Search Results (104)

Migraine is a complex neurological disorder that severely compromises quality of life. Current therapies remain inadequate, creating an urgent need for precision medicine approaches. To bridge this gap, we integrated genome-wide association studies (GWASs) and multi-tissue expression quantitative trait loci (eQTL) data. Using Mendelian randomization (SMR/HEIDI) to identify putatively causal genes, followed by colocalization analysis, protein–protein interaction networks, and gene enrichment, we prioritized druggable targets. Phenome-wide association studies (PheWASs) further assessed their potential safety profiles. We identified 31 migraine-associated genes in whole blood, 20 in brain tissue, and 9 genes shared by both whole blood and brain regions. Among 13 druggable genes identified from the DGIdb and supporting literature, 10 passed colocalization validation. Eight genes (TGFB3, CHRNB1, BACE2, THRA, NCOR2, NR1D1, CHD4, REV3L) showed interactions with known drug targets, enabling the computational prediction of 41 potential repurposable drugs. Based on target druggability, PPI (protein–protein interaction) and favorable PheWAS profiles, NR1D1, THRA, NCOR2, and CHD4 are prioritized for drug development. Additionally, MICU1, UFL1, LY6G5C, and PPP1CC emerged as novel pathophysiological factors. This study establishes a multi-omics framework for precision migraine therapy, translating genetic insights into clinically actionable targets. Full article

Background/Objectives: Prepulse inhibition (PPI) is a robust, reproducible phenotype associated with schizophrenia and other psychiatric disorders. This study was carried out to identify gene(s) influencing PPI. Methods: We performed Quantitative Trait Locus (QTL) analysis of PPI in 59 strains from the BXD recombinant inbred (BXD RI) mouse family and used a 2-LOD region for candidate gene identification. Genes significantly correlated with the candidate gene were identified based on genetic, partial, and literature correlation, and were further studied through gene enrichment and protein–protein interaction analyses. Phenome-wide association study (PheWAS) and differential expression analyses of the candidate gene were performed using human data. Results: We identified one significant (GN Trait 11428) and two suggestive male-specific QTLs (GN Traits 11426 and 11427) on Chromosome 19 between 27 and 36 Mb with peak LRS values of 19.2 (−logP = 4.2), 14.4 (−logP = 3.1), and 13.3 (−logP = 2.9), respectively. Atad1, ATPase family, AAA domain containing 1 was identified as the strongest candidate for the male-specific PPI loci. Atad1 expression in BXDs is strongly cis-modulated in the nucleus accumbens (NAc, LRS = 26.5 (−logP = 5.7). Many of the Atad1-correlated genes in the NAc were enriched in neurotransmission-related categories. Protein–protein interaction analysis suggested that ATAD1 functions through its direct partners, GRIA2 and ASNA1. PheWAS revealed significant associations between Atad1 and psychiatric traits, including schizophrenia. Analysis of a human RNA-seq dataset revealed differential expression of Atad1 between schizophrenia patients and the control group. Conclusions: Collectively, our analyses support Atad1 as a potential candidate gene for PPI and suggest that this gene should be further investigated for its involvement in psychiatric disorders. Full article

Background: The selection of machine learning (ML) models in the biomedical sciences often relies on global performance metrics. When these metrics are closely clustered among candidate models, identifying the most suitable model for real-world deployment becomes challenging. Methods: We developed a novel composite framework that integrates visual inspection of Model Scoring Distribution Analysis (MSDA) with a new scoring metric (MSDscore). The methodology was implemented within the Digital Phenomics platform as the MSDanalyser tool and tested by generating and evaluating 27 predictive models developed for breast, lung, and renal cancer prognosis. Results: Our approach enabled a detailed inspection of true-positive, false-positive, true-negative, and false-negative distributions across the scoring space, capturing local performance patterns overlooked by conventional metrics. In contrast with the minimal variation between models obtained by global metrics, the MSDA methodology revealed substantial differences in score region behaviour, allowing better discrimination between models. Conclusions: Integrating our composite framework alongside traditional performance metrics provides a complementary and more nuanced approach to model selection in clinical and biomedical settings. Full article

The morphological diversity of the domestic turkey is still an open question in poultry research. For this reason, a meta-analysis with 97 reports from 28 morphometric characterization studies covering 15 different turkey genotypes was carried out in the present study. Biometric measurements and indices collected from the articles were used as independent variables in three principal component analyses. The highest variance explaining power was achieved in the analysis including only biometric indices, with more than 70% in the first two principal components for both sexes. The ‘leg length’, ‘body mass’, ‘shape’, and ‘tarsus’ indices were those with higher explanatory power, the latter two particularly so in females. In addition, ‘head’ was such a high variance explaining body region, especially in males, while for females, the ‘leg’ showed high variability between breeds. The spatial representation of observations drew an interesting grouping pattern, proposing an ‘African’ and ‘Mediterranean’ trunk of turkeys based just on biometric traits. The correlation matrix showed positive and negative associations between the variables, especially stronger in females. Breast circumference was negatively correlated with weight and size traits, suggesting that turkey landraces differ in body conformation and environmental requirements. Despite data limitations, particularly in terms of available breed reports and measures taken, consistent results were obtained. The results of the present work could be common guidelines for the phenotypic characterization of turkey breeds worldwide. Full article

With the advancement of remote sensing imagery and multimodal sensing technologies, monitoring plant trait dynamics has emerged as a critical area of research in modern agriculture. Traditional approaches, which rely on handcrafted features and shallow models, struggle to effectively address the complexity inherent in high-dimensional and multisource data. In contrast, deep learning, with its end-to-end feature extraction and nonlinear modeling capabilities, has substantially improved monitoring accuracy and automation. This review summarizes recent developments in the application of deep learning methods—including CNNs, RNNs, LSTMs, Transformers, GANs, and VAEs—to tasks such as growth monitoring, yield prediction, pest and disease identification, and phenotypic analysis. It further examines prominent research themes, including multimodal data fusion, transfer learning, and model interpretability. Additionally, it discusses key challenges related to data scarcity, model generalization, and real-world deployment. Finally, the review outlines prospective directions for future research, aiming to inform the integration of deep learning with phenomics and intelligent IoT systems and to advance plant monitoring toward greater intelligence and high-throughput capabilities. Full article

This review synthesizes advances in livestock genomics by examining the interplay between candidate genes, molecular markers (MMs), signatures of selection (SSs), and quantitative trait loci (QTLs) in shaping economically vital traits across livestock species. By integrating advances in genomics, bioinformatics, and precision breeding, the study elucidates genetic mechanisms underlying productivity, reproduction, meat quality, milk yield, fibre characteristics, disease resistance, and climate resilience traits pivotal to meeting the projected 70% surge in global animal product demand by 2050. A critical synthesis of 1455 peer-reviewed studies reveals that targeted genetic markers (e.g., SNPs, Indels) and QTL regions (e.g., IGF2 for muscle development, DGAT1 for milk composition) enable precise selection for superior phenotypes. SSs, identified through genome-wide scans and haplotype-based analyses, provide insights into domestication history, adaptive evolution, and breed-specific traits, such as heat tolerance in tropical cattle or parasite resistance in sheep. Functional candidate genes, including leptin (LEP) for feed efficiency and myostatin (MSTN) for double-muscling, are highlighted as drivers of genetic gain in breeding programs. The review underscores the transformative role of high-throughput sequencing, genome-wide association studies (GWASs), and CRISPR-based editing in accelerating trait discovery and validation. However, challenges persist, such as gene interactions, genotype–environment interactions, and ethical concerns over genetic diversity loss. By advocating for a multidisciplinary framework that merges genomic data with phenomics, metabolomics, and advanced biostatistics, this work serves as a guide for researchers, breeders, and policymakers. For example, incorporating DGAT1 markers into dairy cattle programs could elevate milk fat content by 15-20%, directly improving farm profitability. The current analysis underscores the need to harmonize high-yield breeding with ethical practices, such as conserving heat-tolerant cattle breeds, like Sahiwal. Full article

Background: We aimed to explore the mechanism by which extracellular-5′-nucleotidase (NT5E) regulates macrophage polarization via regenerating islet-derived protein 3 beta (Reg3β) and other plasma proteins that mediate immune-cell effects on myocarditis. Methods: The involvement of NT5E in Reg3β-induced macrophage polarization was first analyzed using RNA sequencing, Western blotting, and quantitative polymerase chain reaction. Mendelian randomization was employed to identify NT5E and various plasma proteins as potential therapeutic targets for myocarditis. Mediation analysis, enrichment analysis, protein–protein interaction network analysis, drug prediction, molecular docking, and single-cell RNA sequencing were integrated to further evaluate the biological functions and pharmacological potential of the identified targets. Finally, phenome-wide association studies were conducted to assess the safety of targeting these proteins. Results: NT5E expression was elevated in Reg3β-stimulated M2 macrophages. The expression of Arg-1, a marker of M2 macrophages, decreased upon NT5E knockdown, suggesting that NT5E is involved in the Reg3β-mediated polarization of macrophages to the M2 phenotype. Mendelian randomization analysis identified NT5E and 80 other plasma proteins as being causally associated with myocarditis. Mediation analysis revealed 12 immune-cell types were mediators of the effects of plasma protein on myocarditis progression. Drug prediction identified candidates such as ICN 1229 and chrysin, which showed strong binding affinities in molecular docking analyses. These findings may contribute to the development of effective treatments for myocarditis. Conclusions: NT5E plays a dual role in Reg3β-induced macrophage polarization and in interacting with plasma proteins that influence the onset and progression of myocarditis through immune-cell pathways. Full article

Background: Rheumatoid arthritis (RA) is an autoimmune disease that remains incurable. An increasing number of proteomic genome-wide association studies (GWASs) are emerging, offering immense potential for identifying novel therapeutic targets for diseases. This study aims to identify potential therapeutic targets for RA based on human plasma proteome. Methods: Protein quantitative trait loci were extracted and integrated from eight large-scale proteomic GWASs. Proteome-wide Mendelian randomization (Pro-MR) was performed to prioritize proteins causally associated with RA. Further validation of the reliability and stratification of prioritized proteins was performed using MR meta-analysis, colocalization, and transcriptome-wide summary-data-based MR. Subsequently, prioritized proteins were characterized through protein–protein interaction and enrichment analyses, pleiotropy assessment, genetically engineered mouse models, cell-type-specific expression analysis, and druggability evaluation. Phenotypic expansion analyses were also conducted to explore the effects of the prioritized proteins on phenotypes such as endocrine disorders, cardiovascular diseases, and other immune-related diseases. Results: Pro-MR prioritized 32 unique proteins associated with RA risk. After validation, prioritized proteins were stratified into four reliability tiers. Prioritized proteins showed interactions with established RA drug targets and were enriched in an immune-related functional profile. Four trans-associated proteins exhibited vertical or horizontal pleiotropy with specific genes or proteins. Genetically engineered mouse models for 18 prioritized protein-coding genes displayed abnormal immune phenotypes. Single-cell RNA sequencing data were used to validate the enriched expression of several prioritized proteins in specific synovial cell types. Nine prioritized proteins were identified as targets of existing drugs in clinical trials or were already approved. Further phenome-wide MR and mediation analyses revealed the effects and potential mediating roles of some prioritized proteins on other phenotypes. Conclusions: This study identified 32 plasma proteins as potential therapeutic targets for RA, expanding the prospects for drug discovery and deepening insights into RA pathogenesis. Full article

The utilization of saline–alkali land for rice cultivation is critical for global food security. However, most existing studies on rice salt tolerance focus on the seedling stage, with limited insights into tolerance mechanisms during reproductive growth, particularly at the panicle initiation stage (PI). Leveraging precision salinity-control facilities, this study imposed four salt stress gradients (0, 3, 5, and 7‰) to dissect the differential response mechanisms of six rice varieties (YXYZ: Yuxiangyouzhan, JLY3261: Jingliangyou3261, SLY91: Shuangliangyou91, SLY138: Shuangliangyou138, HLYYHSM: Hualiangyouyuehesimiao, and SLY11:Shuangliangyou111) during PI. The results revealed that increasing salinity significantly reduced tiller number (13.14–68.04%), leaf area index (18.58–57.99%), canopy light interception rate (11.91–44.08%), and net photosynthetic rate (2.63–52.42%) (p < 0.001), accompanied by reactive oxygen species (ROS)-induced membrane lipid peroxidation. Integrative analysis of field phenotypic and physiological indices revealed distinct adaptation strategies: JLY3261 rapidly activated antioxidant enzymes under 3‰ salinity, alleviating lipid peroxidation (no significant difference in H₂O₂ or malondialdehyde content compared to 0‰ salinity) and maintaining tillering and aboveground biomass. SLY91 tolerated 7‰ salinity via CAT/POD-mediated lipid peroxide degradation, with H₂O₂ and malondialdehyde contents increasing initially but decreasing with escalating stress. These findings highlight genotype-specific antioxidant strategies underlying salt-tolerance mechanisms and the critical need for integrating phenomics–physiological assessments at reproductive stages into salt-tolerance breeding pipelines. Full article

Background: Heat stress challenges small ruminants in semi-arid regions, requiring integrative multi-modeling approaches to identify adaptive thermotolerance traits. This study aimed to identify phenotypic biomarkers and explore the relationships between thermoregulatory responses and hematological, behavioral, morphometric, carcass, and meat traits in lambs. Methods: Twenty 4-month-old non-castrated male lambs, with an average body weight of 19.0 ± 5.11 kg, were evaluated under natural heat stress. Results: Thermoregulatory variables were significantly associated with non-carcass components (p = 0.002), carcass performance (p = 0.027), commercial meat cuts (p = 0.032), and morphometric measures (p = 0.029), with a trend for behavioral responses (p = 0.078). The main phenotypic traits related to thermoregulation included idleness duration, cold carcass weight, blood, liver, spleen, shank, chest circumference, and body length. Exploratory factor analysis reduced the significant indicators to seven latent domains: carcass traits, commercial meat cuts, non-carcass components, idleness and feeding behavior, and morphometric and thermoregulatory responses. Bayesian network modeling revealed interdependencies, showing carcass traits influenced by morphometric and thermoregulatory responses and non-carcass traits linked to ingestive behavior. Thermoregulatory variables were not associated with meat quality or hematological traits. Conclusions: These findings highlight the complex biological relationships underlying heat adaptation and emphasize the potential of combining phenomic data with computational tools to support genomic selection for climate-resilient and welfare-oriented breeding programs. Full article

Seed quality traits, such as seed size, oil and protein content, mineral accumulation, and morphological characteristics, are crucial for enhancing crop productivity, nutritional value, and marketability. Traditional quantitative trait loci (QTL) mapping methods, such as linkage analysis and genome-wide association studies (GWAS), have played fundamental role in identifying loci associated with these complex traits. However, these approaches often struggle with high-dimensional genomic data, polygenic inheritance, and genotype-by-environment (GXE) interactions. Recent advances in artificial intelligence (AI) and machine learning (ML) provide powerful alternatives that enable more accurate trait prediction, robust marker-trait associations, and efficient feature selection. This review presents an integrated overview of AI/ML applications in QTL mapping and seed trait prediction, highlighting key methodologies such as LASSO regression, Random Forest, Gradient Boosting, ElasticNet, and deep learning techniques including convolutional neural networks (CNNs) and graph neural networks (GNNs). A case study on soybean seed mineral nutrients accumulation illustrates the effectiveness of ML models in identifying significant SNPs on chromosomes 8, 9, and 14. LASSO and ElasticNet consistently achieved superior predictive accuracy compared to tree-based models. Beyond soybean, AI/ML methods have enhanced QTL detection in wheat, lettuce, rice, and cotton, supporting trait dissection across diverse crop species. I also explored AI-driven integration of multi-omics data—genomics, transcriptomics, metabolomics, and phenomics—to improve resolution in QTL mapping. While challenges remain in terms of model interpretability, biological validation, and computational scalability, ongoing developments in explainable AI, multi-view learning, and high-throughput phenotyping offer promising avenues. This review underscores the transformative potential of AI in accelerating genomic-assisted breeding and developing high-quality, climate-resilient crop varieties. Full article

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI’s role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding. Full article

Radiotherapy-induced hearing impairment significantly affects patients’ quality of life, yet its genetic basis remains poorly understood. This study seeks to identify genetic variants associated with radiotherapy-induced tinnitus and hearing loss and explore their functional implications. A genome-wide association study (GWAS) was conducted to identify single-nucleotide polymorphisms (SNPs) associated with radiotherapy-induced tinnitus and hearing loss. Protein–protein interaction networks and functional enrichment analyses were performed to explore underlying biological pathways. A phenome-wide association study (PheWAS) analysis across five databases examined associations between identified SNPs and various phenotypes. The GWAS identified 97 SNPs significantly associated with radiotherapy-induced tinnitus and 76 SNPs with hearing loss. Tinnitus-associated variants were enriched in pathways involving Wnt signaling and telomerase RNA regulation, while hearing-loss-associated variants were linked to calcium-dependent cell adhesion and neurotransmitter receptor regulation. The PheWAS analysis revealed significant associations between these hearing-impairment-related SNPs and metabolic phenotypes, particularly BMI and metabolic disorders. A chromosomal distribution analysis showed concentrated significant SNPs on chromosomes 1, 2, 5, and 10. This study identified distinct genetic architectures underlying radiotherapy-induced tinnitus and hearing loss, revealing different molecular pathways involved in their pathogenesis. The unexpected association with metabolic phenotypes suggests potential interactions between metabolic status and susceptibility to radiotherapy-induced hearing complications. These findings provide insights for developing genetic screening tools and targeted interventions to prevent or mitigate radiotherapy-related hearing damage. Full article

Background: At present, there are still limitations and challenges in the treatment of hyperuricemia (HUA). Mendelian randomization (MR) has been widely used to identify new therapeutic targets. Therefore, we conducted a systematic druggable genome-wide MR to explore potential therapeutic targets and drugs for HUA. Methods: We integrated druggable genome data; blood, kidney, and intestinal expression quantitative trait loci (eQTLs); and HUA-associated genome-wide association study (GWAS) data to analyze the potential causal relationships between drug target genes and HUA using the MR method. Summary-data-based MR (SMR) analysis and Bayesian colocalization were used to assess causality. In addition, we conducted phenome-wide association studies, protein network construction, and enrichment analysis of significant targets to evaluate their biological functions and potential side effects. Finally, we performed drug prediction and molecular docking to identify potential drugs targeting these genes for HUA treatment. Results: Overall, we identified 22 druggable genes significantly associated with HUA through MR, SMR, and colocalization analyses. Among them, two prior druggable genes (ADORA2B and NDUFC2) reached statistically significant levels in at least two tissues in the blood, kidney, and intestine. Further results from phenome-wide studies revealed that there were no potential side effects of ADORA2B or NDUFC2. Moreover, we screened 15 potential drugs targeting the 22 druggable genes that could serve as candidates for HUA drug development. Conclusions: This study provides genetic evidence supporting the potential benefits of targeting 22 druggable genes for HUA treatment, offering new insights into the development of targeted drugs for HUA. Full article

With the surge in digital farming, real-time quality management of fresh produce has become essential. For apples (Malus domestica Borkh.), consumer demand extends beyond sweetness, texture, and appearance to internal quality factors such as moisture content. Existing non-destructive methods, however, involve costly equipment, complex calibration, and sensitivity to environmental conditions. This study hypothesizes that thermal diffusivity indices derived from surface heating and cooling patterns can accurately predict apple moisture content non-destructively. A total of 823 apples from seven varieties were analyzed using a thermal imaging sensor in a 120-s process comprising 40 s of heating and 80 s of cooling. Key thermal diffusivity indices—minimum, maximum, mean, and max–min values—were extracted and correlated with actual moisture content measured via the drying method. Multiple linear regression and leave-one-out cross-validation confirmed that mean temperature-based models provided the most stable predictions ($R_{CV}^2$ ≥ 0.90 for some varieties). Frame optimization and artificial neural networks further improved prediction accuracy for varieties exhibiting higher variability. The proposed method is cost-effective, requires minimal calibration, and is less affected by surface reflectance, outperforming conventional optical methods (e.g., NIR spectroscopy, hyperspectral imaging), especially regarding robustness against surface reflectance variability and calibration complexity. This offers a practical solution for monitoring apple freshness and quality during sorting and distribution processes, with expanded research on sugar content and acidity expected to accelerate commercialization. Full article