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P1‐002: Transcriptome‐guided neurogenesis gene pathway variation is associated with hippocampal volume in mild cognitive impairment and Alzheimer's disease
Author(s) -
Horgusluoglu Emrin,
Nho Kwangsik,
Risacher Shan L.,
Foroud Tatiana,
Saykin Andrew J.
Publication year - 2015
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.2015.06.198
Subject(s) - neurogenesis , hippocampal formation , neuroscience , hippocampus , biology , brain size , gene expression profiling , dentate gyrus , gene , psychology , genetics , gene expression , medicine , magnetic resonance imaging , radiology
Background: Genome-wide association studies (GWAS) have added substantially to our understanding of the genetic factors implicated in late-onset Alzheimer’s disease (LOAD). However, GWAS alone lacks the granularity to determine causal variants from among thousands of candidates in each significant region. Determination of true causal variants must therefore be conducted through additional analyses following GWAS. This is further complicated by the fact that an estimated 93% of GWAS signals implicate variants in non-protein-coding regions where mechanisms involve indirect gene regulation (Maurano et al. 2012). Furthermore, regulatory elements do not necessarily regulate the closest genes. Therefore two key questions arise: (1) How do we distinguish between the multitudes of non-coding variants with significant P-values to determine causal variants? (2) How do we determine which target genes these causal variants regulate?Methods:We developed a pipeline integrating annotation data from ENCODE, NIH Roadmap Epigenomics, and FANTOM5 data among others for the purpose of detecting cell-type-specific enhancer-promoter relationships affected by LOAD-associated non-coding SNPs. From the 21 genome-wide significant LOAD-associated regions identified by the IGAP GWAS, 1,980 candidate SNPs were identified. Candidates were then prioritized for enhancer potential based on consensus among cell-type-specific and non-cell-type-specific enhancer markings. Target genes were predicted for top candidates based on gene expression QTL and chromatin conformation (3C and Hi-C) results, as well as through enhancer-promoter correlation of both open chromatin and RNA expression levels. Results:Using our pipeline we identified a set of five top candidate LOAD-associated enhancer SNPs, which are supported by multiple independent monocyte-specific enhancer marks. These SNPs were linked to the genes PTK2B, CASS4, MS4A4A, MS4A6A, and TAS2R41 by both FANTOM5 enhancer-promoter RNA expression correlation as well as monocyte-specific eQTL studies. We are currently in the process of performing luciferase assays in the THP-1 monocyte cell line in order to validate the enhancers’ activities. Conclusions: Through utilization of genomic annotation data, we were able to identify candidate causal SNPs within LOAD-associated non-protein coding regions. Further wet-lab validations are underway; we will report the findings during the AAIC 2015 meeting.