Premium
P4‐492: GENOME‐WIDE INTEGRATION OF ALZHEIMER'S DISEASE GENETICS AND MYELOID CELL GENOMICS IDENTIFIES NOVEL RISK GENES EXPRESSED IN MICROGLIA
Author(s) -
Marcora Edoardo,
Kapoor Manav,
Novikova Gloriia,
Renton Alan E.,
Hao Ke,
Goate Alison
Publication year - 2019
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.08.038
Subject(s) - genome wide association study , epigenomics , biology , trem2 , genetics , expression quantitative trait loci , genomics , genetic association , genome , gene , candidate gene , computational biology , single nucleotide polymorphism , dna methylation , gene expression , genotype , cell , myeloid cells
Background: In recent years, large-scale and hypothesis-free genetic studies have identifiedw30 genomic regions that pass the genomewide significance threshold for association with Alzheimer’s disease (AD). These risk loci contain hundreds of genes and, since the vast majority of risk variants in these regions are non-coding, it is difficult to identify the causal genes with confidence. Indeed, compelling evidence of causal association exists only for a handful of genes where functional coding risk variants have been identified (e.g., APOE, TREM2, and ABCA7), the majority of which are expressed and execute key functions in microglia.Methods: Herewe apply our previous findings that AD heritability is enriched in enhancer elements active in peripheral blood or brain myeloid cells (microglia) by performing a genome-wide and hypothesis-driven functional genomic scan that integrates Alzheimer’s disease genetics and myeloid cell genomics (eQTL and epigenomic datasets) to discover novel microglial risk genes.Results: Wewere able to nominate several candidate causal genes in knowngenome-wide significant loci, as well as novel genes in loci that are only suggestively associated with AD in standard GWAS analysis but are study-wide significant in our integrative analysis. In addition, we were able to predict how modulation of these genes expression would affect disease risk. Conclusions: Integration of AD genetics data with eQTL and epigenomic datasets obtained from disease-relevant cell types enables the discovery of AD risk genes and how to modulate their activity for disease risk reduction, which is critical for the formulation of novel therapeutic hypotheses for AD.