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Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis

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dc.contributor.authorCho, Seoae-
dc.contributor.authorKim, Haseong-
dc.contributor.authorOh, Sohee-
dc.contributor.authorKim, Kyunga-
dc.contributor.authorPark, Taesung-
dc.date.accessioned2017-03-27T00:51:45Z-
dc.date.available2017-03-27T09:56:47Z-
dc.date.issued2009-12-15-
dc.identifier.citationBMC Proceedings, 3(Suppl 7):S25ko_KR
dc.identifier.urihttps://hdl.handle.net/10371/109988-
dc.description.abstractThe current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.ko_KR
dc.language.isoenko_KR
dc.publisherBioMed Centralko_KR
dc.titleElastic-net regularization approaches for genome-wide association studies of rheumatoid arthritisko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor조서애-
dc.contributor.AlternativeAuthor김하성-
dc.contributor.AlternativeAuthor오소희-
dc.contributor.AlternativeAuthor김경아-
dc.contributor.AlternativeAuthor박태성-
dc.identifier.doi10.1186/1753-6561-3-S7-S25-
dc.language.rfc3066en-
dc.rights.holderCho et al; licensee BioMed Central Ltd.-
dc.date.updated2017-01-06T10:52:20Z-
Appears in Collections:
College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Journal Papers (저널논문_협동과정-생물정보학전공)
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