Biology:GWASdb
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| Description | database for human genetic variants identified by genome wide association studies. |
| Data types captured | Genomic Variants (from GWAS studies), disease annotations (based on Disease-Ontology Lite and Human Phenotype Ontology), computational predictions (on Transcription factor binding sites, microRNA targets, Splicing sites and many genome regulators) |
| Organisms | Homo sapiens (9606) |
| Contact | |
| Research centre | University of Hong Kong |
| Laboratory | GWASdb, University of Hong Kong Bioinformatics Lab of Biochemistry |
| Author(s) | Li Jun Mulin |
| Release date | 2011 |
| Access | |
| Website | http://jjwanglab.org/gwasdb |
| Web service URL | http://wanglab.hku.hk:8080/gwasdbws/ws |
GWASdb is an online bioinformatics database combines collections of GVs from GWAS and their comprehensive functional annotations, as well as disease classifications.
Introduction
Thousands of genetic variants (GVs) associated with human traits and diseases have been identified by Genome-Wide Association Studies (GWAS). The advent of high throughput technologies, such as next-generation sequencing and very high-density microarrays, enables us to capture genome-wide variation on a much larger scale.[1] With increasing sample sizes, GWAS studies based on these technologies will produce more information at higher resolutions. We will be able to detect many traits/diseases associated GVs, such as Single Nucleotide Polymorphisms (SNPs), Copy Number Variations (CNVs), and insertions and deletions (Indels).[2]
Existing resources also have limitations in satisfying the increasing demands of current GWAS research: (i) Many true disease susceptibility loci have relatively moderate P values which are ignored in existing databases. GVs with moderate effect sizes, usually filtered by strict cutoffs, can be directly related to diseases through gene-gene interaction in the context of regulatory networks and pathways. (ii) Most of the existing databases focus only on one or several aspects of the functional annotations, and not on GV-disease relationships. An integrative, comprehensive, up-to-date GWAS-based knowledgebase that focuses on disease classification is needed.
GWASdb, a database that combines collections of GVs from GWAS together with their functional annotations and disease classifications. The database provides the following information: (i) In addition to all the GVs annotated in the NHGRI GWAS Catalog, we manually curated the GVs that are marginally significant (P value < 1.0×10-3) collected from supplementary materials of each original publication. (ii) We provide extensive functional annotations for these GVs. (iii) The GVs have been manually classified according to disease using Disease-Ontology Lite (DOLite) and Human Phenotype Ontology (HPO). The database can be used to conduct gene-based pathway enrichment and PPI network association analysis for diseases with sufficient variants.
References
- ↑ Hindorff, L. A.; Sethupathy, P.; Junkins, H. A.; Ramos, E. M.; Mehta, J. P.; Collins, F. S.; Manolio, T. A. (2009-05-29). "Potential etiologic and functional implications of genome-wide association loci for human diseases and traits". Proc. Natl. Acad. Sci. USA 106 (23): 9362–7. doi:10.1073/pnas.0903103106. PMID 19474294. Bibcode: 2009PNAS..106.9362H.
- ↑ The 1000 Genomes Project Consortium (2010-10-29). "A map of human genome variation from population-scale sequencing". Nature 467 (7319): 1061–73. doi:10.1038/nature09534. PMID 20981092. Bibcode: 2010Natur.467.1061T.
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