Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Guangdong Provincial People's Hospital, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510080, China.
Biomed Res Int. 2021 Sep 8;2021:4229194. doi: 10.1155/2021/4229194. eCollection 2021.
Previous studies have shown that heart failure (HF) and chronic kidney disease (CKD) have common genetic mechanisms, overlapping pathophysiological pathways, and therapeutic drug-sodium-glucose cotransporter 2 (SGLT2) inhibitors.
The genetic pleiotropy metaCCA method was applied on summary statistics data from two independent meta-analyses of GWAS comprising more than 1 million people to identify shared variants and pleiotropic effects between HF and CKD. Targets of SGLT2 inhibitors were predicted by SwissTargetPrediction and DrugBank databases. To refine all genes, we performed using versatile gene-based association study 2 (VEGAS2) and transcriptome-wide association studies (TWAS) for HF and CKD, respectively. Gene enrichment and KEGG pathway analyses were used to explore the potential functional significance of the identified genes and targets.
After metaCCA analysis, 4,624 SNPs and 1,745 genes were identified to be potentially pleiotropic in the univariate and multivariate SNP-multivariate phenotype analyses, respectively. 21 common genes were detected in both metaCCA and SGLT2 inhibitors' target prediction. In addition, 169 putative pleiotropic genes were identified, which met the significance threshold both in metaCCA analysis and in the VEGAS2 or TWAS analysis for at least one disease.
We identified novel variants associated with HF and CKD using effectively incorporating information from different GWAS datasets. Our analysis may provide new insights into HF and CKD therapeutic approaches based on the pleiotropic genes, common targets, and mechanisms by integrating the metaCCA method, TWAS and VEGAS2 analyses, and target prediction of SGLT2 inhibitors.
先前的研究表明，心力衰竭（HF）和慢性肾脏病（CKD）具有共同的遗传机制、重叠的病理生理途径和治疗药物——钠-葡萄糖共转运蛋白 2（SGLT2）抑制剂。
应用遗传多效性元关联分析（metaCCA）方法对两项独立的 GWAS 荟萃分析的汇总统计数据进行分析，这些 GWAS 包含了超过 100 万人的数据，以确定 HF 和 CKD 之间的共同变异和多效性效应。SGLT2 抑制剂的靶点通过 SwissTargetPrediction 和 DrugBank 数据库进行预测。为了细化所有基因，我们分别使用多功能基因关联研究 2（VEGAS2）和全转录组关联研究（TWAS）对 HF 和 CKD 进行了分析。基因富集和 KEGG 通路分析用于探索鉴定基因和靶点的潜在功能意义。
经过 metaCCA 分析，在单变量和多变量 SNP-多变量表型分析的单变量和多变量分析中，分别有 4624 个 SNP 和 1745 个基因被认为是潜在的多效性。在 metaCCA 和 SGLT2 抑制剂的靶点预测中都检测到 21 个共同基因。此外，还鉴定出 169 个假定的多效性基因，这些基因在 metaCCA 分析以及 VEGAS2 或 TWAS 分析中至少有一种疾病的分析中均达到了显著阈值。
我们通过有效地整合来自不同 GWAS 数据集的信息，使用有效方法识别与 HF 和 CKD 相关的新型变异。我们的分析可能为 HF 和 CKD 的治疗方法提供新的见解，这些方法基于多效性基因、共同靶点和机制，整合了 metaCCA 方法、TWAS 和 VEGAS2 分析以及 SGLT2 抑制剂的靶点预测。
