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

J.J. Ware, X. Chen, J.M. Vink, A. Loukola, C.C. Minica, R. Pool, Y. Milaneschi, M. Mangino, C. Menni, J. Chen, R.E. Peterson, K. Auro, L.P. Lyytikäinen, J. Wedenoja, A.I. Stiby, G. Hemani, G. Willemsen, J.J. Hottenga, T. Korhonen, M. Heliövaara & 18 others M. Perola, R.J. Rose, L. Paternoster, N. Timpson, C.A. Wassenaar, A.Z.X. Zhu, G. Davey Smith, O.T. Raitakari, T. Lehtimäki, M. Kähönen, S. Koskinen, T.D. Spector, B.W.J.H. Penninx, V. Salomaa, D.I. Boomsma, R.F. Tyndale, J. Kaprio, M. Munafò

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 â'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
Article number20092
JournalScientific Reports
Volume6
Issue number20092
DOIs
Publication statusPublished - 2016

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Cotinine
Genome-Wide Association Study
Tobacco Products
Meta-Analysis
Nicotine
Genome
Phenotype
Biomarkers
Self Report
Smoking
Linkage Disequilibrium
Environmental Exposure
Proxy
Multigene Family
Metabolic Networks and Pathways
Individuality
Genes

Cite this

Ware, J.J. ; Chen, X. ; Vink, J.M. ; Loukola, A. ; Minica, C.C. ; Pool, R. ; Milaneschi, Y. ; Mangino, M. ; Menni, C. ; Chen, J. ; Peterson, R.E. ; Auro, K. ; Lyytikäinen, L.P. ; Wedenoja, J. ; Stiby, A.I. ; Hemani, G. ; Willemsen, G. ; Hottenga, J.J. ; Korhonen, T. ; Heliövaara, M. ; Perola, M. ; Rose, R.J. ; Paternoster, L. ; Timpson, N. ; Wassenaar, C.A. ; Zhu, A.Z.X. ; Davey Smith, G. ; Raitakari, O.T. ; Lehtimäki, T. ; Kähönen, M. ; Koskinen, S. ; Spector, T.D. ; Penninx, B.W.J.H. ; Salomaa, V. ; Boomsma, D.I. ; Tyndale, R.F. ; Kaprio, J. ; Munafò, M. / Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2. In: Scientific Reports. 2016 ; Vol. 6, No. 20092.
@article{9ee844b6aa6d469d98028ec94b87a2af,
title = "Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2",
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 {\^a}'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.",
author = "J.J. Ware and X. Chen and J.M. Vink and A. Loukola and C.C. Minica and R. Pool and Y. Milaneschi and M. Mangino and C. Menni and J. Chen and R.E. Peterson and K. Auro and L.P. Lyytik{\"a}inen and J. Wedenoja and A.I. Stiby and G. Hemani and G. Willemsen and J.J. Hottenga and T. Korhonen and M. Heli{\"o}vaara and M. Perola and R.J. Rose and L. Paternoster and N. Timpson and C.A. Wassenaar and A.Z.X. Zhu and {Davey Smith}, G. and O.T. Raitakari and T. Lehtim{\"a}ki and M. K{\"a}h{\"o}nen and S. Koskinen and T.D. Spector and B.W.J.H. Penninx and V. Salomaa and D.I. Boomsma and R.F. Tyndale and J. Kaprio and M. Munaf{\`o}",
year = "2016",
doi = "10.1038/srep20092",
language = "English",
volume = "6",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "20092",

}

Ware, JJ, Chen, X, Vink, JM, Loukola, A, Minica, CC, 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, AZX, Davey Smith, G, Raitakari, OT, Lehtimäki, T, Kähönen, M, Koskinen, S, Spector, TD, Penninx, BWJH, Salomaa, V, Boomsma, DI, Tyndale, RF, Kaprio, J & Munafò, M 2016, 'Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2' Scientific Reports, vol. 6, no. 20092, 20092. https://doi.org/10.1038/srep20092

Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2. / Ware, J.J.; Chen, X.; Vink, J.M.; Loukola, A.; Minica, C.C.; Pool, R.; Milaneschi, Y.; Mangino, M.; Menni, C.; Chen, J.; Peterson, R.E.; Auro, K.; Lyytikäinen, L.P.; Wedenoja, J.; Stiby, A.I.; Hemani, G.; Willemsen, G.; Hottenga, J.J.; Korhonen, T.; Heliövaara, M.; Perola, M.; Rose, R.J.; Paternoster, L.; Timpson, N.; Wassenaar, C.A.; Zhu, A.Z.X.; Davey Smith, G.; Raitakari, O.T.; Lehtimäki, T.; Kähönen, M.; Koskinen, S.; Spector, T.D.; Penninx, B.W.J.H.; Salomaa, V.; Boomsma, D.I.; Tyndale, R.F.; Kaprio, J.; Munafò, M.

In: Scientific Reports, Vol. 6, No. 20092, 20092, 2016.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2

AU - Ware, J.J.

AU - Chen, X.

AU - Vink, J.M.

AU - Loukola, A.

AU - Minica, C.C.

AU - Pool, R.

AU - Milaneschi, Y.

AU - Mangino, M.

AU - Menni, C.

AU - Chen, J.

AU - Peterson, R.E.

AU - Auro, K.

AU - Lyytikäinen, L.P.

AU - Wedenoja, J.

AU - Stiby, A.I.

AU - Hemani, G.

AU - Willemsen, G.

AU - Hottenga, J.J.

AU - Korhonen, T.

AU - Heliövaara, M.

AU - Perola, M.

AU - Rose, R.J.

AU - Paternoster, L.

AU - Timpson, N.

AU - Wassenaar, C.A.

AU - Zhu, A.Z.X.

AU - Davey Smith, G.

AU - Raitakari, O.T.

AU - Lehtimäki, T.

AU - Kähönen, M.

AU - Koskinen, S.

AU - Spector, T.D.

AU - Penninx, B.W.J.H.

AU - Salomaa, V.

AU - Boomsma, D.I.

AU - Tyndale, R.F.

AU - Kaprio, J.

AU - Munafò, M.

PY - 2016

Y1 - 2016

N2 - 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 â'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.

AB - 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 â'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.

U2 - 10.1038/srep20092

DO - 10.1038/srep20092

M3 - Article

VL - 6

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 20092

M1 - 20092

ER -