Вавиловский журнал генетики и селекции (Jan 2020)

<i>In silico</i> mapping of coronary artery disease genes

  • I. V. Zorkoltseva,
  • N. M. Belonogova,
  • G. R. Svishcheva,
  • A. V. Kirichenko,
  • T. I. Axenovich

DOI
https://doi.org/10.18699/VJ19.585
Journal volume & issue
Vol. 23, no. 8
pp. 1037 – 1046

Abstract

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To date, more than 100 loci associated with coronary artery disease (CAD) have been detected in large-scale genome-wide studies. For some of the several hundreds of genes located in these loci, roles in the pathogenesis of the disease have been shown. However, the genetic mechanisms and specific genes controlling this disease are still not fully understood. This study is aimed at in silico search for new CAD genes. We performed a gene-based association analysis, where all polymorphic variants within a gene are analyzed simultaneously. The analysis was based on the results of the genome-wide association studies (GWAS) available from the open databases MICAD (120,575 people, 85,112 markers) and UK Biobank (337,199 people, 10,894,597 markers). We used the sumFREGAT package implementing a wide range of new methods for gene-based association analysis using summary statistics. We found 88 genes demonstrating significant gene-based associations. Forty-four of the identified genes were already known as CAD genes. Furthermore, we identified 28 additional genes in the known CAD loci. They can be considered as new candidate genes. Finally, we identified sixteen new genes (AGPAT4, ARHGEF12, BDP1, DHX58, EHBP1, FBF1, HSPB9, NPBWR2, PDLIM5, PLCB3, PLEKHM2, POU2F3, PRKD2, TMEM136, TTC29 and UTP20) outside the known loci. Information about the functional role of these genes allows us to consider many of them as candidates for CAD. The 41 identified genes did not have significant GWAS signals and they were identified only due to simultaneous consideration of all variants within the gene in the framework of gene-based analysis. These results demonstrate that gene-based association analysis is a powerful tool for gene mapping. The method can utilize huge amounts of GWAS results accumulated in the world to map different traits and diseases. This type of studies is widely available, as it does not require additional material costs.

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