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dc.contributor.authorWare, Jennifer J
dc.contributor.authorChen, Xiangning
dc.contributor.authorVink, Jacqueline
dc.contributor.authorLoukola, Anu
dc.contributor.authorMinica, Camelia
dc.contributor.authorPool, Rene
dc.contributor.authorMilaneschi, Yuri
dc.contributor.authorMangino, Massimo
dc.contributor.authorMenni, Cristina
dc.contributor.authorChen, Jingchun
dc.contributor.authorPeterson, Roseann E
dc.contributor.authorAuro, Kirsi
dc.contributor.authorLyytikäinen, Leo-Pekka
dc.contributor.authorWedenoja, Juho
dc.contributor.authorStiby, Alexander I
dc.contributor.authorHemani, Gibran
dc.contributor.authorWillemsen, Gonneke
dc.contributor.authorHottenga, Jouke Jan
dc.contributor.authorKorhonen, Tellervo
dc.contributor.authorHeliövaara, Markku
dc.contributor.authorPerola, Markus
dc.contributor.authorRose, Richard J
dc.contributor.authorPaternoster, Lavinia
dc.contributor.authorTimpson, Nic
dc.contributor.authorWassenaar, Catherine A
dc.contributor.authorZhu, Andy Z X
dc.contributor.authorDavey Smith, George
dc.contributor.authorRaitakari, Olli T
dc.contributor.authorLehtimäki, Terho
dc.contributor.authorKähönen, Mika
dc.contributor.authorKoskinen, Seppo
dc.contributor.authorSpector, Timothy
dc.contributor.authorPenninx, Brenda W J H
dc.contributor.authorSalomaa, Veikko
dc.contributor.authorBoomsma, Dorret I
dc.contributor.authorTyndale, Rachel F
dc.contributor.authorKaprio, Jaakko
dc.contributor.authorMunafò, Marcus R
dc.date.accessioned2023-02-22T17:26:16Z
dc.date.available2023-02-22T17:26:16Z
dc.date.issued2016-02-01
dc.identifier.citationWare JJ, Chen X, Vink J, Loukola A, Minica C, Pool R, Milaneschi Y, Mangino M, Menni C, Chen J, Peterson RE, Auro K, Lyytikäinen LP, Wedenoja J, Stiby AI, Hemani G, Willemsen G, Hottenga JJ, Korhonen T, Heliövaara M, Perola M, Rose RJ, Paternoster L, Timpson N, Wassenaar CA, Zhu AZ, Davey Smith G, Raitakari OT, Lehtimäki T, Kähönen M, Koskinen S, Spector T, Penninx BW, Salomaa V, Boomsma DI, Tyndale RF, Kaprio J, Munafò MR. Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2. Sci Rep. 2016 Feb 1;6:20092. doi: 10.1038/srep20092. PMID: 26833182; PMCID: PMC4735517.en_US
dc.identifier.eissn2045-2322
dc.identifier.doi10.1038/srep20092
dc.identifier.pmid26833182
dc.identifier.urihttp://hdl.handle.net/20.500.12648/8398
dc.description.abstractGenome-wide association studies (GWAS) of complex behavioural phenotypes such as cigarette smoking typically employ self-report phenotypes. However, precise biomarker phenotypes may afford greater statistical power and identify novel variants. Here we report the results of a GWAS meta-analysis of levels of cotinine, the primary metabolite of nicotine, in 4,548 daily smokers of European ancestry. We identified a locus close to UGT2B10 at 4q13.2 (minimum p = 5.89 × 10(-10) for rs114612145), which was consequently replicated. This variant is in high linkage disequilibrium with a known functional variant in the UGT2B10 gene which is associated with reduced nicotine and cotinine glucuronidation activity, but intriguingly is not associated with nicotine intake. Additionally, we observed association between multiple variants within the 15q25.1 region and cotinine levels, all located within the CHRNA5-A3-B4 gene cluster or adjacent genes, consistent with previous much larger GWAS using self-report measures of smoking quantity. These results clearly illustrate the increase in power afforded by using precise biomarker measures in GWAS. Perhaps more importantly however, they also highlight that biomarkers do not always mark the phenotype of interest. The use of metabolite data as a proxy for environmental exposures should be carefully considered in the context of individual differences in metabolic pathways.
dc.language.isoenen_US
dc.relation.urlhttps://www.nature.com/articles/srep20092en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleGenome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2.en_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleScientific reportsen_US
dc.source.volume6
dc.source.beginpage20092
dc.source.endpage
dc.source.countryUnited Kingdom
dc.source.countryUnited States
dc.source.countryUnited Kingdom
dc.source.countryUnited States
dc.source.countryUnited Kingdom
dc.source.countryUnited Kingdom
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited Kingdom
dc.source.countryUnited Kingdom
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited Kingdom
dc.source.countryUnited Kingdom
dc.source.countryUnited Kingdom
dc.source.countryInternational
dc.source.countryUnited Kingdom
dc.source.countryUnited Kingdom
dc.source.countryCanada
dc.source.countryUnited States
dc.source.countryEngland
dc.description.versionVoRen_US
refterms.dateFOA2023-02-22T17:26:17Z
html.description.abstractGenome-wide association studies (GWAS) of complex behavioural phenotypes such as cigarette smoking typically employ self-report phenotypes. However, precise biomarker phenotypes may afford greater statistical power and identify novel variants. Here we report the results of a GWAS meta-analysis of levels of cotinine, the primary metabolite of nicotine, in 4,548 daily smokers of European ancestry. We identified a locus close to UGT2B10 at 4q13.2 (minimum p = 5.89 × 10(-10) for rs114612145), which was consequently replicated. This variant is in high linkage disequilibrium with a known functional variant in the UGT2B10 gene which is associated with reduced nicotine and cotinine glucuronidation activity, but intriguingly is not associated with nicotine intake. Additionally, we observed association between multiple variants within the 15q25.1 region and cotinine levels, all located within the CHRNA5-A3-B4 gene cluster or adjacent genes, consistent with previous much larger GWAS using self-report measures of smoking quantity. These results clearly illustrate the increase in power afforded by using precise biomarker measures in GWAS. Perhaps more importantly however, they also highlight that biomarkers do not always mark the phenotype of interest. The use of metabolite data as a proxy for environmental exposures should be carefully considered in the context of individual differences in metabolic pathways.
dc.description.institutionSUNY Downstateen_US
dc.description.departmentPsychiatry and Behavioral Sciencesen_US
dc.description.departmentInstitute for Genomics in Healthen_US
dc.description.degreelevelN/Aen_US
dc.identifier.journalScientific reports


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