Project Lead: Diane Xue, Institute for Public Health Genetics
Data Science Lead: Bryna Hazelton
One in three people over the age of 65 dies with dementia. The most common cause of dementia is Alzheimer’s disease (AD), a progressive neurodegenerative disorder influenced by genetic and environmental factors. Dozens of genetic loci have been linked to AD and related dementias, and there is growing evidence that social, built, and physical environmental factors are associated with dementia outcomes. Yet, few studies have investigated the effects of social, built, and physical environmental factors after controlling for polygenic risk for AD.
The goal of this project is to model multi-level macro- and meso- environmental factors including ambient pollutants, socioeconomic status, density of physical activity facilities and social engagement destinations. alongside polygenic scores that summarize individual-level genetic risk for AD in order to determine what social and environmental factors remain significantly associated with dementia risk and/or cognitive decline after controlling for PRS. Additionally, we want to investigate whether effects of social and environmental factors differ for high- and low- genetic risk groups. Social, built, and physical environmental variables that are associated with healthy controls who are at high genetic risk can be further investigated as population-level solutions for promoting AD resilience. Furthermore, early prediction of AD is key to prevention. The results of the proposal will prepare us to integrate genetic and non-genetic factors for risk prediction, moving us close to precision treatments.