A Gene-Centric Approach to Elucidating Cardiovascular Risk

Abstract

Although behavioral precedents including diet and cigarette smoking play an important role, coronary artery disease (CAD) exhibits significant heritability,1 a part of which can be attributable to genetic variants affecting known biochemical and metabolic risk factors. In the last 2 years, impressive progress has been made in understanding the genetic basis of several of these using both candidate gene and genome-wide association study (GWAS) approaches. Article see p 16 Candidate genes, coding for proteins of known biological significance in a disease process, provide a logical first step in understanding the genetics of common disease states. Populations of affected and unaffected individuals can be studied by genotyping common single-nucleotide polymorphisms (SNPs) within a gene and its regulatory sequences. Although economically attractive, this approach is acknowledged to have a number of limitations. By definition, studies are limited to genes with a known or suspected role in defining a given phenotype and do not provide new insight into biological pathways leading to disease. Furthermore, candidate gene associations often fail to replicate for multiple reasons. Sample sizes have often been inadequate to provide the statistical power required when multiple variants of small effect are tested. The original observation may have been false positive, emphasizing the need for stringent statistical thresholds. Other issues include heterogeneity of causality. For example, genetic variants affecting plasma lipid traits may have significant effects on CAD risk, but these may be less important than or interact with other risk factors including diabetes and smoking. Finally, appropriate measures must be taken to test for population stratification. Genome wide association studies (GWASs) are is an alternative approach, facilitated by the International HapMap Project and advances in genotyping technology, permitting analysis of a million or more markers across the human genome. GWASs provides an unbiased approach that has led to the identification …