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

Systematic Integration of genomics with transcriptomics for the Study of Coronary Artery Disease and Subclinical Atherosclerosis

Genetics of cardiovascular disease: genes and polymorphisms in pathways underlying atherosclerosis and clinical events.

BACKGROUND Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality worldwide and arise from a complex interplay of environmental, metabolic and genetic factors. Over the past decades, advances in genomic research, particularly genome-wide association studies (GWAS) and high-throughput sequencing, have identified numerous genetic loci associated with cardiovascular risk. These discoveries have greatly expanded our understanding of the genetic architecture of CVD, yet the functional roles of many associated genes and their integration within relevant biological pathways remain incompletely understood. AIM OF REVIEW This review aims to compile current evidence on genes and polymorphisms associated with cardiovascular disease and to organize these findings within a pathway-oriented framework that classifies GWAS-identified genes according to the biological processes in which they are involved. By integrating these genetic associations into their functional biological context, the review provides a structured perspective on the mechanisms linking genetic variation to atherosclerosis and cardiovascular events. KEY SCIENTIFIC CONCEPTS OF REVIEW The genetic architecture of CVD reflects a complex network of interacting genes that contribute to the development and progression of atherosclerosis. Rather than acting independently, many genes exert pleiotropic effects and participate in interconnected biological mechanisms involved in cardiovascular pathology. In this review, genetic associations are interpreted within a pathway-oriented framework that organizes both established and emerging genes according to their functional roles in cardiovascular biology, and highlights the interconnection between the biological processes underlying disease development. By integrating these findings, this review provides an integrated perspective on how genetic variation contributes to the molecular mechanisms underlying CVD, and offers a framework for interpreting genetic discoveries within the biological pathways that drive atherosclerosis and cardiovascular events.

Genetic insights into childhood obesity: A comprehensive bioinformatics analysis

Aim: This study aimed to explore the genetic basis of childhood obesity through systematic identification and examination of genes, pathways, and regulatory elements contributing to disease development using integrated bioinformatic approaches. Methodology: Thirty genes strongly associated with childhood obesity were subjected to detailed computational analysis using DisGeNET, Gene Ontology (GO), and WikiPathways. Gene–gene interaction patterns, biological processes, molecular functions, and cellular components were examined. Regulatory layers involving transcription factor and microRNA interactions were additionally analysed to characterise genetic and post-transcriptional control mechanisms. Results: Significantly enriched pathways included adipogenesis, orexin receptor signalling, hunger and satiety regulation, and broader metabolic control mechanisms. Key genes including LEP, IL6, POMC and ADIPOQ recurred across multiple analyses, indicating central roles within obesity-associated molecular networks. Cross-database integration revealed complex genetic interactions and molecular cross-talk influencing obesity-related phenotypes. Interpretation: This study delineates a comprehensive genetic and molecular landscape underlying childhood obesity, reflecting its multifactorial pathogenesis. The identification of key regulatory genes, transcription factors, and microRNA interactions provides valuable insight into potential molecular targets, with implications for improving future prevention and management strategies for childhood obesity. Key words: Bioinformatics, Childhood obesity, Gene pathways, Molecular networks, Therapeutic targets

The Genetics of Coronary Artery Disease: Insights from Genome-wide Association Studies.

Coronary artery disease (CAD) is a leading cause of mortality worldwide, driven by both environmental and genetic factors. Over the past two decades, our understanding of CAD vulnerability has increased substantially through advancements in genetic investigations. Researchers have extensively examined the genetic basis of CAD through genome-wide association studies (GWAS), identifying numerous loci associated with the disease. The findings have facilitated the early identification of at-risk individuals and the implementation of targeted prevention strategies, while also helping to uncover the pathophysiology of the disease. Despite these advancements, converting genetic discoveries into clinical applications still poses challenges. This review examines the heritable underpinnings of CAD, focusing on the contributions of GWAS to our understanding of the disease. The GWAS methodology, key findings, functional implications, and future prospects are discussed, along with the current status. It highlights how GWAS findings are paving the way for personalized medicine through polygenic risk scores, targeted therapies, and pharmacogenomics-driven interventions for CAD.

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm

Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members ( ADH1A, ADH1B, ADH4, ADH6 ) and ZNF827 , which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

Integrated functional analysis of prostate cancer– associated genes: A multi-dataset bioinformatics approach

The identified genes, microRNAs, and pathways advance mechanistic understanding of disease vulnerability and may ultimately inform the development of biological markers and targeted therapeutic strategies for prostate cancer.