Genetic and molecular insights into bipolar disorder: A genome-wide association study and bioinformatics analysis

Integrated Expression and Bioinformatic Analysis of AKT-Associated lncRNAs Suggests UCA1 as a Candidate Biomarker for Bipolar Disorder

Brain imaging-derived phenotypes influence risk of bipolar disorder: comprehensive exposome-wide association and wide-angled genetic analyses

An atlas of brain IDPs associated with bipolar disorder is established, by integrating epidemiological and genetic evidence, and candidate IDPs that were consistently associated with BD are highlighted across complementary analyses.

Genetic insights into childhood obesity: A comprehensive bioinformatics analysis

A comprehensive genetic and molecular landscape underlying childhood obesity is delineated, reflecting its multifactorial pathogenesis, with implications for improving future prevention and management strategies for childhood obesity.

Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.

BACKGROUND Bipolar disorder (BD) is a severe psychiatric disorder associated with substantial disability. Although genome-wide association studies have identified multiple BD-associated loci, the underlying genes and mechanisms remain incompletely understood. METHODS We integrated a European-ancestry BD genome-wide association dataset with cross-tissue and tissue-specific transcriptome-wide association studies (TWAS) and complementary gene-based analysis. Candidate genes were further evaluated using differential expression analysis, consensus clustering, immune infiltration analysis, machine learning, summary-data-based Mendelian randomization, Mendelian randomization using single-cell expression quantitative trait locus data, single-nucleus transcriptomics, phenome-wide association analysis, and virtual screening. RESULTS The integrative analyses prioritized 37 candidate genes. Peripheral-blood differential-expression analysis identified 14 genes that remained significant after FDR correction, and their expression profiles separated BD samples into two expression-defined clusters. Machine-learning analysis selected UNC50, LMAN2L, LYG2, HSPE1, and KANSL3 for an exploratory classification nomogram. SMR associated genetically predicted higher HSPE1 expression with increased BD risk in two blood eQTL datasets. Cell-type-specific analyses indicated HSPE1-related associations in T-cell and natural killer cell subsets, while single-nucleus analysis descriptively showed higher HSPE1 expression in medial thalamic T cells from BD samples. PheWAS identified no genome-wide significant associations for HSPE1, whereas virtual screening identified candidate compounds with favorable predicted docking scores against the HSPE1 structure. CONCLUSION This integrative multi-omics study identified HSPE1 as a candidate BD risk gene with immune-cell-related regulatory evidence, providing insight into BD pathogenesis and supporting functional validation.

Genetic and molecular insights into amyotrophic lateral sclerosis: Exploring key pathways and disease mechanisms

Aim: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterised by progressive motor neuron degeneration. Despite identification of multiple susceptibility loci, the genetic and molecular basis of ALS pathogenesis remains incompletely understood. The present study aimed to integrate functional and metabolic analyses with GWAS-derived variants to investigate key genes, pathways, and cellular mechanisms underlying ALS. Methodology: Genetic variants associated with ALS were retrieved from published GWAS datasets and analysed through integrative bioinformatic approaches, including Gene Ontology (GO) enrichment and KEGG pathway mapping. Cell Marker enrichment assessed immune cell involvement, whilst metabolomic profiling examined lipid metabolism alterations. Unsupervised machine learning, encompassing clustering and principal component analysis (PCA), identified patterns across susceptibility loci on chromosomes 9, 12 and 19. Results: Core susceptibility genes identified included C9orf72, UNC13A and ITPR2. Enrichment analyses revealed disruptions in synaptic vesicle docking, neurotransmitter release, calcium homeostasis, oxidative stress, and neuroinflammation. Metabolomic profiling implicated disturbed lipid metabolism, whilst chromosomal clustering highlighted a genetic basis for disease heterogeneity. Interpretation: These findings underscore the multifactorial nature of ALS across genetic, molecular and metabolic dimensions, identifying potential molecular targets to guide future therapeutic development. Key words: ALS, Calcium signalling, GWAS, Neurodegeneration, Synaptic dysfunction

Genetic insights into coronary heart disease: A multi-omics approach

Novel insights are revealed into CHD genetics, confirming known loci such as 9p21.3 (CDKN2B-AS1), COL4A2 and PHACTR1, while uncovering their broader functional roles in vascular remodelling, inflammation, and lipid metabolism.