This work used a combination of the latest genome sequencing and resequencing approaches to assemble a high-quality reference genome for Malus angustifolia, a native apple to the Southeastern U.S., and resequence its germplasm to enable genome-wide association study identify regions and structural variants associated with abiotic stress resistance.
Abstract
Apple (Malus domestica Borkh.) production faces many challenges stemming from abiotic stresses such as extreme temperatures, droughts, and spring frosts. The introduction of resiliency traits from wild Malus relatives that originate from high-stress environments could offer new genetic solutions to a changing climate. Use of wild Malus relatives in breeding is constrained by the lack of genomic resources and base knowledge of genetics associated with abiotic stress. To address this gap, we used a combination of the latest genome sequencing and resequencing approaches to assemble a high-quality reference genome for Malus angustifolia, a native apple to the Southeastern U.S., and resequence its germplasm to enable genome-wide association study identify regions and structural variants associated with abiotic stress resistance. The resulting genome assembly exhibited scaffold N50 of >40 Mb and BUSCO scores >98.7% complete for both haplotype assemblies. We used climate data from the origin of each resequenced sample as a phenotype to identify 242 regions including SNPs and structural variants (deletions, duplications, and inversions) of the genome of M. angustifolia that were significantly associated with abiotic factors such as seasonal precipitation and maximum seasonal temperature. These results will enable identification of resilient M. angustifolia accessions and genetic loci for use in breeding of climate-resilient apples.
Horticultural crops, particularly Solanaceae and Cucurbitaceae, represent a major component of global vegetable production and are increasingly exposed to climate variability and environmental constraints. Landraces and crop wild relatives constitute essential reservoirs of adaptive genetic diversity; however, their effective utilization in breeding programs remains limited by fragmented characterization, incomplete passport information, and reliance on labor-intensive morphological descriptors. These limitations hinder the systematic exploitation of conserved germplasm and restrict its integration into modern predictive breeding frameworks. Recent advances in high-throughput phenotyping, genomics, and multi-omics technologies have created new opportunities to bridge the gap between genotype and phenotype. Next-generation phenomics enables non-destructive, high-resolution quantification of plant physiological and structural traits across environments, while genomic approaches, including whole-genome resequencing, support comprehensive assessment of genetic diversity. Pangenome frameworks further extend this resolution by capturing core and variable genomic fractions, collectively defining the species variome and enabling improved identification of structural and allelic variants associated with adaptive traits. The integration of phenomic and genomic datasets through multi-omics approaches enhances the functional interpretation of trait-associated variation and strengthens the predictive capacity of breeding strategies. In this context, the genome as a functional passport constitutes a unified reference layer linking germplasm identity with genomic, phenotypic, and functional trait information, thereby enabling more systematic germplasm characterization, reduced redundancy, and improved identification of elite parental material. This mini-review highlights how the integration of phenomics, pangenomics, and multi-omics enables the transition from descriptive germplasm cataloguing toward more systematic, data-driven, and predictive breeding systems for the development of climate-resilient horticultural crops.
P. Mylona, L. Barchi, Luciana Gaccione et al.· Frontiers in Plant Science· 0 citations
Abstract Red clover (Trifolium pratense L.) is a globally important temperate forage legume. Its symbiosis with soil‐borne rhizobia enables nitrogen fixation, and its ability to produce quality forage under diverse soil conditions enhances pasture productivity, particularly during water deficits. With increasing climate‐related stresses, harnessing adaptive traits absent in current cultivars is critical. Genebanks conserve diverse red clover germplasm, providing genetic variation for agronomic and adaptive traits. In this study, we introgressed novel germplasm into locally adapted cultivars to track the inheritance of allelic variants using genotyping‐by‐sequencing. Multi‐location, multi‐year trials evaluated half‐sib families (generation two [Gen 2]) two generations removed from the exotic germplasm (genereation zero [Gen 0]) against local cultivars. Several Gen 2 populations matched or outperformed local cultivars and exhibited a moderate family mean heritability (h 2 > 0.40) for most traits. Integrating genomic, phenotypic, and environmental data, 77 bioclimatic‐associated single nucleotide polymorphisms (SNPs) were identified, of which 35 SNPs and 27 associated genes were significantly linked to trait expression. By using the original germplasm (Gen 0) as a training population and the derived half‐sib families (Gen 2) as a validation population, genomic prediction models were developed to calculate prediction accuracies for key agronomic traits. Biomass and plot density traits showed high predictive abilities and the highest prediction accuracies across generations. This study demonstrates a route by which genetic diversity from genebanks can be successfully incorporated into local populations, enabling evaluation and selection of key traits. The identified molecular markers and genomic prediction models provide a pathway to efficiently develop climate‐adaptive red clover cultivars.
A. Heslop, S. Arojju, R. Hofmann et al.· The Plant Genome· 0 citations
There is an urgent need to expand groundnut genomics knowledge base to improve yield and adaptability in the target environments. Whole-genome sequencing can discover selective sweeps in the genomic regions distinguishing adaptive from non-adaptive germplasm within target environments. When combined with genome-wide association studies (GWAS), this approach can reveal genes underpinning local adaptability and yield advantage. The objective of this study was to establish a genome-wide quantitative framework form identifying genomic regions under selection, with a particular focus on narrowing down regions associated with important yield and adaptive traits. Moreover, validation of elite haplotype distributions in an independent fully sequenced groundnut panel. A panel of 197 groundnut accessions was subjected to whole-genome sequencing and phenotypic evaluation to dissect the collection into adaptive and non-adaptive subsets to uncover the genomic regions under selection. This was then combined with GWAS to uncover genetic variants governing agronomic traits associated with yield and adaptability. The stringent single and multi-trait analysis identified 60 loci for 12 agronomic traits, of which seven loci controlled multiple yield related traits and were pleiotropic. Within our diversity panel, 48 genomic regions showed signs of selection. Among these selective sweeps, six positively selected loci were co-localized with trait associated loci. A large genomic region on chr2 spanning ∼78 Mb was under divergent selection and harbored genes underpinning yield and 20-pod length. A F-box transcription factor, Arahy.37HYKA, on chr9, and an alanine transferase protein gene, Arahy.E9MTVL, on chr12 carried peak SNPs associated with yield and related traits. We further cross-validated our results in another groundnut355 panel, where the corresponding genes within LD blocks showed significant effects on HKW, pod length and pod weight. The genomic resources developed here provide a high-resolution variation map to delineate the genes underpinning yield and developmental traits in groundnut, improved our understanding of the genetic basis of important agronomic traits, and provide a valuable resource for further functional genomics studies and groundnut improvement programs.
M. Jahanzaib, Kun-Hui He, S. Rehman et al.· bioRxiv· 0 citations
Drought threatens global crop yields, and common oat, a vital nutritional source for food and feed, is particularly constrained in the semi‑arid regions where it is widely cultivated. Here, we report two high-quality genome assemblies for drought-resilient (Borris37) and drought-sensitive (XymC06) oat accessions with distinct seedling survival rates and genome sizes of 10.92 Gb and 10.96 Gb, and construct comprehensive landscapes of insertion‑deletions (InDels) and structural variants (SVs). Integrating population-level genomic, transcriptomic and phenotypic (seedling survival rate), we demonstrate that InDels and SVs underpin divergent drought resilience and identify 52 candidate genes associated with drought resistance whose expression is significantly modulated by these variants. Borris37 accumulates 36 favorable alleles of these genes. An InDel in the AsNF-YB3 promoter enhances binding to AsARF1, upregulating AsNF‑YB3 under drought, and overexpression of AsNF‑YB3 reduces ROS accumulation. Our findings provide resources and targets for drought‑resistance breeding in oat, thereby supporting global food security. Drought severely constrains common oat production. By generating genome assemblies for oat accessions with contrasting drought tolerance, the authors identify genetic variants and candidate genes underlying drought resistance, including an NF-Y transcription factor and its putative upstream regulator.
Shu-Hui Wang, Dong-Qing Liu, Ying-Ying Li et al.· Nature Communications· 0 citations
Background Sesame (Sesamum indicum L.) is one of the oldest oilseed crops. Its seeds accumulate lignans and antioxidants that determine their nutritional value. Global demand for sesame is rising rapidly, but climate change increasingly threatens sesame yields and seed quality. Here, we analyzed a worldwide panel of 300 sesame accessions to explore the genetic basis of key adaptive and quality traits, specifically flowering time, seed lignan content, and seed antioxidant capacity. Results Whole-genome resequencing revealed previously unreported genetic diversity, expanding the resources available for sesame breeding. By integrating k-mer-based genome-wide association analysis with a graph-based sesame genome, we identified structural variants associated with differences in flowering time and lignan accumulation. A 9.4-kb deletion on chromosome 6 that disrupted SIN_1018434 (an ortholog of Arabidopsis PHOTOPERIOD-INDEPENDENT EARLY FLOWERING 1) and a 6.2-kb Copia-type retrotransposon insertion upstream of SIN_1004470 on chromosome 11 were associated with early flowering. The intact alleles at both loci were associated with delayed flowering and were predominant in low-latitude accessions. High seed lignan content was associated with non-synonymous mutations and copy number variants in glycosyl hydrolase genes on chromosome 6 and an 8.2-kb deletion spanning SIN_1019378 on chromosome 13. Association signals for seed antioxidant traits coincided with loci involved in abiotic stress responses and seed-coat pigmentation. Conclusions These findings highlight the importance of exploring diverse germplasm to uncover previously unrecognized adaptive and quality-associated genomic variation. The loci identified here provide molecular targets for developing climate-adapted cultivars with improved seed quality.
Sookyeong Lee, S. Lee, J. Ahn et al.· bioRxiv· 0 citations
The first draft genome assembly of the MHR genome is presented, providing a foundation to understand the genetic architecture underlying key phenotypic traits and identifying potential novel gene sources in MHR for rice improvement in the Caribbean region.
Uddesh M. Sahadeo, Omar Ali, A. Ramsubhag et al.· BioTech· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
