Search Results (1,975)

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

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

Ascochyta blight (AB), caused by the necrotrophic fungus [Ascochyta rabiei (Pass.) Labr.], is one of the most destructive diseases of chickpea (Cicer arietinum L.), causing yield losses of up to 100% under favorable conditions. The pathogen possesses a heterothallic mating system with two mating-type idiomorphs, MAT1-1 and MAT1-2, which contribute to high genetic diversity and the frequent breakdown of host resistance. This review summarizes current knowledge of AB biology, epidemiology, and management, highlighting recent advances in molecular diagnostics, host–pathogen interactions, and population dynamics. Conventional and modern detection methods, including PCR-based assays, field-deployable diagnostic tools, and high-throughput phenotyping approaches, are discussed. Resistance to AB is genetically complex and predominantly polygenic, involving multiple quantitative trait loci (QTLs), although major resistance genes have also been reported. Genomic tools such as QTL mapping, genome-wide association studies (GWAS), and genomic selection have accelerated the identification of resistance loci and improved the efficiency of chickpea breeding. The potential of wild Cicer species as sources of novel resistance alleles is also emphasized. Integrating genetic resistance with effective disease monitoring and management strategies remains essential for sustainable AB control and the development of durable resistant cultivars. 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

The Jishen Black pig is a synthetic breed incorporating the genetic backgrounds of Chinese indigenous pigs and Western lean-type pigs. The objective of this study was to characterize phenotypic variation, estimate genetic parameters, and identify genomic loci associated with rib number (NR) and the total number of thoracolumbar vertebrae (NTLV) in Jishen Black pigs. NR, NTLV, and lumbar vertebra number (NLV) were measured in 389 pigs, and genotyping was performed using a 70K SNP chip. Genetic parameters were estimated using HIBLUP, and genome-wide association studies (GWAS) were conducted using the MLM and BLINK models in GAPIT v3.0. The mean values of NR, NTLV, and NLV were 14.88, 20.94, and 6.05, respectively, and their heritability estimates were 0.568, 0.493, and 0.140, respectively. GWAS identified a shared major association peak for NR and NTLV on SSC7 at 91.19–98.15 Mb, centered on VRTN and the adjacent ABCD4-LTBP2-AREL1-PGF linkage region, among which the loci at 96.12 Mb, 96.56 Mb and 97.79 Mb are NR-specific loci. Additional signals included MIB1 on SSC6 and MMRN2 on SSC14 for NTLV, together with NR-specific loci on SSC7 at 111.59 Mb and on SSC8. Database annotation indicated that several significant SNPs overlapped with records for rib number, carcass length, loin muscle area, backfat thickness, or teat number. This study identifies major genomic regions associated with vertebral-number traits in Jishen Black pigs and expands current knowledge of their genetic basis. Full article

Chronic lung diseases, including asthma, chronic obstructive pulmonary disease, bronchiectasis, and idiopathic pulmonary fibrosis, are clinically distinct but share epithelial injury, host-defense, inflammatory, and remodeling processes. We integrated European-ancestry genome-wide association study (GWAS) summary statistics for these four diseases using genomic structural equation modeling to construct a multivariate chronic lung disease (mvCLD) factor. Variant-level association testing was combined with genomic control assessment, locus annotation, GWAS-by-subtraction, fine-mapping, transcriptomic prioritization, pathway enrichment, single-cell spatial mapping, and heritability partitioning. For discovery, across 6,255,777 autosomal variants, mvCLD identified 2067 genome-wide significant variants, 30 loci, and 53 lead variants. Five lead variants were genome-wide significant for mvCLD but not for any component disease and showed high-confidence fine-mapping support. For gene prioritization, transcriptomic and gene-level analyses prioritized 21 candidate genes, including ORMDL3, GSDMB, IL18RAP, IL18R1, IL1R1, SMAD3, and CLEC16A. In exploratory functional analyses, enrichment analyses converged on interleukin, cytokine-receptor, JAK-STAT, interleukin-4/interleukin-13, thymic stromal lymphopoietin, asthma, and lung fibrosis pathways. Exploratory single-cell spatial mapping, based on a mouse embryonic atlas, showed the strongest overall enrichment in the lung annotation, although cross-dataset cell-type and tissue analyses did not reach significance after false discovery rate correction; heritability partitioning implicated conserved and active regulatory elements. These findings support a shared epithelial–immune and cytokine-regulatory genetic architecture across major chronic lung diseases and nominate biologically coherent candidate genes and pathways for future functional and translational studies. Full article