Clinical Relevance and Disease Prediction

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As described by McCarthy and colleagues (McCarthy et al, 2008) clinical translation of recent GWA successes in the identification of susceptibility variants for complex diseases can take two routes. In the first, detection of novel loci - even with small effect sizes - may reveal new insights into disease pathogenesis leading to identification of new therapeutic targets. Such treatments may well be effective in individuals without the specific genetic variant that led to its discovery. Perhaps the best example is the development of HMG-CoA reductase inhibitors (statins) that effectively lower cholesterol levels in nearly everyone, except in individuals with homozygous absence of LDL-receptors who were instrumental in identifying this key metabolic pathway (Manolio et al, 2008).

The second translational route is through the use of genetic knowledge to develop more personalized approaches to disease prediction, prevention, and management. The major limitation here for most complex diseases is that the variants so far identified provide limited information on disease risk above and beyond conventional risk factors. For example, a recent study concluded that the combined impact of 18 risk variants for type 2 diabetes does not provide strong predictive value at the population level in addition to age, sex, and body mass index

(Lango et al, 2008). Before genetic profiling can be widely applied in clinical practice, the accuracy of risk prediction needs to be improved through identification of additional susceptibility variants and demonstration of their predictive value in prospective studies (Arnett et al, 2007; McCarthy et al, 2008).

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