Cognitive decline among older people and Alzheimer's disease (AD), the specific condition most frequently responsible for severe decline in this age group, are large and growing humanistic and public health problems. This trajectory is a human trait with a heritable component, but is also associated with non-genetic factors. We propose to explore the architecture of the phenotype of decline in global cognition to inform the functional characterization of both known and novel risk factors. Since the decline in cognition may not follow a linear trend, we will compare the performance of multiple statistical models with which to examine this trait, starting with a linear mixed effects model and moving to models of increasing complexity, including penalized and quadratic spline models. Using the optimal statistical model determined in these efforts, we will explore the relationship between this enhanced estimate of cognitive trajectories with existing sets of data cataloguing (1) genome-wide genotype data and (2) genome-wide DNA methylation profiles of the dorsolateral prefrontal cortex from the same individuals. In addition, these estimates of cognitive decline will also be examined for associations with a novel dataset of microRNA (miRNA) profiles from the same region of the brain in the same subjects. Further, limiting our analyses to DNA methylation and miRNA features associated with cognitive decline, we will assess for evidence of interaction in these two types of measures, which capture different aspects of the transcriptional state of the brain. While examining genetic, methylomic and transcriptomic factors separately is important, informative results will also come from assessing how they all work together to affect cognition. Therefore, we propose to use model reduction techniques, such as LASSO, to create a comprehensive model of cognitive decline that incorporates non-redundant clinical, neuropathologic, genetic, DNA methylation and miRNA features that are risk factors for cognitive decline. We will start with the known clinical and genetic components and then consider genomic, DNA methylation and miRNA traits discovered in earlier analyses in this proposal. Throughout this proposal, we will produce integrated models of known and new risk factors that relate to the genetic architecture of cognitive decline, and secondarily to AD. Ultimately, these models will provide an ideal basis for developing new studies and analytic plans that will drive my own career as an independent investigator. Moreover, many leads will certainly emerge from these studies, the pursuit of which will serve to further my career by creating additional funding opportunities.

Public Health Relevance

The goal of this proposal is to understand the genomic, epigenomic and transcriptomic architecture of cognitive decline. In understanding how this variation affects aging and human cognitive function will allow investigators to better understand the onset of cognitive decline and diseases of cognition, such as AD. This may help to develop new treatments to delay and hopefully prevent onset of Alzheimer's disease and cognitive decline, and to develop clinical tests that may be able to predict who is at risk of developing Alzheimer's disease and cognitive decline.

National Institute of Health (NIH)
Mentored Quantitative Research Career Development Award (K25)
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Study Section
Neuroscience of Aging Review Committee (NIA)
Program Officer
Wagster, Molly V
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Brigham and Women's Hospital
United States
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