With a death rate of nearly 18 million people each year, cardiovascular diseases (CVDs) are the most common cause of death worldwide. The central Diagnostic Laboratory of the University Medical Centre (UMC) in Utrecht conducts research into CVDs such as ischemic stroke, coronary artery disease and subclinical carotid atherosclerosis.
Studying traits is seen as a useful approach to identifying genetic risk factors for vascular diseases and biomarker development. An important trait marker for the progression of carotid atherosclerosis is carotid intima-media thickness (cIMT). Respective genetic association studies of cIMT have been conducted in global populations and have provided insight into the link between genetic effects and regulatory mechanisms of atherosclerosis. This has resulted in significant genetic correlations between cIMT and CVDs. Research of genetic variation and their association with CVDs is therefore of critical importance. Significant sex differences in the prevalence and severity of cIMT and carotid plaques have also been observed. Men statistically tend to have increased cIMT than women.
However, analysis of about sex-specific effects on atherosclerosis has been limited and underpowered. Men and women are generally analysed together in sex-combined genetic analyses, which estimate the homogeneity of the genetic effects between men and women. The power of sex-stratified analysis is often decreased due to the lower sample size. The power of sex-specific effects could be enhanced by sex-stratified meta-analysis of multiple studies.
This research aims to gain new insights into sexual dimorphic effects on the development of atherosclerosis using sex-stratified and sex-interaction genome-wide association–meta-studies (GWAMA) between genetic variants and cIMT for more powerful analysis.
Data from multiple individual cohort studies with over 70,000 men and women from different populations have been collected. Except for the SMART cohort, individual imputation and quality control had already been performed on the cohorts, and sample files containing stratified sexes had been generated. The multiple cohorts were cleaned, reformatted, and harmonized in a standardized way for meta-analysis. It was expected that meta-analysis from multiple populations would increase the power and reliability of the analysis. It was also expected that the results of the sex-stratified meta-analysis would show sexual dimorphic effects on cIMT and that higher effect sizes will be shown in men than in women as men have a higher plaque burden than women.
A combined and stratified single analysis of the SMART cohort has been performed before the meta-analysis. A sex-stratified meta-analysis on separate male and female samples has been performed to test for sex-specific significant variants. A sex-interaction analysis has been performed to test for the difference between sex-specific effect sizes and post-GWAS analysis has been performed to gain more insight into the significant variants and genes.
The project consists of two parts; the analysis of SMART cohort with regenie, and the meta-analysis with MetaGWASToolKit (includes interaction analysis).