The blunt snout bream (Megalobrama amblycephala, BSB) is a freshwater economic fish with Chinese characteristics, and its genetic characteristics have unique value for studying fish evolution. The gynogenetic blunt snout bream (GBSB) obtained through distant hybridization between cross-order species, which showed a faster growth rate than the female parent, but its appearance is similar to that of BSB and is difficult to distinguish. Therefore, by comparing the transcriptome sequencing data of BSB and GBSB (SRA number: PRJNA893089, not released yet), we identified 30 SNPs associated with genes related to muscle growth, protein synthesis, and glycolysis that are unique to GBSB. Through multi-sample PCR detection and sequencing analysis, 16 SNPs with stable differences in GBSB and BSB were obtained. The polymorphism analysis of 16 SNP sites showed that 9 SNP sites were polymorphic in GBSB, which could be used to identify GBSB and its female parent, BSB. In addition, the 9 SNP sites are located in the myoz1a (myozenin 1a) gene, which is related to muscle development, and may provide insights for further study of muscle growth regulation. Therefore, this study provides candidate marker resources for GBSB germplasm resource identification and molecular marker-assisted breeding, which is beneficial for improving the efficiency and reliability of selection and breeding work.

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F2 population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation.

Reliable classification of rose cultivars is complicated by their long and complex breeding history, frequent hybridization, and the coexistence of traditional horticultural categories with genetically heterogeneous groups. While molecular marker sets such as SSRs have been applied to assess genetic relationships, studies across cultivated roses and species are rare. Using 18 SNP markers on 1,345 accessions in combination with machine learning now offers an opportunity to systematically evaluate how well horticultural classes align with underlying genomic structure and to provide robust tools for the management of large germplasm collections. Using a panel of SNP markers across 1,345 rose accessions from the Europa Rosarium Sangerhausen, multiple unsupervised (hierarchical, spectral, k-means, DBSCAN, HDBSCAN) and supervised (SVM, decision tree, naive Bayes, XGBoost) machine learning approaches were applied to identify genetic clusters and predict horticultural classifications. Across clustering methods, certain groups consistently emerged as genetically distinct, such as the alba and damask roses, which clustered together with low internal diversity, reflecting their shared historic origin. In contrast, tea, bengal, lutea, and remontant hybrids were repeatedly grouped together and predicted with high classification accuracies (up to 100%) but displayed high within-group diversity, which is consistent with complex breeding backgrounds. Miniature, kordesii, and rubiginosa hybrids also tended to cluster together, despite their differing horticultural labels. Overall, the labels obtained from unsupervised clustering were consistently confirmed by supervised models, which achieved balanced accuracies of up to 100%, highlighting the robustness of the observed groupings. Our results demonstrate that machine learning applied to SNP marker data can robustly resolve genetic relationships among rose cultivars and provide novel insights into the alignment of horticultural classifications with genomic structure. The high predictive accuracies obtained suggest that marker-based classification can serve as a reliable complementary tool for genebank management, cultivar identification, and reassessment of traditional rose categories.

Pelodiscus sinensis (Chinese soft-shelled turtle) is a freshwater species with economic and biomedical value. Here, we report a near-telomere-to-telomere (near-T2T) chromosome-level genome assembly. Using a combination of PacBio HiFi, Oxford Nanopore ultra-long reads, and Hi-C data, we assembled a 2.27 Gb genome (contig N50 = 132.52 Mb). Hi-C contact maps guided scaffolding and curation, anchoring 40 contigs to 34 chromosomes and resolving telomeres for 61 out of the 68 chromosomal ends. Gap filling and polishing yielded a chromosome-level assembly with no ambiguous bases (QV = 41.19; GC = 45.8%). BUSCO analysis indicated 97.9% completeness. Mapping rates for BGI short reads (99.4%), ONT long reads (100%), and HiFi reads (99.98%) confirm high accuracy across platforms. We annotated 21,124 protein-coding genes (97.77% functionally annotated) and 12,868 non-coding RNAs, with support from RNA-seq and full-length transcript data. Sex chromosomes (chrZ and chrW) were inferred from male-to-female depth ratios and validated using two known sex-linked SNP markers. This high-quality genome provides an essential resource for studying reptilian chromosome evolution, sex determination, and molecular breeding.

Navigating rice culm resilience: High-throughput quantitative trait locus mapping in indica and tropical japonica derived population.

The aim of this study was to apply DArTseq technology to analyze T. spelta L. (spelt wheat) genotypes in order to eliminate duplicates in the gene bank and ensure the high quality and purity of the stored material. The research included the analysis of genetic similarity, the construction of dendrograms, and association mapping, which enabled the identification of specific molecular diagnostic markers for spelt wheat. Spelt is an ancient cereal species gaining popularity, especially in organic farming. It is characterized by natural resistance to biotic factors and tolerance to environmental stress. Spelt is a valuable material in plant resistance breeding aimed at developing varieties resistant to diseases and well adapted to unfavourable environmental conditions. In this study, molecular characterization of 27 spelt genotypes was carried out using high-throughput DArTseq technology, enabling simultaneous analysis of SilicoDArT and SNP markers. A total of 96,136 markers were identified, of which 16,712 met the quality criteria and were used for genetic similarity and association mapping. Based on similarity coefficients, a dendrogram was created, distinguishing four main genotype groups. Association mapping revealed over 2,600 markers significantly associated with the virulence level of the B. graminis f. sp. tritici pathogen. Particular attention was paid to SilicoDArT 7,492,586 and SNP 1,126,088 markers, showing significant associations with plant response to three of the five analyzed isolates. Chromosomal regions (1D, 3D, 5B, 6A) associated with resistance were also identified, confirming the polygenic nature of this trait. Results indicate high genetic variability of the analyzed material and the usefulness of DArTseq technology in identifying markers for resistance breeding. The presented markers can be used in marker-assisted breeding programs, especially considering the growing interest in spelt as a cereal for organic farming. These findings provide a valuable basis for further improvement of spelt resistance and sustainable cereal breeding.

This study investigates the integration of Bayesian networks (BN) and structural equation models (SEM) to explore genomic relationships among nine traits related to productivity, defense, and climate-adaptability in an interior lodgepole pine breeding program. Data from 392 open-pollinated trees, genotyped with 25,099 SNP markers, were analyzed. The traditional multi-trait model (MTM) served as a benchmark for comparing SEM in estimating genetic (co)variance components, genetic correlations, breeding value (BV) predictions, and predictive ability, using both pedigree- (ABLUP) and genomic-based (GBLUP) individual-tree mixed models. The Hill-Climbing algorithm identified 12 significant causal structures (λ) among traits. Strong positive causal effects included tree height (HT) on wood density (WD) (λHT→WD = 0.413) and on stable carbon isotope ratio (C13) (λHT→C13 = 0.565), and limonene (LIMO) on carbon assimilation rate (CAR) (λLIMO→CAR = 0.368). The most influential causal relationship was HT → C13, followed by resistance to western gall rust (WGR) → CAR, CAR → LIMO, and WGR → C13. SEM incorporated these relationships, capturing both direct and indirect effects. Compared with MTM, SEM yielded lower residual variances, higher additive variances, and higher heritability estimates for all traits. The λ values from SEM correlated strongly with genetic correlations (0.932), with similarly high correlations between models (0.929), though SEM produced lower posterior mean correlations. BV correlations between models were high (ABLUP > 0.82, GBLUP > 0.84), but some reranking occurred among the top 39-trees (ABLUP > 0.71, GBLUP > 0.42). ABLUP and GBLUP-SEM models outperformed MTM in predictive ability, with mean gains of 6.62% and 6.03%, mainly for conditioned traits. BN-SEM enhances understanding of trait networks, improving genomic evaluations and breeding strategies in forest trees.

Salinity is one of the most detrimental abiotic stressors, harming agricultural plants and reducing production. Barley (Hordeum vulgare L.) is the fourth most produced cereal crop in the world, which tolerates salt stress conditions. However, the genetic basis of salinity tolerance under hydro and nano priming in barley remains poorly understood. Therefore, understanding the genetics of seed germination under salt stress can aid in improving the development and production of barley. This study aimed to detect the genetic associations underpinning the impact of hydro and zinc oxide nanoparticles (100 ppm) priming compared to unprimed conditions on 170 spring barley cultivars that were exposed to 200 mM sodium chloride during seed germination and seedling growth using GWAS (MLM + K, p-value ≤ 0.0001 and FDR α = 0.01). High phenotypic variations were detected among all genotypes under control and salinity for all studied traits under all treatments. The reduction rate in root length and shoot length was lower in nano and hydro priming compared to unprimed conditions. The reduction rates were higher in the nano priming compared to hydropriming and unprimed conditions for fresh weight. GWAS analysis reveals 137 and 79 significant SNPs at p ≤ 0.0001 and FDR ≤ 0.01, respectively. Thirty pleiotropic markers linked with salt tolerance at p ≤ 0.0001 for unprimed and priming conditions. For instance, on chr 7H at position 111,292,841bp, the SNP marker BOPA1_1518 - 624 was significantly associated with twelve traits related to germination under hydro and nano priming was found near the gene HORVU.MOREX.r3.7HG0670060 coding for F-box family protein (protein turnover). Also, five SNPs were detected that control seven germination-related traits under all treatments. For seedling-related traits, the SNP marker SCRI_RS_168580 on 4H was found to be associated with the salt tolerance index of both root and shoot length under unprimed conditions, which lies near the gene model HORVU.MOREX.r3.4HG0345880 coding for peroxidase (ROS detoxification). Hydro and nano priming significantly enhanced seed germination and the seedling establishment traits under salinity. This study highlights the potential of employing pleiotropic markers linked to multiple traits to advance our understanding and improvement of salinity tolerance in barley.

Soybean (Glycine max (L.) Merr.), as a crucial source of oil and protein globally, is widely cultivated in many countries. Low-temperature stress has become one of the major environmental factors affecting soybean production, especially in colder regions, making the improvement of cold tolerance traits in soybean a key breeding objective. This study integrates Genome-Wide Association Studies (GWAS) and Marker-Assisted Selection (MAS) to enhance the predictive performance of soybean cold tolerance traits. First, three GWAS methods—Fast3VmrMLM, fastGWA, and FarmCPU—were used to analyze soybean cold tolerance traits, and significant SNP markers were identified. Principal Component Analysis (PCA) was employed to reveal genetic differences among various soybean germplasm. Then, based on the identified SNP markers, multiple Genomic Selection (GS) models, such as GBLUP, BayesA, BayesB, BayesC, BL, and BRR, were used for prediction to evaluate the contribution of genetic effects to phenotypic variation. The results showed that the markers selected through GWAS significantly improved the prediction accuracy of genomic selection, especially with the Fast3VmrMLM and FarmCPU methods in larger datasets. Finally, Gene Ontology (GO) analysis was performed to further identify candidate genes associated with cold tolerance traits and their biological functions, providing theoretical support for molecular breeding of cold-tolerant soybean varieties.

Perilla [Perilla frutescens (L.) Britton] is an annual herbaceous species of the Lamiaceae family native to Northeast Asia, cultivated for both seed oil production and as a leafy vegetable. We developed and validated Kompetitive Allele-Specific PCR (KASP) markers for accurate identification of Korean Perilla cultivars. Whole-genome resequencing of 16 representative cultivars yielded 9686,199 SNPs, with 6183 high-confidence SNPs identified after stringent filtering. From these, 237 KASP markers were designed, and 150 polymorphic markers were validated across 48 cultivars. Principal coordinate analysis (PCoA) and phylogenetic analyses mostly distinguished seed-type from leaf-type Perilla. Minimal KASP marker sets (five for seed Perilla, six for leaf Perilla) were established, sufficient to distinguish widely cultivated Korean cultivars. These markers, encoded by a binary barcode system, enabled rapid and precise cultivar identification. Application tests demonstrated their utility for evaluating seed purity by quantifying contamination. This work provides substantial genomic resources for cultivar authentication, genetic purity assessment, and molecular breeding. The new KASP system offers a cost-effective, high-throughput, and reliable approach for managing and enhancing Perilla genetic resources, ultimately advancing breeding programs and improving seed industry processes.

Net-form net blotch (NFNB) is a devastating fungal disease for barley. Three potentially novel QTL for resistance and identified SNP markers can contribute to the global control efforts of NFNB. Pyrenophora teres f. teres, the fungus responsible for the barley disease, net-form net blotch (NFNB), leads to considerable yield and quality reductions. This research involved collecting phenotypic and genotypic data from a barley doubled haploid (DH) mapping population consisting of 277 lines, which were exposed to the highly virulent Ptt isolate GPS18. The DH lines were derived via anther culture from second-generation hybrids of a cross between the disease-resistant barley cultivar Avcı 2002 ("A") and the susceptible cultivar Bülbül 89 ("B"). Anther pretreatment with 1.0M mannitol resulted in a statistically superior response compared to 0.7M mannitol in the F2 progeny of the A × B cross. The highest callus induction rate was 37.6% in the "Br_Ind" medium, and the highest green plant formation rate was 24.7% in the "PD_Reg" medium. The use of sequencing-based diversity array technology (DArT-seq) identified 9170 SNP markers, which facilitated the creation of a linkage map spanning 1682.97cM, with an average density of 1.49 markers/cM. Quantitative trait loci (QTL) analysis identified three QTL associated with Ptt resistance located on chromosomes 3H, 4H, and 6H. All three can be considered novel with the 3H QTL mapping in between Rpt1 and QRptta3, the 4H QTL maps to a distinct region of Rpt7, and the 6H QTL maps in between the Qns-6H.3 and SFNB-6H-33.74 loci. The SNPs associated with disease resistance identified within these QTL offer a foundation for developing DNA-based tests for resistance.

SNP markers represent the most extensively distributed and abundant type of polymorphic markers. Although researchers have developed high-throughput detection methods for SNP markers, there remains a lack of simple, flexible, and cost-effective approaches for detecting small numbers of SNPs. To address this need, this study proposes a method for detecting SNPs based on T-C mismatch. Specifically, two upstream primers were designed with a single base difference at the 3’ end. Additionally, a T-C mismatch was introduced within 5 bp upstream of the 3’ end to distinguish SNPs. Detection was achieved through PCR amplification and subsequent gel electrophoresis analysis. In this study, twelve random SNPs from bitter gourd were selected, and the detection efficiency was found to be 83.3%. The proposed SNP detection method is characterized by its simplicity and flexibility, offering an effective tool for molecular marker-assisted selection breeding in applications such as stress resistance improvement and agricultural productivity enhancement.

The mithun (Bos frontalis), a unique bovine species of the Eastern Himalayas, remains underutilized and poorly characterized in terms of genetic and phenotypic attributes. This review consolidates current knowledge on the phenotypic diversity of mithun populations both within India across Arunachal Pradesh, Nagaland, Manipur, Mizoram, and Assam and internationally. Distinct morphological traits, management systems, and cultural practices were documented across regional populations, underscoring their evolutionary adaptations and the need for population-specific conservation strategies. Genomic and cytogenetic studies revealed mithun’s close phylogenetic relationship with wild gaur (Bos gaurus), confirmed through mitochondrial DNA analyses and chromosomal configurations, including species-specific Robertsonian translocations. Molecular diversity assessments using microsatellite and highdensity SNP markers highlight moderate genetic variability with minimal substructure but also indicated signs of inbreeding and genetic erosion in isolated populations. The availability of a high-quality de novo genome assembly offered valuable insights into gene content, repetitive elements, and evolutionary divergence from other bovine species. This review emphasized the urgent need for targeted genetic improvement, sustainable breeding practices, and conservation programs. Strategic recommendations include phenotypic and genotypic characterization of distinct populations, formation of mithun federations, development of semi-intensive rearing systems, and international collaboration through a proposed mithun genome consortium. Investing in mithun conservation and improvement offers a viable pathway for enhancing tribal livelihoods, ensuring food and nutritional security, and preserving agro biodiversity in fragile highland ecosystems. As a neglected but invaluable genetic resource, the mithun holds substantial promise for climate-resilient livestock production and sustainable rural development in the Eastern Himalayan region.

Grape (Vitis vinifera spp.) accessions exhibit rich diversity, and understanding their genetic variation and evolutionary relationships is crucial for cultivar selection and utilization. A highly representative SNP marker set was developed in this study based on re-sequencing data analysis, to clarify the phylogenetic relationships among 96 grape accessions and to evaluate the genetic resolution of core markers. Using PN40024 as the reference genome, high-quality SNP loci were screened from resequencing data of the 96 accessions. A phylogenetic tree was constructed, and genetic diversity was analyzed using PCA and population structure analysis. The results showed that the 96 accessions were mainly divided into four groups: European (‘Merlot’, ‘Chardonnay’), American (‘Beta’, ‘Concord’), Euro-American hybrids (‘Vidal’, ‘Miguang’), and wild populations along with their hybrid progeny (‘Zuoyouhong’, ‘Huajia 8’). PCA and ADMIXTURE validated population differentiation, revealing clear separation between wild and cultivated accessions. Through screening of core SNP markers, 384,304 candidate SNPs suitable for probe design were identified. Further refinement yielded 2000 and 10,000 SNP markers. Detailed analysis of core marker characteristics showed that their minor allele frequency (MAF) was predominantly between 0.1 and 0.3, with the majority distributed in CDS (38.65%), intronic (30.2%), and intergenic regions. The most common mutation types were [A/G] (35%) and [C/T] (34%) transitions. The 2000 core SNPs were associated with 1220 functional genes and were significantly enriched in pathways such as protein binding, RNA transport, and plant–pathogen interaction. These findings provide an efficient tool for grape genetic diversity analysis, cultivar identification, and molecular breeding, laying the groundwork for the precise utilization of grape germplasm resources.

Understanding the genetic structure and evolutionary history of endangered species is crucial for effective conservation planning. The Spanish toothcarp, Aphanius iberus (Valenciennes, 1846), an endemic and euryhaline fish of the Mediterranean coast of the Iberian Peninsula, is currently threatened by habitat destruction, climate change, and anthropogenic translocations. Here, we employed both a single genetic marker (cytochrome b) and genome-wide SNP data from medium-coverage whole genomes to investigate the population structure, genetic diversity, and demographic history of A. iberus, especially focussing on its northern distribution, which has remained poorly studied. Our analyses revealed a well-structured genetic pattern across the species’ range, with four main genetic lineages: Northern Catalonia, Southern Catalonia, Levantine, and Murcian. Genomic indicators, including heterozygosity, ROHs, and migration analyses, suggest higher inbreeding and genetic erosion in the northernmost populations, likely due to long-term isolation, whereas southern populations maintain higher genetic diversity. We also identified several admixed and potentially translocated populations. These findings underscore the importance of accurately determining the origin of populations before any translocation or reintroduction, as misguided management may compromise the genetic integrity of native lineages. This work provides essential genomic insights to guide conservation strategies and emphasizes the need for lineage-aware management of endemic species like A. iberus.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-31909-y.

Seed biochemical composition critically influences the quality and industrial value of oilseed rape (Brassica napus L.). Understanding the genetic basis of seed oil, protein, and fiber content is essential for breeding improved cultivars. Here we conducted a genome-wide association study (GWAS) on 350 diverse winter oilseed rape lines over three years, using near-infrared reflectance spectroscopy (NIRS) to measure seed traits and SNP genotyping for association mapping. We identified numerous SNP markers significantly associated with seed oil, protein, acid detergent fiber (ADF), and neutral detergent fiber (NDF) content. From 18,566 detected SNPs, 3782 met stringent criteria and were used for association mapping, resulting in 3189 significant associations across three years. The highest number of associations was observed for protein (3480), followed by NDF (3662), ADF (3422), and oil (2046). Individual markers explained up to 35% of phenotypic variation, indicating strong genetic control of these traits. Gene ontology enrichment analyses linked candidate genes to key metabolic and regulatory pathways influencing these traits: protein biosynthesis and post-translational modification, lipid metabolism regulated by phosphorylation, and transcriptional control of cell wall polysaccharide synthesis. These findings provide valuable molecular markers that can be validated for further use in marker-assisted selection, supporting the development of rapeseed cultivars with optimized seed quality for food, feed, and industrial applications.

IntroductionIn North India, Puccinia striiformis f. sp. triticii (Pst), the causal agent of stripe rust, poses a significant challenge to wheat productivity. The frequent emergence of new virulent Pst strains has rendered many resistance genes ineffective. Hence, continuous identification and deployment of novel resistance genes are crucial for maintaining durable resistance and ensuring sustainable wheat cultivation.Materials and MethodsA genome-wide association study (GWAS) was conducted on 652 elite, diverse wheat genotypes using 1,938 DArTseq SNP markers. Field evaluations were performed at the adult plant stage across four locations—Hisar, Karnal, Gurdaspur, and Khudwani—under natural disease conditions. Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant.ResultsAnalysis revealed 27 genomic regions significantly associated with stripe rust resistance across environments. Among these, four loci were located on chromosomes 2B and 6B, and three on 6A. Several loci corresponded to resistance-related genes, including NBS-LRR, F-box, LRR, protein kinase, Ser/Thr_kinase, Znf_RING-CH, E3-ubiquitin ligase, and ABC transporter genes, suggesting their potential role in rust resistance mechanisms.DiscussionThe study identified novel genomic regions associated with Pst resistance, providing valuable resources for wheat improvement. The functional annotation of these loci highlights their involvement in plant defense pathways. Conversion of these loci into breeder-friendly molecular markers will facilitate marker-assisted selection (MAS) and accelerate the development of durable stripe rust-resistant wheat cultivars suited to North Indian agro-ecological conditions.

ABSTRACTA better understanding of the possible adaptive response and genomic vulnerability of forest trees is needed to properly assist future forest management and develop adequate resilience strategies to changing environments. Scots pine (Pinus sylvestris L.), a keystone species with extensive distribution and a broad ecological niche, is expected to be directly impacted by climate change due to maladaptation and associated fitness declines. Despite extensive studies that have clarified the broad‐scale history and genetic structure of the species, understanding the genetic basis for local adaptation and the extent of genomic offset in Scots pine remains incomplete. Here, we used thousands of genotyped SNP markers in 39 natural populations (440 trees) along a broad latitudinal gradient of species distribution to examine molecular signatures of local adaptation. Specifically, this landscape genomics approach aimed to assess fine‐scale patterns of SNPs associated with environmental gradients, estimate genomic offset as a proxy for exposure and sensitivity components of vulnerability, and evaluate the adaptive response of populations to projected climate shifts. The variation of outlier SNPs, which exhibit selection signatures between genetically very similar populations in the analysed distribution range, was highly correlated with mean annual temperature, a key limiting factor for the growth and survival of tree species. Furthermore, our simulation results indicated a high genomic offset on a large spatial scale in P. sylvestris, with the time frame required to close the offset gap by natural selection estimated to be in the range of hundreds of years. We evaluate the genomic offset in the coming decades and indicate the optimal allelic frequency spectra required in the future to ensure resilience of Scots pine populations. We discuss forest assisted migration (FAM) as a management strategy, involving the relocation of genotypes to areas with matching environmental conditions. By evaluating adaptive responses, the study adds to the discussion on the long‐term sustainability of forest ecosystems.

ABSTRACTGenetic markers can assist in the identification of the stock origin in different organisms. Comparative studies of forest tree provenances have demonstrated that forest tree populations differ in performance across environments and at multiple geographic levels: populations nested within regions nested within gene pools. These levels are critical for conservation and sustainable use of genetic resources: regions of provenance are key units for seed marketing, while populations guide reproductive material collection under most seed regulations. Despite their potential, genetic methods have rarely been applied to identify forest tree origins due to methodological (sufficient number of highly discriminatory markers) and practical (construction of a baseline composed of a representative selection of samples) challenges. In our study, we analyzed a genomic dataset comprising 10,185 SNPs from 1579 samples of Pinus pinaster, a species with strong population structure, across 86 populations, 45 regions of provenance, and 10 gene pools, to discriminate among these hierarchical levels and assign individuals to them. We used two software packages to evaluate the reliability of our baseline dataset (i.e., reference data) for genetic discrimination and assignment: RUBIAS, which performs genetic stock identification and associated tasks, and assignPOP, implementing a supervised machine‐learning genetic‐assignment framework. Using numerical validation analyses, we assessed their suitability and limitations for origin inference at each geographical level. Our results indicate that origin assignment is reliable in P. pinaster at the gene pool and region of provenance levels, but less so at the population level, provided that the 10 K SNP markers and a comprehensive genetic baseline are used. Incomplete baselines may result in wrong assignments at any hierarchical level, irrespective of sampling intensity for sampled candidate origins. We provide an extensive and publicly available baseline for P. pinaster, offering a useful tool for the management of forest genetic resources of this economically and ecologically important tree species.

Localization of heterosis loci for quality traits and identification of candidate genes in Brassica napus.