Search Results (1,970)

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 need 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

Soybean pod-related traits are important for seed development, yield formation, and cultivar evaluation. However, conventional measurements are inefficient and have limited ability to characterize complex pod features such as curvature, local enlargement, and continuous color variation. In this study, mature pod images of 187 cultivated soybean accessions collected across two successive years were analyzed using a deep learning-assisted image phenotyping approach. Eight pod-related traits related to size, morphology, and color were extracted from pod images. A genome-wide association study (GWAS) was performed using 61,541 high-quality SNP markers to dissect the genetic basis of these image-derived pod traits. A total of 16 stable loci associated with pod size, morphology, and color traits were identified across 11 chromosomes. Among these loci, eight were not reported in the previous image-based soybean pod GWAS study. Based on SoyBase gene annotation and Gene Ontology biological process information, 32 biologically relevant candidate gene records were prioritized within the corresponding candidate genomic intervals, while pod-related expression profiles and SoyBase association information were used as supporting evidence for candidate gene evaluation. These findings indicate that refined image-derived traits can provide complementary genetic information beyond conventional pod measurements and offer additional opportunities for dissecting soybean pod development, morphology, and mature pod color variation. Full article

Elucidating the evolutionary trajectories and genetic basis of critical agronomic traits in regional rice germplasm is paramount for discovering elite allelic variations for crop improvement. Here, we systematically characterized a panel of 109 rice accessions from Zhejiang Province through whole-genome resequencing (~10× coverage) coupled with two years of rigorous field phenotypic evaluations. A total of 4,753,071 high-quality genomic variants, including 4,147,316 SNPs, were identified across the genome. Population structure and evolutionary analyses revealed sharp genetic differentiation at the subspecies level, partitioning the panel into distinct indica and japonica clusters accompanied by intricate subpopulation stratification and historical gene flow. Through a joint scanning of the fixation index (Fst) and nucleotide diversity (Pi) ratios, three prominent selective sweep regions (qSS1, qSS10, and qSS12) driving subspecific differentiation were captured on chromosomes 1, 10, and 12. Notably, the qSS12 locus harbors the sucrose transporter gene OsSUT2, indicating that carbohydrate transport and energy metabolism served as core genomic targets driving the indica–japonica divergence. Furthermore, genome-wide association studies (GWAS) successfully mapped 9 significant loci modulating heading date, effective tiller number, and grain size. Subsequent gene-based haplotype analyses within these target intervals pinpointed elite allelic variations in core candidate genes, including OsSPX1 (phosphate homeostasis, 1000-grain weight), Chl9 (chlorophyll synthesis, grain width), and OsCER1 (wax biosynthesis, panicle length). Collectively, this study deciphers the genomic landscape and subspecies differentiation patterns of Zhejiang rice germplasm, providing pivotal molecular targets and invaluable genomic resources for germplasm conservation and precision molecular breeding. 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

Exserohilum spike blight, caused by Exserohilum rostratum, is an emerging constraint in wheat production, and improving host resistance is a sustainable strategy because the disease is governed largely by quantitative, environment-responsive loci rather than single major genes. Identifying genomic regions and biological pathways underlying quantitative resistance is therefore essential for developing durable resistant varieties. In this study, the Wheat Association Mapping Initiative spring wheat panel (n = 289) was evaluated across two contrasting Indian agroclimatic zones over two seasons. Resistance was quantified using two traits: incubation period (IP) and area under the disease progress curve (AUDPC). Significant genotype, environment, and genotype × environment interaction effects were observed for both traits (p < 0.001). IP and AUDPC were weakly correlated (r = −0.18 to 0.16), indicating that these traits represent partially distinct resistance components. Genome-wide association mapping identified 25 marker–trait associations, 13 for AUDPC and 12 for IP, with most associations showing environment dependence. Putative candidate genes highlighted defense-relevant loci, including an LRR receptor-like kinase and a WRKY transcription factor for IP, and an RGA2-like resistance gene and PHLOEM UNLOADING MODULATOR-like gene for AUDPC. These findings provide the first GWAS-based framework for E. rostratum resistance in wheat, prioritizing loci for validation and marker deployment. Full article

It is well known that genetic variants contribute to cellular sensitivity to chemotherapeutic agents and ionizing radiation (IR). The aim of this study was to identify single nucleotide polymorphisms (SNPs) and genes associated with the spectrum of normal cellular sensitivity of lymphoblastoid cell lines (LCLs) towards ionizing radiation and mitomycin C (MMC). In the first step, we determined the viability of LCLs established from male participants of the Berlin Aging Study II (BASE-II) aged ≥62 years following treatments with increasing doses of IR (n = 137 cell lines) or MMC (n = 140 cell lines) using the alamarBlue assay. Results from intra-experimental triplicates and three independent experiments for each cell line and treatment were used to calculate the area under the curves (AUCs) representing the specific sensitivity to IR and MMC of each LCL. The data from these experiments were subsequently used as outcomes in genome-wide association studies (GWASs). In addition, we calculated polygenic risk scores (PGS) from UK Biobank GWAS results for four cancer-related phenotypes and assessed the extent to which the variance in the IR and MMC sensitivity is explained by these PGS. The GWAS analyses revealed one variant, rs74728080, located in CDH13 on chromosome 16, to show genome-wide significant (p < 5 × 10⁻⁸, ß = 2.81) association with cellular viability after treatment with IR. In the GWAS on MMC sensitivity the most interesting signal was elicited by SNP rs113978558 in an intron of the PLD5 gene on chromosome 1 (p = 9.232 × 10⁻⁸; ß = 1.44). Several other SNPs with statistically suggestive (i.e., p < 1 × 10⁻⁵) evidence of association with IR or MMC sensitivity were identified. PGS calculations from GWAS of four cancer-related traits in UKB explained ~5% and ~3% of phenotypic variance in IR- and MMC-induced cell viability, respectively. The genome-wide significant association of rs74728080 with IR sensitivity and the location of this variant in CDH13 is interesting and functionally highly plausible given its known involvement in oxidative stress response and function as a tumor suppressor. Taken together, our novel data suggest that CDH13 may be genuinely involved in regulating cellular IR sensitivity. Full article