Creating and evaluating polygenic scores for risk and response for common complex chronic diseases in multiple populations
Approved Research ID: 63258
Approval date: November 9th 2020
Chronic diseases like heart diseases, diabetes, and obesity are leading causes of death in the U.S. Genetic risk scores can identify risks for disease independent of traditional clinical risk factors. Another important issue is that polygenic risk scores have largely been developed in European population groups, and therefore, do not perform well in non-white population group.
The main goal of this project is to develop genetic risk scores that can be used to help predict a person's risk of a condition, and response to drug treatment. Ancestry-specific risk scores will be created and evaluated in non-white population groups, utilizing effect estimates generated in European and African-ancestry population groups. This work will improve the individualization of risk scores and, therefore, the application of precision treatments. We expect to complete the proposed work within 36 months. At the end of the proposed work, we expect to have validated risk prediction model on a few complex diseases and their related drug response phenotypes for multiple populations. The potential public health impact is enormous because the diseases of interest are very common and quite morbid, and we currently have inadequate tools to predict who will develop the disease or whether an individual will respond to the usual treatments. This is particularly true of non-white ancestral groups for whom the genetic factors influencing disease and drug response are less explored. For example current guidelines suggest treating all heart failure patients with Beta blockers but our preliminary data suggests that most of this benefit occurs in only one-third of patients with the other two-thirds deriving little benefit. If polygenic scores can better direct our preventative and treatment efforts to the subgroups of patients at highest risk or with enhanced benefit, this would revolutionize medical care and make it much more efficient.