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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.

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.

Drought will reduce global crop production by >10% in 2050 substantially worsening global malnutrition. Breeding for resistance to drought will require accessing crop genetic diversity found in the wild accessions from the driest high stress ecosystems. Genome-environment associations (GEA) in crop wild relatives reveal natural adaptation, and therefore can be used to identify adaptive variation. We explored this approach in the food crop L., characterizing 86 geo-referenced wild accessions using genotyping by sequencing (GBS) to discover single nucleotide polymorphisms (SNPs). The wild beans represented Mesoamerica, Guatemala, Colombia, Ecuador/Northern Peru and Andean groupings. We found high polymorphism with a total of 22,845 SNPs across the 86 accessions that confirmed genetic relationships for the groups. As a second objective, we quantified allelic associations with a bioclimatic-based drought index using 10 different statistical models that accounted for population structure. Based on the optimum model, 115 SNPs in 90 regions, widespread in all 11 common bean chromosomes, were associated with the bioclimatic-based drought index. A gene coding for an ankyrin repeat-containing protein and a phototropic-responsive NPH3 gene were identified as potential candidates. Genomic windows of 1 Mb containing associated SNPs had more positive Tajima's D scores than windows without associated markers. This indicates that adaptation to drought, as estimated by bioclimatic variables, has been under natural divergent selection, suggesting that drought tolerance may be favorable under dry conditions but harmful in humid conditions. Our work exemplifies that genomic signatures of adaptation are useful for germplasm characterization, potentially enhancing future marker-assisted selection and crop improvement.

The work provides an integrative analysis of a gene family with widespread phenotypic effects in a polyploid crop, offering valuable insights into the effects of polyploidization on gene family evolution and function. [...]on a more downstream level,Lin et al.delved the intricate relationship between gene expression and metabolic profiles during sweet potato tuber development. The authors further pinpointed 14 candidate genes related to starch, carotenoids and anthocyanins contents in sweet potato tubers, i.e., chalcone isomerase (CHI) gene in flavonoid biosynthesis, and UDP-glucose pyrophosphorylase 2 (UGP2) and glycogen synthase (glgA) genes in starch biosynthesis. The studies presented here focus on a select group of crop species, yet future research should expand the species spectrum to encompass a broader representation of crops, particularly those with unique evolutionary histories (e.g.,Cortés et al., 2018), orphan research (Hu et al., 2025), or paramount importance for the food security (e.g.,López-Hernández et al., 2023), nutrition (Wu et al., 2020,2024), sustainability (Benitez-Alfonso et al., 2023) and self-sufficiency (Scherer et al., 2020;Varshney et al., 2021b) targets.

Climate change is altering spring snowmelt patterns in alpine and arctic ecosystems, and these changes may alter plant phenology, growth and reproduction. To predict how alpine plants respond to shifts in snowmelt timing, we need to understand trait plasticity, its effects on growth and reproduction, and the degree to which plants experience a home-site advantage. We tested how the common, long-lived dwarf shrub Salix herbacea responded to changing spring snowmelt time by reciprocally transplanting turfs of S. herbacea between early-exposure ridge and late-exposure snowbed microhabitats. After the transplant, we monitored phenological, morphological and fitness traits, as well as leaf damage, during two growing seasons. Salix herbacea leafed out earlier, but had a longer development time and produced smaller leaves on ridges relative to snowbeds. Longer phenological development times and smaller leaves were associated with reduced sexual reproduction on ridges. On snowbeds, larger leaves and intermediate development times were associated with increased clonal reproduction. Clonal and sexual reproduction showed no response to altered snowmelt time. We found no home-site advantage in terms of sexual and clonal reproduction. Leaf damage probability depended on snowmelt and thus exposure period, but had no short-term effect on fitness traits. We conclude that the studied populations of S. herbacea can respond to shifts in snowmelt by plastic changes in phenology and leaf size, while maintaining levels of clonal and sexual reproduction. The lack of a home-site advantage suggests that S. herbacea may not be adapted to different microhabitats. The studied populations are thus unlikely to react to climate change by rapid adaptation, but their responses will also not be constrained by small-scale local adaptation. In the short term, snowbed plants may persist due to high stem densities. However, in the long term, reduction in leaf size and flowering, a longer phenological development time and increased exposure to damage may decrease overall performance of S. herbacea under earlier snowmelt.

Human-driven plant selection, a practice as ancient as agriculture itself, has laid the foundations of plant breeding and contemporary farming [...].Human-driven plant selection, a practice as ancient as agriculture itself, has laid the foundations of plant breeding and contemporary farming [...].

Wild accessions of crops and landraces are valuable genetic resources for plant breeding and for conserving alleles and gene combinations in planta. The primary genepool of cultivated common beans includes wild accessions of Phaseolus vulgaris. These are of the same species as the domesticates and therefore are easily crossable with cultivated accessions. Molecular marker assessment of wild beans and landraces is important for the proper utilization and conservation of these important genetic resources. The goal of this research was to evaluate a collection of wild beans with fluorescent microsatellite or simple sequence repeat markers and to determine the population structure in combination with cultivated beans of all known races. Marker diversity in terms of average number of alleles per marker was high (13) for the combination of 36 markers and 104 wild genotypes that was similar to the average of 14 alleles per marker found for the 606 cultivated genotypes. Diversity in wild beans appears to be somewhat higher than in cultivated beans on a per genotype basis. Five populations or genepools were identified in structure analysis of the wild beans corresponding to segments of the geographical range, including Mesoamerican (Mexican), Guatemalan, Colombian, Ecuadorian-northern Peruvian and Andean (Argentina, Bolivia and Southern Peru). The combined analysis of wild and cultivated accessions showed that the first and last of these genepools were related to the cultivated genepools of the same names and the penultimate was found to be distinct but not ancestral to the others. The Guatemalan genepool was very novel and perhaps related to cultivars of race Guatemala, while the Colombian population was also distinct. Results suggest geographic isolation, founder effects or natural selection could have created the different semi-discrete populations of wild beans and that multiple domestications and introgression were involved in creating the diversity of cultivated beans.

Recombination (R) rate and linkage disequilibrium (LD) analyses are the basis for plant breeding. These vary by breeding system, by generation of inbreeding or outcrossing and by region in the chromosome. Common bean (Phaseolus vulgaris L.) is a favored food legume with a small sequenced genome (514 Mb) and n = 11 chromosomes. The goal of this study was to describe R and LD in the common bean genome using a 768-marker array of single nucleotide polymorphisms (SNP) based on Trans-legume Orthologous Group (TOG) genes along with an advanced-generation Recombinant Inbred Line reference mapping population (BAT93 x Jalo EEP558) and an internationally available diversity panel. A whole genome genetic map was created that covered all eleven linkage groups (LG). The LGs were linked to the physical map by sequence data of the TOGs compared to each chromosome sequence of common bean. The genetic map length in total was smaller than for previous maps reflecting the precision of allele calling and mapping with SNP technology as well as the use of gene-based markers. A total of 91.4% of TOG markers had singleton hits with annotated Pv genes and all mapped outside of regions of resistance gene clusters. LD levels were found to be stronger within the Mesoamerican genepool and decay more rapidly within the Andean genepool. The recombination rate across the genome was 2.13 cM / Mb but R was found to be highly repressed around centromeres and frequent outside peri-centromeric regions. These results have important implications for association and genetic mapping or crop improvement in common bean.

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.

[...]new strategies for genebank utilization must be empowered in order to meet increasing global food demand (McCouch, 2013;Bohra et al., 2021) with crop alternatives resilient to climate change, sustainable to the environment and the biodiversity, and profitable for communities (Scherer et al., 2020). [...]in order to contribute filling this gap on genebank mining, this Research Topic compiles recent developments able to speed up crop improvement processes by leveraging high-throughput phenotyping and genotyping of crop wild relatives (CWR) and landraces (Singh et al., 2022). Mining and unlocking the hidden value of CWR and landraces Crop wild relatives are feasible sources for novel genetic and phenotypic variation (Bohra et al., 2021), as exemplified byInagaki et al.The team demonstrated that a rice (Oryza sativa L.) near-isogenic line (NIL) that carries a genomic segment on chromosome seven from a Thai O. rufipogon CWR, narrowed by marker-assisted selection from a BC4F2 generation, is able to capture an optimum spectrum of plant shapes for sunlight receiving efficiency, which in turn maximizes vegetative growth and weed suppression. [...]Nuraga et al.introduced enset (Ensete ventricosum), a multipurpose crop grown in southern Ethiopia for human food, animal feed, and fiber. Yet, their factual utilization has been hampered by poor characterizations, genetic incompatibilities, and polygenic variance (Coyne et al., 2020). [...]an improved roadmap to address these bottlenecks with modern technologies is a prerequisite for their effective utilization (Figure 1).