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Meta-Analysis
. 2020 Oct;25(10):2392-2409.
doi: 10.1038/s41380-018-0313-0. Epub 2019 Jan 7.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

A Mesut Erzurumluoglu #  1 Mengzhen Liu #  2 Victoria E Jackson #  1   3   4 Daniel R Barnes  5 Gargi Datta  2   6 Carl A Melbourne  1 Robin Young  5 Chiara Batini  1 Praveen Surendran  5 Tao Jiang  5 Sheikh Daud Adnan  7 Saima Afaq  8 Arpana Agrawal  9 Elisabeth Altmaier  10 Antonis C Antoniou  11 Folkert W Asselbergs  12   13   14   15 Clemens Baumbach  10 Laura Bierut  16 Sarah Bertelsen  17 Michael Boehnke  18 Michiel L Bots  19   20 David M Brazel  6   21 John C Chambers  8   22   23   24 Jenny Chang-Claude  25   26 Chu Chen  27   28 Janie Corley  29   30 Yi-Ling Chou  9 Sean P David  31 Rudolf A de Boer  32 Christiaan A de Leeuw  33 Joe G Dennis  11 Anna F Dominiczak  34 Alison M Dunning  35 Douglas F Easton  11   35 Charles Eaton  28 Paul Elliott  36   37   38   39 Evangelos Evangelou  8   40 Jessica D Faul  41 Tatiana Foroud  42 Alison Goate  17 Jian Gong  27 Hans J Grabe  43 Jeff Haessler  27 Christopher Haiman  44 Göran Hallmans  45 Anke R Hammerschlag  33 Sarah E Harris  29   46 Andrew Hattersley  47 Andrew Heath  9 Chris Hsu  48 William G Iacono  2 Stavroula Kanoni  49   50 Manav Kapoor  17 Jaakko Kaprio  51   52 Sharon L Kardia  53 Fredrik Karpe  54   55 Jukka Kontto  56 Jaspal S Kooner  23   24   37   57 Charles Kooperberg  27   58 Kari Kuulasmaa  56 Markku Laakso  59 Dongbing Lai  42 Claudia Langenberg  60 Nhung Le  61 Guillaume Lettre  62   63 Anu Loukola  51   52 Jian'an Luan  60 Pamela A F Madden  9 Massimo Mangino  64   65 Riccardo E Marioni  29   46 Eirini Marouli  49   50 Jonathan Marten  66 Nicholas G Martin  67 Matt McGue  2 Kyriaki Michailidou  11   68 Evelin Mihailov  69 Alireza Moayyeri  70 Marie Moitry  71 Martina Müller-Nurasyid  72   73   74 Aliya Naheed  75 Matthias Nauck  76   77 Matthew J Neville  54   55 Sune Fallgaard Nielsen  78 Kari North  79 Markus Perola  51   56 Paul D P Pharoah  11   35 Giorgio Pistis  80 Tinca J Polderman  33 Danielle Posthuma  33   81 Neil Poulter  82 Beenish Qaiser  51   52 Asif Rasheed  83 Alex Reiner  28   27 Frida Renström  84   85 John Rice  86 Rebecca Rohde  87 Olov Rolandsson  88 Nilesh J Samani  89 Maria Samuel  83 David Schlessinger  90 Steven H Scholte  91 Robert A Scott  60 Peter Sever  57   82 Yaming Shao  87 Nick Shrine  1 Jennifer A Smith  53 John M Starr  29   92 Kathleen Stirrups  49   93 Danielle Stram  94 Heather M Stringham  18 Ioanna Tachmazidou  95 Jean-Claude Tardif  62   63 Deborah J Thompson  11 Hilary A Tindle  96 Vinicius Tragante  97 Stella Trompet  98   99 Valerie Turcot  62 Jessica Tyrrell  47 Ilonca Vaartjes  19   20 Andries R van der Leij  91 Peter van der Meer  32 Tibor V Varga  84 Niek Verweij  32   100 Henry Völzke  77   101 Nicholas J Wareham  60 Helen R Warren  102   103 David R Weir  41 Stefan Weiss  77   104 Leah Wetherill  42 Hanieh Yaghootkar  47 Ersin Yavas  105   106 Yu Jiang  107 Fang Chen  107 Xiaowei Zhan  108 Weihua Zhang  8   109 Wei Zhao  110 Wei Zhao  53 Kaixin Zhou  111 Philippe Amouyel  112 Stefan Blankenberg  113   114 Mark J Caulfield  102   103 Rajiv Chowdhury  5 Francesco Cucca  80 Ian J Deary  29   30 Panos Deloukas  95   49   115 Emanuele Di Angelantonio  5   116 Marco Ferrario  117 Jean Ferrières  118 Paul W Franks  84   119 Tim M Frayling  47 Philippe Frossard  83 Ian P Hall  120 Caroline Hayward  66 Jan-Håkan Jansson  121 J Wouter Jukema  122   123 Frank Kee  124 Satu Männistö  56 Andres Metspalu  69 Patricia B Munroe  102   103 Børge Grønne Nordestgaard  78 Colin N A Palmer  125 Veikko Salomaa  56 Naveed Sattar  126 Timothy Spector  65 David Peter Strachan  127 Understanding Society Scientific Group, EPIC-CVD, GSCAN, Consortium for Genetics of Smoking Behaviour, CHD Exome+ consortiumPim van der Harst  32   128 Eleftheria Zeggini  95 Danish Saleheen  5   129   130 Adam S Butterworth  5   116 Louise V Wain  1   131 Goncalo R Abecasis  18 John Danesh  5   95   116 Martin D Tobin #  1   131 Scott Vrieze #  2 Dajiang J Liu #  132 Joanna M M Howson #  133
Affiliations
Meta-Analysis

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

A Mesut Erzurumluoglu et al. Mol Psychiatry. 2020 Oct.

Abstract

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

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Conflict of interest statement

Paul W. Franks has been a paid consultant for Eli Lilly and Sanofi Aventis and has received research support from several pharmaceutical companies as part of European Union Innovative Medicines Initiative (IMI) projects. Neil Poulter has received financial support from several pharmaceutical companies that manufacture either blood pressure lowering or lipid lowering agents or both and consultancy fees. Peter Sever has received research awards from Pfizer. Mark J. Caulfield is Chief Scientist for Genomics England, a UK government company. Adam Butterworth reports grants from Merck and Pfizer and grants outside of this work from AstraZeneca, Biogen, and Novartis, and personal fees from Novartis. John Danesh reports grants, personal fees and non-financial support from Merck Sharp & Dohme (MSD), grants, personal fees and non-financial support from Novartis, grants from British Heart Foundation, grants from European Research Council, grants from NIHR, grants from NHS Blood and Transplant, grants from Pfizer, grants from UK MRC, grants from Wellcome Trust, grants from AstraZeneca, outside the submitted work. Laura J. Bierut is listed as an inventor on Issued U.S. Patent 8,080,371,“Markers for Addiction” covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction.

Figures

Fig. 1
Fig. 1
Study design including the discovery and replication stages. NB: Gene-based studies, conditional analyses, and replication in African American ancestry samples not shown here for clarity. *GFG and NAGOZALC studies contributed additional custom content
Fig. 2
Fig. 2
A concentric Circos plot of the association results for smoking initiation (SI; outer ring), cigarettes per day (CPD) and smoking cessation (SC; inner ring) for chromosomes 1–22 (Pack-years results, which can be found in Supp. Figure 1, are omitted for clarity). Each dot represents a SNV, with the X and Y axes corresponding to genomic location in Mb and -log10P-values, respectively. Labels show the nearest gene to the novel sentinel variants identified in the discovery stage and taken forward to replication. The top signals were truncated at 10−10 for clarity. Novel and previously reported signals are highlighted in red and dark blue, respectively. Grey rings on the y-axis increase by increments of 2 (initial ring corresponding to P = 0.001, then 0.00001 etc.); and the outer and inner red rings correspond to the genome-wide significance level (P = 5 × 10−8) and P = 5 × 10−7, respectively. Image was created using Circos (v0.65)

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