Show simple item record

dc.contributor.authorYao, C.en_US
dc.contributor.authorChen, B. H.en_US
dc.contributor.authorJoehanes, R.en_US
dc.contributor.authorOtlu, B.en_US
dc.contributor.authorZhang X.en_US
dc.contributor.authorLiu, C.en_US
dc.contributor.authorHuan, T.en_US
dc.contributor.authorTastan, O.en_US
dc.contributor.authorCupples, L. A.en_US
dc.contributor.authorMeigs, J. B.en_US
dc.contributor.authorFox, C. S.en_US
dc.contributor.authorFreedman, J. E.en_US
dc.contributor.authorCourchesne, P.en_US
dc.contributor.authorO'Donnell, C. J.en_US
dc.contributor.authorMunson, P. J.en_US
dc.contributor.authorKeles, S.en_US
dc.contributor.authorLevy, D.en_US
dc.date.accessioned2016-02-08T10:30:12Z
dc.date.available2016-02-08T10:30:12Z
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/11693/24492
dc.description.abstractBACKGROUND - : Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies have reported numerous single nucleotide polymorphisms (SNPs) to be associated with CVD, the role of most of these variants in disease processes remains unknown. METHODS AND RESULTS - : We built a CVD network using 1512 SNPs associated with 21 CVD traits in genome-wide association studies (at P≤5×10) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-expression quantitative trait loci (eQTLs; SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples in which the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in genome-wide association studies may exert their function by altering expression of eQTL genes (eg, LDLR and PCSK7), which in turn may promote interindividual variation in phenotypes. CONCLUSIONS - : Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.en_US
dc.language.isoEnglishen_US
dc.source.titleCirculationen_US
dc.relation.isversionofhttp://dx.doi.org/10.1161/CIRCULATIONAHA.114.010696en_US
dc.subjectRegulation networken_US
dc.subjectCardiovascular diseaseen_US
dc.subjectGene expression/regulationen_US
dc.subjectGenetic variationen_US
dc.subjectAdulten_US
dc.subjectCardiovascular diseaseen_US
dc.subjectCardiovascular risken_US
dc.subjectCross linkingen_US
dc.subjectDisease courseen_US
dc.subjectFemaleen_US
dc.subjectGene expressionen_US
dc.subjectGenetic analysisen_US
dc.subjectGenetic associationen_US
dc.subjectGenetic variabilityen_US
dc.subjectHumanen_US
dc.subjectIntegromic analysisen_US
dc.subjectMajor clinical studyen_US
dc.subjectMaleen_US
dc.subjectMiddle ageden_US
dc.subjectPhenotypeen_US
dc.subjectPriority journalen_US
dc.subjectQuantitative trait locusen_US
dc.subjectSingle nucleotide polymorphismen_US
dc.titleIntegromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypesen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage536en_US
dc.citation.epage549en_US
dc.citation.volumeNumber131en_US
dc.citation.issueNumber6en_US
dc.identifier.doi10.1161/CIRCULATIONAHA.114.010696en_US
dc.publisherLippincott Williams & Wilkinsen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record