P2‐123: Bioinformatics Strategy to Advance the Interpretation of Alzheimer's Disease Gwas Discoveries: the Roads From Association to Causation
Year: 2019
Type: article
Abstract: Late-onset Alzheimer's disease (LOAD) is a genetically heterogenous disease. Genome wide association studies (GWAS) and whole genome/exome sequencing (WGS/WES) discovered about 40 LOAD-associated SNPs, navigating the genetic research of LOAD to specific regions in the genome. While the target genes have been inferred by proximity to the most significantly associated SNPs, the actual causal genes are yet to be identified. Thus, there is an unmet need for post-GWAS research that applies a multifaceted strategy combining in silico, in vitro and in vivo approaches to identify and validate the precise causal gene/s within the associated loci. We propose a bioinformatic strategy built on the hypothesis that dysregulation of gene expression mediated by genetic and epigenetic mechanisms contributes, at least in part, to LOAD pathogenesis. We defined LOAD-GWAS regions by the associated SNP±0.5Mb and extended the loci boundaries such that they are not within a gene. We developed a bioinformatics pipeline that utilizes the hippocampus-specific chromatin state segmentation track available from the Roadmap Epigenomics Project to map active enhancers and data on hippocampus specific frequencies of chromatin interaction using the virtual 4C software to visualize interactions between active enhancers and gene promoters. We augment our pipeline with biomedical and functional information from publications. We applied the bioinformatics strategy using three ∼1Mb LOAD-GWAS loci known by the proximate genes: BIN1, PICALM, CELF1. These loci contain 10–24 genes, an average of 106 active enhancers and 80 CTCF sites. We integrated the active enhancers from the chromatin state segmentation track with the enhancer-promoter interactions from 4C data and identified all genes corresponding to the promoters that interact with the active enhancer positioned the closest to the LOAD-GWAS-SNP. This strategy generated a shorter list of prioritized candidate LOAD genes, ranging from 5–14 per locus. We suggest that the interpretation of LOAD-GWAS discoveries requires the integration of brain-specific functional genomic and epigenomic datasets and information related to regulatory activity, rather than inference based on proximity to the GWAS-SNPs. We demonstrated that our bioinformatics pipeline is feasible to identify the highest priority LOAD candidate genes and to guide post-GWAS laboratory experiments to investigate causality.
Source: Alzheimer s & Dementia
Institution: Duke University
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4248560958