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Genome-wide search for genetic modulators in gene regulatory pathways: Weighted window-based peak identification algorithm

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dc.contributor.authorLee, Eunjee-
dc.contributor.authorKim, Kyunga-
dc.contributor.authorPark, Taesung-
dc.date.available2021-02-22T13:17:09Z-
dc.date.issued2011-06-
dc.identifier.issn0888-7543-
dc.identifier.issn1089-8646-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/12562-
dc.description.abstractGenome-wide gene expression and genotype data have been integratively analyzed in expression quantitative trait loci (eQTL) studies to elucidate the genetics of gene transcription. Most eQTL analyses have focused on identifying polymorphic genetic variants that influence the expression levels of individual genes, and such analyses may have limitations in explaining gene regulatory pathways that are likely to involve multiple genes and their genetic and/or non-genetic modulators. We have developed a novel two-step method for identifying potential genetic modulators of transcription processes for multiple genes in a biological pathway. We proposed a new weighted window-based peak identification algorithm to improve the detection of genetic modulators for individual genes and employed a Poisson-based test to search for master genetic modulators of multiple genes. Here, we have illustrated this two-step approach by analyzing the gene expression data in the Centre d'Etude du Polymorphisme Humain (CEPH) lymphoblast cells and single nucleotide polymorphism chip data. (C) 2011 Elsevier Inc. All rights reserved.-
dc.format.extent8-
dc.language영어-
dc.language.isoENG-
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE-
dc.titleGenome-wide search for genetic modulators in gene regulatory pathways: Weighted window-based peak identification algorithm-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1016/j.ygeno.2011.03.006-
dc.identifier.scopusid2-s2.0-79957532872-
dc.identifier.wosid000291459800008-
dc.identifier.bibliographicCitationGENOMICS, v.97, no.6, pp 386 - 393-
dc.citation.titleGENOMICS-
dc.citation.volume97-
dc.citation.number6-
dc.citation.startPage386-
dc.citation.endPage393-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasssci-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaGenetics & Heredity-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryGenetics & Heredity-
dc.subject.keywordPlusHUMAN DNAJ PROTEIN-
dc.subject.keywordPlusT-CELL-
dc.subject.keywordPlusEXPRESSION-
dc.subject.keywordPlusHTID-1-
dc.subject.keywordPlusCOMPLEX-
dc.subject.keywordPlusPOLYMORPHISM-
dc.subject.keywordPlusASSOCIATION-
dc.subject.keywordPlusNETWORKS-
dc.subject.keywordPlusBINDING-
dc.subject.keywordAuthorGene regulatory pathway-
dc.subject.keywordAuthorExpression quantitative trait loci (eQTL)-
dc.subject.keywordAuthorPeak identification algorithm-
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