Molecular genetic epidemiology of human diseases: from patterns to predictions

Databases of disease-associated or disease-causing mutations allow the study, not only of the molecular mechanisms underlying the primary lesions at the DNA level, but also of the functional consequences of mutation at the phenotypic level. The Human Gene Mutation Database (HGMD) and the bioinformatics analyses of its content provide an illustrative example of this indirect approach to molecular genetic epidemiology. In fact, the Bayesian type of reasoning underlying previous scientific analyses of HGMD data is also reflected in current software tools used to predict the likely disease relevance of a newly detected genetic variant. After a brief resume of the past scientific utility of HGMD, we, therefore, shortly review three representative and commonly used examples of these tools, namely SIFT, PolyPhen-2 and NNSplice.