Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption

Cornelis, Marilyn C.; Byrne, Enda M.; Esko, Tõnu; Nalls, Michael A.; Ganna, Andrea; Paynter, Nina P.; Monda, Keri L.; Amin, Najaf; Fischer, Krista; Renström, Frida; Ngwa, Julius S.; Huikari, Ville; Cavadino, Alana; Nolte, Ilja M.; Teumer, Alexander; Yu, Kai; Marques‐Vidal, Pedro; Rawal, Rajesh; Manichaikul, Ani; Wojczynski, Mary K.; Vink, Jacqueline M.; Zhao, Jing Hua; Burlutsky, George; Lahti, Jari; Mikkilä, Vera; Lemaître, Rozenn N.; Eriksson, Joel; Musani, Solomon K.; Tanaka, Toshiko; Geller, Frank; Luan, Jian’an; Hui, Jennie; Mägi, Reedik; Dimitriou, Maria; García, Melissa; Ho, Weang Kee; Wright, Margaret J.; Rose, Lynda M.; Magnusson, Patrik K. E.; Pedersen, Nancy L.; Couper, David J.; Oostra, Ben A.; Hofman, Albert; Ikram, M. Arfan; Tiemeier, Henning; Uitterlinden, André G.; Rooij, Frank J.A. van; Barroso, Inês; Johansson, Ingegerd; Xue, Li; Kaakinen, Marika; Milani, Lili; Power, Chris; Snieder, Harold; Stolk, Ronald P.; Baumeister, Sebastian E.; Biffar, Reiner; Gu, Fangyi; Bastardot, François; Kutalik, Zoltán; Jacobs, David R.; Forouhi, Nita G.; Mihailov, Evelin; Lind, Lars; Lindgren, Cecilia M.; Michaëlsson, Karl; Morris, Huw R.; Jensen, Majken K.; Khaw, K-T; Luben, Robert; Wang, J. J.; Männistö, Satu; Perälä, Mia-Maria; Kähönen, Mika; Lehtimäki, Terho; Viikari, Jorma; Mozaffarian, Dariush; Mukamal, Kenneth J.; Psaty, Bruce M.; Döring, Angela; Heath, Andrew C.; Montgomery, Grant W.; Dahmen, Norbert; Carithers, T.; Tucker, Katherine L.; Ferrucci, Luigi; Boyd, Heather A.; Melbye, Mads; Treur, Jorien L.; Mellström, Dan; Hottenga, Jouke Jan; Prokopenko, Inga; Tönjes, Anke; Deloukas, Panos; Kanoni, Stavroula; Lorentzon, Mattias; Houston, Denise K.; Liu, Y.; Danesh, John; Rasheed, Awais; Mason, Marc A.; Zonderman, Alan B.; Franke, Lude; Kristal, Bruce S.; Karjalainen, Juha; Reed, Danielle R.; Westra, Harm Jan; Evans, Michele K.; Saleheen, Danish; Harris, Tamara B.; Dedoussis, George; Curhan, Gary C.; Stümvoll, Michael; Beilby, John; Pasquale, Louis R.; Feenstra, Bjarke; Bandinelli, Stefania; Ordovás, José M.; Chan, Andrew T.; Peters, Ulrike; Ohlsson, Claes; Gieger, Christian; Martin, Nicholas G.; Waldenberger, Mélanie; Siscovick, David S.; Raitakari, Olli T.; Eriksson, Johan G.; Mitchell, Paul; Hunter, David J.; Kraft, Peter; Rimm, Eric B.; Boomsma, Dorret I.; Borecki, Ingrid B.; Loos, Ruth J.F.; Wareham, Nick; Vollenweider, Péter; Caporaso, Neil E.; Grabe, Hans J.; Neuhouser, Marian L.; Wolffenbuttel, Bruce H. R.; Hu, Frank B.; Hyppönen, Elina; Järvelin, Marjo Riitta; Cupples, L. Adrienne; Franks, Paul W.; Ridker, Paul M.; Duijn, Cornelia M. van; Heiss, Gerardo; Metspalu, Andres; North, Kari E.; Ingelsson, Erik; Nettleton, Jennifer A.; Dam, Rob M. van; Chasman, Daniel I.

doi:10.1038/mp.2014.107

Cited by 238 publications

(250 citation statements)

References 41 publications

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“…Estimates for the associations of SNPs with coffee consumption were taken from analyses restricted to individuals of European ancestry. Full details of how this coffee consumption was assessed and control for population stratification in each contributing cohort are available in the supplementary material of the GWAS publication 8. Gene outcome associations were obtained from summary level data from the Tobacco and Genetics (TAG) consortium (cigarettes per day phenotype, n ≤ 38 181) and the cotinine consortium for the gene–outcome associations ( n ≤ 4548) (see Supporting information).…”

Section: Methodsmentioning

confidence: 99%

“…Three specific combinations of single nucleotide polymorphisms (SNPs) that capture coffee consumption were used, corresponding to three models: (1) a set of eight independent ( r 2 < 0.0006) SNPs (rs1260326, rs1481012, rs4410790, rs7800944, rs17685, rs6265, rs2470893, rs9902453), comprising variants which met the threshold for genome‐wide significance (log 10 Bayes factor > 5.64) in the trans‐ethnic GWAS meta‐analysis of coffee consumption reported by the CCGC; (2) a moderate set of six SNPs (rs1260326, rs4410790, rs7800944, rs17685, rs2470893, rs9902453), which limited the full set to those variants which met the threshold of P < 0.05 in the CCGC stage 2 European sample GWAS; and (3) a conservative set of two SNPs (rs4410790, rs2470893), which limited the full set to variants in loci also identified in previous GWAS, in or near genes with a biological role in caffeine metabolism 8, 9, 10, 11, 12. The gene–exposure and gene–outcome associations were identified in the CCGC 8, TAG 15 and cotinine 16 consortia summary‐level data.…”

Section: Methodsmentioning

confidence: 99%

“…The gene–exposure and gene–outcome associations were identified in the CCGC 8, TAG 15 and cotinine 16 consortia summary‐level data. Models 2 and 3 were included as sensitivity analyses, as recommended by Burgess and colleagues 14.…”

Section: Methodsmentioning

confidence: 99%

“…This approach has been used to examine the causal impact of cigarette smoking on a variety of health outcomes 5, 6, 7. The identification of genetic variants associated with coffee consumption 8, 9, 10, 11, 12 presents the opportunity to use MR to examine the causal impact of coffee consumption on smoking.…”

Section: Introductionmentioning

confidence: 99%

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Does coffee consumption impact on heaviness of smoking?

et al. 2017

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Background and AimsCoffee consumption and cigarette smoking are strongly associated, but whether this association is causal remains unclear. We sought to: (1) determine whether coffee consumption influences cigarette smoking causally, (2) estimate the magnitude of any association and (3) explore potential mechanisms.DesignWe used Mendelian randomization (MR) analyses of observational data, using publicly available summarized data from the Tobacco and Genetics (TAG) consortium, individual‐level data from the UK Biobank and in‐vitro experiments of candidate compounds.SettingThe TAG consortium includes data from studies in several countries. The UK Biobank includes data from men and women recruited across England, Wales and Scotland.ParticipantsThe TAG consortium provided data on n ≤ 38 181 participants. The UK Biobank provided data on 8072 participants.MeasurementsIn MR analyses, the exposure was coffee consumption (cups/day) and the outcome was heaviness of smoking (cigarettes/day). In our in‐vitro experiments we assessed the effect of caffeic acid, quercetin and p‐coumaric acid on the rate of nicotine metabolism in human liver microsomes and cDNA‐expressed human CYP2A6.FindingsTwo‐sample MR analyses of TAG consortium data indicated that heavier coffee consumption might lead to reduced heaviness of smoking [beta = −1.49, 95% confidence interval (CI) = −2.88 to −0.09]. However, in‐vitro experiments found that the compounds investigated are unlikely to inhibit significantly the rate of nicotine metabolism following coffee consumption. Further MR analyses in UK Biobank found no evidence of a causal relationship between coffee consumption and heaviness of smoking (beta = 0.20, 95% CI = –1.72 to 2.12).ConclusionsAmount of coffee consumption is unlikely to have a major causal impact upon amount of cigarette smoking. If it does influence smoking, this is not likely to operate via effects of caffeic acid, quercetin or p‐coumaric acid on nicotine metabolism. The observational association between coffee consumption and cigarette smoking may be due to smoking impacting on coffee consumption or confounding.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Does coffee consumption impact on heaviness of smoking?

et al. 2017

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“…1,2 Multiple successful GWAS studies have been reported ranging from body size, metabolomics, and medically relevant traits (reviewed), 3,4 to hormones, 5 personality, 6 educational attainment, 7 and lifestyle characteristics. [8][9][10] The major technology behind these successes is the relatively cheap genotyping, in comparison with full genome sequencing, of DNA samples on genotyping arrays with 300 K-5 M single-nucleotide polymorphisms (SNPs), followed by imputation of the unmeasured SNPs. Initially, the contents of these arrays were determined by the manufacturers, but recently companies also allow researchers to select the variants on an array.…”

Section: Introductionmentioning

confidence: 99%

A method to customize population-specific arrays for genome-wide association testing

et al. 2016

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As an example of optimizing population-specific genotyping assays using a whole-genome sequence reference set, we detail the approach that followed to design the Axiom-NL array which is characterized by an improved imputation backbone based on the Genome of the Netherlands (GoNL) reference sequence and, compared with earlier arrays, a more comprehensive inclusion of SNPs on chromosomes X, Y, and the mitochondria. Common variants on the array were selected to be compatible with the Illumina Psych Array and the Affymetrix UK Biobank Axiom array. About 3.5% of the array (23 977 markers) represents SNPs from the GWAS catalog, including SNPs at FTO, APOE, Ion-channels, killer-cell immunoglobulin-like receptors, and HLA. Around 26 000 markers associated with common psychiatric disorders are included, as well as 6705 markers suggested to be associated with fertility and twinning. The platform can thus be used for risk profiling, detection of new variants, as well as ancestry determination. Results of coverage tests in 249 unrelated subjects with GoNL-based sequence data show that after imputation with 1000G as a reference, the median concordance between original and imputed genotypes is above 98%. The median imputation quality R 2 for MAF thresholds of 0.001, 0.01, 0.05, and 40.05 are 0.05, 0.28, 0.80, 0.99, respectively, for the 1000G imputed SNPs, with a similar quality for the autosomes and X chromosome, showing a good genome-wide coverage for association studies after imputation. 3,4 to hormones, 5 personality, 6 educational attainment, 7 and lifestyle characteristics. [8][9][10] The major technology behind these successes is the relatively cheap genotyping, in comparison with full genome sequencing, of DNA samples on genotyping arrays with 300 K-5 M single-nucleotide polymorphisms (SNPs), followed by imputation of the unmeasured SNPs. Initially, the contents of these arrays were determined by the manufacturers, but recently companies also allow researchers to select the variants on an array. Here, we focus on the Axiom array, a genotyping solution from Affymetrix, Inc., which provides a high throughput platform for high-density SNP genotyping on a diverse range of sample types. This array has been used for several large population-wide genome-screening projects including the UK biobank 11 and the GERA cohorts. 12 We describe a similar custommade Axiom array for the Netherlands population, the Axiom-NL that allows good imputation and enhances association, and risk score analysis on DNA samples collected within Dutch Biobanks, such as the large number of Biobanks collaborating in BBMRI-NL. 13 Notwithstanding the application to this specific population, the SNP selection procedures and coverage testing can provide a general guideline for customizing the Axiom array in other populations for which valid reference sequence genomes are available. MATERIALS AND METHODS SNP selection for the Axiom-NL arrayAn overview of the SNP selection is provided in Figure 1, a stepwise procedure of the selection is given in the Supple...

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Association of Coffee Drinking With Mortality by Genetic Variation in Caffeine Metabolism

et al. 2018

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and Asia show consistent inverse associations between coffee drinking and mortality, including deaths from cardiovascular disease and some cancers. However, concerns about coffee, particularly among people with common genetic polymorphisms affecting caffeine metabolism and among those drinking more than 5 cups per day, remain.OBJECTIVE To evaluate associations of coffee drinking with mortality by genetic caffeine metabolism score. DESIGN, SETTING, AND PARTICIPANTSThe UK Biobank is a population-based study that invited approximately 9.2 million individuals from across the United Kingdom to participate. We used baseline demographic, lifestyle, and genetic data form the UK Biobank cohort, with follow-up beginning in 2006 and ending in 2016, to estimate hazard ratios (HRs) for coffee intake and mortality, using multivariable-adjusted Cox proportional hazards models. We investigated potential effect modification by caffeine metabolism, defined by a genetic score of previously identified polymorphisms in AHR, CYP1A2, CYP2A6, and POR that have an effect on caffeine metabolism. Of the 502 641 participants who consented with baseline data, we included those who were not pregnant and had complete data on coffee intake and smoking status (n = 498 134).EXPOSURES Total, ground, instant, and decaffeinated coffee intake. MAIN OUTCOMES AND MEASURESAll-cause and cause-specific mortality. RESULTSThe mean age of the participants was 57 years (range, 38-73 years); 271 019 (54%) were female, and 387 494 (78%) were coffee drinkers. Over 10 years of follow-up, 14 225 deaths occurred. Coffee drinking was inversely associated with all-cause mortality. Using non-coffee drinkers as the reference group, HRs for drinking less than 1, 1, 2 to 3, 4 to 5, 6 to 7, and 8 or more cups per day were 0.94 (95% CI, 0.88-1.01), 0.92 (95% CI, 0.87-0.97), 0.88 (95% CI, 0.84-0.93), 0.88 (95% CI, 0.83-0.93), 0.84 (95% CI, 0.77-0.92), and 0.86 (95% CI, 0.77-0.95), respectively. Similar associations were observed for instant, ground, and decaffeinated coffee, across common causes of death, and regardless of genetic caffeine metabolism score. For example, the HRs for 6 or more cups per day ranged from 0.70 (95% CI, 0.53-0.94) to 0.92 (95% CI, 0.78-1.10), with no evidence of effect modification across strata of caffeine metabolism score (P = .17 for heterogeneity). CONCLUSIONS AND RELEVANCECoffee drinking was inversely associated with mortality, including among those drinking 8 or more cups per day and those with genetic polymorphisms indicating slower or faster caffeine metabolism. These findings suggest the importance of noncaffeine constituents in the coffee-mortality association and provide further reassurance that coffee drinking can be a part of a healthy diet.

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Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption

Cited by 238 publications

References 41 publications

Does coffee consumption impact on heaviness of smoking?

Does coffee consumption impact on heaviness of smoking?

A method to customize population-specific arrays for genome-wide association testing

Association of Coffee Drinking With Mortality by Genetic Variation in Caffeine Metabolism

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