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Article: A knowledge-based weighting framework to boost the power of genome-wide association studies
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TitleA knowledge-based weighting framework to boost the power of genome-wide association studies
 
AuthorsLi, MX1
Sham, PC1
Cherny, SS1
Song, YQ1
 
Issue Date2010
 
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
 
CitationPlos One, 2010, v. 5 n. 12 [How to Cite?]
DOI: http://dx.doi.org/10.1371/journal.pone.0014480
 
AbstractBackground: We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances. Methodology/Principal Findings: We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance. Conclusions/Significance: With user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases. © 2010 Li et al.
 
ISSN1932-6203
2012 Impact Factor: 3.73
2012 SCImago Journal Rankings: 1.512
 
DOIhttp://dx.doi.org/10.1371/journal.pone.0014480
 
PubMed Central IDPMC3013112
 
ISI Accession Number IDWOS:000285838900009
Funding AgencyGrant Number
Research Grant Council of Hong KongHKU7688/05M
HKU7752/08M
Research Fund for the Control of Infectious Diseases (RFCID)08070652
Funding Information:

This work was supported by the Research Grant Council of Hong Kong (HKU7688/05M and HKU7752/08M, YQS) and the Research Fund for the Control of Infectious Diseases (RFCID) (no. 08070652, YQS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

 
ReferencesReferences in Scopus
 
GrantsFine mapping candidate loci for nasopharyngeal carcinoma (NPC) in southern Chinese specifically linked to EBV aetiopathogenesis
 
DC FieldValue
dc.contributor.authorLi, MX
 
dc.contributor.authorSham, PC
 
dc.contributor.authorCherny, SS
 
dc.contributor.authorSong, YQ
 
dc.date.accessioned2011-07-27T01:25:59Z
 
dc.date.available2011-07-27T01:25:59Z
 
dc.date.issued2010
 
dc.description.abstractBackground: We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances. Methodology/Principal Findings: We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance. Conclusions/Significance: With user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases. © 2010 Li et al.
 
dc.description.naturepublished_or_final_version
 
dc.identifier.citationPlos One, 2010, v. 5 n. 12 [How to Cite?]
DOI: http://dx.doi.org/10.1371/journal.pone.0014480
 
dc.identifier.citeulike8501520
 
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pone.0014480
 
dc.identifier.hkuros188454
 
dc.identifier.hkuros186079
 
dc.identifier.isiWOS:000285838900009
Funding AgencyGrant Number
Research Grant Council of Hong KongHKU7688/05M
HKU7752/08M
Research Fund for the Control of Infectious Diseases (RFCID)08070652
Funding Information:

This work was supported by the Research Grant Council of Hong Kong (HKU7688/05M and HKU7752/08M, YQS) and the Research Fund for the Control of Infectious Diseases (RFCID) (no. 08070652, YQS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

 
dc.identifier.issn1932-6203
2012 Impact Factor: 3.73
2012 SCImago Journal Rankings: 1.512
 
dc.identifier.issue12
 
dc.identifier.openurl
 
dc.identifier.pmcidPMC3013112
 
dc.identifier.pmid21217833
 
dc.identifier.scopuseid_2-s2.0-79251484337
 
dc.identifier.urihttp://hdl.handle.net/10722/135018
 
dc.identifier.volume5
 
dc.languageeng
 
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
 
dc.publisher.placeUnited States
 
dc.relation.ispartofPLoS ONE
 
dc.relation.projectFine mapping candidate loci for nasopharyngeal carcinoma (NPC) in southern Chinese specifically linked to EBV aetiopathogenesis
 
dc.relation.referencesReferences in Scopus
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subject.meshAlzheimer Disease - genetics
 
dc.subject.meshBinding Sites
 
dc.subject.meshComputational Biology - methods
 
dc.subject.meshGenome-Wide Association Study
 
dc.subject.meshPolymorphism, Single Nucleotide
 
dc.titleA knowledge-based weighting framework to boost the power of genome-wide association studies
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong