Identification of complex molecular networks underltying common human phenotypes is a major challenge of modern genetics. Deapite the identification of multiple relevant loci, molecular mechanisms of many common human diseases remain largely unclear. We have developed several network-based computational approaches (such as NETBAG+) that allow an integrated analysis of diverse genetic data using a unified statistical framework. The application of these approaches to psychiatric disorders (autism, schizophrenia) allowed us to implicate several molecular processes involved in synapse development, axon targeting, neuronal mobility, and chromosomal modification.

The genes forming the implicated networks are highly expressed in the brain, with higher brain expression during prenatal development. The obtained results reveal an amazing phenotypic and genetic complexity of the psychiatric phenotypes. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

More generally, our results provide a proof of the principle that networks underlying complex human diseases can be identified by a network-based functional analysis of rare and common genetic variants.