Scientists from the international CARDIoGRAMplusC4D Consortium, including those from Oxford Population Health, have linked dozens of new genome sites to coronary artery disease (CAD) risk and pioneered a powerful method for illuminating the biological roots of common disease in new research published today in Nature Genetics.
This study of over a million individuals, including over 200,000 with CAD, provides a more complete picture of the genetic roots of CAD, outlines a list of genes and genetic variants for future study, and demonstrates an analytical framework for identifying causal genes that can be used to enhance research on other diseases involving genome-wide association studies (GWAS).
The researchers discovered 68 new genome regions, or loci, associated with increased risk for CAD, bringing the total of known loci to more than 250. Loci are the parts of our DNA that contain genetic markers that can indicate a likelihood towards developing some diseases. The researchers also developed a sweeping approach that incorporates eight diverse lines of evidence and used it to pinpoint 220 candidate causal genes (genes that could be responsible for causing the onset of disease) that underlie the associated loci. The researchers verified the role of one of these potential causal genes through genome-editing and cell-based experiments, showing the power of their method to reveal how specific genes might be involved in the development of CAD.
The team wanted to go further and not only find these GWAS 'hits', but also link them to the nearby genes that cause CAD when they are disrupted. A variety of methods exist for working out which gene near a GWAS hit is likely to have a causal role in disease, so the researchers pioneered an innovative, systematic approach that incorporates evidence from eight of these methods. Some of the methods look for the closest or potentially most disruptive variants, while others look for genes known to be altered in people with the disease.
To explore the potential clinical use of their findings, the researchers also generated a new polygenic risk score that incorporates more than 2 million genomic variants. Polygenic risk scores can be used to estimate a person’s risk of developing some diseases based on genetic variants within their DNA. While the team’s score better predicted an individual’s risk for new and recurrent CAD, the improvement was surprisingly modest given the large increase in GWAS sample size. This suggests that more ancestral diversity and advances in polygenic scoring methods may be more likely to lead to substantive improvements in polygenic risk score performance than can be achieved through increasingly large, single-ancestry GWAS.
Jemma C Hopewell, Professor of Precision Medicine and Epidemiology at Oxford Population Health, co-author of the paper and current co-chair of CARDIoGRAMplusC4D research efforts said ‘This study provides another landmark in our understanding of CAD and highlights the unique strength of large-scale studies and global collaborative efforts. This comprehensive research provides insights into the genetic architecture and biological mechanisms involved in CAD and not only provides a clearer understanding of the extent to which genetics can predict an individual’s risk of CAD, but also has the potential to help guide the development of new treatments that can impact patient lives.’
The researchers have shared their findings openly in the Cardiovascular Disease Knowledge Portal, developed by scientists at the Broad Institute.
The work was funded in part by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services, National Human Genome Research Institute, American Heart Association, DZHK, National Institute of Health Research, the UK Medical Research Council, Health Data Research UK, and the British Heart Foundation.