Southern California Undiagnosed Disease Network

PROJECT SUMMARY Rare diseases have a negative impact on the quality of life of patients, who are left on the road of a psychologically challenging, expensive and uncertain diagnostic odyssey that sometimes lasts years. In many cases, patients receive multiple wrong diagnoses in the interim and may miss windows of intervention. Our proposal leverages the broad clinical, research and data science expertise at UC Irvine (UCI) and Children’s Hospital of Orange County (CHOC) in conjunction with Ambry Genetics’ expertise in clinical testing and infrastructure to provide affordable and comprehensive evaluations for patients with undiagnosed diseases as well as several Community Health Partners with access to medically underserved populations. The result will be an academic + non-profit + community + commercial partnership based in Orange County that will serve as a regional hub covering a large US Southwestern area within the national UDN. The Southern California UDN (SoCal UDN) site will be built on the foundation of the existing UCI/CHOC Diagnostic Center of Excellence, which is able to collect in-depth phenotype and genotype data in a sustainable fashion by performing studies on a clinical basis whenever possible and collaborating with existing translational research studies for genomics, research phenotyping, or functional evaluations. Our partnership with Ambry will further allow research genomic data to be generated from clinically covered exome sequencing. This proposal will promote rapid expansion of our program. Through partnerships with regional tertiary medical centers with whom we have a history of collaboration, we will bring UDN expertise and resources to patients throughout the American Southwest that would not otherwise have access to a local UDN clinical site, facilitate appropriate evaluation prior to acceptance, and avoid unnecessary travel for clinical phenotyping studies. We will work with community partners to improve equity for underserved populations in our region including the economically disadvantaged and Vietnamese Americans. To improve efficiency and promote sustainability, we will leverage the extensive machine learning and AI infrastructure at UCI to include machine-assisted subject identification, informed consent, record review, data management, and result disclosure into our workflow. By participating in the larger UDN, we will facilitate data and sample sharing in a way that will improve the understanding of rare diseases and advance the diagnostic process for patients with previously undiagnosed diseases.