A novel genetic region prioritization tool using a new statistical framework to analyse oligogenic diseases

Nowadays, few bioinformatics tools are able to identify the genetic cause of oligogenic diseases. To allow such analyses and make the best use of public and in-house available exome and whole genome data, our goal is to develop a new prioritization tool. Its function will be to discover the genetic underpinnings of human disease using 3 main components: (1) Identification of new regions of interest (beyond single variants). First, we will consider the entire gene to be the analyzed unit (aggregating all variants within a gene). In a second layer, we will group genes based on their involvement in the same molecular/biological pathways. Finally, for whole genome data, we will need to define completely new units of analysis. (2) Development of a new statistical framework for testing association of variants within the genetic regions of interest defined, comprised of tests representative of all main classes (Burden, Variance-Component and Omnibus tests). (3) A dedicated automated pipeline made of the statistical framework (as main component), along with other modules that will provide information to weight the results (e.g. associated biological data like pathways, spatial distribution of variants within regions of interest). The pipeline will automatically generates hypotheses using heuristic methods and will be available as a free, stand-alone prioritization tool. Finally, we will benchmark it against published and in-house data, and perform use-cases to identify novel associations of variants with disease. This approach is critical in the analysis of genetically heterogeneous and complex, multigenic disorders such as breast cancer, primary lymphedema, and cleft lip and/or palate, subjects of large-scale sequencing projects in our lab, which will require these tools to effectively exploit the large volumes of data being generated. We aim at proposing this tool as a clinically competent application, introducing a new step toward oligogenic disease understanding.