Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2

Jennifer J. Ware, Xiangning Chen, Jacqueline Vink, Anu Loukola, Camelia Minica, Rene Pool, Yuri Milaneschi, Massimo Mangino, Cristina Menni, Jingchun Chen, Roseann E. Peterson, Kirsi Auro, Leo-Pekka Lyytikainen, Juho Wedenoja, Alexander I. Stiby, Gibran Hemani, Gonneke Willemsen, Jouke Jan Hottenga, Tellervo Korhonen, Markku HeliovaaraMarkus Perola, Richard J. Rose, Lavinia Paternoster, Nic Timpson, Catherine A. Wassenaar, Andy Z. X. Zhu, George Davey Smith, Olli T. Raitakari, Terho Lehtimaki, Mika Kahonen, Seppo Koskinen, Timothy Spector, Brenda W. J. H. Penninx, Veikko Salomaa, Dorret I. Boomsma, Rachel F. Tyndale, Jaakko Kaprio, Marcus R. Munafo

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Genome-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.
Original languageEnglish
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 1 Feb 2016

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