File Download
Supplementary

Conference Paper: A systematic comparison of GWAS pathway analysis methods

TitleA systematic comparison of GWAS pathway analysis methods
Authors
KeywordsStatistical Genetics
Genetic Epidemiology
KW008 - Bioinformatics
KW080 - Genome-wide Association
Issue Date2011
PublisherAmerican Society of Human Genetics.
Citation
The 12th International Congress of Human Genetics (ICHG 2011), Montreal, Canada, 11-15 October 2011. How to Cite?
Abstract
Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it provides a way of discovering hidden disease causal mechanisms by combining statistical methods with biological knowledge. Algorithms or programs currently available can be categorized by different types of input data, null hypothesis or number of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive empirical distributions for test statistics, and then evaluate the significance of candidate pathways. However, thorough performance evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be done before these methods should become common practice in GWAS. Seven algorithms were selected to conduct pathway analysis on SNP genotypes together with simulated and real phenotypes from the WTCCC Crohn’s disease study. All 7 methods control type I error rate (at 0.05) well, and are mostly slightly conservative. However, the methods varied greatly in terms of power and running time. In real data analysis, raw data-based algorithms turn out to be best, provided sufficient computation capacity is available. Given the variability in performance, in general, particularly when underlying disease causal mechanism is ambiguous, it is worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset.
DescriptionPoster Session - Statistical Genetics and Genetic Epidemiology: Program no. 619W
Persistent Identifierhttp://hdl.handle.net/10722/153114

 

DC FieldValueLanguage
dc.contributor.authorGui, Hen_US
dc.contributor.authorLi, Men_US
dc.contributor.authorSham, PCen_US
dc.contributor.authorCherny, SSen_US
dc.date.accessioned2012-07-16T09:57:19Z-
dc.date.available2012-07-16T09:57:19Z-
dc.date.issued2011en_US
dc.identifier.citationThe 12th International Congress of Human Genetics (ICHG 2011), Montreal, Canada, 11-15 October 2011.en_US
dc.identifier.urihttp://hdl.handle.net/10722/153114-
dc.descriptionPoster Session - Statistical Genetics and Genetic Epidemiology: Program no. 619W-
dc.description.abstractThough rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it provides a way of discovering hidden disease causal mechanisms by combining statistical methods with biological knowledge. Algorithms or programs currently available can be categorized by different types of input data, null hypothesis or number of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive empirical distributions for test statistics, and then evaluate the significance of candidate pathways. However, thorough performance evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be done before these methods should become common practice in GWAS. Seven algorithms were selected to conduct pathway analysis on SNP genotypes together with simulated and real phenotypes from the WTCCC Crohn’s disease study. All 7 methods control type I error rate (at 0.05) well, and are mostly slightly conservative. However, the methods varied greatly in terms of power and running time. In real data analysis, raw data-based algorithms turn out to be best, provided sufficient computation capacity is available. Given the variability in performance, in general, particularly when underlying disease causal mechanism is ambiguous, it is worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset.-
dc.languageengen_US
dc.publisherAmerican Society of Human Genetics.-
dc.relation.ispartofInternational Congress of Human Genetics, ICHG 2011en_US
dc.subjectStatistical Genetics-
dc.subjectGenetic Epidemiology-
dc.subjectKW008 - Bioinformatics-
dc.subjectKW080 - Genome-wide Association-
dc.titleA systematic comparison of GWAS pathway analysis methodsen_US
dc.typeConference_Paperen_US
dc.identifier.emailLi, M: mxli@hku.hken_US
dc.identifier.emailSham, PC: pcsham@hku.hken_US
dc.identifier.emailCherny, SS: cherny@hku.hken_US
dc.identifier.authoritySham, PC=rp00459en_US
dc.identifier.authorityCherny, SS=rp00232en_US
dc.description.naturelink_to_OA_fulltext-
dc.identifier.hkuros200605en_US
dc.publisher.placeUnited States-
dc.description.otherThe 12th International Congress of Human Genetics (ICHG 2011), Montreal, Canada, 11-15 October 2011.-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats