Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

PurposeThere is ongoing debate about whether testing low-risk genes at multiple loci will be useful in clinical care and public health. We investigated the usefulness of multiple genetic testing using simulated data.MethodsUsefulness was evaluated by the area under the receiver-operating characteristic curve (AUC), which indicates the accuracy of genetic profiling in discriminating between future patients and nonpatients. The AUC was investigated in relation to the number of genes assumed to be involved, the risk allele frequency, the odds ratio of the risk genotypes, and to the proportion of variance explained by genetic factors as an approximation of the heritability of the disease.ResultsWe demonstrated that a high (AUC > 0.80) to excellent discriminative accuracy (AUC > 0.95) can be obtained by simultaneously testing multiple susceptibility genes. A higher discriminative accuracy is obtained when genetic factors play a larger role in the disease, as indicated by the proportion of explained variance. The maximum discriminative accuracy of future genetic profiling can be estimated at present from the heritability and prevalence of disease.ConclusionsGenetic profiling may have the potential to identify individuals at higher risk of disease depending on the prevalence and heritability of the disease.

Original publication




Journal article


Genetics in medicine : official journal of the American College of Medical Genetics

Publication Date





395 - 400


Erasmus MC, University Medical Center Rotterdam, The Netherlands.


Humans, Genetic Predisposition to Disease, Area Under Curve, Risk Factors, Gene Expression Profiling, Gene Frequency, Genotype, Multifactorial Inheritance, Models, Genetic, Forecasting, Computer Simulation, Statistics as Topic, Genetic Variation, Genetic Testing