Epidemiological evaluation of the use of genetics to improve the predictive value of disease risk factors.

Abstract

The prevention of common diseases relies on identifying risk factors and implementing intervention in high-risk groups. Nevertheless, most known risk factors have low positive predictive value (PPV) and low population-attributable fraction (PAF) for diseases (e.g., cholesterol and coronary heart disease). With advancing genetic technology, it will be possible to refine the risk-factor approach to target intervention to individuals with risk factors who also carry disease-susceptibility allele(s). We provide an epidemiological approach to assess the impact of genetic testing on the PPV and PAF associated with risk factors. Under plausible models of interaction between a risk factor and a genotype, we derive values of PPV and PAF associated with the joint effects of a risk factor and a genotype. The use of genetic testing can markedly increase the PPV of a risk factor. PPV increases with increasing genotype-risk factor interaction and increasing marginal relative risk associated with the factor, but it is inversely proportional to the prevalences of the genotype and the factor. For example, for a disease with lifetime risk of 1%, if all the risk-factor effect is confined to individuals with a susceptible genotype, a risk factor with 10% prevalence and disease relative risk of 2 in the population will have a disease PPV of 1.8%, but it will have a PPV of 91.8% among persons with a genotype of 1% prevalence. On the other hand, genetic testing and restriction of preventive measures to those susceptible may decrease the PAF of the risk factor, especially at low prevalences of the risk factor and genotype.(ABSTRACT TRUNCATED AT 250 WORDS)