GWAS and multimodal transcriptomic profiling of human otolithic organs characterize the genetic architecture of balance in the elderly

Genetic and biological mechanisms of pediatric enuresis: integrating pathophysiology, GWAS associations, and multi-omics research

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.

CNNM2 in schizophrenia: multilevel evidence of genetic susceptibility, magnesium homeostasis, neurodevelopment and cognitive dysfunction.

Schizophrenia (SCZ) is a common psychiatric disorder with a complex, genetically and environmentally influenced etiology, but the specific pathogenesis remains unclear. In recent years, the SCZ susceptibility gene CNNM2 (encoding cyclin M2) located at the 10q24.32-33 locus has received widespread attention. The well-validated SCZ risk interval 10q24.32-33 harbors two independent risk variants: rs11191580 in NT5C2 (significantly associated with CNNM2 mRNA and protein levels) and rs7914558 in CNNM2. Results from functional genomic analyses indicate that lower CNNM2 expression is significantly associated with SCZ. Imaging genetics studies have demonstrated that carriers of risk alleles of CNNM2 SNPs exhibit alterations in brain structure. Animal model studies have revealed that Cnnm2 downregulation in mice leads to impairments in sensorimotor gating and cognitive function. As an Mg2+ transporter, CNNM2 primarily maintains systemic Mg2+ homeostasis. According to clinical studies, a proportion of patients with SCZ exhibit reduced Mg2+ concentrations in plasma and cerebrospinal fluid. CNNM2 dysfunction may contribute to the pathology of SCZ by disrupting Mg2+ homeostasis, thereby affecting neurodevelopment and synaptic plasticity. A systematic consolidation of current evidence supporting the involvement of CNNM2 in SCZ pathogenesis provides a direction for further investigation of the pathological mechanisms underlying this disease, and for identification of novel targets for clinical intervention.

Genomic Architecture of Migraine: A Multi ancestry GWAS Meta analysis of 2.5 Million Participants

Migraine is a leading cause of disability, yet preventive treatment remains largely empirical despite the availability of several mechanistically distinct therapies. Genetic data can clarify mechanisms and therapeutic hypotheses when association signals are integrated with molecular and clinical data. We meta-analyzed migraine GWAS data from 12 European ancestry cohorts (206,893 cases and 2,093,175 controls) and four African ancestry cohorts (22,115 cases and 178,626 controls). We identified 311 lead variants in European-ancestry analyses and 316 lead variants in trans-ancestry analysis. Fine-mapping and transcriptome-wide analyses prioritized variants and genes implicated in sensory neuronal signaling, vascular tone, and immune regulation, with convergent evidence at several established loci including TRPM8 and PHACTR1. Drug-repurposing analyses identified therapeutic targets and compounds, including established migraine treatments and candidates requiring experimental validation. Genetic correlations, Mendelian randomization, and a phenome-wide scan linked migraine liability to psychiatric, pain, and gastrointestinal phenotypes. Together, these findings expand the known genetic architecture of migraine across ancestries and provide a genetics-led map connecting association signals with biological pathways, multimorbidity and candidate therapeutic mechanisms, providing a foundation for future functional and translational studies.

Serum metabolomic profiling identifies a biomarker panel associated with age-related hearing loss

Age-related hearing loss (ARHL) is a progressive neurodegenerative disorder for which reliable biomarkers are lacking. This study aimed to identify serum metabolic markers associated with ARHL that may improve early screening and diagnostic performance. A total of 300 subjects, including 125 healthy controls and 175 patients with ARHL from three independent centers, were enrolled. Untargeted and targeted metabolomics based on liquid chromatography–quadrupole time-of-flight mass spectrometry were performed to characterize metabolic alterations. Multivariable regression analysis was used to examine metabolite–phenotype associations, and logistic regression combined with receiver operating characteristic curve analysis evaluated diagnostic performance. Ten candidate metabolites were initially identified. Stepwise regression analysis demonstrated that d-glutamine (OR 1.81, 95% CI 1.29–2.53), sphingomyelin (d18:1/20:0) (OR 1.24, 95% CI 1.12–1.37) and N6-methyladenosine (OR 1.22, 95% CI 1.09–1.42) were independently associated with ARHL after controlling for age, sex, body mass index, smoking status, triglycerides and high-density lipoprotein cholesterol. The validation cohort confirmed that the biomarker panel exhibited strong diagnostic potential for ARHL (AUC 0.8523, 95% CI 0.7429–0.9704). These findings identify and validate a serum metabolomic signature for ARHL, providing new insight into early detection and clinical assessment of ARHL.

Identification of novel loci regulating circulating melatonin and its causal relationship with hypertension

Findings reveal novel genetic contributors to melatonin regulation and establish their putative causal relationship with hypertension.