Leveraging high-throughput functional data for personalized, quantitative disease risk estimates
Approved Research ID: 99208
Approval date: January 18th 2023
Alzheimer's diseases (AD) is a major growing cause of morbidity and mortality worldwide, with AD expected to impact 135 million individuals by 2050. As potential new treatments become available, improved risk assessment for AD is urgently needed to empower patients and physicians to make informed decisions about screening, therapy and long-term care well before the onset of symptoms. Given that AD risk is largely heritable, genetic testing is a promising avenue for risk assessment. The situation is similar for other diseases with a substantial genetic component, including cancer and cardiac conditions. Current genetic testing efforts are aimed at classifying gene variants as "pathogenic" or "benign", but these do not provide quantitative information about a patient's risk of developing disease (e.g. 60% risk of developing AD by age 70). Such quantitative estimates are needed to help patients and physicians make informed decisions about screening, lifestyle change, treatments, and long-term care. Both population data (e.g. from the UK biobank) and functional data (i.e. from laboratory experiments that measure gene function) are important sources of evidence to assess genetic risk. Each of these sources, however, has advantages and disadvantages. For example, functional data can provide evidence for many more variants than population data, but does not contain information about disease risk. Population data, on the other hand, contains information about disease risk, but only for a handful of variants. To better estimate risk of developing AD, cancer and cardiac conditions, we are combining both population and functional data into a model that leverages the advantages of both sources of information. The result will be accurate personalized risk estimates, which will vastly improve medical management of inherited diseases, including better recommendations for screening, and improved diagnosis and treatment. This will help to ameliorate the looming public health crisis of AD, cancer and heart disease. The expected duration of our research project is three years.