Validation of heritability estimation software and subtyping algorithms in stroke, epilepsy, and Chronic Obstructive Pulmonary Disease (COPD) using UK BioBank electronic health record and genomic data
Principal Investigator: Dr Nicholas Tatonetti
Approved Research ID: 41039
Approval date: August 31st 2018
Many diseases and disorders are inherited, and many common diseases have genetic risk factors that have yet to be identified. Understanding heritability, or the component of disease variation attributable to genetic variation, is important for both identifying patient risk factors in clinical practice and motivating future disease-specific genetic analyses. Understanding particular genomic factors that confer risk for or alter the progression of our individual diseases and disorders will hopefully impact future clinical care for patients. To this end, our first aim will be to use additional data from a separate population to validate heritability estimation software previously developed in our lab. One crucial issue in identifying genetic risk factors is having limited genetic resources, which dilutes the signal inherent in genome-wide association studies. In addition, genetic differences across subtypes of disease has hindered the discovery of disease-causative mutations. This is often not captured by physician diagnosis in the clinic. Successful estimation of disease heritability requires the curation of a cohort with a homogenous phenotype. Therefore, our second aim will focus on defining stroke, epilepsy, and Chronic Obstructive Pulmonary Disease (COPD) subtypes using a data-driven approach to recover more genetic signal in these diseases. In doing so, so we hope to increase our understanding of the genetic causes of these diseases, the correlation between physician diagnosis and patient genetics, and whether demographic factors impact disease severity. We expect our project to be completed within 2-3 years.