High-throughput genotyping, expression, and sequencing technologies and the development of increasingly sophisticated methods for predicting gene-disease interactions have given the new field of genomic-based personalized medicine a wealth of data: more, in fact, than can easily be processed and interpreted. Omicia is in the business of developing computational tools and diagnostics in the field of personalized medicine for cardiovascular disease (CVD). As part of that effort we are developing a software infrastructure that uses these abundant data to identify and prioritize candidate genes and their sequence variations for clinical evaluation. The research proposed in this application has three aims. In Phase II, we will enhance and optimize Omicia's Gene Inference System (GIS), prototyped during the SBIR Phase I project. In Phase II we will add additional capabilities to its candidate gene identification methods. This will build upon the Omicia Disease Genes (ODG) ontology built in Phase I, and add pathway and protein-interaction data and include """"""""top"""""""" candidates from external publicaly-available clinical studies.
In Aim 2, we will enhance the ability of GIS to prioritize sequence variations by improving our novel paralogous-gene variation identification algorithm (iDIP) and by integrating existing amino-acid substitution (AAS) methods. By running these algorithms over all known human genes via dbSNP, we expect to identify a list of candidate variations that will be evaluated in Aim 3 including an in vitro experiment. The goal of Aim 3 is to test the gene- and variant-predictive power of GIS and to compare it to other selection methods. The clinical study will be a single-stage case control design to test the """"""""top"""""""" variant candidates from Aims 1 &2 and compare the potential association to well-established genetic markers for the risk of myocardial infarction (MI). With approximately 700 cases and 700 matched controls, our association study will be well powered to test our predicted functional markers for MI. The GIS infrastructure is an integral part of the commercial workflow of Omicia, and will form the basis of the product pipeline. As such, it will serve as licensable commercial technology for the company by helping other biotechnology companies to develop their genetic biomarkers for diagnostic and therapeutic developments (theranostics). In addition, any novel variants drawn from this Phase II study will be licensable and exploitable intellectual property, useful both as the basis for future products in our internal pipeline, as well as potentially valuable additions to our patent portfolio. The Phase II goals of enhancing and clinically validating GIS serve three purposes: proving our methodology as applied to CVD and opening the door to applications in other disease areas;showcasing GIS as a key technology for managing complexity in the post-genomic era and providing clinically-relevant insights;and finally, potentially identifying valuable IP in the form of novel genetic markers for MI.
The outcome of this project will be an evaluated gene inference system (GIS) for identifying gene-disease interactions, with a focus in the area of cardiovascular disease (CVD). This system will be used as part of the Omicia product pipeline, and can also be licensed to third parties. In addition, any novel genetic markers identified as part of the validation study will themselves be valuable additions to the Omicia product and IP portfolio. Omicia's goal is to provide content and analysis tools for molecular diagnostic tests for cardiovascular conditions, with the promise of identifying patients at high risk to enable them to begin preventive care before symptoms appear. Given the prevalence of CVD in the developed world, these products are potentially a great boon to public health, as well as being significant commercial opportunities.
