Exploring the common genetic basis of metabolic syndrome-related diseases and chronic kidney disease: insights from extensive genome-wide cross-trait analyses.
IF 6.1 3区 生物学Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
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引用次数: 0
Abstract
Background: Chronic kidney disease (CKD) is a globally prevalent chronic condition characterized by progressive renal function decline, imposing significant economic and psychological burdens on patients. Metabolic syndrome (MetS), characterized by obesity, hypertension, hyperglycemia, and dyslipidemia, is a significant risk factor for CKD. A strong epidemiological association exists between CKD and MetS. This study explores the genetic connections between MetS-related diseases and CKD, focusing on identifying shared risk loci, key tissues, and underlying genetic mechanisms.
Methods: We performed a cross-trait pleiotropy analysis using summary-level GWAS data from ten MetS-related diseases and CKD obtained from the IEU database to detect shared pleiotropic loci and genes. Functional annotation and tissue-specific analyses were conducted to reveal potential associations between CKD and MetS. Additionally, we used metabolite colocalization methods to explore the metabolic perspective of these diseases' associations. Finally, Mendelian randomization (MR) was employed for further association analysis.
Results: The study identified shared genetic mechanisms between mental disorders and prostatitis, revealing 1,437 pleiotropic loci at genome-wide significance. Forty-four dominant risk SNP loci were annotated, with 11 loci confirmed through causal colocalization analysis. Further gene-level analysis identified eight unique pleiotropic genes, including APOC1, APOE, BICC1, and PDILT. Pathway analysis identified the significant involvement of the Metabolism of Fat-Soluble Vitamins, Positive Regulation of Plasma Membrane-Bounded Cell Projection Assembly, and Positive Regulation of RNA Metabolic Process pathways in these diseases. Tissue enrichment analyses at the SNP and gene levels indicated that pleiotropic mechanisms play crucial roles in the Adipose Visceral Omentum, Brain Cerebellum, and Testis. Ultimately, phenotypic-level metabolite colocalization analysis revealed a metabolic intermediary mechanism linking MetS-related diseases and CKD.
Conclusion: This study uncovers the complex genetic interactions between CKD and MetS-related diseases, identifying shared genetic loci and biological pathways, providing novel insights for future therapeutic strategies.
期刊介绍：
BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.
Topical areas include, but are not limited to:
-Development, evaluation, and application of novel data mining and machine learning algorithms.
-Adaptation, evaluation, and application of traditional data mining and machine learning algorithms.
-Open-source software for the application of data mining and machine learning algorithms.
-Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies.
-Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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