Show simple item record

dc.contributor.authorZhao, Yang
dc.contributor.authorChen, Feng
dc.contributor.authorZhai, Rihong
dc.contributor.authorLin, Xihong
dc.contributor.authorDiao, Nancy
dc.contributor.authorChristiani, David C.
dc.date.accessioned2013-04-25T18:29:51Z
dc.date.issued2012
dc.identifier.citationZhao, Yang, Feng Chen, Rihong Zhai, Xihong Lin, Nancy Diao, and David C. Christiani. 2012. Association test based on SNP set: logistic kernel machine based test vs. principal component analysis. PLoS ONE 7(9): e44978.en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10587975
dc.description.abstractGWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdoi:10.1371/journal.pone.0044978en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3441747/pdf/en_US
dash.licenseLAA
dc.subjectBiologyen_US
dc.subjectGeneticsen_US
dc.subjectHuman Geneticsen_US
dc.subjectGenetic Association Studiesen_US
dc.subjectGenome-Wide Association Studiesen_US
dc.subjectMathematicsen_US
dc.subjectStatisticsen_US
dc.subjectBiostatisticsen_US
dc.subjectMedicineen_US
dc.subjectEpidemiologyen_US
dc.subjectEpidemiological Methodsen_US
dc.subjectGenetic Epidemiologyen_US
dc.titleAssociation Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysisen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS ONEen_US
dash.depositing.authorLin, Xihong
dc.date.available2013-04-25T18:29:51Z
dc.identifier.doi10.1371/journal.pone.0044978*
dash.contributor.affiliatedDiao, Nancy
dash.contributor.affiliatedZhai, Rihong
dash.contributor.affiliatedLin, Xihong
dash.contributor.affiliatedChristiani, David


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record