Team science approach to integrate machine-learning models and functional genomics to study aging in the context of Alzheimer's disease

PROJECT SUMMARY
Alzheimer's disease (AD) is an age-related neurodegenerative disease characterized by progressive cognitive
decline and dementia. It accounts for approximately two-thirds of all dementia cases, and its pathogenesis may
predate its clinical manifestations by decades. With ~44 million patients worldwide, AD is placing an everincreasing
burden on long-term well-being, healthcare costs, and family life. Despite more than fifty years of
research, no cures exist, and the standard of treatment remains unsatisfactory; available therapies only partially
alleviate select clinical symptoms. Decades of genetic research have demonstrated the high heritability of AD,
and identified dozens of genetic variants that are associated with AD, but it has not been straightforward to
connect these to disease mechanisms. Disentangling the impact of the normal aging process on disease risk
and progression is not straightforward and has hampered efforts to develop effective treatment or prevention
strategies for AD. In the R21 phase of this proposal, we will leverage and integrate large-scale epigenomic and
transcriptomic datasets from multiple consortia and projects to develop a cell-type specific regulatory network
model for normal brain aging and AD brain aging (Aim 1), while we simultaneously generate the first empirical
dataset to resolve AD risk regulatory loci with differential activity in donor-matched "young" and "old" human
neurons and microglia (Aim 2). Reciprocal use of computational and experimental models will benchmark the
extent to which we can recapitulate the hallmarks of AD brain aging in silica and in vitro (.R21 Milestone). In the
R33 phase of this proposal we will model the epigenetic regulation of gene expression changes in brain aging
and AD progression (Aim 3) and conduct an unbiased examination of the role of human brain cell aging in AD
risk, validating age-dependent regulatory activity and resolving convergent downstream impacts of ADassociated
variants and drivers of aging (Aim 4). Our objective is to couple emerging computational and
experimental approaches to refine in silica and in vitro experimental models of aging, towards resolving how
aging processes initiate and/or increase genetic risk for AD (R33 milestone). Overall, we test the hypothesis
that aging-related processes and AD-associated risk variants independently alter chromatin
accessibility and gene expression, acting in a combinatorial manner to drive aberrant cell type-specific
function in AD. We propose to predict and measure the molecular and functional effects of aging on neural and
glia function. Our hope is that this work may identify novel therapeutic points of intervention, in order to prevent
or slow disease course in individuals with AD.

Grant Number: 5R21AG087875-02
NIH Institute/Center: NIH
Principal Investigator: Kristen Brennand