Genetic insights into childhood obesity: A comprehensive bioinformatics analysis

Unraveling the pathogenic mechanisms of osteoarthritis and obesity: An integration of GWAS, cellular specificity, and spatial transcriptomics

Objective This study aims to systematically elucidate the shared and specific genetic basis of osteoarthritis (OA) and obesity by integrating large-scale genome-wide association study (GWAS) summary statistics, cross-tissue quantitative trait loci (QTLs), and single-cell and spatial transcriptomic data. Method The research employed a multi-omics integrative analysis pipeline. First, a meta-analysis was conducted on GWAS data for OA and obesity. Next, tissue- and spatial-specific enrichment analyses were performed using methods such as QTLEnrich, MAGMA, and gsMap. Key steps included the application of single-cell analysis, Cell-stratified mendelian randomization (csMR), and the ECLIPSER/CELLECT framework to identify specific cell types. Finally, hub genes were identified using hdWGCNA. Results The results revealed significant enrichment of genetic risk signals for OA and obesity in brain tissues, including the cortex and pituitary gland. At the cellular level, T cells were identified as the highest-priority shared cell type for both diseases. Hub genes—GSN, CALD1, EBF1, LHFPL6, and TIMP3—were identified through co-expression network analysis. Spatial transcriptomic analysis further mapped the genetic risk signals to brain regions during embryonic development. Conclusion This study precisely anchors the genetic risk of OA and obesity to specific brain regions, cell types, and developmental time windows, providing a novel perspective for understanding the pathological mechanisms of OA.

GENOMIC INSIGHTS INTO CHRONIC DISEASE SUSCEPTIBILITY: IMPLICATIONS FOR PERSONALISED MEDICINE

The complex interplay of genetic, metabolic and immune-related processes influences chronic diseases. Even though genomic research has found susceptibility loci to specific conditions, there have been fewer studies that combine variant level, pathway-level, and cumulative risk using a single framework to examine chronic disease susceptibility. This study aimed to investigate genomic variation associated with susceptibility patterns and examine its implications for personalised medicine using type 2 diabetes mellitus as a model chronic disease. A secondary analysis was conducted using SNP genotype data from the GEO dataset GSE226084. Following quality control, principal component analysis was applied for dimensionality reduction, and K-means clustering was used to derive genomic susceptibility groups. Logistic regression identified associated SNPs, which were subsequently annotated, aggregated at the gene level, and evaluated through pathway enrichment analysis. A weighted genetic risk score was calculated to assess cumulative genetic burden across clusters. Two distinct genomic clusters were identified, comprising 63 and 243 individuals. Among 3,733 tested SNPs, 871 remained significant after false discovery rate correction. Key loci included HLA-DPA1, ETV6, TRIM15, TRIM26, and TCF7L2, with notable signal concentration on chromosome 6. Enrichment analysis revealed pathways related to immune regulation, inflammatory response, and cellular signaling. Genetic risk scores differed markedly between clusters, with one group exhibiting consistently higher cumulative genetic burden. These findings demonstrate that genomic susceptibility is organised into biologically distinct profiles defined by coordinated variant, gene, pathway, and cumulative risk signals. Integrating these layers provides a practical basis for risk stratification and supports the application of genomics in personalised medicine.

Common genetic mechanisms between obesity and COVID-19 severity: unravelling pleiotropic loci and biological pathways

COVID-19 and obesity are complex conditions marked by immune and metabolic dysfunction, with the former still ranking among the leading causes of death from infectious diseases worldwide and the latter reaching pandemic proportions. Clinical evidence consistently shows that obesity increases the risk of severe COVID-19, yet the biological mechanisms underlying this association remain unclear. Given their physiological and clinical overlap, they may share genetic pathways. We investigated genetic variants jointly associated with body mass index (BMI) and COVID-19 using publicly available genome-wide data. A conjunctional false discovery rate (conjFDR) approach identified shared variants between BMI and three COVID-19 phenotypes: infection, hospitalization and very severe respiratory illness. Functional annotation and pathway enrichment analyses were performed to explore the biological context of these variants, followed by a phenome-wide association study (PheWAS) to characterize pleiotropy. Shared variants were enriched in immune, metabolic and hormonal signaling pathways, including metal ion transport and glycosylation. The overlap with BMI was strongest for hospitalized and severe cases, suggesting common mechanisms underlying disease progression rather than infection. These findings suggest a biologically meaningful genetic overlap between obesity and COVID-19 severity, highlighting pleiotropy as a key feature in complex disease interactions and potential shared therapeutic targets.

Genetically predicted triglyceride levels and endometriosis risk: Mendelian randomization with exploratory multi-omics analyses

This study explored the genetic evidence linking triglyceride-related pathways and lipid-lowering drug targets with endometriosis risk and investigated the genetic association between TG levels and EM. Using genome-wide association study (GWAS) data, a two-sample Mendelian randomization (MR) analysis was conducted, estimating the association between genetically predicted TG levels and EM risk (OR = 1.1853, 95% CI: 1.070–1.313, p = 0.0011). Seven TG-lowering drug target genes (SLC9A1, SCN3A, VEGFA, APOA1, TNF, ITGAV, BACE1) were identified. Transcriptome analysis revealed that SCN3A, BACE1, and APOA1 were significantly upregulated in EM patients compared to healthy controls. Enrichment pathway analysis showed that these genes are involved in lipid transport, localization, cell adhesion, and blood vessel development. PPARA was predicted to act as a transcription factor for five of these genes, while estrogen receptor 1 (ESR1) was predicted to regulate the majority of the others. Among the TG inhibitors examined, fibrate-related targets showed relatively broader target coverage in the present genetic analyses. These findings suggest that TG-related pathways and lipid-lowering drug targets, particularly fibrate-related targets, may be relevant to EM biology and warrant further experimental and clinical validation.

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction

Key genetic biomarkers and therapeutic potential in multiple sclerosis: Insights from integrated bioinformatics and mendelian randomization.

This study integrates transcriptomic, genetic-causal, immunological, and structural analyses to prioritize STAT6 as a potentially druggable MS-associated target and strengthen the computational plausibility of STAT6-ligand interactions.