Search Results (1,045)

The rice field eel (Monopterus albus) is an economically important aquaculture species widely distributed in Southeast Asia. However, the lack of nationally approved varieties and the increasing demand for high-quality eel products have highlighted the necessity of developing improved germplasm resources for aquaculture production. In this study, morphometric morphology and muscle texture traits were evaluated in 367 wild eel individuals, and a genome-wide association study (GWAS) based on whole-genome resequencing was conducted to identify genetic variants and candidate genes associated with economically important traits, including body length (BL), tail length (TL), tail height (TH), head length (HL), head width (HW), head height (HH), hardness, chewiness, springiness, and resilience. The GWAS identified three significant and seventeen suggestive SNPs associated with body shape traits, with most loci located on chromosome 4. Candidate gene analysis of the associated genomic regions revealed several candidate genes potentially associated with growth regulation, nutrient metabolism, and skeletal development, including b4galnt4a, tmem86a, cpt1b, mrpl23, lama1, gcm2, wnt3a, thrab, and bmpr1a. Additionally, four suggestive SNPs associated with muscle texture traits were detected, and candidate genes related to muscle development and extracellular matrix regulation, including fstl1b, col5a3a, and pnn, were identified. These findings provide new insights into the genetic basis underlying morphometric variation and muscle texture diversity in M. albus. The identified SNP markers and candidate genes provide potential molecular resources for understanding the genetic basis of body morphology and muscle texture variation in rice field eel. Full article

Major airway diseases, including chronic obstructive pulmonary disease (COPD), asthma, bronchiectasis and chronic rhinosinusitis without nasal polyps (CRSsNP), frequently coexist and share inflammatory, epithelial and remodeling features. However, whether these clinically distinct airway disorders are driven by a unified genetic liability and how this shared liability maps to disease-relevant tissues, genes and immune-regulatory programs remain incompletely understood. We integrated GWAS summary statistics for COPD, asthma, bronchiectasis and CRSsNP using linkage disequilibrium score regression, local genetic correlation analysis and Genomic structural equation modeling. A latent shared airway disease factor, termed gAirwayDisease, was constructed to capture common genetic liability across the four conditions. We then applied an integrative functional genomics framework, including gsMap spatial enrichment, PoPS gene prioritization, MAGMA gene-set enrichment, GTEx v8 lung MTWAS, OneK1K and DICE immune-cell MTWAS, scMORE regulon analysis and phenome-wide Mendelian randomization. All six airway disease pairs showed positive genetic correlations, with estimates ranging from 0.508 to 0.685. Genomic SEM supported a single shared factor, with positive standardized loadings for COPD, asthma, bronchiectasis and CRSsNP and excellent model fit. Spatial mapping localized gAirwayDisease-associated signals to airway- and epithelial-associated anatomical domains. PoPS prioritized immune and airway-relevant genes, including SMAD3, GATA3, IL1R1, RUNX3 and STAT6, while MAGMA enrichment highlighted B-cell activation, T-cell activation and transcriptional regulatory pathways. Lung MTWAS identified SLC9A2 and ORMDL3 as top genetically regulated expression signals. OneK1K immune-cell MTWAS highlighted recurrent IL18R1 associations across CD4 and CD8 T-cell subsets. scMORE further identified 36 significant regulon–cell type pairs across dendritic cells, B cells, monocytes, T cells and NK cells, including BCL11A, TCF4, KLF4, RUNX1 and STAT4 regulons. MR-PheWAS linked genetically predicted gAirwayDisease to respiratory, allergic, lung function and immune-related traits. This study defines gAirwayDisease as a genetically informed latent factor capturing shared liability across major airway diseases. Integrated functional genomic analyses highlight airway epithelial and immune regulatory programs associated with shared disease susceptibility and prioritize candidate genes and regulons for future experimental validation. Full article

Growth and body conformation traits are key determinants of meat production efficiency and economic performance in sheep. However, the genetic architecture underlying these complex traits remains incompletely understood, particularly across multi-breed populations. In this study, we performed a genome-wide association study (GWAS) for seven growth and developmental traits in a combined population of 401 sheep, including Qira Black, Kyrgyz, Dorset × Hu crossbred, and Suffolk × Karakul crossbred sheep. After genotype harmonization and quality control, 47,674 autosomal SNPs were retained for analysis. Population structure was assessed using principal component analysis, and association testing was conducted using a mixed linear model incorporating breed, principal components, and a kinship matrix. A total of 44 independent loci were detected at a nominal significance threshold, encompassing 112 candidate genes. The strongest association was identified for cannon bone circumference near RPS6KA5 (Chr7; p = 1.40 × 10⁻⁷). Several biologically relevant genes involved in osteogenesis, cartilage development, and metabolic regulation were detected, including STEAP3, SLC26A2, PPARGC1B, COL11A1, CALN1, and CITED2. Two genomic regions exhibited pleiotropic effects, which were identified as being associated with multiple traits, suggesting shared genetic regulation of correlated skeletal characteristics. These findings are consistent with a polygenic architecture underlying growth trait in sheep and highlight candidate genomic regions potentially involved in skeletal development and body conformation. Although further validation is required, the identified loci provide preliminary evidence for regions that may influence growth-related phenotypes and offer a reference for future molecular breeding efforts in indigenous and crossbred sheep populations. Full article

Average daily gain (ADG) is a core quantitative trait determining the economic benefits of Ashidan yak, a polled new breed adapted to cold barn feeding on the Qinghai–Tibet Plateau. Unraveling its complex genetic architecture is crucial for early molecular breeding selection. In this study, high-depth whole-genome resequencing (WGS) data from 474 Ashidan yaks were used to conduct combined evaluation of genome-wide association study (GWAS) and genomic selection (GS). During GWAS analysis, sex and measurement batch were included as fixed effects, while birth weight and principal components (PC1–PC3) were incorporated as covariates. Multi-model association analysis using GLM, MLM, and FarmCPU was performed on 3.36 million LD-pruned SNPs. The genomic inflation factors (λ ≈ 1.0) for MLM and FarmCPU confirmed effective elimination of population stratification. A total of 11 genome-wide significant SNP loci and 7 key candidate genes including PDE10A, RAD51B, BCAS3 and KCNH8 were identified via the FarmCPU model. Functional enrichment analysis indicated that these gene clusters are significantly involved in cAMP signaling pathway, regulation of ion channel activity, as well as extracellular matrix remodeling of blood vessels and skeletal muscle cells. For genomic selection, a single-trait GBLUP model was constructed using 22.87 million high-density raw SNPs to fully capture polygenic minor effects. Moderately high narrow-sense genomic heritability of ADG was estimated at h² = 0.3233 (p < 0.05). The average independent prediction accuracy across the 10-fold cross-validation reached an average of R = 0.14 ± 0.07. To evaluate marker prioritized genomic evaluation without data leakage, a strict 10-fold cross-validation scheme was implemented, where the top 1% high-priority variant set (~228,000 SNPs) was screened independently within each training fold. The resulting unbiased prediction accuracy reached R = 0.1328 ± 0.1566 (with an average RMSE of 0.3793 ± 0.0066 and a regression slope of 0.4183 ± 0.5055). Comparing this with the unselected whole-genome baseline (R = 0.14 ± 0.07) indicates that naive marker selection based solely on GBLUP effect size in small reference cohorts is influenced by sampling variance, highlighting the need to integrate multi-omics functional annotations for future custom breeding array development. This study provides quantitative insights into the polygenic architecture of ADG in yaks, offering baseline data for genomic selection and custom array development for indigenous livestock on the Qinghai–Tibet Plateau. Full article

Seed protein content, oil content, and yield are key agronomic traits that determine the economic value of soybean. For decades, soybean has served as a leading source of plant protein for human and animal nutrition due to its high protein concentration. Manipulating amino acid transporters to regulate the direction of nitrogen allocation represents a promising strategy for improving seed protein content. Multiple studies have employed this strategy by targeting amino acid importers. Recently, the Usually Multiple Amino acids Move In and Out Transporter (UMAMIT) family has been characterized as amino acid exporters; nevertheless, their role in regulating the seed protein content of soybean has not yet been investigated. In this study, we identified 120 soybean UMAMIT genes via a genome-wide search and designated them according to chromosomal location. Phylogenetic analysis grouped these genes into 10 clades (A–J). Whole-genome duplication (WGD)/segmental duplication served as the main driver of the GmUMAMIT family expansion, followed by tandem duplication. By integrating transcriptome data with QTL/GWAS loci, we identified twelve candidate genes associated with seed protein content and verified their expression patterns during seed development via qPCR. One candidate gene, GmUMAMIT118, was selected and overexpressed in Arabidopsis thaliana, resulting in transgenic lines with significantly higher seed protein content and yield. Collectively, these results provided a comprehensive overview of the soybean UMAMIT family and offered a preliminary exploration of its role in improving seed protein content. Full article

Sweet corn is a globally important dual-purpose crop for both food and fresh vegetables. The plant architecture and ear-related traits directly determine its yield potential and field ecological adaptability. To elucidate the genetic architecture of these traits and identify superior alleles for breeding, we conducted a genome-wide association study (GWAS) on 11 agronomic traits using 30,597 high-quality SNP markers in a panel of 101 elite sweet corn inbred lines. Population genetic structure was analyzed using sparse non-negative matrix factorization (sNMF) and discriminant analysis of principal components (DAPC) algorithms, revealing three main clusters and six subpopulations. The clustering pattern was highly consistent with germplasm origin. Association mapping with the fixed and random Circulating Probability Unification (FarmCPU) model identified 16 significant marker–trait associations (MTAs), distributed across seven target agronomic traits. The phenotypic variance explained (PVE) by individual loci ranged from 8.0% to 16.0%. Among these, five stable MTAs across environments, a novel ERN locus (SNP25518) specific to sweet corn, and most association intervals overlapped with previously reported quantitative trait loci (QTLs). Within the ±0.15 Mb (defined by LD decay) flanking windows around the significant SNP loci, a total of 236 candidate genes were annotated, which are primarily involved in hormone signaling, carbon and nitrogen metabolism, cell division, and plant growth and development. In summary, this study dissected the genetic basis of key agronomic traits in sweet corn and provides a foundation for marker-assisted selection and functional validation. Full article

Fusarium root rot, primarily caused by Fusarium solani, is a damaging soil-borne disease that restricts soybean growth and reduces yield. In the present study, a panel of 330 soybean germplasm accessions was inoculated with F. solani. Disease responses were evaluated using the disease severity index (DSI). The soybean accessions displayed substantial variation in their susceptibility to F. solani. Based on DSI, 39 accessions were classified as highly resistant, 94 as resistant, 125 as susceptible, and 72 as highly susceptible, accounting for 11.82%, 28.48%, 37.88%, and 21.82% of the panel, respectively. The genome-wide association study (GWAS) was performed using a genotyping dataset of 627,436 high-quality single nucleotide polymorphisms (SNPs) and two models, Fixed and random model Circulating Probability Unification (FarmCPU) and mixed linear model (MLM). Both GWAS models detected putative SNP associations across seven chromosomes. Based on SNP allelic-effect analysis and gene function annotation, eight genes were prioritized and subsequently evaluated by quantitative reverse transcription PCR (qRT-PCR) for their responses to F. solani infection. Two Kompetitive allele-specific PCR (KASP) markers, KASP-S13_37431242 and KASP-S13_37529208, were developed from resistance-associated SNPs on chromosome 13 and evaluated across the diverse soybean association panel used in this study. Accessions carrying the favorable genotypes of these markers were enriched for resistant germplasm, with positive predictive values of 65.52% and 66.67%, respectively, indicating their potential value for preliminary favorable-allele tracking and germplasm prioritization. Collectively, these results improve our understanding of the genomic components underlying soybean responses to F. solani. The identified loci, candidate genes, and KASP assays provide a basis for further functional validation and the development of multi-locus strategies for improving soybean resistance to Fusarium root rot. Full article

The production of Kimchi cabbage (Brassica rapa L. ssp. pekinensis) is increasingly threatened by concurrent heat and drought stress, which induce physiological disorders and severe heading failure. In this study, we implemented a dual-environment screening strategy to identify robust indicators of heat resilience in an F₂ population. First, seedling-stage heat tolerance was evaluated in a controlled growth chamber using qualitative visual scoring (Scale 1–4) for shoot and root vigor. Second, the population was validated under summer field conditions in South Korea. Machine learning (ML) analysis revealed that the seedling-stage qualitative shoot-to-root (S/R) ratio was the most robust predictor of mature heat resilience, achieving a classification accuracy of 0.770 via SVM algorithms. The study also revealed a strong positive correlation (r = 0.71) between shoot apical meristem (SAM) wilting and localized calcium deficiency (tipburn). Individuals with severe SAM wilting were predominantly associated with severe tipburn and inner-leaf decay. Genome-wide association studies (GWAS) pinpointed the MIZU-KUSSEI 1-like (MIZ1-like) gene on Chromosome 2 as a primary candidate for S/R ratio regulation, potentially acting through the optimization of root hydrotropism. Our findings highlight that qualitative seedling-stage S/R ratio screening, integrated with ML models, serves as an efficient, breeder-friendly tool for early selection of climate-resilient B. rapa cultivars before field transplantation. Full article

Background/Objectives: Precision medicine requires translation of genetic and molecular variation into clinically actionable therapeutic targets. However, many disease-associated signals identified by genome-wide association studies (GWAS) reside in non-coding regulatory regions, making biological interpretation and therapeutic prioritization difficult. Long non-coding RNAs (lncRNAs) are regulatory molecules with growing relevance to disease mechanisms, biomarker discovery, patient stratification, and RNA-based therapeutics. This review presents a translational framework for AI-guided lncRNA target discovery, linking non-coding genetic signals to experimental validation and clinical implementation. Methods: This narrative review synthesizes literature on GWAS interpretation, quantitative trait loci analysis, epigenomic annotation, single-cell and spatial transcriptomics, multi-omics integration, artificial intelligence and machine learning, experimental validation, RNA therapeutic modality selection, delivery assessment, safety evaluation, and biomarker-informed precision medicine. Results: Genetic and multi-omics data can nominate disease-relevant lncRNAs, but no single evidence layer is sufficient to establish causality, mechanism, druggability, or clinical utility. AI can integrate heterogeneous biomedical datasets, rank candidate lncRNAs, detect regulatory patterns, and prioritize transcripts for validation. However, computational prediction should be interpreted as decision support rather than proof of therapeutic relevance. Candidate targets require disease-context expression validation, functional perturbation, mechanistic assessment, appropriate model systems, therapeutic modulation, delivery-feasibility assessment, safety evaluation, and patient-selection strategies. Conclusions: LncRNAs represent a promising but challenging therapeutic target class. A responsible translational pipeline should connect non-coding genetic evidence and multi-omics support with AI-guided prioritization, experimental validation, RNA therapeutic strategy selection, delivery assessment, safety evaluation, and clinical implementation. The framework defines qualification criteria and decision gates to reduce premature target claims during translational development. Full article

Background: The directional relationship between epigenetic age acceleration (EAA) and musculoskeletal disease remains unresolved. This study integrated bidirectional Mendelian randomization (MR) with multi-layer genomic evidence to evaluate directionality, shared genetic architecture, and robustness to instrument definition. Methods: Four EAA clocks (IEAA, PhenoAA, HannumAA, and GrimAA) and ten musculoskeletal phenotypes were analyzed in a 10 × 4 bidirectional two-sample MR design. EAA instruments underwent GRCh37 functional annotation, genome-wide-significant external-association screening for the index variants and European linkage-disequilibrium proxies, pair-specific Steiger filtering, and conservative Set A/B/C sensitivity analyses. The juvenile-arthritis reverse models underwent instrument-flow reconstruction, strength assessment, liability-scale directionality testing, and minimum-detectable-effect analysis. Additional analyses comprised LD score regression (LDSC), PLACO+ cross-trait locus mapping, Bayesian colocalization, multivariable MR (MVMR) with exact-SNP matched univariable comparators, and integrated evidence synthesis. Results: Forward MR yielded two nominal HannumAA associations. The inverse HannumAA–spondyloarthritis estimate remained directionally consistent across the original, Steiger-filtered, and conservative external-association-filtered sets, whereas the HannumAA–pain-in-thoracic-spine estimate lost nominal significance in the conservative set; no forward result survived correction across 40 tests. GrimAA forward estimates were sensitive to use of the fallback instrument threshold. Reverse MR identified ten nominal associations. For juvenile arthritis, three harmonized instruments had F statistics of 51.25–102.35; liability-scale Steiger comparisons supported the tested direction under all 16 outcome-by-prevalence combinations, although the 788-case discovery GWAS and possible winner’s curse remained important limitations. LDSC identified FDR-significant positive genetic correlations of GrimAA with hip osteoarthritis (r_g = 0.267, p = 8.49 × 10⁻⁵, q = 0.0019) and knee osteoarthritis (r_g = 0.269, p = 9.52 × 10⁻⁵, q = 0.0019). PLACO+ identified 738 genome-wide-significant cross-trait variants and 65 independent loci; six of 37 evaluable loci showed strong colocalization. Of 96 MVMR models, 43 had primary-exposure conditional F ≥ 10, and 32 also had candidate-trait conditional F ≥ 10. After exact-SNP matching, the 43 primary-strength models were operationally classified as 35 partially attenuated and eight independent-signal models, with no fully attenuated model; no adjusted association survived multiplicity correction. Conclusions: The results support a prioritized genomic map with substantial instrument- and model-specific uncertainty. Disease-to-clock signals were richer than clock-to-disease signals, GrimAA shared polygenic architecture with osteoarthritis, and selected loci showed strong shared-variant evidence, while the MR and MVMR findings remained unsuitable for definitive causal or mediation claims. Full article

Hulless barley (HB) is a nutrient-rich cereal gaining renewed research interest in the Tibetan regions of China. Grain protein content (GPC) is a quality-determining trait in HB that significantly affects end-use quality and is strongly influenced by environmental factors. Identifying stable association regions and developing breeding-applicable molecular markers are breeding objectives for high-GPC HB variety improvement. This study analyzed 266 HB accessions grown across six environments (2018–2021). Phenotypic GPC ranged from 5.02% to 18.31%, with moderate heritability (H² = 0.473) and significant G×E interaction. A genome-wide association study (GWAS) using 168,983 GBS-derived SNPs and three models (GLM, MLM, FarmCPU) revealed no Bonferroni-significant SNPs in the BLUP meta-analysis. At the suggestive threshold (p < 1 × 10⁻⁵), 40 SNPs (18 loci) showed nominal association, with chromosome 7H harboring the most robust cross-model signal. Two SNPs on chromosome 6H and 7H exceeded Bonferroni correction in Yangma-2018 under GLM and FarmCPU. Candidate gene mining revealed seven functional genes, including 30S ribosomal protein S13 and glutamate-cysteine ligase. The lead SNP S7H_6966377 was located 165 kb from 30S ribosomal protein S13. These findings establish chromosome 7H as a regulatory hub for GPC and provide markers for marker-assisted selection. Full article

Turbot (Scophthalmus maximus) is a key farmed flatfish species in North China, where growth performance directly determines aquaculture profitability. To elucidate the genetic architecture underlying growth traits, we performed whole-genome resequencing of 177 turbot individuals and obtained 5.29 million high-quality single nucleotide polymorphisms (SNPs). Genome-wide association studies (GWAS) were conducted for eleven growth traits—body weight (BW), total length (TL), body length (BL), body depth (BD), trunk length (TUL), head length (HL), snout length (SnL), caudal peduncle depth (CPD), eye diameter (ED), post-orbital head length (PoL), and interorbital width (IW)—using both single-trait and multi-trait approaches. Heritability estimates ranged from 0.001 (IW) to 0.259 (BW), with BW showing the highest heritability, and genetic correlation analysis revealed strong positive correlations between BW and most body size traits (r_g > 0.7). Critically, extensive genetic pleiotropy governing these traits was uncovered. In single-trait GWAS, two pleiotropic loci—19:16017254 (within fam81b) and 15:2921091 (within atrn)—were repeatedly associated with the same six body-size traits (BD, BL, CPD, HL, TL, and TUL), demonstrating that single variants can exert coordinated effects across multiple morphological dimensions. Furthermore, multi-trait GWAS captured additional pleiotropic signals that remained undetected in single-trait analyses, identifying 20 novel loci encompassing key candidates such as iqgap1 (cytoskeletal signaling), enah (actin organization), and baz2a (chromatin remodeling). KEGG enrichment further consolidated these findings, with candidate genes significantly enriched in the “Regulation of actin cytoskeleton” pathway, while amd1 emerged as a hub gene integrating three metabolic pathways. Collectively, these results highlight the pervasive pleiotropy underlying turbot growth and serve as promising candidate loci for marker-assisted selection, although independent validation in larger populations is required before practical application. Full article

Background/Objectives: Substance use behaviors share a complex, overlapping polygenic architecture, yet translating genome-wide association study (GWAS) findings into actionable biological mechanisms remains challenging. This study aimed to characterize the genetic architecture of five substance use traits (alcohol consumption, alcohol dependence, nicotine use, illicit drug use, and behavioral disinhibition) and identify shared and distinct gene expression signatures within the neural circuits governing addiction. Methods: We reanalyzed 7188 individuals from the Minnesota Center for Twin and Family Research (MCTFR) cohort utilizing longitudinal composite phenotypes spanning five substance-use domains and general behavioral disinhibition. Post-QC, 6874 individuals were retained for downstream analysis. Following genomic imputation and linear mixed model GWAS (GEMMA), we utilized the SNipar framework to partition polygenic risk scores (PRS) into direct and indirect genetic effects, investigating intergenerational shifts in genetic penetrance and effects of assortative mating. Finally, we integrated our summary statistics with brain tissue reference panels to perform a transcriptome-wide association study (TWAS) modeling genetically regulated gene expression within neural circuits relevant to addiction. Results: Partitioning of polygenic risk revealed that while surface-level parental DNA correlations were modest (r = 0.08), underlying latent genetic correlations approached unity (R_δ ≈ 0.99), indicating that addiction risk clustering in families is driven by intense assortive mating and concentrated biological inheritance. Multi-phenotype TWAS identified several significant gene–phenotype associations—notably ADAM32 and SLC9A3, which demonstrated pleiotropic effects across multiple substance use categories. Crucially, these significant TWAS signals were enriched in striatal structures (caudate, putamen, substantia nigra) and frontal cortical regions. Conclusions: Our findings support a model of shared genetic liability across diverse substance use behaviors, mediated by specific gene expression patterns in the mesolimbic dopamine system and frontal cortex. By integrating multi-phenotype GWAS and TWAS, this study highlights pleiotropic candidate genes and provides critical insights into the tissue-specific neurobiological pathways underlying addiction vulnerability. Full article

Single nucleotide polymorphisms (SNPs) are widely used for genetic diversity analysis, linkage mapping, genome-wide association studies (GWAS), and molecular marker development in crop plants. Genotyping-by-sequencing (GBS) enables cost-effective SNP discovery; however, achieving sufficient marker density in polyploid crops remains challenging because of complex genome structures, high sequence similarity among homologous chromosomes, and repetitive genomic regions. In this study, we optimized a GBS-based bioinformatics pipeline for polyploid kiwifruit (Actinidia spp.) by evaluating restriction enzyme combinations through in silico digestion analysis and comparing the SNP detection efficiency of three variant-calling tools, namely freebayes, bcftools, and Genome Analysis Tool Kit (GATK). The methylation-sensitive ApeKI/TfiI combination generated the highest proportion of DNA fragments within the target size range (200–500 bp) in the kiwifruit reference genome cv. Hongyang (A. chinensis). Using GATK, 828,257 SNPs were identified, approximately 22-fold higher than those detected using freebayes and bcftools, with a comparable transition/transversion (Ts/Tv) ratio. GATK also identified substantially higher absolute numbers of SNPs in genic regions, while the proportion of genic-region SNPs was similar across all three tools. Notably, only the GATK-derived SNP dataset exceeded the estimated marker density discussed in this study for high-density genomic coverage of the kiwifruit genome. These results demonstrate that the combination of methylation-sensitive restriction enzymes and GATK-based variant calling generated a high-density SNP dataset for mixed-ploidy kiwifruit germplasm. Because independent validation of SNP accuracy was beyond the scope of this study, the observed differences should be interpreted as differences in SNP discovery rather than comparative variant-calling accuracy. Full article

Growth traits, as core economic indicators in pig breeding, are closely associated with production costs, rearing duration, and final carcass quality and have thus consistently been a major focus of genetic improvement. This study aimed to identify candidate genes affecting Age to 120 kg live weight (AGE120), Backfat thickness at 120 kg (BF120), and Loin muscle depth at 120 kg (LMD120) in pigs. Ear tissue samples were collected from 3364 healthy adult pigs (including 558 boars and 2805 sows) from three breeds: Large White, Landrace, and Duroc. Genotyping was performed using an 80 K functional site array, and quality-controlled SNP (Single-Nucleotide Polymorphism) loci were subjected to genotype imputation, resulting in 15,447,611 loci obtained. Genome-wide association studies (GWASs) for Age to 120 kg live weight, Back fat thickness at 120 kg, and Loin muscle depth at 120 kg were conducted using a mixed linear model in Genome-wide Complex Trait Analysis (GCTA). Genes located within 500 kb upstream and downstream of significant GWAS loci were extracted using the biomaRt package in R. Furthermore, colocalization analysis was performed using expression Quantitative Trait Locus (eQTL) data of 34 tissues from the PigGTEx database to identify genes that share the same causal variant as the GWAS signals. Through integrated GWAS and eQTL colocalization analysis, in addition to five previously reported genes associated with pig growth traits (TAF11, ZC3HAV1L, ANKS1A, USP20, and TBC1D1), a set of novel, high-confidence candidate genes was identified: ZNF215, UBE2Z, HOXB7, SARDH, ADAMTSL2, ATP6V0A4, RPL10A, PGM2, and RELL1. These findings enrich our understanding of the genetic architecture underlying growth traits in pigs at heavy body weights and provide an important foundation for subsequent functional validation and molecular breeding applications. Full article