Tag: multi-omics

Multi-Omics Exploration of Risk Factors and Pathways Linking Respiratory Conditions and Abdominal Aortic Aneurysm

Multi-Omics Exploration of Risk Factors and Pathways Linking Respiratory Conditions and Abdominal Aortic AneurysmAnnouncing a new article publication for Cardiovascular Innovations and Applications journal. This editorial highlights a landmark study that applies an integrative multi-omics framework to uncover a causal connection between chronic obstructive pulmonary disease (COPD) and abdominal aortic aneurysm (AAA). Through the combined use of bidirectional Mendelian randomization (MR), tissue-specific expression quantitative trait loci (eQTL) analysis, single-cell RNA sequencing (scRNA-seq), and phenome-wide association studies (PheWAS), the study provides robust genetic and molecular evidence linking the two diseases.

A key discovery is the identification of 48 genes associated with both conditions, including KIF3A, a gene found to inhibit disease activity in both COPD and AAA. The integration of scRNA-seq data allowed localization of relevant genes—such as PLTP, RIF1, and IFI27L2—to immune and stromal cells, providing insights into tissue-specific mechanisms of pathogenesis.

Read more: https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2025.0019

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Genetic and Molecular Relationships Between Chronic Obstructive Pulmonary Disease and Abdominal Aortic Aneurysm: Insights from a Multi-Omics Approach

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Chronic obstructive pulmonary disease (COPD) and abdominal aortic aneurysm (AAA) are both severe conditions with complex etiologies and substantial comorbidities. Previous studies have suggested a potential relationship between these diseases, but the underlying genetic and molecular mechanisms remain unclear.

Bidirectional two-sample Mendelian randomization (MR) was used to investigate the causal relationship between COPD and AAA. Expression quantitative trait loci (eQTL) analysis was performed with GTEx V8 summary statistics for aortic and lung tissues. Single-cell sequencing data from GEO datasets were analyzed to identify differentially expressed genes. Finally, a phenome-wide association study (PheWAS) was conducted to explore the broader implications of identified pathogenic genes. (more…)

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