Webinar: Identifying disease associated multi-gene networks with Synomics Studio. Case Studies on Bipolar Disease and Breast Cancer.
September 12, 2017
Complex chronic diseases are usually polygenic, heterogeneous and have highly interrelated networks of metabolic processes. Multiple factors (including maybe 5 or 10 genes and many non-genomic factors such as co-morbidities, assay results, treatment history & environment) act in combination to determine the disease risk. Identifying clinically relevant networks of multi-modal biomarkers associated with specific outcomes, e.g. disease risk or therapy response, is hugely complex.
This talk will describe a new platform (Synomics) that enables very rapid discovery and validation of multi-omics biomarker networks, associating up to 30 features in combination across the largest genomics and clinical studies of complex diseases. An example association analysis of a breast cancer population of 14,777 people, all of whom had BRCA1 and/or BRCA2 mutations will be described. The most complex biomarker networks identified, with high clinical penetrance, contain 17 SNPs acting in combination. These results were found and validated in 6 days on a single 4 GPU POWER8 server.