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A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure

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dc.contributor.author Sung, Y.J.
dc.contributor.author Winkler, T.W.
dc.contributor.author de Las Fuentes, L.
dc.contributor.author Bentley, A.R.
dc.contributor.author Brown, M.R.
dc.contributor.author Kraja, A.T.
dc.contributor.author Schwander, K.
dc.contributor.author Ntalla, I.
dc.contributor.author Guo, X.
dc.contributor.author Franceschini, N.
dc.contributor.author Lu, Y.
dc.contributor.author Cheng, C.Y.
dc.contributor.author Sim, X.
dc.contributor.author Vojinovic, D.
dc.contributor.author Marten, J.
dc.contributor.author Musani, S.K.
dc.contributor.author Li, C.
dc.contributor.author Feitosa, M.F.
dc.contributor.author Kilpelainen, T.O.
dc.contributor.author Richard, M.A.
dc.contributor.author Noordam, R.
dc.contributor.author Aslibekyan, S.
dc.contributor.author Aschard, H.
dc.contributor.author Bartz, T.M.
dc.contributor.author Dorajoo, R.
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dc.contributor.author Tajuddin, S.M.
dc.contributor.author Tayo, B.O.
dc.contributor.author Warren, H.R.
dc.contributor.author Zhao, W.
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dc.contributor.author Divers, J.
dc.contributor.author Gandin, I.
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dc.contributor.author Giulianini, F.
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dc.contributor.author Harris, S.E.
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dc.contributor.author Horimoto, A.R.V.R.
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dc.contributor.author Campbell, A.
dc.contributor.author Canouil, M.
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dc.contributor.author CHARGE Neurology Working Group
dc.contributor.author Chauhan, G.
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dc.contributor.author COGENT-Kidney Consortium
dc.contributor.author Collins, F.S.
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dc.contributor.author GIANT Consortium
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dc.contributor.author Lifelines Cohort Study
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dc.contributor.author Pankow, J.S.
dc.contributor.author Pedersen, N.L.
dc.contributor.author Peters, A.
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dc.contributor.author Polasek, O.
dc.contributor.author Raitakari, O.T.
dc.contributor.author Renstrom, F.
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dc.contributor.author Rudan, I.
dc.contributor.author Salako, B.L.
dc.contributor.author Sandow, K.
dc.contributor.author Schmidt, C.O.
dc.contributor.author Schreiner, P.J.
dc.contributor.author Scott, W.R.
dc.contributor.author Seshadri, S.
dc.contributor.author Sever, P.
dc.contributor.author Sitlani, C.M.
dc.contributor.author Smith, J.A.
dc.contributor.author Snieder, H.
dc.contributor.author Starr, J.M.
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dc.contributor.author Waldenberger, M.
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dc.contributor.author Kamatani, Y.
dc.contributor.author Laurie, C.C.
dc.contributor.author Bouchard, C.
dc.contributor.author Cooper, R.S.
dc.contributor.author Evans, M.K.
dc.contributor.author Gudnason, V.
dc.contributor.author Kardia, S.L.R.
dc.contributor.author Kritchevsky, S.B.
dc.contributor.author Levy, D.
dc.contributor.author O'Connell, J.R.
dc.contributor.author Psaty, B.M.
dc.contributor.author van Dam, R.M.
dc.contributor.author Sims, M.
dc.contributor.author Arnett, D.K.
dc.contributor.author Mook-Kanamori, D.O.
dc.contributor.author Kelly, T.N.
dc.contributor.author Fox, E.R.
dc.contributor.author Hayward, C.
dc.contributor.author Fornage, M.
dc.contributor.author Rotimi, C.N.
dc.contributor.author Province, M.A.
dc.contributor.author van Dujin, C.M.
dc.contributor.author Tai, E.S.
dc.contributor.author Wong, T.Y.
dc.contributor.author Loos, R.J.F.
dc.contributor.author Reiner, A.P.
dc.contributor.author Rotter, J.I.
dc.contributor.author Zhu, X.
dc.contributor.author Bierut, L.J.
dc.contributor.author Gauderman, W.J.
dc.contributor.author Caulfield, M.J.
dc.contributor.author Elliott, P.
dc.contributor.author Rice, K.
dc.contributor.author Munroe, P.B.
dc.contributor.author Morrison, A.C.
dc.contributor.author Cupples, L.A.
dc.contributor.author Rao., D.C.
dc.contributor.author Chasman, D.I.
dc.date.accessioned 2018-03-09T05:38:22Z
dc.date.available 2018-03-09T05:38:22Z
dc.date.issued 2018
dc.identifier.citation American journal of human genetics.2018;102(3):375-400 en_US
dc.identifier.issn 0002-9297 (Print)
dc.identifier.issn 1537-6605 (Electronic)
dc.identifier.issn 0002-9297 (Linking)
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/18626
dc.description Indexed In MEDLINE en_US
dc.description.abstract Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10-8) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10-8). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2). en_US
dc.language.iso en_US en_US
dc.publisher University of Chicago Press en_US
dc.subject Blood pressure en_US
dc.title A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure en_US
dc.type Article en_US


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