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Conference Paper: A systematic comparison of GWAS pathway analysis methods
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TitleA systematic comparison of GWAS pathway analysis methods
 
AuthorsGui, H
Li, M
Sham, PC
Cherny, SS
 
KeywordsStatistical Genetics
Genetic Epidemiology
KW008 - Bioinformatics
KW080 - Genome-wide Association
 
Issue Date2011
 
PublisherAmerican Society of Human Genetics.
 
CitationThe 12th International Congress of Human Genetics (ICHG 2011), Montreal, Canada, 11-15 October 2011. [How to Cite?]
 
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.
 
DescriptionPoster Session - Statistical Genetics and Genetic Epidemiology: Program no. 619W
 
DC FieldValue
dc.contributor.authorGui, H
 
dc.contributor.authorLi, M
 
dc.contributor.authorSham, PC
 
dc.contributor.authorCherny, SS
 
dc.date.accessioned2012-07-16T09:57:19Z
 
dc.date.available2012-07-16T09:57:19Z
 
dc.date.issued2011
 
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.description.naturelink_to_OA_fulltext
 
dc.descriptionPoster Session - Statistical Genetics and Genetic Epidemiology: Program no. 619W
 
dc.description.otherThe 12th International Congress of Human Genetics (ICHG 2011), Montreal, Canada, 11-15 October 2011.
 
dc.identifier.citationThe 12th International Congress of Human Genetics (ICHG 2011), Montreal, Canada, 11-15 October 2011. [How to Cite?]
 
dc.identifier.hkuros200605
 
dc.identifier.urihttp://hdl.handle.net/10722/153114
 
dc.languageeng
 
dc.publisherAmerican Society of Human Genetics.
 
dc.publisher.placeUnited States
 
dc.relation.ispartofInternational Congress of Human Genetics, ICHG 2011
 
dc.subjectStatistical Genetics
 
dc.subjectGenetic Epidemiology
 
dc.subjectKW008 - Bioinformatics
 
dc.subjectKW080 - Genome-wide Association
 
dc.titleA systematic comparison of GWAS pathway analysis methods
 
dc.typeConference_Paper
 
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