, TAO1-like disease resistance protein, receptor-like protein 12, and At5g42460-like F-box protein, contained more than two variable positions, with an impact on their biological activities. Nevertheless, the obtained mutant lines were able to survive and reproduce. Mutant lines, which include moderate and high impact mutations in encoding genes, are a perfect pool not only for MAS but also for the investigation of the fundamental basis of protein functions. For the first time, a collection of mutant lines was developed in our country to improve the selection of local rapeseed cultivars.

Delineating the role of plant stature towards heat stress tolerance in field pea (Pisum sativum L.)

Terminal heat stress severely affects field pea production in tropical climates. Identifying and characterizing marker-trait(s) remain vital for breeding heat-tolerant cultivars of field pea. Field pea genotypes are highly variable for plant stature; however, the significance of plant stature for yield stability under high-temperature conditions is not yet well understood. The study aimed to investigate the sensitivity and significance of plant stature toward yield sustainability of field pea under high-temperature environments. A panel of 150 diverse genotypes with variable plant statures [dwarf (<50 cm), semi-dwarf (50-80 cm), medium-tall (80-150 cm)] were grown under late sowing-induced high-temperature environments for two consecutive years (2017-2019). During the first year of the experiment, the late sown crops (15 and 30 days) were exposed to high-temperatures at flowering (+3.5 to +8.1 °C) and grain-filling (+3.3 to +6.1 °C) over timely sown crops. Likewise, elevated temperature during flowering (+3.7 to +5.2 °C) and grain filling (+5.4 to +9.9 °C) were recorded in late-sown environments (delayed by 27 and 54 days) in the next year. Medium-tall genotypes had longer grain-filling duration (7-10%), higher pod-bearing nodes (8-18%) and yield (22-55%), and lower yield losses (13-18%) over semi-dwarf and dwarf genotypes under high-temperature environments. Significant associations of plant height with yield, yield loss, and heat-susceptibility index in high-temperature environments suggested higher heat tolerance capacity of tall-type plants compared to dwarf and semi-dwarf types. GGEbiplot analysis revealed that the heat-tolerant genotypes were all medium tall-type (mean = 108 cm), while the heat-susceptible genotypes were mostly dwarf in stature. Hence, tall-type genotypes had better adaptability to high-temperature environments. Henceforth, the breeding approach for high-temperature tolerance in field pea may be designed by embracing tall-type backgrounds over dwarf plant to develop climate resilient cultivars.

Evolution and Expression Patterns of the Fructose 1,6-Bisphosptase Gene Family in a Miracle Tree (Neolamarckia cadamba)

Neolamarckia cadamba (N. cadamba) is a fast-growing tree species with tremendous economic and ecological value; the study of the key genes regulating photosynthesis and sugar accumulation is very important for the breeding of N. cadamba. Fructose 1,6-bisphosptase (FBP) gene has been found to play a key role in plant photosynthesis, sugar accumulation and other growth processes. However, no systemic analysis of FBPs has been reported in N. cadamba. A total of six FBP genes were identifed and cloned based on the N. cadamba genome, and these FBP genes were sorted into four groups. The characteristics of the NcFBP gene family were analyzed such as phylogenetic relationships, gene structures, conserved motifs, and expression patterns. A cis-acting element related to circadian control was first found in the promoter region of FBP gene. Phylogenetic and quantitative real-time PCR analyses showed that NcFBP5 and NcFBP6 may be chloroplast type 1 FBP and cytoplasmic FBP, respectively. FBP proteins from N. cadamba and 22 other plant species were used for phylogenetic analyses, indicating that FBP family may have expanded during the evolution of algae to mosses and differentiated cpFBPase1 proteins in mosses. This work analyzes the internal relationship between the evolution and expression of the six NcFBPs, providing a scientific basis for the evolutionary pattern of plant FBPs, and promoting the functional studies of FBP genes.

Biochemical and Transcriptional Responses in Cold-Acclimated and Non-Acclimated Contrasting Camelina Biotypes under Freezing Stress

Cold-acclimated and non-acclimated contrasting Camelina (Camelina sativa L.) biotypes were investigated for changes in stress-associated biomarkers, including antioxidant enzyme activity, lipid peroxidation, protein, and proline content. In addition, a well-known freezing tolerance pathway participant known as C-repeat/DRE-binding factors (CBFs), an inducer of CBF expression (ICE1), and a cold-regulated (COR6.6) genes of the ICE-CBF-COR pathway were studied at the transcriptional level on the doubled-haploid (DH) lines. Freezing stress had significant effects on all studied parameters. The cold-acclimated DH34 (a freezing-tolerant line) showed an overall better performance under freezing stress than non-acclimated plants. The non-cold-acclimated DH08 (a frost-sensitive line) showed the highest electrolyte leakage after freezing stress. The highest activity of antioxidant enzymes (glutathione peroxidase, superoxide dismutase, and catalase) was also detected in non-acclimated plants, whereas the cold-acclimated plants showed lower enzyme activities upon stress treatment. Cold acclimation had a significantly positive effect on the total protein and proline content of stressed plants. The qRT-PCR analysis revealed significant differences in the expression and cold-inducibility of CsCBF1-3, CsICE1, and CsCOR6.6 genes among the samples of different treatments. The highest expression of all CBF genes was recorded in the non-acclimated frost-tolerant biotype after freezing stress. Interestingly a significantly higher expression of COR6.6 was detected in cold-acclimated samples of both frost-sensitive and -tolerant biotypes after freezing stress. The presented results provide more insights into freezing tolerance mechanisms in the Camelina plant from both a biochemical point of view and the expression of the associated genes.

Overexpression of Terpenoid Biosynthesis Genes Modifies Root Growth and Nodulation in Soybean (Glycine max)

Root nodule formation in many leguminous plants is known to be affected by endogen ous and exogenous factors that affect formation, development, and longevity of nodules in roots. Therefore, it is important to understand the role of the genes which are involved in the regulation of the nodulation signaling pathway. This study aimed to investigate the effect of terpenoids and terpene biosynthesis genes on root nodule formation in Glycine max. The study aimed to clarify not only the impact of over-expressing five terpene synthesis genes isolated from G. max and Salvia guaranitica on soybean nodulation signaling pathway, but also on the strigolactones pathway. The obtained results revealed that the over expression of GmFDPS, GmGGPPS, SgGPS, SgFPPS, and SgLINS genes enhanced the root nodule numbers, fresh weight of nodules, root, and root length. Moreover, the terpene content in the transgenic G. max hairy roots was estimated. The results explored that the monoterpenes, sesquiterpenes and diterpenes were significantly increased in transgenic soybean hairy roots in comparison with the control. Our results indicate the potential effects of terpenoids and terpene synthesis genes on soybean root growth and nodulation. The study provides novel insights for understanding the epistatic relationship between terpenoids, root development, and nodulation in soybean.

Genome–Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives

Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome–environment associations (GEAs). We first exemplify how GEA utilizes in situ georeferencing from genotypically characterized, gene bank accessions to pinpoint genomic signatures of natural selection. We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat–based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change.

“I shall endeavor to find out how nature’s forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature” Alexander von Humboldt—Letter to Karl Freiesleben, June 1799.

Delving the Role of Caralluma fimbriata: An Edible Wild Plant to Mitigate the Biomarkers of Metabolic Syndrome

Metabolic syndrome (MS), commonly known as syndrome X or insulin resistance syndrome, is a collection of risk factors for cardiovascular diseases and type II diabetes. MS is believed to impact over a billion individuals worldwide. It is a medical condition defined by visceral obesity, insulin resistance, high blood pressure, and abnormal cholesterol levels, according to the World Health Organization. The current dietary trends are more focused on the use of functional foods and nutraceuticals that are well known for their preventive and curative role against such pathological disorders. Caralluma fimbriata is one such medicinal plant that is gaining popularity. It is a wild, edible, succulent roadside shrub with cactus-like leaves. Besides its main nutrient contents, various bioactive constituents have been identified and linked with positive health outcomes of appetite-suppressing, hypolipidemic, antioxidant, hepatoprotective, and anticancer potentials. Hence, such properties make C. fimbriata an invaluable plant against MS. The current review compiles recent available literature on C. fimbriata's nutritional composition, safety parameters, and therapeutic potential for MS. Summarized data in this review reveals that C. fimbriata remains a neglected plant with limited food and therapeutic applications. Yet various studies explored here do prove its positive health-ameliorating outcomes.

Full-Length Transcriptome Sequencing Reveals Alternative Splicing and lncRNA Regulation during Nodule Development in Glycine max

Alternative splicing (AS) is a ubiquitous phenomenon among eukaryotic intron-containing genes, which greatly contributes to transcriptome and proteome diversity. Here we performed the isoform sequencing (Iso-Seq) of soybean underground tissues inoculated and uninoculated with Rhizobium and obtained 200,681 full-length transcripts covering 26,183 gene loci. It was found that 80.78% of the multi-exon loci produced more than one splicing variant. Comprehensive analysis of these identified 7874 differentially splicing events with highly diverse splicing patterns during nodule development, especially in defense and transport-related processes. We further profiled genes with differential isoform usage and revealed that 2008 multi-isoform loci underwent stage-specific or simultaneous major isoform switches after Rhizobium inoculation, indicating that AS is a vital way to regulate nodule development. Moreover, we took the lead in identifying 1563 high-confidence long non-coding RNAs (lncRNAs) in soybean, and 157 of them are differentially expressed during nodule development. Therefore, our study uncovers the landscape of AS during the soybean-Rhizobium interaction and provides systematic transcriptomic data for future study of multiple novel directions in soybean.

Identification of Candidate Genes Regulating Drought Tolerance in Pearl Millet

Pearl millet is an important crop of the arid and semi-arid ecologies to sustain food and fodder production. The greater tolerance to drought stress attracts us to examine its cellular and molecular mechanisms via functional genomics approaches to augment the grain yield. Here, we studied the drought response of 48 inbreds representing four different maturity groups at the flowering stage. A set of 74 drought-responsive genes were separated into five major phylogenic groups belonging to eight functional groups, namely ABA signaling, hormone signaling, ion and osmotic homeostasis, TF-mediated regulation, molecular adaptation, signal transduction, physiological adaptation, detoxification, which were comprehensively studied. Among the conserved motifs of the drought-responsive genes, the protein kinases and MYB domain proteins were the most conserved ones. Comparative in-silico analysis of the drought genes across millet crops showed foxtail millet had most orthologs with pearl millet. Of 698 haplotypes identified across millet crops, MyC2 and Myb4 had maximum haplotypes. The protein–protein interaction network identified ABI2, P5CS, CDPK, DREB, MYB, and CYP707A3 as major hub genes. The expression assay showed the presence of common as well as unique drought-responsive genes across maturity groups. Drought tolerant genotypes in respective maturity groups were identified from the expression pattern of genes. Among several gene families, ABA signaling, TFs, and signaling proteins were the prospective contributors to drought tolerance across maturity groups. The functionally validated genes could be used as promising candidates in backcross breeding, genomic selection, and gene-editing schemes in pearl millet and other millet crops to increase the yield in drought-prone arid and semi-arid ecologies.

A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.

Chasing genetic correlation breakers to stimulate population resilience to climate change

Global climate change introduces new combinations of environmental conditions, which is expected to increase stress on plants. This could affect many traits in multiple ways that are as yet unknown but will likely require the modification of existing genetic relationships among functional traits potentially involved in local adaptation. Theoretical evolutionary studies have determined that it is an advantage to have an excess of recombination events under heterogeneous environmental conditions. Our study, conducted on a population of radiata pine (Pinus radiata D. Don), was able to identify individuals that show high genetic recombination at genomic regions, which potentially include pleiotropic or collocating QTLs responsible for the studied traits, reaching a prediction accuracy of 0.80 in random cross-validation and 0.72 when whole family was removed from the training population and predicted. To identify these highly recombined individuals, a training population was constructed from correlation breakers, created through tandem selection of parents in the previous generation and their consequent mating. Although the correlation breakers showed lower observed heterogeneity possibly due to direct selection in both studied traits, the genomic regions with statistically significant differences in the linkage disequilibrium pattern showed higher level of heretozygosity, which has the effect of decomposing unfavourable genetic correlation. We propose undertaking selection of correlation breakers under current environmental conditions and using genomic predictions to increase the frequency of these ’recombined’ individuals in future plantations, ensuring the resilience of planted forests to changing climates. The increased frequency of such individuals will decrease the strength of the population-level genetic correlations among traits, increasing the opportunity for new trait combinations to be developed in the future.

Whole Transcriptome Sequencing Unveils the Genomic Determinants of Putative Somaclonal Variation in Mint (Mentha L.)

Mint (Mentha L., Lamiaceae) is a strongly scented herb of the family Lamiaceae that is grown mostly by clonal propagation, making it a valuable species for the study of somaclonal variation and its phenotypic consequences. The recent introduction of a few species of mint in South America, followed by a presumably rampant propagation, make this region particularly ideal for studying the extent of somaclonal genetic diversity. Hence, the objective of this work was to offer a preliminary characterization of somaclonal genetically coding diversity of the mint in the northern Andes in order to address the question of whether somaclonal variants may have emerged despite relatively recent introductions in a region where mint is not native. A total of 29 clonally propagated specimens, collected in mint export farms in the province of Antioquia, a major region for mint production in the northwest Andes of Colombia, were genotyped using RNA sequencing (RNA-Seq). SNP calling was carried out from the leaves’ transcriptome profiles of each plant by combining the GATK4 and TRINITY protocols, obtaining a total of 2033 loci across 912 transcripts with a minimum read depth of 20X and 4% of missing data. Unsupervised machine learning algorithms considered the K-means, AGNES and UPGMA approaches, all of which suggested three genetic clusters for M. spicata and a unique cluster for M. × piperita. The results indicate that at least two different origins of M. spicata reached the eastern region of the Antioquia province, clonally propagated in the locality ever since for local consumption and export. One of these ancestries had more population structure, possibly due to environmental or anthropological pressures that intervened in the fragmentation of this genetic group or to a higher somaclonal mutation rate. This work offers a first step into the study of the accumulation and transmission of presumably quasi-neutral somatic mutations at coding regions in an herbaceous clonally propagated scented species such as mint, likely favored by an expected population expansion after its Andean introduction. These ad hoc hypotheses warrant further study as part of future research.

Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms

Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.

riceExplorer: Uncovering the Hidden Potential of a National Genomic Resource Against a Global Database

Agricultural crop breeding programs, particularly at the national level, typically consist of a core panel of elite breeding cultivars alongside a number of local landrace varieties (or other endemic cultivars) that provide additional sources of phenotypic and genomic variation or contribute as experimental materials (e.g., in GWAS studies). Three issues commonly arise. First, focusing primarily on core development accessions may mean that the potential contributions of landraces or other secondary accessions may be overlooked. Second, elite cultivars may accumulate deleterious alleles away from nontarget loci due to the strong effects of artificial selection. Finally, a tendency to focus solely on SNP-based methods may cause incomplete or erroneous identification of functional variants. In practice, integration of local breeding programs with findings from global database projects may be challenging. First, local GWAS experiments may only indicate useful functional variants according to the diversity of the experimental panel, while other potentially useful loci-identifiable at a global level-may remain undiscovered. Second, large-scale experiments such as GWAS may prove prohibitively costly or logistically challenging for some agencies. Here, we present a fully automated bioinformatics pipeline (riceExplorer) that can easily integrate local breeding program sequence data with international database resources, without relying on any phenotypic experimental procedure. It identifies associated functional haplotypes that may prove more robust in determining the genotypic determinants of desirable crop phenotypes. In brief, riceExplorer evaluates a global crop database (IRRI 3000 Rice Genomes) to identify haplotypes that are associated with extreme phenotypic variation at the global level and recorded in the database. It then examines which potentially useful variants are present in the local crop panel, before distinguishing between those that are already incorporated into the elite breeding accessions and those only found among secondary varieties (e.g., landraces). Results highlight the effectiveness of our pipeline, identifying potentially useful functional haplotypes across the genome that are absent from elite cultivars and found among landraces and other secondary varieties in our breeding program. riceExplorer can automatically conduct a full genome analysis and produces annotated graphical output of chromosomal maps, potential global diversity sources, and summary tables.

Genetic and Genomic Resources for Soybean Breeding Research

Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement.

Genetic differentiation of grain, fodder and pod vegetable type cowpeas (Vigna unguiculata L.) identified through single nucleotide polymorphisms from genotyping-by-sequencing

The species Vigna unguiculata L. (Walp), commonly known as cowpea, is a multi-purpose legume that has been selected into three subspecies that are divided into grain, fodder and pod (yardlong bean) types. However, genetic bases for distinctions are not well understood. The purpose of this study was to apply genotyping-by-sequencing (GBS) and current reference genome for V. unguiculata to distinguish three subspecies and identify signatures of divergence. The collection of 130 accessions included 128 cultivated from: 1) ssp. cylindrica, fodder type; 2) ssp. sesquipedalis, pod vegetable type; and 3) ssp. unguiculata, grain type. Two wilds genotypes from spp. dekindtiana and spp. pubescens, were used to anchor phylogeny. A total of 11,083 highly informative single nucleotide polymorphisms (SNPs) were discovered. Wild accessions showed distinct genetic fingerprints and were separated from cultivated subspecies. Principal component analysis showed closer relationship between ssp. unguiculata and ssp. cylindrica compared to ssp. sesquipedalis. Relative differentiation of cultivated subspecies (with Fixation Index, FST) indicated the existence of discrete signatures of selection. This work clarifies the population structure, phylogeny, and domestication of cultivated cowpeas. Furthermore, significant genetic differences between grain and pod vegetable types can provide valuable information for future breeding in three cowpea groups.

Whole-Genome Sequencing and Annotation of the Yeast Clavispora santaluciae Reveals Important Insights about Its Adaptation to the Vineyard Environment

Clavispora santaluciae was recently described as a novel non-Saccharomyces yeast species, isolated from grapes of Azores vineyards, a Portuguese archipelago with particular environmental conditions, and from Italian grapes infected with Drosophila suzukii. In the present work, the genome of five Clavispora santaluciae strains was sequenced, assembled, and annotated for the first time, using robust pipelines, and a combination of both long- and short-read sequencing platforms. Genome comparisons revealed specific differences between strains of Clavispora santaluciae reflecting their isolation in two separate ecological niches—Azorean and Italian vineyards—as well as mechanisms of adaptation to the intricate and arduous environmental features of the geographical location from which they were isolated. In particular, relevant differences were detected in the number of coding genes (shared and unique) and transposable elements, the amount and diversity of non-coding RNAs, and the enzymatic potential of each strain through the analysis of their CAZyome. A comparative study was also conducted between the Clavispora santaluciae genome and those of the remaining species of the Metschnikowiaceae family. Our phylogenetic and genomic analysis, comprising 126 yeast strains (alignment of 2362 common proteins) allowed the establishment of a robust phylogram of Metschnikowiaceae and detailed incongruencies to be clarified in the future.

Evolutionary Genetics of Crop-Wild Complexes

Since Darwin's time, the role of crop wild relatives (CWR), landraces, and cultivated genepools in shaping plant diversity and boosting food resources has been a major question [...].

Identification and Validation of a QTL for Bacterial Leaf Streak Resistance in Rice (Oryza sativa L.) against Thai Xoc Strains

Rice is one of the most important food crops in the world and is of vital importance to many countries. Various diseases caused by fungi, bacteria and viruses constantly threaten rice plants and cause yield losses. Bacterial leaf streak disease (BLS) caused by Xanthomonas oryzae pv. oryzicola (Xoc) is one of the most devastating rice diseases. However, most modern rice varieties are susceptible to BLS. In this study, we applied the QTL-seq approach using an F2 population derived from the cross between IR62266 and Homcholasit (HSC) to rapidly identify the quantitative trait loci (QTL) that confers resistance to BLS caused by a Thai Xoc isolate, SP7-5. The results showed that a single genomic region at the beginning of chromosome 5 was highly associated with resistance to BLS. The gene xa5 was considered a potential candidate gene in this region since most associated single nucleotide polymorphisms (SNPs) were within this gene. A Kompetitive Allele-Specific PCR (KASP) marker was developed based on two consecutive functional SNPs in xa5 and validated in six F2 populations inoculated with another Thai Xoc isolate, 2NY2-2. The phenotypic variance explained by this marker (PVE) ranged from 59.04% to 70.84% in the six populations. These findings indicate that xa5 is a viable candidate gene for BLS resistance and may help in breeding programs for BLS resistance.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

Integrative Pre-Breeding for Biotic Resistance in Forest Trees

Climate change is unleashing novel biotic antagonistic interactions for forest trees that may jeopardize populations' persistence. Therefore, this review article envisions highlighting major opportunities from ecological evolutionary genomics to assist the identification, conservation, and breeding of biotic resistance in forest tree species. Specifically, we first discuss how assessing the genomic architecture of biotic stress resistance enables us to recognize a more polygenic nature for a trait typically regarded Mendelian, an expectation from the Fisherian runaway pathogen-host concerted arms-race evolutionary model. Secondly, we outline innovative pipelines to capture and harness natural tree pre-adaptations to biotic stresses by merging tools from the ecology, phylo-geography, and omnigenetics fields within a predictive breeding platform. Promoting integrative ecological genomic studies promises a better understanding of antagonistic co-evolutionary interactions, as well as more efficient breeding utilization of resistant phenotypes.

Review and comparative analysis of machine learning-based predictors for predicting and analyzing of anti-angiogenic peptides

Cancer is one of the leading causes of death worldwide and underlying this is angiogenesis that represents one of the hallmarks of cancer. Ongoing effort is already under way in the discovery of anti-angiogenic peptides (AAPs) as a promising therapeutic route by tackling the formation of new blood vessels. As such, the identification of AAPs constitutes a viable path for understanding their mechanistic properties pertinent for the discovery of new anti-cancer drugs. In spite of the abundance of peptide sequences in public databases, experimental efforts in the identification of anti-angiogenic peptides have progressed very slowly owing to its high expenditures and laborious nature. Owing to its inherent ability to make sense of large volumes of data, machine learning (ML) represents a lucrative technique that can be harnessed for peptide-based drug discovery. In this review, we conducted a comprehensive and comparative analysis of ML-based AAP predictors in terms of their employed feature descriptors, ML algorithms, cross-validation methods and prediction performance. Moreover, the common framework of these AAP predictors and their inherent weaknesses are also discussed. Particularly, we explore future perspectives for improving the prediction accuracy and model interpretability, which represents an interesting avenue for overcoming some of the inherent weaknesses of existing AAP predictors. We anticipate that this review would assist researchers in the rapid screening and identification of promising AAPs for clinical use.

Genome-Wide Association Study and Genomic Prediction for Soybean Cyst Nematode Resistance in USDA Common Bean (Phaseolus vulgaris) Core Collection

Soybean cyst nematode (SCN, Heterodera glycines) has become the major yield-limiting biological factor in soybean production. Common bean is also a good host of SCN, and its production is challenged by this emerging pest in many regions such as the upper Midwest USA. The use of host genetic resistance has been the most effective and environmentally friendly method to manage SCN. The objectives of this study were to evaluate the SCN resistance in the USDA common bean core collection and conduct a genome-wide association study (GWAS) of single nucleotide polymorphism (SNP) markers with SCN resistance. A total of 315 accessions of the USDA common bean core collection were evaluated for resistance to SCN HG Type 0 (race 6). The common bean core set was genotyped with the BARCBean6K_3 Infinium BeadChips, consisting of 4,654 SNPs. Results showed that 15 accessions were resistant to SCN with a Female Index (FI) at 4.8 to 9.4, and 62 accessions were moderately resistant (10 < FI < 30) to HG Type 0. The association study showed that 11 SNP markers, located on chromosomes Pv04, 07, 09, and 11, were strongly associated with resistance to HG Type 0. GWAS was also conducted for resistance to HG Type 2.5.7 and HG Type 1.2.3.5.6.7 based on the public dataset (N = 276), consisting of a diverse set of common bean accessions genotyped with the BARCBean6K_3 chip. Six SNPs associated with HG Type 2.5.7 resistance on Pv 01, 02, 03, and 07, and 12 SNPs with HG Type 1.2.3.5.6.7 resistance on Pv 01, 03, 06, 07, 09, 10, and 11 were detected. The accuracy of genomic prediction (GP) was 0.36 to 0.49 for resistance to the three SCN HG types, indicating that genomic selection (GS) of SCN resistance is feasible. This study provides basic information for developing SCN-resistant common bean cultivars, using the USDA core germ plasm accessions. The SNP markers can be used in molecular breeding in common beans through marker-assisted selection (MAS) and GS.

Harnessing Crop Wild Diversity for Climate Change Adaptation

Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent 'big data' developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these 'big data' approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

Back to the future: Reintegrating biology to understand how past eco-evolutionary change can predict future outcomes

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: 1) utilizing knowledge of biological systems to better inform eco-evolutionary models, 2) generating models with more accurate predictions, and 3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.

Allelic Diversity at Abiotic Stress Responsive Genes in Relationship to Ecological Drought Indices for Cultivated Tepary Bean, Phaseolus acutifolius A. Gray, and Its Wild Relatives

Some of the major impacts of climate change are expected in regions where drought stress is already an issue. Grain legumes are generally drought susceptible. However, tepary bean and its wild relatives within Phaseolus acutifolius or P. parvifolius are from arid areas between Mexico and the United States. Therefore, we hypothesize that these bean accessions have diversity signals indicative of adaptation to drought at key candidate genes such as: Asr2, Dreb2B, and ERECTA. By sequencing alleles of these genes and comparing to estimates of drought tolerance indices from climate data for the collection site of geo-referenced, tepary bean accessions, we determined the genotype x environmental association (GEA) of each gene. Diversity analysis found that cultivated and wild P. acutifolius were intermingled with var. tenuifolius and P. parvifolius, signifying that allele diversity was ample in the wild and cultivated clade over a broad sense (sensu lato) evaluation. Genes Dreb2B and ERECTA harbored signatures of directional selection, represented by six SNPs correlated with the environmental drought indices. This suggests that wild tepary bean is a reservoir of novel alleles at genes for drought tolerance, as expected for a species that originated in arid environments. Our study corroborated that candidate gene approach was effective for marker validation across a broad genetic base of wild tepary accessions.

Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as a model fruit tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, biomass/reproductive, and fruit yield and quality traits) in the scions for 3 years (2015-2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a "genetic prediction" model to calculate narrow-sense heritabilities (h 2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e., total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in "Hass" avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work highlights the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit tree breeding programs while enhancing our understanding of the consequences of grafting.

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.