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Robust Analysis of related samples under the presence of population substructure

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dc.contributor.authorChoi, Sungkyoung-
dc.contributor.authorWon, Sungho-
dc.date.accessioned2021-06-23T12:07:29Z-
dc.date.available2021-06-23T12:07:29Z-
dc.date.created2021-01-21-
dc.date.issued2011-11-
dc.identifier.issn2163-6966-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/39244-
dc.description.abstractWe propose a new method for genome-wide association analysis with a family-based design. The proposed method is robust against population substructure while it is more efficient than the traditional method such as transmission disequilibrium test for related samples. The proposed method estimates the correlation matrix between individuals and then the principal component analysis is applied. To maximize the statistical power, we consider the additive polygenic model and a best linear unbiased predictor is used as offset. We confirmed that the proposed method is always efficient by simulation studies. The method will be applied to Framingham Heart study.-
dc.language영어-
dc.language.isoen-
dc.publisherIEEE COMPUTER SOC-
dc.titleRobust Analysis of related samples under the presence of population substructure-
dc.typeArticle-
dc.contributor.affiliatedAuthorChoi, Sungkyoung-
dc.identifier.doi10.1109/BIBMW.2011.6112458-
dc.identifier.scopusid2-s2.0-84862962115-
dc.identifier.wosid000407067100111-
dc.identifier.bibliographicCitation2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, pp.714 - 720-
dc.relation.isPartOf2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS-
dc.citation.title2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS-
dc.citation.startPage714-
dc.citation.endPage720-
dc.type.rimsART-
dc.type.docTypeProceedings Paper-
dc.description.journalClass3-
dc.description.isOpenAccessN-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.subject.keywordPlusFAMILY-BASED ASSOCIATION-
dc.subject.keywordPlusGENOME-WIDE ASSOCIATION-
dc.subject.keywordPlusTRAITS-
dc.subject.keywordPlusTESTS-
dc.subject.keywordAuthorBioinformatics-
dc.subject.keywordAuthorGenomics-
dc.subject.keywordAuthorDiseases-
dc.subject.keywordAuthorCorrelation-
dc.subject.keywordAuthorRobustness-
dc.subject.keywordAuthorTesting-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/6112458?arnumber=6112458&SID=EBSCO:edseee-
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ERICA 과학기술융합대학 (ERICA 수리데이터사이언스학과)
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