Personalized Genetic Prediction: Too Limited, Too Expensive, or Too Soon?

Genetic epidemiology has identified many common genetic variants that are associated with common diseases, and the list is growing monthly (1, 2). This success has boosted expectations for personalized genetic prediction. According to these expectations, genetic information can tell people about their risk for various diseases and which medications they should use or avoid. However, 2 articles in this issue (3, 4) suggest that this promise may be exaggerated and premature. Paynter and colleagues (3) evaluated the predictive performance of rs10757274 for cardiovascular disease (CVD) in 22129 white women. This gene variant has emerged from genome-wide association studies, and its association with CVD has been extensively and consistently replicated across many data sets (5). Although the authors confirmed that this polymorphism is associated with incident CVD risk, even after adjustment for other risk factors, no improvement in the prediction of CVD occurred. Adding the genotypic information to traditional risk factorsincluding family historyreclassified 2.6% of the women to different risk categories, but 63.4% of them were incorrectly reclassified. Knowing a patient's rs10757274 genotype would not help a clinician make better preventive or therapeutic decisions to reduce the future risk for heart disease. The genetic effect of rs10757274, as for most associations discovered through current genome-wide association studies, is subtle, conferring an odds ratio for future heart disease of 1.25 for heterozygotes and 1.32 for homozygotes. Such effects typically explain a small proportion (<1%) of the risk variance for the disease of interest. Thus, 1 variant alone is unlikely to convey any important predictive information. The situation could improve if we discover many variants with small effects or at least a few variants with large effects. Recent studies on the predictive performance of common genetic variants (Table) (69) are prone to several shortcomings. Typically, they have casecontrol designs or are cohort studies with relatively few events. The list of potential traditional risk factors is often incomplete (for example, a study on diabetes did not consider family history). Moreover, researchers sometimes report strength-of-association measures that are derived from the data sets in which the genetic variants were discovered and first replicated. This practice may exaggerate the genetic effects because the statistical models are overfitted to the data. Generalizability to different populations, for example, people of different ancestry or different environmental and lifestyle exposures, is unknown. Table. Recent Studies of Predictive Performance With Several Common Genetic Variants, Traditional Risk Factors, and Both As these studies show (Table), we are still far from personalized medicine. The improvement in prediction of future disease by adding genotypic information to traditional risk factors is subtle, if any, for CVD, prostate cancer, and type 2 diabetes. Genetic information increases the ability to predict progression of age-related macular degenerationwhich has no strong traditional risk factors and has gene variants with large effects (homozygotes have more than a 3-fold increased risk)but even with that genetic information, the predictive power to individualize risk remains relatively weak. Improved prediction does not necessarily make a predictive test useful. Utility depends also on whether the disease of interest is common in the tested population. For example, approximately 10% of women develop breast cancer in the general population. Currently, only 7 common genetic variants are robustly associated with breast cancer (10) and cumulatively they explain only about 5% of the risk variance. Therefore, they can barely distinguish between women whose risk for breast cancer differs by 2-fold. Nevertheless, some scientists argue that separating women with 2-fold differences in risk suffices to decide who should have screening mammography early in life and who will never need it (10). One might make similar claims for CVD because it is also a very common disease. Conversely, for a relatively uncommon disease, we need very high predictive discrimination to identify subpopulations at considerable absolute risk. For example, scientists have discovered at least 32 robustly replicated genetic variants associated with Crohn disease (11), but the average prevalence of the disease in the general population is less than 0.2%. Persons with several high-risk genes may have a 20-fold, higher-than-average risk but still a negligible probability (<4%) that they will receive a diagnosis of Crohn disease. Furthermore, the clinical utility of genetic prediction also depends on whether we have effective interventions whose deployment depends on more accurately measuring perceived risk. When we have no effective interventions or effective interventions that benefit everyone (regardless of the level of absolute risk), more accurate prediction will not change the indicated action plan and outcomes. Cardiovascular disease may be a disease in which the optimal action plan for prevention depends on the level of perceived risk. For example, the risks of using statins or aspirin may exceed the benefits in low-risk patients, making accurate classification of risk clinically useful. However, for breast cancer and mammography and CVD and early intervention, the utility of the genetic tests rests on the brittle assumption that there is a very specific and noncontestable risk threshold that leads to very different action plans. This prerequisite applies to any test, genetic or other, that claims to improve decision making. Even if a genetic test improves prediction for common outcomes and the effective action plan depends on perceived risk, the test may still be too expensive to adopt. Cost-effectiveness depends largely on the ability to isolate a target population in which testing will yield maximal benefits. A source of justified anxiety about the emerging genetic tests is that they are advertised for indiscriminate, widespread use in the general population. The cumulative cost of such population-wide testing can be very large. Conversely, a more rational use of genetic information might involve specific, circumscribed indications. Pharmacogenetics is one such field. Only a small proportion of the population takes a specific drug, not the general population at large. Pharmacogenetics may inform about maximizing the chances of benefiting from treatment or avoiding a serious adverse event. Unfortunately, despite several thousands of articles on pharmacogenetics of common genetic variants, few practical applications have emerged to date. One of them, also approved by the U.S. Food and Drug Administration, is genetic testing to inform about the risk for bleeding with warfarin treatment. The CYP2C9 and VKORC1 genes are implicated in warfarin and vitamin K metabolism, and variants in these genes are strongly and consistently associated with bleeding while taking warfarin. Is this information worth its cost? Eckman and coworkers (4) found that genotype-guided dosing based on CYP2C9 and VKORC1 was not cost-effective for patients requiring initiation of warfarin treatment. Despite better outcomes, the cost exceeded $170000 per quality-adjusted life-year, on the basis of the current cost of testing (about $400). To bring the cost-effectiveness ratio down to the range that is considered cost-effective, the test would have to cost $100 or be restricted to patients at high risk for bleeding. Even with imposing these conditions, testing for polymorphisms in these genes may not be ready for routine practice. For example, although their association with bleeding risk is probably beyond doubt, we do not know whether genotyping in everyday clinical practice reduces the risk for hemorrhage. Such knowledge would require large, well-designed, pragmatic randomized trials. The 3 small randomized trials published to date on this question have captured cumulatively only 11 cases of major bleeding. Despite these caveats, genetic tests have already invaded the health market and are here to stay (12). Although we may lament their poor evidence base, we should remember that we adopted nearly all pregenomic-era tests without waiting for large, well-designed, pragmatic randomized trials. However, we should not use past mistakes as an excuse for present inaction. As we prepare for an unprecedented flood of genetic information, we should observe some safeguards. These could include transparent and thorough reporting of data on genetic associations (13); unbiased, continuous, and updated synthesis of the pertinent evidence (14); careful grading of the evidence (15); appropriately linking between the evidence base and the information conveyed to consumers (16); and alerting the public that most genetic tests have not yet been shown to be clinically useful. Helping patients and physicians to decide when to do genetic tests will be a tough task because neither knows much about the rapidly emerging field of genomics (17). We need to learn more about what our genome can tell us and, more important, what it cannot tell us (18).