Alzheimer?s Disease (AD) is a leading cause of cognitive decline and mortality in the elderly. Treatment options for AD are limited, and there is a huge need for better medical options. AD is associated with progressive expansion of beta amyloid plaques in the brain, leading over time to loss of brain tissue and cognitive function. However, at present, understanding of the underlying drivers of AD is incomplete. In this project, we will use a combination of large data sets of genetic and phenotypic data, as well as functional genomics, to further elucidate the biological processes, pathways, and cell types leading to AD. Specifically, we will use existing functional genomics data, as well as newly generated Massively Parallel Reporter Assays and advanced colocalization and outlier-based statistical approaches to identify functional regulatory variants at disease-relevant loci; these will be used to increase the power to detect AD-associated variants, particularly for low-frequency sites. Furthermore, we will focus on linking intermediate biomarkers such as metabolites and brain imaging data, and traits such as diabetes, cardiovascular disease and sleep patterns to AD risk using Mendelian Randomization and clustering techniques. We will further aim to partition the GWAS signal into discrete biological factors, both by pathways/processes and by tissue. In sum, this work will lead to deeper understanding of the causal genetic drivers of Alzheimer?s Disease.
We propose to study diverse phenotypic, metabolic pathways and low-frequency variants to identify new high- effect and actionable targets for AD. We will develop methods and validation data which will enhance the interpretability of AD variants across the allele-frequency spectrum. This is an essential step towards interpretation of an individual?s AD risk and enabling identification of new target genes and pathways.