Genetic Dissection of Cognitive Resilience

Our goal is an improved understanding of the genetic contributors to cognitive resilience in neurodegenerative disease (ND) and normal aging, and the development of machine-learning (ML) models for the prediction of expected cognition and its decline. Our specific aims are:
I: Identify novel genetic factors for cognitive resilience through the development of genetically informed, ML models of cognition. We will define a novel measure of residual cognition using ML to identify novel genetic associations for cognitive resilience. To achieve this aim, we will: (1) Characterize and compare the degree of association between polygenic risk scores (PRS) and measured risk and resilience factors with risk of diagnosis, cognition, and cognitive decline across NDs. (2) Develop integrated, multi-PRS predictive models of cognition in normal aging, which will be further refined to account for neuropathology in AD, PD, and ADRSs. Comparison of these models will highlight differences in factors driving cognition across ND conditions. (3) Conduct genome-wide and rare variant association studies of cognitive resilience, as quantified by a novel continuous residual cognition measure defined in (2).
II: Identify and characterize genetic signatures of cognitive resilience in healthy aging. Similarly, we will characterize if and how ND, cognitive reserve, and brain structure PRS associated with cognition in healthy aging - in cohorts including ADSP and Wellderly. To identify novel genetic contributors, we will identify individuals with outlying genetic signatures of risk to cognitive decline – and perform association studies and analyses as in I.3.
III: Genetically informed dissection of the causal contributions of cognitive / brain reserve in cognitive resilience. We will dissect the causal relationship between cognitive resilience, educational attainment, and neuroimaging features. Using polygenic Mendelian randomization analysis, educational attainment, and brain structure PRS as instrument variables, and measured educational attainment and brain imaging as exposures, we can dissect the relationship between cognitive reserve, brain reserve, and cognitive decline in ND.

Cognitive decline occurs during normal brain aging and during neurodegeneration. Understanding shared genetic factors that protect from cognitive decline in aging and neurodegeneration may help inform strategies to stop its decline. The focus of this proposal is to identify those genetic factors that protect against cognitive decline. In particular, we aim to identify novel genetic factors involved in cognitive resilience through associative, predictive, and causal analysis strategies. The overarching goal is an improved understanding of the genetic contributors to cognitive resilience in neurodegenerative disease and normal aging, and the development of machine-learning (ML) models for the prediction of expected cognition and its decline, which will have downstream utility in clinical trial design, clinical decision-making, and have the potential to reveal determinants of therapeutic response in neurodegenerative diseases.