Genetic cardiovascular risk prediction: will we get there?

Abstract

Major advances in genetics, including the sequencing of the human genome in 20011,2 and the publication of the HapMap in 2005,3 have paved the way for a revolution in our understanding of the genetics of complex diseases, including cardiovascular disease (CVD). After years of inconsistent results and failure to replicate putative candidate gene associations, high-throughput technologies (which genotype more than 500 000 genetic markers known as single-nucleotide polymorphisms [SNPs]) and novel statistical tools have led to a virtual explosion of novel genetic markers associated with complex human diseases. In the context of CVD, these advances have been remarkably successful in uncovering many novel genetic associations with myocardial infarction (MI) and cardiovascular risk factors such as lipids, blood pressure, diabetes, and obesity. A major objective of these studies has always been to provide new insights into the biology of CVD. However, a highly touted additional aim of these discoveries has been to use these genetic markers to usher in a new era of personalized medicine by incorporating genetic information into risk prediction (including for the primary prevention of CVD). In fact, direct-to-consumer testing of recently discovered genetic markers has proliferated despite a lack of evidence for clinical use.4 As with all nascent technologies, many fundamental questions remain to be answered: Can genetic markers or gene scores improve CVD risk prediction over and above validated risk algorithms such as the Framingham risk score and a family history of CVD? How many SNPs are responsible for the genetic component of CVD, and how many genetic markers will we need to discover to reliably improve risk prediction? What are the implications of the allelic architecture of CVD and other complex diseases for risk prediction? And, finally, what steps will be needed before this information is brought to patients? In …