GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits
Open accessAbstract
Genome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.
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Restricting to Alzheimer's disease-relevant tissues appears to aid in regulatory variant prioritization, with probabilities for functional variants increasing substantially when using GWAS4D cell-dependent analyses with brain or monocytes.
“restricting to Alzheimer’s disease–relevant tissues (via Ensembl Regulatory Build58 and GWAS4D59) appears to aid in regulatory variant prioritization, with probabilities for functional variants increasing substantially when using GWAS4D cell-dependent analyses with brain or monocytes”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [59]
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Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
- Authors
- Huang, D., Yi, X., Zhang, S., Zheng, Z., Wang, P., Xuan, C., Sham, P. C., Wang, J., & Li, M. J.
- Affiliations
- Tianjin Medical University (CN)
- Mayo Clinic (US)
- University of Hong Kong (HK)
- Corresponding author
- Li, Mulin Jun
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-16
- Volume
- 46
- Issue
- W1
- Pages
- W114-W120
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6030885
- OpenAlex
- W2811129245
- Cited by
- 83
- References
- 61
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 14 · 2022: 13 · 2021: 16 · 2020: 8 · 2019: 12 · 2018: 4
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (31701143)
- 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
- causal variant identification, complex diseases, complex traits, transcription factor binding motifs, web server
- MeSH terms
- Humans, Software, Computational Biology, Polymorphism, Single Nucleotide, Genomics, Genetic Diseases, Inborn, Quantitative Trait Loci, Genome-Wide Association Study
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