C32: Identification of causal genes in Alzheimer’s Disease using epigenomic annotations and interactions in myeloid cells

Presenter(s)

Aryan Pradhan

Abstract or Description

Background: Genome-wide association studies (GWAS) of Alzheimer’s disease (AD) have identified ~75 risk loci to date. However, pinpointing the causal genes underlying these genetic associations remains challenging as many of these variants reside in non-coding regions of the genome. It is likely that these genes are regulated by a combination of coding and non-coding risk variants. Additionally, pathway-based analyses have shown that many AD risk loci reside in or near genes with important roles in microglia-related pathways. Integration of genetics with epigenomic data from myeloid cell types will enable us better capture the regulatory elements in which disease risk variants operate and assign AD risk variants to their target genes with greater precision. 

Methods: Using AD GWAS summary statistics, we performed variant-to-gene mapping using by incorporating epigenomic annotation and chromatin interaction data. This approach allows for more relevant assignment of non-coding variants to their putative functional genes. We obtained enhancer-gene connections for myeloid cell types using an activity-by-contact (ABC) model. GWAS summary statistics and MAGMA were used to identify myeloid-specific genes associated with AD.

Results: Integration of chromatin interaction ABC scores with AD GWAS Summary Statistics in 6 myeloid cell types revealed 100 candidate risk genes in at least 1 cell type. There were 16 genes significant for AD in every cell type. There were 43 genes significant for AD when combining significance across cell types. 

Discussion: Variants in regulatory regions may affect distal genes or regulate several genes, and this approach allows us to incorporate these non-coding variants in gene-set analyses. We plan to use the ABC scores as weights in the genetic association analysis, which will allow us to leverage their effect on the genes based on the strength of interaction. We anticipate that this will also identify stronger genetic associations with AD.