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Article: GWASdb: A database for human genetic variants identified by genome-wide association studies
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TitleGWASdb: A database for human genetic variants identified by genome-wide association studies
 
AuthorsLi, MJ2
Wang, P2
Liu, X2
Lim, EL2 4
Wang, Z2 5
Yeager, M1 3
Wong, MP2
Sham, PC2
Chanock, SJ1
Wang, J2
 
Issue Date2012
 
PublisherOxford University Press. The Journal's web site is located at http://nar.oxfordjournals.org/
 
CitationNucleic Acids Research, 2012, v. 40 D1, p. D1047-D1054 [How to Cite?]
DOI: http://dx.doi.org/10.1093/nar/gkr1182
 
AbstractRecent advances in genome-wide association studies (GWAS) have enabled us to identify thousands of genetic variants (GVs) that are associated with human diseases. As next-generation sequencing technologies become less expensive, more GVs will be discovered in the near future. Existing databases, such as NHGRI GWAS Catalog, collect GVs with only genome-wide level significance. However, many true disease susceptibility loci have relatively moderate P values and are not included in these databases. We have developed GWASdb that contains 20 times more data than the GWAS Catalog and includes less significant GVs (P < 1.0 × 10 -3) manually curated from the literature. In addition, GWASdb provides comprehensive functional annotations for each GV, including genomic mapping information, regulatory effects (transcription factor binding sites, microRNA target sites and splicing sites), amino acid substitutions, evolution, gene expression and disease associations. Furthermore, GWASdb classifies these GVs according to diseases using Disease-Ontology Lite and Human Phenotype Ontology. It can conduct pathway enrichment and PPI network association analysis for these diseases. GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences. It is freely available at http://jjwanglab.org/gwasdb and will be updated frequently. © The Author(s) 2011.
 
ISSN0305-1048
2012 Impact Factor: 8.278
2012 SCImago Journal Rankings: 5.125
 
DOIhttp://dx.doi.org/10.1093/nar/gkr1182
 
PubMed Central IDPMC3245026
 
ISI Accession Number IDWOS:000298601300157
Funding AgencyGrant Number
University of Hong Kong201007176262
Research Grants Council of Hong Kong781511M
778609M
N_HKU752/10
Food and Health Bureau of Hong Kong10091262
National Cancer Institute (NCI), NIH, USA
Funding Information:

The Small Project Fund (201007176262) of the University of Hong Kong; Research Grants Council of Hong Kong (781511M, 778609M, N_HKU752/10); Food and Health Bureau of Hong Kong (10091262); The intramural research program of the National Cancer Institute (NCI), NIH, USA. Funding for open access charge: Research Grants Council (781511M) of Hong Kong.

 
ReferencesReferences in Scopus
 
GrantsA Novel Hidden Markov Model to Predict microRNAs and their Targets Simultaneously and its Application to the Epstein-Barr virus
 
DC FieldValue
dc.contributor.authorLi, MJ
 
dc.contributor.authorWang, P
 
dc.contributor.authorLiu, X
 
dc.contributor.authorLim, EL
 
dc.contributor.authorWang, Z
 
dc.contributor.authorYeager, M
 
dc.contributor.authorWong, MP
 
dc.contributor.authorSham, PC
 
dc.contributor.authorChanock, SJ
 
dc.contributor.authorWang, J
 
dc.date.accessioned2012-05-31T07:52:33Z
 
dc.date.available2012-05-31T07:52:33Z
 
dc.date.issued2012
 
dc.description.abstractRecent advances in genome-wide association studies (GWAS) have enabled us to identify thousands of genetic variants (GVs) that are associated with human diseases. As next-generation sequencing technologies become less expensive, more GVs will be discovered in the near future. Existing databases, such as NHGRI GWAS Catalog, collect GVs with only genome-wide level significance. However, many true disease susceptibility loci have relatively moderate P values and are not included in these databases. We have developed GWASdb that contains 20 times more data than the GWAS Catalog and includes less significant GVs (P < 1.0 × 10 -3) manually curated from the literature. In addition, GWASdb provides comprehensive functional annotations for each GV, including genomic mapping information, regulatory effects (transcription factor binding sites, microRNA target sites and splicing sites), amino acid substitutions, evolution, gene expression and disease associations. Furthermore, GWASdb classifies these GVs according to diseases using Disease-Ontology Lite and Human Phenotype Ontology. It can conduct pathway enrichment and PPI network association analysis for these diseases. GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences. It is freely available at http://jjwanglab.org/gwasdb and will be updated frequently. © The Author(s) 2011.
 
dc.description.naturepublished_or_final_version
 
dc.identifier.citationNucleic Acids Research, 2012, v. 40 D1, p. D1047-D1054 [How to Cite?]
DOI: http://dx.doi.org/10.1093/nar/gkr1182
 
dc.identifier.citeulike10142837
 
dc.identifier.doihttp://dx.doi.org/10.1093/nar/gkr1182
 
dc.identifier.eissn1362-4962
 
dc.identifier.epageD1054
 
dc.identifier.hkuros208295
 
dc.identifier.isiWOS:000298601300157
Funding AgencyGrant Number
University of Hong Kong201007176262
Research Grants Council of Hong Kong781511M
778609M
N_HKU752/10
Food and Health Bureau of Hong Kong10091262
National Cancer Institute (NCI), NIH, USA
Funding Information:

The Small Project Fund (201007176262) of the University of Hong Kong; Research Grants Council of Hong Kong (781511M, 778609M, N_HKU752/10); Food and Health Bureau of Hong Kong (10091262); The intramural research program of the National Cancer Institute (NCI), NIH, USA. Funding for open access charge: Research Grants Council (781511M) of Hong Kong.

 
dc.identifier.issn0305-1048
2012 Impact Factor: 8.278
2012 SCImago Journal Rankings: 5.125
 
dc.identifier.issueD1
 
dc.identifier.pmcidPMC3245026
 
dc.identifier.pmid22139925
 
dc.identifier.scopuseid_2-s2.0-84861023442
 
dc.identifier.spageD1047
 
dc.identifier.urihttp://hdl.handle.net/10722/148737
 
dc.identifier.volume40
 
dc.languageeng
 
dc.publisherOxford University Press. The Journal's web site is located at http://nar.oxfordjournals.org/
 
dc.publisher.placeUnited Kingdom
 
dc.relation.ispartofNucleic Acids Research
 
dc.relation.projectA Novel Hidden Markov Model to Predict microRNAs and their Targets Simultaneously and its Application to the Epstein-Barr virus
 
dc.relation.referencesReferences in Scopus
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.titleGWASdb: A database for human genetic variants identified by genome-wide association studies
 
dc.typeArticle
 
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<description.abstract>Recent advances in genome-wide association studies (GWAS) have enabled us to identify thousands of genetic variants (GVs) that are associated with human diseases. As next-generation sequencing technologies become less expensive, more GVs will be discovered in the near future. Existing databases, such as NHGRI GWAS Catalog, collect GVs with only genome-wide level significance. However, many true disease susceptibility loci have relatively moderate P values and are not included in these databases. We have developed GWASdb that contains 20 times more data than the GWAS Catalog and includes less significant GVs (P &lt; 1.0 &#215; 10 -3) manually curated from the literature. In addition, GWASdb provides comprehensive functional annotations for each GV, including genomic mapping information, regulatory effects (transcription factor binding sites, microRNA target sites and splicing sites), amino acid substitutions, evolution, gene expression and disease associations. Furthermore, GWASdb classifies these GVs according to diseases using Disease-Ontology Lite and Human Phenotype Ontology. It can conduct pathway enrichment and PPI network association analysis for these diseases. GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences. It is freely available at http://jjwanglab.org/gwasdb and will be updated frequently. &#169; The Author(s) 2011.</description.abstract>
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Author Affiliations
  1. National Cancer Institute
  2. The University of Hong Kong
  3. SAIC-Frederick
  4. University of Oxford
  5. University of California, Los Angeles