ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2019
Bioinformatics strategy to advance the interpretation of Alzheimer's disease GWAS discoveries: The roads from association to causation.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 28 citations in OpenAlex.
- Functional Annotation of Alzheimer's Disease-Associated SNPs Based on Three-Dimensional Chromatin Structure.International journal of molecular sciences · 2026Article
- Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer's disease brains.Cell & bioscience · 2023Article
- Bioinformatics pipeline to guide post-GWAS studies in Alzheimer's: A new catalogue of disease candidate short structural variants.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort.Genes · 2023Article
- Bioinformatics pipeline to guide late-onset Alzheimer's disease (LOAD) post-GWAS studies: Prioritizing transcription regulatory variants within LOAD-associated regions.Alzheimer's & dementia (New York, N. Y.) · 2022Article
- Translational animal models for Alzheimer's disease: An Alzheimer's Association Business Consortium Think Tank.Alzheimer's & dementia (New York, N. Y.) · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
introductionGenome-wide association studies (GWAS) discovered multiple late-onset Alzheimer's disease (LOAD)-associated SNPs and inferred the genes based on proximity; however, the actual causal genes are yet to be identified.
methodsWe defined LOAD-GWAS regions by the most significantly associated SNP ±0.5 Mb and developed a bioinformatics pipeline that uses and integrates chromatin state segmentation track to map active enhancers and virtual 4C software to visualize interactions between active enhancers and gene promoters. We augmented our pipeline with biomedical and functional information.
resultsWe applied the bioinformatics pipeline using three ∼1 Mb LOAD-GWAS loci: BIN1, PICALM, CELF1. These loci contain 10-24 genes, an average of 106 active enhancers and 80 CTCF sites. Our strategy identified all genes corresponding to the promoters that interact with the active enhancer that is closest to the LOAD-GWAS-SNP and generated a shorter list of prioritized candidate LOAD genes (5-14/loci), feasible for post-GWAS investigations of causality. DISCUSSION: Interpretation of LOAD-GWAS discoveries requires the integration of brain-specific functional genomic data sets and information related to regulatory activity.
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