Dissemin is shutting down on January 1st, 2025

Published in

Nature Research, Scientific Reports, 1(6), 2016

DOI: 10.1038/srep20092

Links

Tools

Export citation

Search in Google Scholar

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

Journal article published in 2016 by Jennifer J. Ware, Xiangning Chen, Jacqueline Vink, Anu Loukola, Camelia Minica ORCID, Rene Pool ORCID, Yuri Milaneschi, Massimo Mangino, Cristina Menni, Jingchun Chen, Roseann E. Peterson ORCID, Kirsi Auro, Leo-Pekka Lyytikäinen, Juho Wedenoja, Alexander I. Stiby and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Red circle
Postprint: archiving forbidden
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

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.