Predictive testing for complex diseases using multiple genes: fact or fiction?

Abstract

Purpose: There 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.

Methods: Usefulness 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.

Results: We 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.

Conclusions: Genetic profiling may have the potential to identify individuals at higher risk of disease depending on the prevalence and heritability of the disease.