Genomics + Health

Large-scale genomic studies in millions of individuals have identified thousands of genetic variants that increase the risk of disease, thus opening the door for novel drug targets, better identification of high-risk patients, and personalized treatments. The genomics revolution in medicine has been particularly fruitful for rare genetic disorders (typically caused by mutations in one gene), where genomic data is routinely used for diagnosis and treatment planning. To unlock the potential of genomic medicine for all patients, health systems focus on biobanks, which directly link thousands of multi-omics data to the electronic medical records (EMR) of every patient in the system, thus enabling the integration of genomics in medical decision. For example, the Institute of Precision Health at UCLA is collecting genome-wide genomic data for more than 150,000 patients, out of about 5 million with EMR.  There are many similar efforts at our peer institutions.  The management and analysis of such multi-layered data have the potential of transforming medicine, but it comes with many unique computational and statistical challenges. 

The Department of Computational Medicine is developing computational methods for combining genomic and health system data in order to apply genomics to patient care. Computational medicine faculty collaborate with physicians from various clinical departments at UCLA (e.g., Pediatrics, Psychiatry) to develop novel computational methods to improve patient outcomes.  Through collaborations, these methods can be applied to genomic and health record data at other institutions.

Pilot project:

In collaboration with the Pediatrics Department and the California Center for Rare Diseases at UCLA, faculty from Genomics + Health successfully built an EMR score to identify undiagnosed Common Variable Immune Deficiency (CVID) patients. Adult patients with CVID, a collection of over 30+ genetic diseases of B-cell memory, can linger for years in pulmonary, gastro-intestinal, or rheumatology clinics receiving symptomatic care but lacking a definitive genetic diagnosis. The extra cost in delaying the start of proper treatments can often cost the system an extra $50K each year, but more importantly, late identification of CVID can be devastating to patients and result in severe debilitation and/or death. The EMR score combines ICD codes with laboratory values to accurately predict patients suspected for CVID. Building on the success of this pilot, faculty in the Genomics + Health together with collaborators at UCLA and at other biobanks are applying similar principles to other diseases to uncover high-risk patients.