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Article: Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset

TitleComparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset
Authors
Issue Date2011
PublisherBioMed Central Ltd.. The Journal's web site is located at http://www.biomedcentral.com/bmcresnotes/
Citation
Bmc Research Notes, 2011, v. 4 How to Cite?
AbstractBackground: Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence. Findings. Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest. Conclusions: Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease. © 2011 Cherny et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/152817
ISSN
2015 SCImago Journal Rankings: 0.702
PubMed Central ID
References

 

DC FieldValueLanguage
dc.contributor.authorGui, Hen_HK
dc.contributor.authorLi, Men_HK
dc.contributor.authorSham, PCen_HK
dc.contributor.authorCherny, SSen_HK
dc.date.accessioned2012-07-16T09:49:38Z-
dc.date.available2012-07-16T09:49:38Z-
dc.date.issued2011en_HK
dc.identifier.citationBmc Research Notes, 2011, v. 4en_HK
dc.identifier.issn1756-0500en_HK
dc.identifier.urihttp://hdl.handle.net/10722/152817-
dc.description.abstractBackground: Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence. Findings. Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest. Conclusions: Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease. © 2011 Cherny et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_US
dc.publisherBioMed Central Ltd.. The Journal's web site is located at http://www.biomedcentral.com/bmcresnotes/ en_HK
dc.relation.ispartofBMC Research Notesen_HK
dc.rightsBMC Research Notes. Copyright © BioMed Central Ltd..-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleComparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataseten_HK
dc.typeArticleen_HK
dc.identifier.emailLi, M: mxli@hku.hken_HK
dc.identifier.emailSham, PC: pcsham@hku.hken_HK
dc.identifier.emailCherny, SS: cherny@hku.hken_HK
dc.identifier.authorityLi, M=rp01722en_HK
dc.identifier.authoritySham, PC=rp00459en_HK
dc.identifier.authorityCherny, SS=rp00232en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1756-0500-4-386en_HK
dc.identifier.pmid21981765-
dc.identifier.pmcidPMC3199264-
dc.identifier.scopuseid_2-s2.0-80053478943en_HK
dc.identifier.hkuros200598en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80053478943&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridGui, H=16645619300en_HK
dc.identifier.scopusauthoridLi, M=17135391100en_HK
dc.identifier.scopusauthoridSham, PC=34573429300en_HK
dc.identifier.scopusauthoridCherny, SS=7004670001en_HK

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