Search Results (585)

Pumpkin (Cucurbita spp.) is a globally significant vegetable crop known for its high nutritional value and remarkable phenotypic diversity. Yet, the surge in new cultivar releases has overwhelmed traditional morphological descriptors, creating critical gaps in variety purity control and breeders’ rights enforcement. Despite the established utility of SNP markers as the gold standard for genetic analysis, a dedicated high-resolution molecular database for modern pumpkin cultivars remains unavailable. To address this gap, we conducted whole-genome resequencing (WGS) on 94 representative pumpkin cultivars (spanning C. moschata, C. maxima, and C. pepo). Clean reads were mapped to the Cucurbita maxima reference genome. We employed a stringent pipeline to identify genomic variants and utilized STRUCTURE software, Principal Component Analysis (PCA), and Neighbor-Joining (NJ) trees to evaluate population stratification. Linkage disequilibrium (LD) decay and DNA fingerprinting barcodes were also developed. A total of 8,873,150 high-quality variants were identified, including 7,345,007 SNPs and 1,528,143 InDels, with an average SNP density of 21,281.50 SNPs/Mb. Population analysis consistently categorized the 94 cultivars into two primary subpopulations (G1 and G2). The first two PCs accounted for 74.06% of the total genetic variance. Further analysis revealed that G1 possessed a more complex genetic architecture and slower LD decay compared to G2, suggesting distinct selection histories. Finally, we screened for highly informative biallelic SNPs to construct a DNA fingerprinting database, enabling precise sample discrimination through unique chromatic barcodes. This study fills a critical gap in pumpkin genomics by establishing a high-density SNP database and a robust fingerprinting system. These resources provide a definitive tool for variety certification, seed purity testing, and the advancement of molecular-assisted breeding in pumpkin. Full article

To explore the genetic diversity and evolutionary differentiation of Mastacembelus armatus from the headwaters of the Dongjiang and Ganjiang Rivers, we performed whole-genome resequencing on three populations, including Xunyushui (XW) and Jiuqu River (DN) from the Dongjiang River source as well as Taojiang River (XF) from the Ganjiang River source. We analyzed population structure, genetic differentiation, nucleotide diversity (π), pairwise FST, linkage disequilibrium, kinship, and neutrality tests (Tajima’s D, Fu and Li’s D). A total of 209.05 Gbp of clean data were obtained, with high quality and reliable alignment. Average nucleotide diversity (π) was higher in XW (0.00490 ± 0.00248) and DN (0.00478 ± 0.00312) and lower in XF (0.00463 ± 0.00158). Pairwise FST values revealed moderate differentiation between XW and DN (FST = 0.12) and strong divergence between XF and the other two populations (FST = 0.19 and 0.17). Neutrality tests showed no significant deviation from neutrality. XW and DN exhibited positive values, indicating stable demography, while XF showed negative values, suggesting a tendency of population expansion. Phylogenetic, admixture, and PCA analyses supported that all three populations belonged to one evolutionary clade with two ancestral components. XF showed the slowest linkage disequilibrium decay and distant kinship, indicating a small effective population size. Significant genetic divergence was primarily driven by geographic isolation and limited gene flow. This study reveals the genetic diversity and differentiation pattern of M. armatus and provides a genomic basis for its conservation and management. Full article

Sweet cherry (Prunus avium L.) is an economically important fruit species with considerable genetic variability, particularly among autochthonous cultivars. This study aimed to evaluate the genetic diversity and population structure of six indigenous sweet cherry cultivars from the Ohrid region in North Macedonia using whole-genome resequencing. A total of approximately 2.27 million single-nucleotide polymorphisms (SNPs) and 263 thousand insertions and deletions (InDels) were identified, indicating high genomic variability. Population structure and phylogenetic analyses revealed two distinct genetic clusters among the studied cultivars. The Ohridska bela cultivar showed the highest level of genetic differentiation, highlighting its importance as a valuable genetic resource. Functional annotation of genetic variants demonstrated significant variability in genes associated with flowering time, dormancy, and stress response, suggesting adaptation to local environmental conditions, while genes related to fruit ripening were highly conserved. Additionally, the rapid linkage disequilibrium decay confirmed the high genetic diversity within the population. These findings emphasize the importance of Macedonian autochthonous sweet cherry germplasm for breeding programs, conservation efforts, and future adaptation to changing environmental conditions. Full article

Several gene-based tests, such as the sequence kernel association test, have been developed to assess associations between rare single nucleotide variants (SNVs) and disease traits. However, these aggregate methods do not distinguish potentially causal variants from null variants within associated regions. To address this limitation, we propose gvClust, a clustering approach that classifies rare variants into null and signal groups using a Gaussian mixture model applied to variant-level summary statistics from multiple-variant models. Signal variants are further partitioned into risk and protective subgroups according to their effect direction and magnitude. We evaluated gvClust in simulation studies using the adjusted Rand index (ARI), mean squared error (MSE), and accuracy of cluster number selection under different sample sizes, effect configurations, outcome types, and linkage disequilibrium (LD) structures. In simulations, gvClust showed improved performance with increasing sample size, achieved high accuracy in determining the number of clusters for continuous traits at large sample sizes, and outperformed both k-means clustering and initialization-only clustering. We then applied gvClust to rare variants in six genes associated with blood pressure traits from a large genome-wide association study and meta-analysis. In the real-data application, gvClust identified distinct null, risk, and protective clusters. These results suggest that gvClust provides a practical framework for classifying rare variants within associated regions and may help improve the biological interpretation of rare variant signals. Full article

Recurrent pregnancy loss (RPL) is a multifactorial reproductive disorder with important genetic, endocrine, immune, and metabolic determinants. Growing evidence links vitamin D deficiency and vitamin D receptor (VDR) gene polymorphisms to pregnancy complications, including RPL. A narrative review was conducted via a literature search in major databases: PubMed, EMBASE, Scopus, and Web of Science from January 2010 to January 2026, which synthesized observational studies on maternal 25-hydroxyvitamin D [25(OH)D] status and/or VDR polymorphisms in RPL, with predefined eligibility criteria and qualitative risk-of-bias assessment. Most studies focused on FokI (rs2228570) and the 3′ regulatory block BsmI/ApaI/TaqI. FokI is the most extensively studied VDR variant in RPL and showed the most consistent directional association compared with other variants, particularly in Asian and Middle Eastern populations, though the effect varied by study design, ancestry, and covariate adjustment. Contrary to that, investigations of BsmI/ApaI/TaqI loci were not consistent due to ancestry-specific linkage disequilibrium (LD). Genotype and vitamin D interaction effects were scarcely studied, with stratified analyses indicating a more significant genotype effect under vitamin D deficiency. From clinical practice perspectives, VDR polymorphisms may explain why some patients with RPL have a poor response to standard vitamin D supplementation. Women with the FokI f allele polymorphism associated with lower VDR activity require higher vitamin D dosing or earlier immunomodulatory support to achieve adequate endometrial receptivity. VDR variation, particularly FokI, may contribute to RPL susceptibility within an endocrine–immune–metabolic framework. Clinical translation will require standardized RPL definitions, concurrent 25(OH)D assessment, robust control for confounders, and analytical models that account for gene–environment interactions. Full article

Background: Maize shoot height is an important component of early vigor and plant architecture; however, its genetic basis during seedling development and its relationship with modern breeding remain insufficiently understood. This study aimed to investigate the genetic architecture of maize seedling shoot height across different breeding eras. Methods: Shoot height at 21 days after sowing was evaluated in 363 maize inbred lines representing three breeding eras in China. Genome-wide association analysis was performed to identify loci and candidate genes associated with shoot height variation, and selective sweep analysis was used to detect breeding-era differentiated genomic regions. Results: Modern breeding lines from the 2000–2010s exhibited significantly greater shoot height than lines from earlier breeding periods. Pearson’s correlation analysis revealed that 3-week shoot height showed highly significant positive correlations with plant height and ear height. Selective sweep analysis identified multiple differentiated genomic regions harboring previously reported height- and architecture-related genes, including ZmBR2, ZmLIL1, ZmNA1, ZmTE1, ZmSPL12, ZmBV1, ZmDIL1, ZmKN1 and ZmACS7. The GWAS identified 43 SNPs exceeding the GEC-derived suggestive threshold for shoot height, with the strongest and most continuous association signal located on chromosome 8. GWAS, together with LD analysis, haplotype analysis, and expression profiling, prioritized ZmGDCL (Zm00001d009163) as a promising candidate gene because of its strong association signal, local linkage disequilibrium support, broad expression profile, and significant haplotype effect on shoot height. Conclusions: Our results indicate that maize breeding has reshaped the genetic architecture of seedling shoot growth. ZmGDCL represents a promising candidate gene for future functional studies, while breeding-era differentiated regions provide useful genomic context for understanding maize architecture improvement. Full article

The descendants of the first cattle that arrived in America in 1493 are known as Creole cattle. The objective of this study was to characterize the diversity and genetic structure of two Creole cattle populations in Mexico. A total of 75 cattle were included: 36 Coreño (CC) and 39 Raramuri (CR). Genotyping was performed using an SNP microarray. Quality control, estimation of genetic diversity, and analysis of homozygosity by runs of homozygosity (ROH) and linkage disequilibrium (LD) analyses were performed in PLINK v1.9. The effective population size (N_e) was calculated using GONE2 and CurrentNe2. The structure was analyzed by principal component analysis (PCA), discriminant analysis of principal components (DAPC) and ADMIXTURE. The expected heterozygosity was high in both populations (CC 0.379 and CR 0.398). The short segments of classes > 0.5–4 represented 84.41% in the CC cattle and 92.83% in the CR cattle. The contemporary N_e was 32 in the CC cattle and 473 in the CR cattle. The Creole cattle were closely grouped with European cattle populations. The ADMIXTURE revealed 70.60% of a Creole ancestral component in the CR cattle, whereas the CC cattle shared 38.67% of this component with the CR cattle and exhibited 46.44% of a differentiated Creole component. In conclusion, the two Mexican Creole cattle populations had high genetic diversity and low homozygosity; a greater extension of LD was observed for the CC cattle, and consequently, the N_e was lower. The CC cattle exhibited shared ancestry with the CR cattle. Full article

A comprehensive evaluation of phenotypic diversity, genetic structure, and marker–trait associations was conducted in a pea (Pisum sativum L.) collection of 184 accessions, using multi-environment field trials and genome-wide SNP data. Agronomic traits were assessed using best linear unbiased estimates, and statistical analyses included correlation, analysis of variance, heritability estimation, population structure, linkage disequilibrium, and genome-wide association study of 10,289 SNP markers. Phenological traits showed low variability, with flowering and maturity averaging 36.08 and 79.19 days (coefficient of variation of 6.17% and 3.79%, respectively), whereas yield-related traits varied more widely, with the number of pods per plant showing a coefficient of variation of 26.14%. Strong correlations were observed between plant height and height of the lowest pod attachment (r = 0.89, p < 0.001), while moderate positive correlations were found between flowering and maturity time (r = 0.43, p < 0.001) and between number of pods per plant and plant height (r = 0.44, p < 0.001); meanwhile, thousand seed weight exhibited significant negative correlation number of pods per plant (r = −0.42, p < 0.001). Heritability was highest for plant height (H² = 0.925), height of the lowest pod attachment (H² = 0.889), and thousand seed weight (H² = 0.883), while yield showed lower heritability (H² = 0.672) and strong environmental influence. Linkage disequilibrium decay was 1.78 Mb at r² = 0.2. GWAS identified 163 quantitative trait loci, including 19 stable loci, with strong effects such as −19.27 cm for q.PH.5-1 and +24.62 g for q.TSW.4-2. Candidate genes associated with key biological processes were identified, thereby enhancing understanding of the genetic control of traits. Full article

Soybean rust caused by the fungus Phakopsora pachyrhizi threatens global soybean production, causing yield losses of up to 80%. Race-specific Rpp genes provide short-term resistance due to pathogen variability, whereas partial resistance (PR) offers durable, broad-spectrum protection, though its genetic basis remains unclear. This study aimed to identify genomic regions and candidate genes underlying PR using the Fixed and Random Model Circulating Probability Unification (FarmCPU) genome-wide association study (GWAS) and machine learning (ML) methods, Random Forest (RF) and Support Vector Regression (SVR). A panel of 312 soybean accessions was evaluated under natural infection across six Ugandan environments. Rust index (RI), derived from rust severity and sporulation level, was used to estimate heritability (H²) and rank genotypes through Best Linear Unbiased Predictions (BLUPs), while Best Linear Unbiased Estimators (BLUEs) supported GWAS input. After quality control, 8272 SNPs were analyzed within a ±60 kb linkage disequilibrium (LD) window. Multi-environmental Analysis (MEA) of RI showed significant genetic effects (p < 0.01); H² = 0.57–0.68. Sixty-one loci were detected: six by FarmCPU, 15 by RF, and 41 by SVR. Key genes included Glyma.01G128100 (a WRKY transcription factor) and Glyma. 13G228000, receptor-like kinase) and Glyma.20G173100 (WD40-domain regulator). Integrating ML with GWAS improved locus detection, confirming the polygenic nature of PR and supporting the use of genomic selection and locus pyramiding for durable rust resistance. Full article

Background: We investigated whether common variants in SCN1A are associated with antiepileptic drug (AED) non-response in Jordanian patients with epilepsy. Methods: We recruited 114 patients (105 successfully genotyped) and Sanger-sequenced five loci spanning rs6432860 and its flanking region of rs1531380, rs1531379, rs1531378, and rs10198801. Genotype–response associations were tested using contingency analyses and multivariable logistic regression adjusting for age at the time of the interview, number of AEDs, and carbamazepine use. Pre-specified secondary analyses included (i) stratification by AED class (voltage-gated sodium channel [VGSC]-acting vs. non-VGSC agents) and (ii) sensitivity analyses using alternative non-response thresholds (seizures > 0/year and ≥4/year). Linkage disequilibrium (LD) and exact Hardy–Weinberg equilibrium (HWE) tests were evaluated. Cohort minor allele frequencies (MAFs) were compared with global population estimates. Results: The four upstream previously cataloged intronic variant SNPs (rs1531380, rs1531379, rs1531378, and rs6432860) were in a complete pattern of LD association in this population (D′ = 1; r^{2 =} 1) whereas each upstream variant with rs10198801 showed D′ = 1 with inverse correlation (r ≈ −0.53). All loci conformed to the exact HWE tests. Upstream variants had novel associations with a non-response in unadjusted analyses and remained significant after adjustment (genotype aOR = 2.8; 95% CI = 1.1–7.2; p value = 0.03), alongside independent effects of carbamazepine use (aOR = 3.3; 95% CI = 1.3–8.0; p value = 0.009) and a number of AEDs (aOR = 0.17; 95% CI = 0.06–0.50; p value = 0.002). In AED-class stratification, upstream additional intronic variants had novel associations with a non-response among VGSC-treated patients (OR = 3.8; 95% CI = 1.1–13.6; p value = 0.03) whereas rs10198801 was associated among non-VGSC patients (OR = 7.9; 95% CI = 0.9–70; p value = 0.04). Findings were robust using a ≥4 seizures/year threshold (recessive model significant) but not using any seizures > 0/year. Cohort MAFs for upstream variants (~48.6%) exceeded European, African, and Asian estimates. Significance: SCN1A upstream intronic variation has a novel association with AED non-response in the Jordanian cohort, shows mechanism-aligned patterns by AED class, persists after covariate adjustment and under a clinically used seizure-frequency threshold, and warrants ancestry-informed replication and functional validation. Full article

Background: Early-onset Alzheimer’s disease (EOAD) caused by autosomal dominant mutations provides a deterministic framework for investigating genetic modifiers of neurodegeneration. Telomere biology has emerged as a central regulator of genomic stability, cellular ageing, and stress response integration, yet its role in EOAD, particularly in under-represented populations, remains poorly defined. Methods: We conducted a cross-sectional case–control study to evaluate the genetic distribution, disease association, and predicted regulatory consequences of common variants in the telomere maintenance genes TERT and TERC in individuals from Western Mexico. The EOAD group comprised genetically confirmed carriers of the PSEN1 p.Ala431Glu (A431E) founder mutation with clinical EOAD (n = 69), and controls were unrelated individuals without dementia (n = 179). Five common variants were analyzed: rs2242652, rs2853677, rs2736100, and rs10069690 (TERT), and rs12696304 (TERC). Results: Genotype distributions in controls conformed to the Hardy–Weinberg equilibrium. Single-variant analyses showed no significant allele-level associations. Most TERT variants did not show significant allele-level associations with EOAD. However, a preliminary genotype-level enrichment for the GC allele at rs12696304 (TERC) was observed among EOAD cases compared with controls; allele-level associations were not significant. Linkage disequilibrium analysis revealed low r² values (<0.20), supporting variant independence. Population-level allele frequency comparisons revealed ancestry-dependent divergence across loci; in silico functional annotation localised all variants to non-coding regulatory regions. GTEx-based analyses indicated that rs12696304 acts as an eQTL for ACTRT3 in whole blood and pituitary, as well as for LRRC34 in the cerebellar hemisphere, suggesting a potential regulatory network within the TERC locus (3q26.2). Conclusions: Overall, common regulatory variants in TERT did not show strong independent effects on EOAD susceptibility in PSEN1 A431E carriers. However, the convergence of association patterns, functional annotation, and regulatory evidence provides hypothesis-generating support for the TERC locus (3q26.2), particularly rs12696304, as a candidate region for further investigation. Additional studies integrating telomere dynamics, functional validation, and multi-omics analyses are needed to clarify the role of telomere biology in the pathogenesis of autosomal dominant EOAD. Full article

Indigenous pig breeds represent valuable reservoirs of genetic diversity but face increasing risks of genetic erosion due to uncontrolled crossbreeding with commercial lines. The Ashanti Dwarf Pig (ADP) of Ghana is an important local genetic resource well-adapted to tropical environments but poorly characterised at the genomic level. Using high-density SNP data from the ADPs and publicly available datasets from other African, European, and Asian pig populations, we examined genetic diversity, population structure, inbreeding, and selection signatures. After quality control, 59,124 SNPs across 875 individuals were retained. ADPs exhibited high polymorphism (~99%) and moderate heterozygosity but also elevated inbreeding (FIS = 0.15; FROH = 0.40), indicating recent inbreeding under free-range management. Population structure revealed that ADPs cluster closely with other African pigs and European breeds more than Chinese breeds. ADMIXTURE analysis, however, indicated recent introgression from both European and Chinese lines. Selection scans revealed candidate genes linked to metabolism-Zinc Finger Ran-Binding Protein 3 (ZRANB3), growth-Sortilin Related VPS10 Domain Containing Receptor 1 (SORCS1), reproduction–Sus Scrofa Chromosome 9 quantitative trait loci (SSC9 QTLs), and immunity-Tudor Domain-Containing Protein 3 and CKLF-like MARVEL transmembrane Domain Containing 7 (TDRD3, CMTM7), reflecting adaptation to tropical production systems. Our results provide a comprehensive genomic characterisation of the ADP within a global context, revealing both genetic richness and vulnerability to genetic erosion. These findings underscore the importance of structured breeding and conservation strategies in preserving this unique African genetic resource and supporting sustainable pig production under changing climatic conditions. Full article

Background: Breast cancer (BC) is a leading cause of cancer-related mortality worldwide and a major public health concern in Mexico. Regulatory variants in KRAS, particularly within the 3′UTR and intronic regions, may influence gene expression through microRNA binding and transcriptional regulation. Methods: Five regulatory single-nucleotide variants (SNVs) in KRAS (rs12228277, rs1137196, rs8720, rs12587, and rs12245) were genotyped in BC patients and cancer-free controls. Associations were evaluated using odds ratios (ORs) with 95% confidence intervals (CIs), adjusting for age, alcohol, and tobacco use. Multiple testing was corrected using the Benjamini–Hochberg false discovery rate (FDR). Linkage disequilibrium (LD), multilocus combinations, and in silico functional analyses were also performed. Results: Variants rs12228277, rs1137196, rs8720, and rs12245 showed significant genotype-level associations with BC susceptibility, all remaining significant after FDR correction (pFDR < 0.05). No clinicopathological associations remained significant after correction in single-variant analyses. Multilocus analysis identified specific high-risk combinations (e.g., involving rs12228277, rs1137196, and rs8720) associated with increased BC susceptibility. At the nominal level, these combinations showed associations with clinicopathological features, including hormone receptor–positive status (PR and ER), proliferation markers, and Luminal B subtype; however, none remained significant after FDR correction. LD analysis indicated weak linkage among variants. In silico analyses suggested potential regulatory effects on microRNA binding and KRAS expression. Conclusions: Regulatory variants in KRAS are associated with BC susceptibility through independent effects and potential combinatorial patterns. These findings support the relevance of non-coding variation in cancer risk and warrant further functional and replication studies. Full article

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by heterogeneous behavioral symptoms and systemic comorbidities, including immune and gastrointestinal dysfunctions. Emerging studies suggest that glycosylation—a fundamental post-translational modification regulating cellular communication and immune responses—may play a role in ASD pathophysiology, yet its contribution remains underexplored. Methods: In this study, we developed an integrative transcriptomic and network analysis framework to investigate glycosylation-related gene expression changes and their functional associations in ASD. Using publicly available datasets from bulk and single-cell RNA sequencing of brain and blood tissues, we focused on four prior-knowledge gene subsets: glycogenes, extracellular matrix glycoproteins, immune response genes, and autism risk genes. Results: Differential expression and pathway enrichment analyses revealed consistent dysregulation of glycosylation pathways, including mucin-type O-glycan biosynthesis, glycosaminoglycan metabolism, GPI-anchor formation, and sialylation, across ASD tissues. These transcriptional changes were functionally linked to altered immune signaling (e.g., IL-17, Toll-like receptor, and complement pathways) and synaptic development pathways, forming a distinct glyco-immune axis. Network analysis identified key glycogenes such as GALNT10, NEU1, LMAN2L, and CHST1 as central molecular nodes, interacting with immune and neuronal regulators. Linkage disequilibrium analysis further revealed ASD-associated SNPs influencing the expression of these glycogenes in both blood and brain tissues. Conclusions: Together, these findings support a model in which disrupted glycosylation contributes to ASD pathophysiology by mediating immune dysregulation and altered neuronal connectivity. This study offers a systems-level framework to understand the molecular complexity of ASD and highlights glycogenes as potential biomarkers and targets for future therapeutic exploration. Full article

This study assesses regional science and technology (S&T) innovation efficiency across 30 Chinese provinces from 2011 to 2022, incorporating a sustainable development perspective. Employing a non-oriented global frontier super-slack-based measure (SBM) model that accounts for undesirable outputs, along with kernel density estimation, cluster analysis, and Moran’s I, the research investigates the spatiotemporal evolution of innovation dynamics. The findings demonstrate a marked upward trend, with the national average efficiency score rising from 0.260 to 0.703. Temporally, efficiency advanced through three stages: an initial period of universally low efficiency, a phase of widening disparities, and a final stage of overall improvement and stabilization. Spatial analysis reveals a persistent “strong in the east, weak in the west” disequilibrium; however, absolute β-convergence tests indicate a significant reduction in regional disparities (p < 0.05). Kernel density estimation reveals a shift from a polarized “pyramid” shape to a more balanced “spindle-shaped” distribution. This is evidenced by a decrease in kurtosis and a rightward shift in the median. Spatial autocorrelation, as measured by the Global Moran’s I, evolved from a statistically insignificant distribution in 2011 to a strong positive correlation (0.223, p < 0.05) by 2022. This progression reflects a transition from isolated “unipolar” hubs to integrated “multi-center block linkages.” The results suggest that, although polarization is diminishing and the national innovation baseline is improving, policy efforts should prioritize the development of emerging innovation corridors to address the remaining east–west divide. Full article