ArticlePLoS genetics2017
Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 6 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
64 citing papers in PubMed, 6 syntheses or guidelines pooled it, 115 citations in OpenAlex.
- Integrative Genome-wide Association Meta-analysis of Aortic Aneurysm and Dissection Identifies Five Novel Genes.Genomics, proteomics & bioinformatics · 2025Pooled it
- Identification of susceptibility loci and relevant cell type for IgA nephropathy in Han Chinese by integrative genome-wide analysis.Frontiers of medicine · 2024Pooled it
- Integrative genome-wide analyses identify novel loci associated with kidney stones and provide insights into its genetic architecture.Nature communications · 2023Pooled it
- Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes.Nature genetics · 2021Pooled it
- Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals.Nature communications · 2020Pooled it
- A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.Frontiers in genetics · 2020Pooled it
- TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.American journal of human genetics · 2025Article
- Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk.PLoS computational biology · 2025Article
- ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.Genome biology · 2025Article
- Article
- Valid inference for machine learning-assisted genome-wide association studies.Nature genetics · 2024Article
- HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data.Genome biology · 2024Article
- multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results.PLoS computational biology · 2023Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value.Communications biology · 2023Article
- Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Epigenome-wide association study of peripheral immune cell populations in Parkinson's disease.NPJ Parkinson's disease · 2023Article
- XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.Nature communications · 2023Article
- Neurogenetic mechanisms of risk for ADHD: Examining associations of polygenic scores and brain volumes in a population cohort.Journal of neurodevelopmental disorders · 2023Article
- A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways.GeroScience · 2023Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer's disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson's disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at http://genocanyon.med.yale.edu/GenoSkyline.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.