Human GeneticsGenetic & Genomic MedicinePeer-reviewed journal article
Genome-wide analyses of early-onset acute myocardial infarction identify 29 novel loci by whole genome sequencing
Yeonsu JeoniD, Sungwon JeoniD, Whan-Hyuk ChoiiD, Kyungwhan AniD, Hansol ChoiiD, Byoung-Chul KimiD, Weon KimiD, Sang-Yeob LeeiD, Jang‐Whan BaeiD, Jin‐Yong HwangiD, Min Gyu KangiD, Yeonkyung KimiD, Younghui KangiD, Yeo Jin KimiD, Byung Chul KimiD, Jong BhakiD, Eun‐Seok ShiniD
Abstract
Early-onset acute myocardial infarction (AMI) may have a higher genetic predisposition than late-onset AMI does. The present study aimed to identify and characterize germline variants that affect early-onset AMI using whole-genome sequencing (WGS). We performed a genome-wide association study based on WGS of 1,239 Koreans, including 596 early-onset AMI patients and 643 healthy individuals. Patients with AMI who underwent percutaneous coronary intervention (PCI) caused by atherothrombotic occlusive lesions were included in the study. A total of 29 novel loci were found to be associated with early-onset AMI. These loci are involved in thrombosis, fibrinolysis, inflammation, and lipid metabolism. One of the associated single nucleotide variants (SNVs), rs1614576, located upstream of PRKCB, is known to be associated with thrombus formation. Additionally, the results revealed a novel locus, rs78631167, located upstream of PLAUR which plays a critical role in regulating plasminogen activation and is related to fibrinolysis. The association between early-onset AMI and rs9357455, which is located upstream of PHACTR1 that regulates inflammation in AMI, was found. Moreover, we could identify a lipid metabolism related genetic risk locus, rs5072, in the APOA1-AS gene. This study provides new evidence supporting the genetic association between early-onset AMI and thrombosis and fibrinolysis, as well as inflammation and lipid metabolism, by analyzing the whole-genome of 596 patients with early-onset AMI who have been treated with PCI. Our findings highlight potential genetic markers for the prediction and management of AMI, as well as for understanding the etiology of AMI.
- organism: human
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 9
- FWCI
- 1.21
- Field percentile
- top 22%
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