Description
Neural cell type-specific epigenomics reveals impact of Alzheimer's associated variants on Microglial regulatory mechanisms
Easwaran Ramamurthy1, Jemmie Cheng2, Gwyneth Welch2, Laura Gunsalus1, Andy Lee1, Morgan Wirthlin1, Li-Huei Tsai2, Andreas Pfenning1
1Carnegie Mellon University, Department of Computational Biology, School of Computer Science, Pittsburgh, PA; 2Massachusetts Institute of Technology, Picower Institute for Learning and Memory, Cambridge, MA
Genome wide associations studies (GWAS) are revealing an increasing number of variants associated with Alzheimer's Disease (AD) risk, majority of which fall in non-coding regions of the genome. Identification of causal variants and impacted cellular mechanisms remains a huge challenge however, due to non-random association or linkage disequilibrium (LD) between variants in the population and incomplete knowledge of the cell type-specific functions of non-coding regions in the genome.
In this study, we attempt to overcome this challenge using new cell type-specific epigenomics data from the brain. We first identify regulatory elements in three different cell types in the human brain using cell type-specific ChIP-Seq of H3K27ac. In parallel, we collect cell type-specific H3K27ac ChIP-Seq from mouse brain and compare data from the two species to show that these elements are conserved and functional. Then, using a computational overlap enrichment analysis, we compare our database of human regulatory elements with variant data from an existing Alzheimer's GWAS. Our analysis reveals that microglial regulatory elements have significant overlap with AD associated GWAS variants relative to neuronal and glial regulatory elements (p<10-4) suggesting that microglial gene regulation is significantly altered in AD.
We further train machine learning models to ascertain which of multiple AD associated variants in LD are causal and to interpret their role in cell type-specific gene regulation. In addition to revealing a role for microglial gene regulation in AD progression, our analyses provide a candidate set of regulatory elements, cell types and variants that can be experimentally followed up on to ascertain their roles in driving the disease.