[Methodological challenges for genome-based prediction of diseases]

The rapidly developing genotyping technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective.