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Mapping Gene and Gene Pathways Associated with Coronary Artery Disease: a CARDIoGRAM Exome and Multi-ancestry UK Biobank Analysis

Overview
Journal Sci Rep
Specialty Science
Date 2021 Aug 13
PMID 34385509
Citations 3
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Abstract

Coronary artery disease (CAD) genome-wide association studies typically focus on single nucleotide variants (SNVs), and many potentially associated SNVs fail to reach the GWAS significance threshold. We performed gene and pathway-based association (GBA) tests on publicly available Coronary ARtery DIsease Genome wide Replication and Meta-analysis consortium Exome (n = 120,575) and multi ancestry pan UK Biobank study (n = 442,574) summary data using versatile gene-based association study (VEGAS2) and Multi-marker analysis of genomic annotation (MAGMA) to identify novel genes and pathways associated with CAD. We included only exonic SNVs and excluded regulatory regions. VEGAS2 and MAGMA ranked genes and pathways based on aggregated SNV test statistics. We used Bonferroni corrected gene and pathway significance threshold at 3.0 × 10 and 1.0 × 10, respectively. We also report the top one percent of ranked genes and pathways. We identified 17 top enriched genes with four genes (PCSK9, FAM177, LPL, ARGEF26), reaching statistical significance (p ≤ 3.0 × 10) using both GBA tests in two GWAS studies. In addition, our analyses identified ten genes (DUSP13, KCNJ11, CD300LF/RAB37, SLCO1B1, LRRFIP1, QSER1, UBR2, MOB3C, MST1R, and ABCC8) with previously unreported associations with CAD, although none of the single SNV associations within the genes were genome-wide significant. Among the top 1% non-lipid pathways, we detected pathways regulating coagulation, inflammation, neuronal aging, and wound healing.

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References
1.
Pencina M, Navar A, Wojdyla D, Sanchez R, Khan I, Elassal J . Quantifying Importance of Major Risk Factors for Coronary Heart Disease. Circulation. 2018; 139(13):1603-1611. PMC: 6433489. DOI: 10.1161/CIRCULATIONAHA.117.031855. View

2.
Barth A, Tomaselli G . Gene scanning and heart attack risk. Trends Cardiovasc Med. 2015; 26(3):260-5. PMC: 5266753. DOI: 10.1016/j.tcm.2015.07.003. View

3.
Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F . Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004; 364(9438):937-52. DOI: 10.1016/S0140-6736(04)17018-9. View

4.
Lu X, Peloso G, Liu D, Wu Y, Zhang H, Zhou W . Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat Genet. 2017; 49(12):1722-1730. PMC: 5899829. DOI: 10.1038/ng.3978. View

5.
van der Harst P, Verweij N . Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease. Circ Res. 2017; 122(3):433-443. PMC: 5805277. DOI: 10.1161/CIRCRESAHA.117.312086. View