Li-San Wang, United States of America
University of Pennsylvania Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics CenterAuthor Of 1 Presentation
CHARACTERIZING TRANSCRIPTIONAL REGULATORY MECHANISMS OF ALZHEIMER’S DISEASE NONCODING RISK VARIANTS IDENTIFIED BY AGGREGATING RESULTS ACROSS MULTIPLE META-ANALYZED GENOME-WIDE ASSOCIATION STUDIES
Abstract
Aims
Recent meta-analyses of genome-wide association studies (GWASs) have found many non-coding genetic variants associated with Alzheimer’s Disease (AD), but functional interpretation of these signals remains challenging. By integrating diverse functional genomics (FG) data-types and aggregating results from several GWASs, we aim to identify functional variants, affected genes, and cell-type specific enhancer-based regulatory mechanisms underlying these genetic associations.
Methods
We applied SparkINFERNO (Kuksa et al, 2020) to analyze genome-wide three of the largest AD GWASs (Lambert et al, 2013; Kunkle et al; Jansen et al, 2019). First, we integrated epigenetic, transcription, and chromatin interaction datasets from >1,000 tissues/cell types. For validation, we used brain-specific datasets, including 18 eQTL, 7 single-cell/bulk RNA-seq, and network analyses from multiple labs/consortiums. All FG datasets were systematically harmonized to prioritize noncoding genetic variants and target genes for functional follow-up.
Results
We identified 278 potentially causal variants targeting 361 genes via GWAS-eQTL colocalization analyses across GWASs. 10% of colocalized variants, and 7% of colocalized variant-gene pairs were found in all GWASs. 71% variant-gene pairs were supported by >=2 brain-specific eQTL datasets, and 77 pairs were further validated by enhancer-promoter interaction data (Nott et al, 2019). Using RNA-seq, we determined brain regions (temporal cortex, cerebellum) and cell types in which these target genes were differentially expressed in AD. We found microglial (FGF22), oligodendrocyte (BIN1), and neuronal genes (DPYSL2, MADD) across analyzed GWASs.
Conclusions
The principled integration of different FG data-types and GWAS datasets revealed variant-gene pairs with layered evidence (epigenetics+chromatin+RNA+eQTL) and identified cell-type specific regulatory mechanisms underlying genetic susceptibility to AD.