Genome-based prediction of common diseases: advances and prospects.
Common diseases such as type 2 diabetes and coronary heart disease result from a complex interplay of genetic and environmental factors. Recent developments in genomics research have boosted progress in the discovery of susceptibility genes and fueled expectations about opportunities of genetic profiling for personalizing medicine. Personalized medicine requires a test that fairly accurately predicts disease risk, particularly when interventions are invasive, expensive or have major side effects. Recent studies on the prediction of common diseases based on multiple genetic variants alone or in addition to traditional disease risk factors showed limited predictive value so far, but all have investigated only a limited number of susceptibility variants. New gene discoveries from genome-wide association studies will certainly further improve the prediction of common diseases, but the question is whether this improvement is sufficient to enable personalized medicine. In this paper, we argue that new gene discoveries may not evidently improve the prediction of common diseases to a degree that it will change the management of individuals at increased risk. Substantial improvements may only be expected if we manage to understand the complete causal mechanisms of common diseases to a similar extent as we understand those of monogenic disorders. Genomics research will contribute to this understanding, but it is likely that the complexity of complex diseases may ultimately limit the opportunities for accurate prediction of disease in asymptomatic individuals as unraveling their complete causal pathways may be impossible.
Department of Public Health, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands. a.janssens@erasmusmc.nl
A Cecile J W
AC
Janssens
Cornelia M
CM
van Duijn
D004194:Disease / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing; D015894:Genome, Human; D023281:Genomics / Q000379:methods / Q000639:trends*; D006801:Humans