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Article: Genova: GENe OVerlap Analysis of GWAS results

TitleGenova: GENe OVerlap Analysis of GWAS results
Authors
KeywordsAnnotation
Enrichment
Method
Gene
Issue Date2012
Citation
Statistical Applications in Genetics and Molecular Biology, 2012, v. 11, n. 3 How to Cite?
AbstractIn many published genome-wide association studies (GWAS), the top few strongly associated variants are often located in or near known genes. This observation raises the more general hypothesis that variants nominally associated with a phenotype are more likely to overlap genes than those not associated with a phenotype. We developed a simple approach - named GENe OVerlap Analysis (GENOVA) - to formally test this hypothesis. This approach includes two steps. First, we define largely independent groups of highly correlated SNPs (or "clumps") and classify each clump as intersecting a gene or not. Second, we determine how strongly associated each clump is with the phenotype and use logistic regression to formally test the hypothesis that clumps associated with the phenotype are more likely to intersect genes. Simulations suggest that the power of GENOVA is affected by at least three factors: GWAS sample size, the gene boundaries used to define gene-intersecting clumps and the P-value threshold used to define phenotype-associated clumps. We applied GENOVA to results from three recent GWAS meta-analyses of height, body mass index (BMI) and waist-hip ratio (WHR) conducted by the GIANT consortium. SNPs associated with variation in height were 1.44-fold more likely to be in or near genes than SNPs not associated with height (P = 5 × 10 -28). A weaker association was observed for BMI (1.09-fold, P = 0.008) and WHR (1.09-fold, P = 0.014). GENOVA is implemented in C++ and is freely available at https://genepi.qimr.edu.au/staff/manuelF/genova/main.html. © 2012 De Gruyter. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/221327

 

DC FieldValueLanguage
dc.contributor.authorTang, Clara S.-
dc.contributor.authorFerreira, Manuel A R-
dc.date.accessioned2015-11-18T06:09:00Z-
dc.date.available2015-11-18T06:09:00Z-
dc.date.issued2012-
dc.identifier.citationStatistical Applications in Genetics and Molecular Biology, 2012, v. 11, n. 3-
dc.identifier.urihttp://hdl.handle.net/10722/221327-
dc.description.abstractIn many published genome-wide association studies (GWAS), the top few strongly associated variants are often located in or near known genes. This observation raises the more general hypothesis that variants nominally associated with a phenotype are more likely to overlap genes than those not associated with a phenotype. We developed a simple approach - named GENe OVerlap Analysis (GENOVA) - to formally test this hypothesis. This approach includes two steps. First, we define largely independent groups of highly correlated SNPs (or "clumps") and classify each clump as intersecting a gene or not. Second, we determine how strongly associated each clump is with the phenotype and use logistic regression to formally test the hypothesis that clumps associated with the phenotype are more likely to intersect genes. Simulations suggest that the power of GENOVA is affected by at least three factors: GWAS sample size, the gene boundaries used to define gene-intersecting clumps and the P-value threshold used to define phenotype-associated clumps. We applied GENOVA to results from three recent GWAS meta-analyses of height, body mass index (BMI) and waist-hip ratio (WHR) conducted by the GIANT consortium. SNPs associated with variation in height were 1.44-fold more likely to be in or near genes than SNPs not associated with height (P = 5 × 10 -28). A weaker association was observed for BMI (1.09-fold, P = 0.008) and WHR (1.09-fold, P = 0.014). GENOVA is implemented in C++ and is freely available at https://genepi.qimr.edu.au/staff/manuelF/genova/main.html. © 2012 De Gruyter. All rights reserved.-
dc.languageeng-
dc.relation.ispartofStatistical Applications in Genetics and Molecular Biology-
dc.subjectAnnotation-
dc.subjectEnrichment-
dc.subjectMethod-
dc.subjectGene-
dc.titleGenova: GENe OVerlap Analysis of GWAS results-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1515/1548-923X.1784-
dc.identifier.scopuseid_2-s2.0-84857773365-
dc.identifier.volume11-
dc.identifier.issue3-
dc.identifier.eissn1544-6115-

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