Search Results (1,980)

Growth is a critical economic trait in all aquaculture industries. To address issues such as germplasm degradation, a comprehensive understanding of the growth and development mechanisms, along with genetic improvement strategies, for Procambarus clarkii (P. clarkii) is urgently required. In this study, we performed whole-genome resequencing on 89 individuals from five cultured stocks to investigate growth traits (body length) and identified a total of 46,919,297 high-quality single nucleotide polymorphisms (SNPs). Based on these SNPs, we conducted principal component analysis (PCA), phylogenetic analysis, and population genetic structure analysis. Furthermore, we performed selective sweep analysis (using FST, Pi, and XP-CLR) and a genome-wide association study (GWAS) to identify genetic variants associated with growth traits. The results revealed significant genetic differentiation among the five cultured stocks, with the Ma’anshan cultured stock exhibiting the fastest linkage disequilibrium (LD) decay. Additionally, long-term aquaculture in different geographical regions resulted in distinct genetic differences among cultured stocks. Through selective sweep analysis, the intersection of FST, Pi, and XP-CLR across the five populations yielded several growth-related candidate genes: Nephrin, Somatostatin, zinc finger protein 154, and yeti. Subsequent the GWAS identified two candidate genes associated with growth traits: Cullin-associated and neddylation-dissociated protein 1 (CAND1) and Baculoviral IAP repeat-containing protein 8 (BIRC8). These genes are presumed to play pivotal roles in the growth and development of P. clarkii. Overall, our findings provide new insights into the genetic mechanisms underlying growth and development in P. clarkii, and these identified genes serve as promising candidates for further functional studies and genetic improvement of this species. Full article

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

Background: Sarcopenia is a multidimensional musculoskeletal-aging phenotype defined by reduced muscle quantity together with weakness and impaired performance, but public genetic resources rarely capture sarcopenia as a single uniformly labeled phenotype. Methods: Official National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) genome-wide association studies (GWAS) Catalog association files archived locally on 15 April 2026 were grouped into muscle-quantity and function-frailty domains. Exact shared rsIDs were identified, assigned to catalog representative/mapped genes, evaluated against exploratory skeletal-muscle transcriptomic context, benchmarked with trait-grouping sensitivity analyses, and ranked with a secondary multi-criteria evidence-integration framework. Results: The muscle-quantity domain contained 1580 unique single-nucleotide polymorphisms (SNPs) and the function-frailty domain contained 383 unique SNPs, with 14 exact rsIDs shared between domains. A trait-file permutation benchmark did not show that this overlap exceeded a catalog-level null expectation (empirical p = 0.514), so the shared set is interpreted as a descriptive, high-specificity candidate intersection rather than statistically enriched sharing. Sensitivity analyses showed that the 14-rsID set was retained after excluding falling/fall and chronic obstructive pulmonary disease (COPD)-related proxy traits, indicating that the primary shared set was driven mainly by grip/low-grip function traits. Exploratory transcriptomic context and evidence integration organized candidates for follow-up but did not validate causal or linkage disequilibrium (LD)-level sharing. Conclusions: This framework generates candidate hypotheses for sarcopenia-related musculoskeletal aging but does not establish causal, fine-mapped, or statistically enriched genetic sharing. Full article

Salt stress is an abiotic constraint on crop production, impairing growth while reducing yield and quality. As a source of edible oil and protein, soybean is particularly vulnerable to salt stress during emergence and seedling establishment, so the genetic basis of salt tolerance bears directly on productive cultivation. Here, a natural population of 256 soybean accessions was phenotyped for salt tolerance at emergence and seedling stages and genotyped using the Zhongdouxin No.1 (ZDX1) single nucleotide polymorphism (SNP) array. Genome-wide association analysis identified 60 salt tolerance-associated SNPs consolidated into 19 quantitative trait loci (QTL), 16 associated with emergence-stage and 3 with seedling-stage salt tolerance. Five QTLs mapped to chromosomal regions harboring previously reported stress-tolerance genes, including GmSALT3, approximately 192 kb from qSTG-SSB-03. Four putative candidate genes were prioritized: Glyma.10G040000, encoding a glutathione S-transferase (GST), was identified at the emergence stage, while Glyma.10G148700, Glyma.10G149200, and Glyma.10G149600, encoding a calmodulin-binding protein (CaM), drought-induced protein 19 (Di19), and a protein phosphatase 2C (PP2C), respectively, were identified at the seedling stage, all with reported roles in salt-stress responses. Full article

Background: Interindividual variability in intestinal cholesterol absorption contributes to differences in serum lipid concentrations and cardiovascular risk. Total cholesterol (TC)-standardized campesterol and sitosterol levels are established markers of cholesterol absorption. However, genetic variants in Europeans associated with these markers remain incompletely characterized. Methods: A genome-wide association study (GWAS) was performed in 398 healthy individuals of European ancestry. Samples were genotyped using the Precision Medicine Research Array (PMRA). After quality control, 166,037 common genetic variants with a minor allele frequency (MAF) > 20% were analyzed. Associations between genetic variants and intestinal cholesterol absorption markers (campesterol/TC and sitosterol/TC) were evaluated using additive and recessive genetic models. Results: A total of 16 SNPs were identified. Eight SNPs overlapped with both campesterol/TC and sitosterol/TC, of which 2 reached genome-wide significance. Six overlapping SNPs were associated with higher concentrations of both markers: 3 SNPs in ABCG8 (rs4299376, rs6544713, and rs4245791), 1 SNP in ADAM12 (rs4962526), and 2 SNPs in non-coding regions (rs260769 and rs5011112). Additionally, two SNPs (rs2033254 and rs12708980) in CETP were associated with lower concentrations of these markers. Five of the identified SNPs have not previously been linked to markers of intestinal cholesterol absorption. Conclusions: This GWAS confirmed previously reported associations within ABCG8 and identified candidate loci in CETP and ADAM12 that may be involved in intestinal cholesterol absorption. These findings contribute to our understanding of genetic factors underlying intestinal cholesterol absorption and highlight candidate loci for future replication and functional studies. Full article

Background/Objectives: Post-Traumatic Stress Disorder (PTSD) is a complex psychiatric condition with a strong polygenic and stress-related biological basis. Although Genome-Wide Association Studies (GWAS) have acknowledged abundant risk variants, translating these findings into biologically meaningful candidates remains challenging. This study introduces an integrative computational approach from raw file preparation by python-coded application into downstream in-depth silico analyses designed to systematically refine GWAS signals for PTSD using fine-mapping, linkage disequilibrium (LD), and haplotype analyses. Methods: GWAS source file for PTSD was obtained from the GWAS Catalog (EFO_0001358) and analyzed using a custom Python pipeline integrating data harmonization, genome-wide visualization, LD estimation via 1000 Genomes reference panels, approximate Bayesian fine-mapping, and Haploview-inspired haplotype inference. SNPs were filtered based on statistical significance, LD structure, and posterior inclusion probability. Downstream systems’ biology analyses included protein–protein interaction modeling and pharmacogenomics (PGx) annotations. Results: From the primary GWAS dataset, 100 top-ranked SNPs were selected, leading to the identification of 58 significant loci. LD and haplotype analyses refined these signals to 93 candidate SNPs (66 genes). Following the exclusion of non-protein-coding genes, 45 genes remained, and network-based prioritization generated a final list of 20 biologically connected and pharmacogenetically relevant genes associated with PTSD. Conclusions: This integrative approach provided a robust and reproducible framework for GWAS fine-mapping and variant prioritization, effectively reducing large-scale GWAS outputs to biologically interpretable PTSD risk loci and genes. Full article

Geographic atrophy (GA) represents the advanced, non-neovascular (dry) form of age-related macular degeneration (AMD), a chronic, progressive, and currently irreversible neurodegenerative disease of the retina. The clinical consequences of GA are severe; it is characterized by the insidious, expanding loss of the retinal pigment epithelium (RPE), the overlying photoreceptors, and the underlying choriocapillaris. This state of complete RPE and outer retinal atrophy (cRORA) permanently destroys the neural architecture required for high-acuity central vision. For decades, the pathophysiological etiology of geographic atrophy was framed principally in terms of cumulative oxidative stress, lipid peroxidation, and cellular senescence. However, the foundational understanding of AMD pathophysiology changed substantially following the landmark genomic discoveries published in 2005. Multiple independent genome-wide association studies (GWAS) linked specific single-nucleotide polymorphisms in the CFH gene to a substantially increased risk of developing AMD. The CFH gene encodes Complement Factor H (FH) and its alternative splice variant, Factor H-like protein 1 (FHL-1), which are the primary soluble regulators of the alternative complement pathway. This genetic discovery established GA not merely as a disease of metabolic wear-and-tear, but fundamentally as an immunologic disorder driven by the chronic dysregulation of the innate immune system. With the rapid emergence and clinical validation of targeted gene-silencing technologies, particularly small interfering RNA (siRNA) and antisense oligonucleotides, there is substantial scientific and pharmaceutical interest in modulating the complement cascade at the post-transcriptional level. This narrative review examines the structural biology, spatial partitioning, and pathophysiological roles of the FHL-1/CFH axis in GA focusing on the possibilities of using siRNA as a new potential therapy for GA. Full article

To address the core breeding demand for greater seedling vigor in bread wheat (Triticum aestivum L.) varieties adapted to the uniform sowing system in China’s Huang-Huai Wheat Region, we aimed to dissect the genetic architecture and identify core candidate genes for shoot length (SL) and root length (RL) at the germination stage. A natural population consisting of 204 wheat accessions was genotyped using the 660K SNP array, and a genome-wide association study (GWAS) integrated with transcriptomic, metabolomic, and proteomic analyses of extreme phenotypic accessions was performed. Both SL and RL were typical quantitative traits with an extremely significant positive correlation. A total of 111 and 176 significant SNPs were associated with SL and RL, respectively, including 56 shared loci. Glutathione metabolism was identified as the conserved core pathway for both tissues, while photosynthesis–antenna proteins and phenylpropanoid biosynthesis were identified as tissue-specific pathways in shoots and roots, respectively. Finally, we identified 7 core candidate genes for SL and 13 for RL, including three pleiotropic genes regulating both traits. This study provides valuable genetic resources and key targets for the molecular breeding of wheat varieties specialized for uniform sowing. Full article

Body weight is the principal economic trait in snakehead aquaculture, yet the genetic architecture of growth in the northern snakehead (Channa argus) remains largely unexplored. We performed a genome-wide association study (GWAS) for body weight in 205 cultured C. argus genotyped with a 50K SNP array. After quality control, 47,123 autosomal SNPs were tested under a univariate linear mixed model in GEMMA that accounted for genomic relatedness. One SNP on chromosome 7 (Chr07:1,329,732) exceeded the Bonferroni genome-wide threshold, and 14 SNPs reached a suggestive threshold, forming two candidate clusters on Chr07 and Chr24. Annotating candidate intervals (±100 kb) against the C. argus genome assembly yielded 93 candidate genes. Hypergeometric enrichment identified 44 Gene Ontology (GO) terms and 4 KEGG pathways, dominated by endolysosomal lumen, serine-type endopeptidase activity, collagen/extracellular-matrix catabolism, bone resorption, and the apoptosis, lysosome, phagocytosis and antigen-processing pathways. The single genome-wide significant SNP fell in an intron of a collagen alpha-1(II) chain gene (COL2A1/zgc:113232), consistent with the collagen/extracellular-matrix enrichment signal; other plausible growth-related candidates included CTSS, S100A4, PYGM, DLAT, MSX2 and ZNRF3. The functional signal was largely driven by a tandem protease/cathepsin gene cluster on Chr07, indicating a regional rather than a genome-wide pathway effect. The lead SNP genotype was associated with a ~240 g (≈30%) difference in body weight, and this effect was reproduced in an independent cohort of 200 fish. These findings provide the first GWAS-based candidate loci for body weight in C. argus and a promising marker for future marker-assisted breeding, though validation to date is limited to a single additional cohort from the same broodstock source; broader multi-population testing will be needed before routine application. 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

Genomic selection (GS) provides an effective approach to accelerating genetic gain in dairy goats, but the prediction performance is strongly influenced by the statistical model, marker density, phenotype adjustment strategy, and biological architecture of the target trait. In this study, dairy goat populations comprising Xinong Saanen and Saanen dairy goats from major production regions in China were used to evaluate genomic prediction for milk yield (MY), milk fat percentage (MFP), and milk protein percentage (MPP). Genotypes from 1034 dairy goats were generated using low-coverage whole-genome sequencing (lcWGS), imputed to improve genotype completeness and accuracy; a high-quality chip-based dataset was also constructed from previously developed 25K single-nucleotide polymorphism (SNP) chip loci. Conventional genomic best linear unbiased prediction (GBLUP) models, Bayesian regression models, and machine learning algorithms were compared using 10-fold cross-validation. Bayesian models showed clear trait-specific advantages, with BayesB improving MFP prediction by approximately 12.9% relative to GBLUP under the 25K chip-based strategy. Among machine learning methods, gradient boosting models performed strongly; extreme gradient boosting (XGBoost) improved the prediction accuracy for MY, MFP, and MPP by 14.3%, 17.9%, and 18.5%, respectively, relative to GBLUP under the chip-based strategy. Incorporating genome-wide association study (GWAS)-derived prior information and selection signature priors further improved the prediction accuracy, particularly for milk composition traits. Overall, the results indicate that genomic prediction in dairy goats can be optimized by matching models, genotyping platforms, and prior biological information to the genetic characteristics of the target trait. Full article