ArticleJournal of Alzheimer's disease : JAD2019
Inferring the Molecular Mechanisms of Noncoding Alzheimer's Disease-Associated Genetic Variants.
Article in Journal of Alzheimer's disease : JAD, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Integrative analysis of summary data from GWAS and eQTL studies implicates genes differentially expressed in Alzheimer's disease.BMC genomics · 2022Pooled it
- The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases.BMC medical genomics · 2022Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Deciphering the Impact of EPHA1-AS1 Gene Polymorphism on Social Cognition Deficits in Parkinson's Disease.CNS neuroscience & therapeutics · 2026Article
- Integrated genomic analysis and CRISPRi implicatesNPJ dementia · 2025Article
- A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis.Osteoarthritis and cartilage open · 2024Article
- Three-dimensional chromatin architecture datasets for aging and Alzheimer's disease.Scientific data · 2023Article
- Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes.Human molecular genetics · 2022Review
- Alzheimer's Disease Variant Portal: A Catalog of Genetic Findings for Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- 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
- MissingGenes · 2021Review
- Relevance of PD-L1 Non-Coding Polymorphisms on the Prognosis of a Genetically Admixed NSCLC Cohort.Pharmacogenomics and personalized medicine · 2021Article
- Molecular mechanisms underlying actions of certain long noncoding RNAs in Alzheimer's disease.Metabolic brain disease · 2020Review
- The Genetics of Alzheimer's Disease in the Chinese Population.International journal of molecular sciences · 2020Review
- The multiplex model of the genetics of Alzheimer's disease.Nature neuroscience · 2020Review
- Bioinformatics strategy to advance the interpretation of Alzheimer's disease GWAS discoveries: The roads from association to causation.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2019Article
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Authors and funding
14 authors.
Funding
Abstract
Most of the loci identified by genome-wide association studies (GWAS) for late-onset Alzheimer's disease (LOAD) are in strong linkage disequilibrium (LD) with nearby variants all of which could be the actual functional variants, often in non-protein-coding regions and implicating underlying gene regulatory mechanisms. We set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying these associations. We applied our INFERNO algorithm to the top 19 non-APOE loci from the IGAP GWAS study. INFERNO annotated all LD-expanded variants at each locus with tissue-specific regulatory activity. Bayesian co-localization analysis of summary statistics and eQTL data was performed to identify tissue-specific target genes. INFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). In several cases, we identified dysregulation of long noncoding RNA (lncRNA) transcripts and applied the lncRNA target identification algorithm from INFERNO to characterize their downstream biological effects. We also validated the allele-specific effects of several variants on enhancer function using luciferase expression assays. By integrating functional genomics with GWAS signals, our analysis yielded insights into the regulatory mechanisms, tissue contexts, genes, and biological processes affected by noncoding genetic variation associated with LOAD risk.
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