Methods in Genetics and Clinical Interpretation Randomized Trial of Personal Genomics for Preventive Cardiology Design and Challenges

Background Genome-wide association studies (GWAS) have identified more than 1500 disease-associated single nucleotide polymorphisms (SNPs), including many related to atherosclerotic cardiovascular disease (CVD). Associations have been found for most traditional risk factors (TRFs), including lipids,1,2 blood pressure/hypertension,3,4 weight/body mass index,5,6 smoking behavior,7 and diabetes.8–13 GWAS have also identified susceptibility variants for coronary heart disease (CHD). The first and, so far, strongest of these signals was found in the 9p21.3 locus, where common variants in this region increase the relative risk of CVD by 15% to 30% per risk allele in most race/ethnic groups.13–20 Subsequent largescale GWAS meta-analyses and replication studies in largely white/European populations have led to the reliable identification of an additional 26 loci conferring susceptibility to CHD,2,20–23 all with substantially lower effects sizes compared with the 9p21 locus. Many of these CVD susceptibility loci appear to be conferring risk independent of TRFs and thus cannot currently be assessed by surrogate clinical measures (Table 1). Among the 27 independent loci identified in the most recent large meta-analyses of CVD, 21 were reported not to be associated with any of the TRFs.20,21 Several studies have explored whether initial CVD-related genetic markers can improve risk prediction over standard models restricted to TRFs using a genetic risk score (GRS) constructed on the basis of the number of risk alleles inherited.24–26 Results to date have been mixed. Although all have shown that a GRS is strongly associated with the outcome of interest independent of TRFs, none were able to demonstrate a significant improvement in the c-statistic. Two of the 3 studies showed some modest improvement in newly defined discrimination indices, including the integrated discrimination index, the net reclassification index, and the clinical net reclassification index (net reclassification index in the intermediate-risk subjects). Thus, the use of these markers has not yet been shown to convincingly outperform models that include TRFs and family history alone. One important reason for the failure of these markers to demonstrate clinically meaningful improvement of risk prediction relates to the small proportion of the genetic variance explained by these markers, a phenomenon commonly referred to as the heritability gap. The basis for this heritability gap is the focus of intense investigation. Despite this gap, it is still possible that knowledge of genetic risk may improve patient outcomes through means other than enhanced risk reclassification. For instance, genetic testing may improve patient adherence and CVD risk factor reduction for Mendelian disorders related to CHD, such as familial hypercholesterolemia.27 This effect may be owing to an increase in patient motivation (eg, people who recognize and accept their high risk are more encouraged to reduce it); however, no clinical trial to date has demonstrated that newly discovered genetic markers improve risk factor profiles by improving adherence to prescribed therapy for complex (garden variety) CVD. Here, we describe the design of an ongoing randomized trial to investigate whether CVD risk factor profiles can be improved by providing participants with knowledge related to their inherited risk of CVD in addition to information on their risk related to measured TRFs. We also discuss some of the challenges that arise in the design and conduct of such a trial and how they were addressed.