Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease
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Abstract
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 . Author Summary After years of community efforts, many experimental and computational approaches have been developed and applied for functional annotation of the human genome, yet proper annotation still remains challenging, especially in non-coding regions. As complex disease research rapidly advances, increasing evidence suggests that non-coding regulatory DNA elements may be the primary regions harboring risk variants in human complex diseases. In this paper, we introduce GenoSkyline-Plus, a principled annotation framework to identify tissue and cell type-specific functional regions in the human genome through integration of diverse high-throughput epigenomic and transcriptomic data. Through validation of known non-coding tissue-specific regulatory regions, enrichment analyses on 45 complex traits, and an in-depth case study of neurodegenerative diseases, we demonstrate the ability of GenoSkyline-Plus to accurately identify tissue-specific functionality in the human genome and provide unbiased, genome-wide insights into the genetic basis of human complex diseases.
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Similar enrichment for immune and liver cell categories was replicated by a second group utilizing an expanded set of tissue-specific annotations.
“Similar enrichment for immune and liver cell categories was replicated by a second group utilizing an expanded set of tissue-specific annotations [185].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [185]
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Kroll, F., Donnelly, J., Özcan, G. G., Mackay, E., & Rihel, J. (2024) · eLife · doi:10.7554/elife.96839.1 · Cited by 2 · see citing works
Li, M., Hao, X., Shi, D., Cheng, S., Zhong, Z., Cai, L. F., Jiang, M., Ding, L., Ding, L., Wang, C., et al. (2024) · Frontiers of Medicine · doi:10.1007/s11684-024-1086-2 · Cited by 1 · see citing works
Yu, X., Xiao, J., Cai, M., Jiao, Y., Wan, X., Liu, J., & Yang, C. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad068 · Cited by 1 · see citing works
Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer DiseasePreprint
Sahelijo, N. J., Rajagopalan, P., Lu, Q., Rahman, R., Priyadarshi, D., Goldstein, D., Thomopoulos, S. I., Bennett, D. A., Farrer, L. A., Stein, T. D., et al. (2024) · medRxiv · doi:10.1101/2024.06.21.24309255 · Cited by 1 · see citing works
Self-citationKroll, F., Donnelly, J., Özcan, G. G., Mackay, E., & Rihel, J. (2025) · eLife · doi:10.7554/elife.96839.2 · Cited by 1 · see citing works
Lu, Q., & Zhao, H. (2019) · Wiley eBooks · doi:10.1002/9781119487845.ch24
Self-citationBahlo, M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735227305.793565249
Zhao, H., Jiang, W., & Girgenti, M. J. (2023) · Research Square · doi:10.21203/rs.3.rs-3266942/v1
Self-citationLeveraging Functional Annotations Improves Cross-Population Genetic Risk Prediction
Ye, Y., Xu, L., & Zhao, H. (2024) · ICSA book series in statistics · doi:10.1007/978-3-031-50690-1_18
Self-citationHu, J., Ye, Y., Zhang, C., Ruan, Y., Natarajan, P., & Zhao, H. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013191
Self-citationTransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study
Lai, D., Wang, H., Gu, T., Wu, S., Liu, D. J., Sham, P. C., & Zhang, Y. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.06.006
- Authors
- Lu, Q., Powles, R. L., Abdallah, S., Ou, D., Wang, Q., Hu, Y., Lu, Y., Liu, W., Li, B., Mukherjee, S., Crane, P. K., & Zhao, H.
- Affiliations
- Yale University (US)
- Peking University (CN)
- University of Washington (US)
- Corresponding author
- Zhao, Hongyu
- ORCID
- Venue
- PLOS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-07-24
- Volume
- 13
- Issue
- 7
- Pages
- e1006933
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC5546707
- OpenAlex
- W2950649723
- Cited by
- 97
- References
- 95
- Citations by year
- 2025: 10 · 2024: 8 · 2023: 18 · 2022: 7 · 2021: 13 · 2020: 20 · 2019: 9 · 2018: 8 · 2017: 4
- FWCI
- 7.44
- Type
- journal article
- Language
- en
- Update notices
- new_version (2017-08-07)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health (R01 GM59507, R01 AG042437, U01 AG006781)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- non-coding genome, heritability enrichment, late-onset Alzheimer's disease, monocytes, Parkinson's disease, cell types
- MeSH terms
- Alzheimer Disease, Humans, Organ Specificity, Genome, Human, Polymorphism, Single Nucleotide, Databases, Genetic, Genome-Wide Association Study, Epigenomics, Molecular Sequence Annotation, Transcriptome
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