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dc.contributor.authorGao, Qingsongen
dc.contributor.authorHe, Yungangen
dc.contributor.authorYuan, Zhongshangen
dc.contributor.authorZhao, Jingen
dc.contributor.authorZhang, Bingbingen
dc.contributor.authorXue, Fuzhongen
dc.date.accessioned2011-09-19T15:39:00Z
dc.date.available2011-09-19T15:39:00Z
dc.date.issued2011-08-26en
dc.identifier.issn1471-2156
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/238982
dc.description.abstractAbstract Background In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits). Kernel principal component analysis combined with logistic regression test (KPCA-LRT) has been successfully used in classifying gene expression data. Nevertheless, the purpose of association study is to detect the correlation between genetic variations and disease rather than to classify the sample, and the genomic data is categorical rather than numerical. Recently, although the kernel-based logistic regression model in association study has been proposed by projecting the nonlinear original SNPs data into a linear feature space, it is still impacted by multicolinearity between the projections, which may lead to loss of power. We, therefore, proposed a KPCA-LRT model to avoid the multicolinearity. Results Simulation results showed that KPCA-LRT was always more powerful than principal component analysis combined with logistic regression test (PCA-LRT) at different sample sizes, different significant levels and different relative risks, especially at the genewide level (1E-5) and lower relative risks (RR = 1.2, 1.3). Application to the four gene regions of rheumatoid arthritis (RA) data from Genetic Analysis Workshop16 (GAW16) indicated that KPCA-LRT had better performance than single-locus test and PCA-LRT. Conclusions KPCA-LRT is a valid and powerful gene- or region-based method for the analysis of GWAS data set, especially under lower relative risks and lower significant levels.
dc.titleGene- or region-based association study via kernel principal component analysisen
dc.typeArticle
dc.date.updated2011-09-19T15:39:00Z
dc.description.versionRIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.en
dc.language.rfc3066en
dc.rights.holderGao et al.; licensee BioMed Central Ltd.
prism.publicationDate2011en
dcterms.dateAccepted2011-08-26en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2011-08-26en
dc.contributor.orcidZhao, Jing [0000-0003-4930-3582]
dc.identifier.eissn1471-2156
rioxxterms.typeJournal Article/Reviewen


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