Abstract
The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene–smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Optimal strategies for learning multi-ancestry polygenic scores vary across traits
Nature Communications Open Access 07 July 2023
-
Protective Association of APOC1/rs4420638 with Risk of Obesity: A case-control Study in Portuguese Children
Biochemical Genetics Open Access 16 June 2023
-
Multi-ancestry meta-analysis and fine-mapping in Alzheimer’s disease
Molecular Psychiatry Open Access 18 May 2023
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Data availability
All summary results will be made available in dbGaP (phs000930.v7.p1).
References
Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).
Do, R. et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45, 1345–1352 (2013).
Peloso, G. M. et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am. J. Hum. Genet. 94, 223–232 (2014).
Spracklen, C. N. et al. Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum. Mol. Genet. 26, 1770–1784 (2017).
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
Kathiresan, S. et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N. Engl. J. Med. 358, 1240–1249 (2008).
Kar, D. et al. Relationship of cardiometabolic parameters in non-smokers, current smokers, and quitters in diabetes: a systematic review and meta-analysis. Cardiovasc. Diabetol. 15, 158 (2016).
Zong, C. et al. Cigarette smoke exposure impairs reverse cholesterol transport which can be minimized by treatment of hydrogen-saturated saline. Lipids Health Dis. 14, 159 (2015).
Manning, A. K. et al. Meta-analysis of gene–environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet. Epidemiol. 35, 11–18 (2011).
Psaty, B. M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from five cohorts. Circ. Cardiovasc. Genet. 2, 73–80 (2009).
Rao, D. C. et al. Multiancestry study of gene–lifestyle interactions for cardiovascular traits in 610 475 individuals from 124 cohorts: design and rationale. Circ. Cardiovasc. Genet. 10, e001649 (2017).
Lanktree, M. B. et al. Genetic meta-analysis of 15,901 African Americans identifies variation in EXOC3L1 is associated with HDL concentration. J. Lipid Res. 56, 1781–1786 (2015).
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890–5890 (2015).
Suzuki, J., Imanishi, E. & Nagata, S. Xkr8 phospholipid scrambling complex in apoptotic phosphatidylserine exposure. Proc. Natl Acad. Sci. USA 113, 9509–9514 (2016).
Wang, J. et al. Genome-wide expression analysis reveals diverse effects of acute nicotine exposure on neuronal function-related genes and pathways. Front. Psychiatry 2, 5 (2011).
International Parkinson Disease Genomics Consortium. Imputation of sequence variants for identification of genetic risks for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet 377, 641–649 (2011).
Ng, M. C. Y. et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 10, e1004517 (2014).
Cai, K., Lucki, N. C. & Sewer, M. B. Silencing diacylglycerol kinase-θ expression reduces steroid hormone biosynthesis and cholesterol metabolism in human adrenocortical cells. Biochim. Biophys. Acta 1841, 552–562 (2014).
Cai, K. & Sewer, M. B. Diacylglycerol kinase θ couples farnesoid X receptor–dependent bile acid signalling to Akt activation and glucose homoeostasis in hepatocytes. Biochem. J. 454, 267–274 (2013).
Cordell, H. J. et al. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways. Nat. Commun. 6, 8019 (2015).
Edwards, T. L. et al. Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease. Ann. Hum. Genet. 74, 97–109 (2010).
Lill, C. M. et al. Comprehensive research synopsis and systematic meta-analyses in Parkinson’s disease genetics: the PDGene Database. PLoS Genet. 8, e1002548 (2012).
Nalls, M. A. et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat. Genet. 46, 989–993 (2014).
Pankratz, N. et al. Meta-analysis of Parkinson disease: identification of a novel locus, RIT2. Ann. Neurol. 71, 370–384 (2012).
Wang, J. et al. Phlegm-dampness constitution: genomics, susceptibility, adjustment and treatment with traditional Chinese medicine. Am. J. Chin. Med. 41, 253–262 (2013).
Choi, J.-H. et al. Variations in TAS1R taste receptor gene family modify food intake and gastric cancer risk in a Korean population. Mol. Nutr. Food Res. 60, 2433–2445 (2016).
Hoffmann, T. J. et al. A large electronic-health-record-based genome-wide study of serum lipids. Nat. Genet. 50, 401–413 (2018).
Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).
Liu, D. J. et al. Exome-wide association study of plasma lipids in ~300,000 individuals. Nat. Genet. 49, 1758 (2017).
Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).
Tobacco Use Among U.S. Racial/Ethnic Minority Groups—African Americans, American Indians and Alaska Natives, Asian Americans and Pacific Islanders, and Hispanics: a Report of the Surgeon General (US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 1998).
Villanti, A. C. et al. Changes in the prevalence and correlates of menthol cigarette use in the USA, 2004–2014. Tobacco Control 25, ii14 (2016).
Ross, K. C., Dempsey, D. A., St.Helen, G., Delucchi, K. & Benowitz, N. L. The influence of puff characteristics, nicotine dependence, and rate of nicotine metabolism on daily nicotine exposure in African American smokers. Cancer Epidemiol. Bomarkers Prev. 25, 936–943 (2016).
Ton, H. T. et al. Menthol enhances the desensitization of human α3β4 nicotinic acetylcholine receptors. Mol. Pharmacol. 88, 256–264 (2015).
Alexander, L. A. et al. Why we must continue to investigate menthol’s role in the African American smoking paradox. Nicotine Tobacco Res. 18, S91–S101 (2016).
Jones, M. R., Tellez-Plaza, M. & Navas-Acien, A. Smoking, menthol cigarettes and all-cause, cancer and cardiovascular mortality: evidence from the National Health and Nutrition Examination Survey (NHANES) and a meta-analysis. PLoS One 8, e77941 (2013).
Munro, H. M., Tarone, R. E., Wang, T. J. & Blot, W. J. Menthol and nonmenthol cigarette smoking: all-cause deaths, cardiovascular disease deaths, and other causes of death among blacks and whites. Circulation 133, 1861–1866 (2016).
Murray, R. P., Connett, J. E., Skeans, M. A. & Tashkin, D. P. Menthol cigarettes and health risks in lung health study data. Nicotine Tobacco Res. 9, 101–107 (2007).
Vozoris, N. T. Mentholated cigarettes and cardiovascular and pulmonary diseases: a population-based study. Arch. Intern. Med. 172, 590–593 (2012).
Pérez-Stable, E. J., Herrera, B., Jacob, I. P. & Benowitz, N. L. Nicotine metabolism and intake in black and white smokers. J. Am. Med. Assoc. 280, 152–156 (1998).
Khariwala, S. S. et al. Cotinine and tobacco-specific carcinogen exposure among nondaily smokers in a multiethnic sample. Nicotine Tobacco Res. 16, 600–605 (2014).
Jain, R. B. Distributions of selected urinary metabolites of volatile organic compounds by age, gender, race/ethnicity, and smoking status in a representative sample of U.S. adults. Environ. Toxicol. Pharmacol. 40, 471–479 (2015).
Benowitz, N. L., Dains, K. M., Dempsey, D., Wilson, M. & Jacob, P. Racial differences in the relationship between number of cigarettes smoked and nicotine and carcinogen exposure. Nicotine Tobacco Res. 13, 772–783 (2011).
The Health Consequences of Smoking—50 Years of Progress: a Report of the Surgeon General (US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014).
Ito, S. et al. Nicotine-induced expression of low-density lipoprotein receptor in oral epithelial cells. PLoS One 8, e82563 (2013).
Dullaart, R. P., Hoogenberg, K., Dikkeschei, B. D. & van Tol, A. Higher plasma lipid transfer protein activities and unfavorable lipoprotein changes in cigarette-smoking men. Arterioscler. Thromb. 14, 1581–1585 (1994).
Frondelius, K. et al. Lifestyle and dietary determinants of serum apolipoprotein A1 and apolipoprotein B concentrations: cross-sectional analyses within a Swedish cohort of 24,984 individuals. Nutrients 9, 211 (2017).
Onat, A. et al. Preheparin serum lipoprotein lipase mass interacts with gender, gene polymorphism and, positively, with smoking. Clin. Chem. Lab. Med. 47, 208 (2009).
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
Winkler, T. W. et al. EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data. Bioinformatics 31, 259–261 (2015).
Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
Kraft, P., Yen, Y. C., Stram, D. O., Morrison, J. & Gauderman, W. J. Exploiting gene–environment interaction to detect genetic associations. Hum. Hered. 63, 111–119 (2007).
Skol, A. D., Scott, L. J., Abecasis, G. R. & Boehnke, M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat. Genet. 38, 209–213 (2006).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284 (2016).
Winkler, T. W. et al. Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: recommendations based on a systematic evaluation. PLoS One 12, e0181038 (2017).
Nikpay, M. et al. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).
Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197 (2015).
Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187 (2015).
Justice, A. E. et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat. Commun. 8, 14977 (2017).
Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).
Liu, C. T. et al. Trans-ethnic meta-analysis and functional annotation illuminates the genetic architecture of fasting glucose and insulin. Am. J. Hum. Genet. 99, 56–75 (2016).
Gaulton, K. J. et al. Genetic fine-mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).
Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).
Mahajan, A. et al. Trans-ethnic fine mapping highlights kidney-function genes linked to salt sensitivity. Am. J. Hum. Genet. 99, 636–646 (2016).
Joehanes, R. et al. Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol. 18, 16 (2017).
Blake, J. A. et al. The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Res. 42, D810–D817 (2014).
Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).
Acknowledgements
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Human Genome Research Institute; the National Institutes of Health; or the US Department of Health and Human Services. This project was largely supported by a grant from the US National Heart, Lung, and Blood Institute of the National Institutes of Health (R01HL118305) and by the Intramural Research Program of the National Human Genome Research Institute of the National Institutes of Health through the Center for Research on Genomics and Global Health (CRGGH). The CRGGH is supported by the National Human Genome Research Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the Center for Information Technology, and the Office of the Director at the National Institutes of Health (Z01HG200362). Additional and study-specific acknowledgments appear in the Supplementary Note.