Application ID: 59345

The impact of rare genetic variants on cardiovascular diseases and their risk factors

Research questions and aims:
This project was focused on the genetic basis of the common cardiovascular diseases: coronary artery disease (CAD) and varicose veins of lower extremities (VVs). The global aim of the research was providing new fundamental knowledge about the genes that directly or indirectly control CAD and VVs development. To achieve this aim, it wass planned to perform genome-wide regional association analysis to identify rare genetic variants influencing the traits of interest and reveal the genes associated with CAD, VVs, and their risk factors.
Data obtained within the project will lay the foundation for the development of fundamentally new drugs. The combination of a huge amount of material and advanced powerful statistical methods of genetic analysis developed by our group will ensure the feasibility of the proposed project.

The background and scientific rationale of the proposed research project in general:
Cardiovascular diseases have a huge socio-economic impact being the leading cause of morbidity and mortality in people of most ethnicities. The most common pathology of the arteries is coronary artery disease (CAD) and the most common venous pathology is varicose veins of lower extremities (VVs). Prevention and treatment strategies developed for CAD led to a significant decrease in CAD mortality, although it still remains very high. Pharmaceutical treatment of VVs is now absent.
Current evidence indicates that genetic factors play an important role in CAD and VVs development. To date, genome-wide association studies of CAD have revealed approximately 300 genetic variants associated with this condition. These are mainly common variants (with a minor allele frequency of at least 5%) having a weak effect on the phenotype. Taken together, these variants account for less than half of the genetic component of this trait. For VVs, nearly 50 susceptibility loci have been identified, and it can be stated that current knowledge of VVs genetic background is far from being complete.
It is obvious that the search for genetic predictors of CAD and VVs should be continued in order to provide further progress in understanding the genetic nature of these diseases and to foster the development of targeted treatment methods. Since common variants are found mainly in the regulatory regions of the genome, coding genome regions carrying rare polymorphic variants have so far been poorly involved in the association analysis. UK databases that comprise data on hundreds of thousands of individuals allow us to study the role of rare genetic variants located in the coding regions of the genome. This opens up great opportunities for the detection of specific genes that control cardiovascular diseases development.

A brief description of the methods to be used:
Traditional methods of association analysis are not powerful enough to detect rare genetic variants influencing the risk of complex traits. In the present project, we will use a new approach called genome-wide regional association analysis. This approach is not aimed at a search for individual genetic variants, but is focused on the identification of the genes containing rare polymorphic variants by means of simultaneous analysis of these variants. It can establish a statistically significant association between the gene and the trait even when none of the variants within the gene gives a statistically significant association signal. In addition to the increased power, the proposed approach provides the results which are easy to interpret. A statistically significant association signal is interpreted as a direct indication that the structure of the corresponding protein is involved in the disease control (not just the fact of the presence of an association between the disease and the genome locus). We will use methods developed in our group (the FREGAT package,

The type and size of dataset required:
We request the following genetic data:

  1. The full cohort genome-wide imputed genotype data;
  2. Exome sequencing data.
Along with genetic data, we request phenotypic data in order to define case and control groups and to be able to adjust for covariates:
  1. All available ICD9,10 codes;
  2. All available OPCS-3,4 codes;
  3. Data on self-reported phenotypes (diseases; interventions and procedures);
  4. Other available data on diagnoses and interventions;
  5. Sex; age and anthropometrics;
  6. Sociodemographic data (social class; ethnicity; employment status; education) and current (or last) occupation;
  7. Lifestyle data (smoking; alcohol consumption; physical activity; sleep)

The expected value of the research:
We plan to perform the most large-scale regional association analysis of CAD and VVs to date. We hope to identify a number of new genes relevant for the studied pathologies and to prioritize the genes at known loci associated with these cardiovascular diseases. Data obtained within the proposed project would expand current knowledge on the genetic background of CAD and VVs, help to identify novel drug targets and aid future therapeutic interventions.

Please provide a lay summary of your research project in plain English, stating the aims, scientific rationale, project duration and public health impact:
The aim of the project is to obtain new fundamental knowledge about the genes involved in the control of coronary artery disease and varicose veins of lower extremities. These cardiovascular diseases affect a large number of people worldwide, significantly impact patients' quality of life and ability to work and impose a high socioeconomic burden. We plan to apply powerful methods of genetic analysis which allow to identify disease-associated genes that carry rare variants changing the protein structure. Project duration will be 36 months. Data obtained within the proposed project would help to identify novel drug targets and aid the development of new therapeutic interventions.

In silico genome-wide gene-based association analysis reveals new genes predisposing to coronary artery disease. Irina Zorkoltseva, Alexandra Shadrina, Nadezhda Belonogova, Anatoly Kirichenko, Yakov Tsepilov, Tatiana Axenovich
sumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics. Nadezhda M. Belonogova, Gulnara R. Svishcheva, Anatoly V. Kirichenko, Irina V. Zorkoltseva, Yakov A. Tsepilov, Tatiana I. Axenovich
Multi-Trait Exome-Wide Association Study of Back Pain-Related Phenotypes. Irina V. Zorkoltseva, Elizaveta E. Elgaeva, Nadezhda M. Belonogova, Anatoliy V. Kirichenko, Gulnara R. Svishcheva, Maxim B. Freidin, Frances M. K. Williams, Pradeep Suri, Yakov A. Tsepilov, Tatiana I. Axenovich
Association of PANX3 with chronic back pain discovered us-ing gene-based analysis. Nadezhda M. Belonogova, Anatoly V. Kirichenko, Maxim B. Freidin, Frances M. K. Williams, Pradeep Suri, Yurii S. Aulchenko, Tatiana I. Axenovich, Yakov A. Tsepilov. BGRS/SB-2022.
Nadezhda M. Belonogova, Elizaveta E. Elgaeva, Irina V. Zorkoltseva, Anatoliy V. Kirichenko, Gulnara R. Svishcheva, Maxim B. Freidin, Frances M. K. Williams, Pradeep Suri, Tatiana I. Axenovich, Yakov A. Tsepilov. Multi-trait gene-based association analysis of back pain-related phenotypes identified seven new genes. In progress.