Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2.
paper
Cited
Public
- Authors
- Ware, Jennifer J; Chen, Xiangning; Vink, Jacqueline; Loukola, Anu; Minica, Camelia; Pool, Rene; Milaneschi, Yuri; Mangino, Massimo; Menni, Cristina; Chen, Jingchun; Peterson, Roseann E; Auro, Kirsi; Lyytikäinen, Leo-Pekka; Wedenoja, Juho; Stiby, Alexander I; Hemani, Gibran; Willemsen, Gonneke; Hottenga, Jouke Jan; Korhonen, Tellervo; Heliövaara, Markku; Perola, Markus; Rose, Richard J; Paternoster, Lavinia; Timpson, Nic; Wassenaar, Catherine A; Zhu, Andy Z X; Davey Smith, George; Raitakari, Olli T; Lehtimäki, Terho; Kähönen, Mika; Koskinen, Seppo; Spector, Timothy; Penninx, Brenda W J H; Salomaa, Veikko; Boomsma, Dorret I; Tyndale, Rachel F; Kaprio, Jaakko; Munafò, Marcus R
- Year
- 2016
- Journal
- Scientific reports
- PMID
- 26833182
- DOI
- 10.1038/srep20092
- PMCID
- PMC4735517
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.
Manhattan and quantile-quantile plots illustrating genome-wide meta-analysis results.Manhattan plot (A): All SNPs plotted on x-axis according to their position on each chromosome, against their association with cotinine level, as shown on the y-axis as –log10 p-value. QQ plot (B): The observed distribution of p-values (y-axis) against the expected distribution of p-values under the null hypothesis (x-axis). Plots includes variants which were genotyped or imputed in at least 3,000 individuals only (~7 M SNPs).
Forest and regional plots of associations for cotinine from genome-wide meta-analysis.Forest plots illustrate effect size and 95% confidence intervals (CIs) observed in each contributing study for chromosome 15 (A) and chromosome 4 (B) SNPs with smallest p-values (“top” SNPs). Regional plots show SNPs plotted by their positions on chromosomes against –log10 p-value for their association with cotinine level in genome-wide meta-analysis. The top SNP in each region is highlighted in purple. The SNPs surrounding each top SNP are colour coded to reflect their LD with this variant (see legend). Estimated recombination rates are plotted in pale blue to reflect local LD structure on secondary y-axis. Genome build = hg19; LD population = 1000 Genomes March 2012 release (EUR). Regional plots generated using Locus Zoom.
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