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Updated: Jul 4, 2025
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Rare and common coding variants in lipid metabolism-related genes and their association with coronary artery disease
Wei Li1, Yongyi Wang1, Ritai Huang1
1Department of Cardiovascular Surgery, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200127, China.
Background:
Coronary artery disease (CAD) is a complex disease that is influenced by environmental and genetic factors. In this study, we aimed to investigate the relationship between coding variants in lipid metabolism-related genes and CAD in a Chinese Han population.
Methods:
A total of 252 individuals were recruited for this study, including 120 CAD patients and 132 healthy control individuals. Rare and common coding variants in 12 lipid metabolism-related genes (ANGPTL3, ANGPTL4, APOA1, APOA5, APOC1, APOC3, CETP, LDLR, LIPC, LPL, PCSK9 and SCARB1) were detected via next-generation sequencing (NGS)-based targeted sequencing. Associations between common variants and CAD were evaluated by Fisher's exact test. A gene-based association test of rare variants was performed by the sequence kernel association test-optimal (SKAT-O test).
Results:
We found 51 rare variants and 17 common variants in this study. One common missense variant, LIPC rs6083, was significantly associated with CAD after Bonferroni correction (OR = 0.47, 95% CI = 0.29-0.76, p = 1.9 × 10- 3). Thirty-three nonsynonymous rare variants were identified, including two novel variants located in the ANGPTL4 (p.Gly47Glu) and SCARB1 (p.Leu233Phe) genes. We did not find a significant association between rare variants and CAD via gene-based analysis via the SKAT-O test.
Conclusions:
Targeted sequencing is a powerful tool for identifying rare and common variants in CAD. The common missense variant LIPC rs6083 confers protection against CAD. The clinical relevance of rare variants in CAD aetiology needs to be investigated in larger sample sizes in the future.
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