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Genome-wide association studies of coffee intake in UK/US participants of European ancestry uncover cohort-specific genetic associations

Abstract

Coffee is one of the most widely consumed beverages. We performed a genome-wide association study (GWAS) of coffee intake in US-based 23andMe participants (N = 130,153) and identified 7 significant loci, with many replicating in three multi-ancestral cohorts. We examined genetic correlations and performed a phenome-wide association study across hundreds of biomarkers, health, and lifestyle traits, then compared our results to the largest available GWAS of coffee intake from the UK Biobank (UKB; N = 334,659). We observed consistent positive genetic correlations with substance use and obesity in both cohorts. Other genetic correlations were discrepant, including positive genetic correlations between coffee intake and psychiatric illnesses, pain, and gastrointestinal traits in 23andMe that were absent or negative in the UKB, and genetic correlations with cognition that were negative in 23andMe but positive in the UKB. Phenome-wide association study using polygenic scores of coffee intake derived from 23andMe or UKB summary statistics also revealed consistent associations with increased odds of obesity- and red blood cell-related traits, but all other associations were cohort-specific. Our study shows that the genetics of coffee intake associate with substance use and obesity across cohorts, but also that GWAS performed in different populations could capture cultural differences in the relationship between behavior and genetics.

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Fig. 1: GWAS and secondary analyses of coffee intake from the 23andMe cohort.
Fig. 2: Genetic and phenotypic associations with genetic disposition to coffee intake in US and UK cohorts.

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Data availability

We will provide 23andMe summary statistics for the top 10,000 SNPs upon publication. 23andMe GWAS and metaGWAS summary statistics will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe research participants. Please visit (https://research.23andme.com/collaborate/#dataset-access/) for more information and to apply to access the data.

References

  1. International Coffee Organization. Annual Review Coffee Year 2019/2020. London: International Coffee Organization; 2021.

  2. Reyes C, Cornelis M. Caffeine in the diet: country-level consumption and guidelines. Nutrients. 2018;10:1772.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Landais E, Moskal A, Mullee A, Nicolas G, Gunter M, Huybrechts I, et al. Coffee and tea consumption and the contribution of their added ingredients to total energy and nutrient intakes in 10 European countries: benchmark data from the late 1990s. Nutrients. 2018;10:725.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rehm CD, Ratliff JC, Riedt CS, Drewnowski A. Coffee consumption among adults in the United States by demographic variables and purchase location: analyses of NHANES 2011-2016 data. Nutrients. 2020;12:2463.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Camandola S, Plick N, Mattson MP. Impact of coffee and cacao purine metabolites on neuroplasticity and neurodegenerative disease. Neurochem Res. 2019;44:214–27.

    Article  CAS  PubMed  Google Scholar 

  6. Grosso G, Godos J, Galvano F, Giovannucci EL. Coffee, caffeine, and health outcomes: an umbrella review. Annu Rev Nutr. 2017;37:131–56.

    Article  CAS  PubMed  Google Scholar 

  7. Poole R, Kennedy OJ, Roderick P, Fallowfield JA, Hayes PC, Parkes J. Coffee consumption and health: umbrella review of meta-analyses of multiple health outcomes. BMJ. 2017;359:j5024.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kositamongkol C, Kanchanasurakit S, Auttamalang C, Inchai N, Kabkaew T, Kitpark S, et al. Coffee consumption and non-alcoholic fatty liver disease: an umbrella review and a systematic review and meta-analysis. Front Pharm. 2021;12:786596.

    Article  CAS  Google Scholar 

  9. Socała K, Szopa A, Serefko A, Poleszak E, Wlaź P. Neuroprotective effects of coffee bioactive compounds: a review. Int J Mol Sci. 2020;22:107.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhao LG, Li ZY, Feng GS, Ji XW, Tan YT, Li HL, et al. Coffee drinking and cancer risk: an umbrella review of meta-analyses of observational studies. BMC Cancer. 2020;20:101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Freedman ND, Park Y, Abnet CC, Hollenbeck AR, Sinha R. Association of coffee drinking with total and cause-specific mortality. N Engl J Med. 2012;366:1891–904.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Svikis DS, Dillon PM, Meredith SE, Thacker LR, Polak K, Edwards AC, et al. Coffee and energy drink use patterns in college freshmen: associations with adverse health behaviors and risk factors. BMC Public Health. 2022;22:594.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Swan GE, Carmelli D, Cardon LR. Heavy consumption of cigarettes, alcohol and coffee in male twins. J Stud Alcohol. 1997;58:182–90.

    Article  CAS  PubMed  Google Scholar 

  14. Treur JL, Taylor AE, Ware JJ, McMahon G, Hottenga JJ, Baselmans BML, et al. Associations between smoking and caffeine consumption in two European cohorts. Addiction. 2016;111:1059–68.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Tang N, Wu Y, Ma J, Wang B, Yu R. Coffee consumption and risk of lung cancer: a meta-analysis. Lung Cancer. 2010;67:17–22.

    Article  PubMed  Google Scholar 

  16. Nehlig A. Effects of coffee on the gastro-intestinal tract: a narrative review and literature update. Nutrients. 2022;14:399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Butt MS, Sultan MT. Coffee and its consumption: benefits and risks. Crit Rev Food Sci Nutr. 2011;51:363–73.

    Article  CAS  PubMed  Google Scholar 

  18. Yang A, Palmer AA, de Wit H. Genetics of caffeine consumption and responses to caffeine. Psychopharmacology. 2010;211:245–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Abdellaoui A, Dolan CV, Verweij KJH, Nivard MG. Gene-environment correlations across geographic regions affect genome-wide association studies. Nat Genet. 2022;54:1345–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kang J, Jia T, Jiao Z, Shen C, Xie C, Cheng W, et al. Increased brain volume from higher cereal and lower coffee intake: shared genetic determinants and impacts on cognition and metabolism. Cereb Cortex. 2022;32:5163–74.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Cornelis MC, van Dam RM. Genetic determinants of liking and intake of coffee and other bitter foods and beverages. Sci Rep. 2021;11:23845.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Said MA, van de Vegte YJ, Verweij N, van der Harst P. Associations of observational and genetically determined caffeine intake with coronary artery disease and diabetes mellitus. J Am Heart Assoc. 2020;9:e016808.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Jin T, Youn J, Kim AN, Kang M, Kim K, Sung J, et al. Interactions of habitual coffee consumption by genetic polymorphisms with the risk of prediabetes and type 2 diabetes combined. Nutrients. 2020;12:2228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vogel CFA, Van Winkle LS, Esser C, Haarmann-Stemmann T. The aryl hydrocarbon receptor as a target of environmental stressors—implications for pollution mediated stress and inflammatory responses. Redox Biol. 2020;34:101530.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Matoba N, Akiyama M, Ishigaki K, Kanai M, Takahashi A, Momozawa Y, et al. GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits. Nat Hum Behav. 2020;4:308–16.

    Article  PubMed  Google Scholar 

  27. Kennedy OJ, Pirastu N, Poole R, Fallowfield JA, Hayes PC, Grzeszkowiak EJ, et al. Coffee consumption and kidney function: a Mendelian randomization study. Am J Kidney Dis. 2020;75:753–61.

    Article  CAS  PubMed  Google Scholar 

  28. Jia H, Nogawa S, Kawafune K, Hachiya T, Takahashi S, Igarashi M, et al. GWAS of habitual coffee consumption reveals a sex difference in the genetic effect of the 12q24 locus in the Japanese population. BMC Genet. 2019;20:61.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zhong VW, Kuang A, Danning RD, Kraft P, van Dam RM, Chasman DI, et al. A genome-wide association study of bitter and sweet beverage consumption. Hum Mol Genet. 2019;28:2449–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Nakagawa-Senda H, Hachiya T, Shimizu A, Hosono S, Oze I, Watanabe M, et al. A genome-wide association study in the Japanese population identifies the 12q24 locus for habitual coffee consumption: the J-MICC Study. Sci Rep. 2018;8:1493.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Cornelis MC, Kacprowski T, Menni C, Gustafsson S, Pivin E, Adamski J, et al. Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior. Hum Mol Genet. 2016;25:ddw334.

    Article  Google Scholar 

  32. Pirastu N, Kooyman M, Robino A, van der Spek A, Navarini L, Amin N, et al. Non-additive genome-wide association scan reveals a new gene associated with habitual coffee consumption. Sci Rep. 2016;6:31590.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Coffee and Caffeine Genetics Consortium, Cornelis MC, Byrne EM, Esko T, Nalls MA, Ganna A, et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol Psychiatry. 2015;20:647–56.

    Article  Google Scholar 

  34. Hamza TH, Chen H, Hill-Burns EM, Rhodes SL, Montimurro J, Kay DM, et al. Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson’s disease modifier gene via interaction with coffee. PLoS Genet. 2011;7:e1002237.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Amin N, Byrne E, Johnson J, Chenevix-Trench G, Walter S, Nolte IM, et al. Genome-wide association analysis of coffee drinking suggests association with CYP1A1/CYP1A2 and NRCAM. Mol Psychiatry. 2012;17:1116–29.

    Article  CAS  PubMed  Google Scholar 

  36. Sulem P, Gudbjartsson DF, Geller F, Prokopenko I, Feenstra B, Aben KKH, et al. Sequence variants at CYP1A1-CYP1A2 and AHR associate with coffee consumption. Hum Mol Genet. 2011;20:2071–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cole JB, Florez JC, Hirschhorn JN. Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations. Nat Commun. 2020;11:1467.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J, Gignoux CR, et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature. 2019;570:514–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Raja A, Ciociola E, Ahmad I, Dar F, Naqvi S, Moaeen-Ud-Din M, et al. Genetic susceptibility to chronic liver disease in individuals from Pakistan. Int J Mol Sci. 2020;21:3558.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gao H, Liu S, Zhao Z, Yu X, Liu Q, Xin Y, et al. Association of GCKR gene polymorphisms with the risk of nonalcoholic fatty liver disease and coronary artery disease in a Chinese Northern Han Population. J Clin Transl Hepatol. 2019;X:1–7.

    Article  Google Scholar 

  41. Cai W, Weng D, Yan P, Lin Y, Dong Z, Mailamuguli ZH, et al. Genetic polymorphisms associated with nonalcoholic fatty liver disease in Uyghur population: a case-control study and meta-analysis. Lipids Health Dis. 2019;18:14.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Xiao X, Ma G, Li S, Wang M, Liu N, Ma L, et al. Functional POR A503V is associated with the risk of bladder cancer in a Chinese population. Sci Rep. 2015;5:11751.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Menashe I, Figueroa JD, Garcia-Closas M, Chatterjee N, Malats N, Picornell A, et al. Large-scale pathway-based analysis of bladder cancer genome-wide association data from five studies of European background. PLoS ONE. 2012;7:e29396.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rotunno M, Yu K, Lubin JH, Consonni D, Pesatori AC, Goldstein AM, et al. Phase I metabolic genes and risk of lung cancer: multiple polymorphisms and mRNA expression. PLoS ONE. 2009;4:e5652.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Aldrich MC, Selvin S, Hansen HM, Barcellos LF, Wrensch MR, Sison JD, et al. CYP1A1/2 haplotypes and lung cancer and assessment of confounding by population stratification. Cancer Res. 2009;69:2340–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Thompson A, Cook J, Choquet H, Jorgenson E, Yin J, Kinnunen T, et al. Functional validity, role, and implications of heavy alcohol consumption genetic loci. Sci Adv. 2020;6:eaay5034.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sanchez-Roige S, Palmer AA, Fontanillas P, Elson SL, 23andMe Research Team tSUDWGotPGC, Adams MJ, et al. Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2019;176:107–18.

    Article  PubMed  Google Scholar 

  48. Clarke TK, Adams MJ, Davies G, Howard DM, Hall LS, Padmanabhan S, et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117). Mol Psychiatry. 2017;22:1376–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yin B, Wang X, Huang T, Jia J. Shared genetics and causality between decaffeinated coffee consumption and neuropsychiatric diseases: a large-scale genome-wide cross-trait analysis and Mendelian randomization analysis. Front Psychiatry. 2022;13:910432.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Treur JL, Taylor AE, Ware JJ, Nivard MG, Neale MC, McMahon G, et al. Smoking and caffeine consumption: a genetic analysis of their association. Addict Biol. 2017;22:1090–102.

    Article  CAS  PubMed  Google Scholar 

  51. Xu J, Zhang S, Tian Y, Si H, Zeng Y, Wu Y, et al. Assessing the association between important dietary habits and osteoporosis: a genetic correlation and two-sample Mendelian randomization study. Nutrients. 2022;14:3683.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Yuan S, Daghlas I, Larsson SC. Alcohol, coffee consumption, and smoking in relation to migraine: a bidirectional Mendelian randomization study. Pain. 2022;163:e342–8.

    Article  PubMed  Google Scholar 

  53. Qi X, Ye J, Wen Y, Liu L, Cheng B, Cheng S, et al. Evaluating the effects of diet-gut microbiota interactions on sleep traits using the UK Biobank cohort. Nutrients. 2022;14:1134.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Wang T, Huang T, Kang JH, Zheng Y, Jensen MK, Wiggs JL, et al. Habitual coffee consumption and genetic predisposition to obesity: gene-diet interaction analyses in three US prospective studies. BMC Med. 2017;15:97.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Laaboub N, Gholam M, Sibailly G, Sjaarda J, Delacrétaz A, Dubath C, et al. Associations between high plasma methylxanthine levels, sleep disorders and polygenic risk scores of caffeine consumption or sleep duration in a Swiss psychiatric cohort. Front Psychiatry. 2021;12:756403.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Song MY, Park S. Association of polygenetic risk scores related to immunity and inflammation with hyperthyroidism risk and interactions between the polygenetic scores and dietary factors in a large cohort. J Thyroid Res. 2021;2021:1–12.

    Article  Google Scholar 

  57. Kim E, Hoffmann TJ, Nah G, Vittinghoff E, Delling F, Marcus GM. Coffee consumption and incident tachyarrhythmias: reported behavior, mendelian randomization, and their interactions. JAMA Intern Med. 2021;181:1185–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sanchez-Roige S, Jennings MV, Thorpe HHA, Mallari JE, van der Werf LC, Bianchi SB, et al. CADM2 is implicated in impulsive personality and numerous other traits by genome- and phenome-wide association studies in humans and mice. Transl Psychiatry. 2023;13:167.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Durand EY, Do CB, Mountain JL, Macpherson JM. Ancestry composition: a novel, efficient pipeline for ancestry deconvolution. bioRxiv. 2014. https://doi.org/10.1101/010512.

  60. Sey NYA, Hu B, Mah W, Fauni H, McAfee JC, Rajarajan P, et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat Neurosci. 2020;23:583–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9:1825.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 2019;15:e1007889.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics C, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Roden D, Pulley J, Basford M, Bernard G, Clayton E, Balser J, et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharm Ther. 2008;84:362–9.

    Article  CAS  Google Scholar 

  65. Dennis J, Sealock J, Levinson RT, Farber-Eger E, Franco J, Fong S, et al. Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease. Mol Psychiatry. 2021;26:4254–64.

    Article  PubMed  Google Scholar 

  66. Ge T, Chen CY, Ni Y, Feng YCA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10:1776.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Carroll RJ, Bastarache L, Denny JC. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics. 2014;30:2375–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ellison-Barnes A, Johnson S, Gudzune K. Trends in obesity prevalence among adults aged 18 through 25 years, 1976-2018. JAMA. 2021;326:2073–74.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Kendler KS, Schmitt E, Aggen SH, Prescott CA. Genetic and environmental influences on alcohol, caffeine, cannabis, and nicotine use from early adolescence to middle adulthood. Arch Gen Psychiatry. 2008;65:674–82.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Karlsson Linnér R, Mallard TT, Barr PB, Sanchez-Roige S, Madole JW, Driver MN, et al. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat Neurosci. 2021;24:1367–76.

    Article  PubMed  Google Scholar 

  72. Pham K, Mulugeta A, Zhou A, O’Brien JT, Llewellyn DJ, Hyppönen E. High coffee consumption, brain volume and risk of dementia and stroke. Nutr Neurosci. 2022;25:2111–22.

    Article  CAS  PubMed  Google Scholar 

  73. Haller S, Montandon ML, Rodriguez C, Herrmann F, Giannakopoulos P. Impact of coffee, wine, and chocolate consumption on cognitive outcome and MRI parameters in old age. Nutrients. 2018;10:1391.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Araújo LF, Mirza SS, Bos D, Niessen WJ, Barreto SM, van der Lugt A, et al. Association of coffee consumption with MRI markers and cognitive function: a population-based study. J Alzheimers Dis. 2016;53:451–61.

    Article  PubMed  Google Scholar 

  75. Perlaki G, Orsi G, Kovacs N, Schwarcz A, Pap Z, Kalmar Z, et al. Coffee consumption may influence hippocampal volume in young women. Brain Imaging Behav. 2011;5:274–84.

    Article  PubMed  Google Scholar 

  76. Magalhães R, Picó-Pérez M, Esteves M, Vieira R, Castanho TC, Amorim L, et al. Habitual coffee drinkers display a distinct pattern of brain functional connectivity. Mol Psychiatry. 2021;26:6589–98.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Laurienti PJ, Field AS, Burdette JH, Maldjian JA, Yen YF, Moody DM. Dietary caffeine consumption modulates fMRI measures. Neuroimage. 2002;17:751–7.

    Article  PubMed  Google Scholar 

  78. Deak JD, Johnson EC. Genetics of substance use disorders: a review. Psychol Med. 2021;51:2189–200.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Vanyukov MM, Tarter RE, Kirillova GP, Kirisci L, Reynolds MD, Kreek MJ, et al. Common liability to addiction and “gateway hypothesis”: theoretical, empirical and evolutionary perspective. Drug Alcohol Depend. 2012;123:S3–17.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Chang LH, Ong JS, An J, Verweij KJH, Vink JM, Pasman J, et al. Investigating the genetic and causal relationship between initiation or use of alcohol, caffeine, cannabis and nicotine. Drug Alcohol Depend. 2020;210:107966.

    Article  CAS  PubMed  Google Scholar 

  81. Hettema JM, Corey LA, Kendler KS. A multivariate genetic analysis of the use of tobacco, alcohol, and caffeine in a population based sample of male and female twins. Drug Alcohol Depend. 1999;57:69–78.

    Article  CAS  PubMed  Google Scholar 

  82. Swan GE, Carmelli D, Cardon LR. The consumption of tobacco, alcohol, and coffee in Caucasian male twins: a multivariate genetic analysis. J Subst Abus. 1996;8:19–31.

    Article  CAS  Google Scholar 

  83. Mallard TT, Sanchez-Roige S. Dimensional phenotypes in psychiatric genetics: lessons from genome-wide association studies of alcohol use phenotypes. Complex Psychiatry. 2021;7:45–48.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Gelernter J, Polimanti R. Genetics of substance use disorders in the era of big data. Nat Rev Genet. 2021;22:712–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, et al. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry. 2020;7:1032–45.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Sanchez-Roige S, Palmer AA, Clarke TK. Recent efforts to dissect the genetic basis of alcohol use and abuse. Biol Psychiatry. 2020;87:609–18.

    Article  CAS  PubMed  Google Scholar 

  87. Ramli NNS, Alkhaldy AA, Mhd Jalil AM. Effects of caffeinated and decaffeinated coffee consumption on metabolic syndrome parameters: a systematic review and meta-analysis of data from randomised controlled trials. Medicina. 2021;57:957.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Lee A, Lim W, Kim S, Khil H, Cheon E, An S, et al. Coffee intake and obesity: a meta-analysis. Nutrients. 2019;11:1274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Schwartz A, Bellissimo N. Nicotine and energy balance: a review examining the effect of nicotine on hormonal appetite regulation and energy expenditure. Appetite. 2021;164:105260.

    Article  PubMed  Google Scholar 

  90. Schubert MM, Irwin C, Seay RF, Clarke HE, Allegro D, Desbrow B. Caffeine, coffee, and appetite control: a review. Int J Food Sci Nutr. 2017;68:901–12.

    Article  PubMed  Google Scholar 

  91. Hou C, Zeng Y, Chen W, Han X, Yang H, Ying Z, et al. Medical conditions associated with coffee consumption: disease-trajectory and comorbidity network analyses of a prospective cohort study in UK Biobank. Am J Clin Nutr. 2022;116:730–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Di Maso M, Boffetta P, Negri E, La Vecchia C, Bravi F. Caffeinated coffee consumption and health outcomes in the US population: a dose-response meta-analysis and estimation of disease cases and deaths avoided. Adv Nutr. 2021;12:1160–76.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Mentis AFA, Dardiotis E, Efthymiou V, Chrousos GP. Non-genetic risk and protective factors and biomarkers for neurological disorders: a meta-umbrella systematic review of umbrella reviews. BMC Med. 2021;19:6.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Chen X, Zhao Y, Tao Z, Wang K. Coffee consumption and risk of prostate cancer: a systematic review and meta-analysis. BMJ Open. 2021;11:e038902.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Hong CT, Chan L, Bai CH. The effect of caffeine on the risk and progression of Parkinson’s disease: a meta-analysis. Nutrients. 2020;12:1860.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Sartini M, Bragazzi N, Spagnolo A, Schinca E, Ottria G, Dupont C, et al. Coffee consumption and risk of colorectal cancer: a systematic review and meta-analysis of prospective studies. Nutrients. 2019;11:694.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Loftfield E, Cornelis MC, Caporaso N, Yu K, Sinha R, Freedman N. Association of coffee drinking with mortality by genetic variation in caffeine metabolism: findings from the UK Biobank. JAMA Intern Med. 2018;178:1086–97.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Wu L, Sun D, He Y. Coffee intake and the incident risk of cognitive disorders: a dose-response meta-analysis of nine prospective cohort studies. Clin Nutr. 2017;36:730–36.

    Article  PubMed  Google Scholar 

  99. Liu QP, Wu YF, Cheng HY, Xia T, Ding H, Wang H, et al. Habitual coffee consumption and risk of cognitive decline/dementia: a systematic review and meta-analysis of prospective cohort studies. Nutrition. 2016;32:628–36.

    Article  PubMed  Google Scholar 

  100. Kennedy OJ, Roderick P, Buchanan R, Fallowfield JA, Hayes PC, Parkes J. Systematic review with meta-analysis: coffee consumption and the risk of cirrhosis. Aliment Pharm Ther. 2016;43:562–74.

    Article  CAS  Google Scholar 

  101. Crippa A, Discacciati A, Larsson SC, Wolk A, Orsini N. Coffee consumption and mortality from all causes, cardiovascular disease, and cancer: a dose-response meta-analysis. Am J Epidemiol. 2014;180:763–75.

    Article  PubMed  Google Scholar 

  102. Ding M, Bhupathiraju SN, Satija A, van Dam RM, Hu FB. Long-term coffee consumption and risk of cardiovascular disease: a systematic review and a dose-response meta-analysis of prospective cohort studies. Circulation. 2014;129:643–59.

    Article  CAS  PubMed  Google Scholar 

  103. Cornelis MC, Bennett DA, Weintraub S, Schneider JA, Morris MC. Caffeine consumption and dementia: are Lewy bodies the link? Ann Neurol. 2022;91:834–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Kim Y, Je Y, Giovannucci E. Coffee consumption and all-cause and cause-specific mortality: a meta-analysis by potential modifiers. Eur J Epidemiol. 2019;34:731–52.

    Article  PubMed  Google Scholar 

  105. Chieng D, Canovas R, Segan L, Sugumar H, Voskoboinik A, Prabhu S, et al. The impact of coffee subtypes on incident cardiovascular disease, arrhythmias, and mortality: long-term outcomes from the UK Biobank. Eur J Prev Cardiol. 2022;29:2240–49.

    Article  PubMed  Google Scholar 

  106. Nordestgaard AT. Causal relationship from coffee consumption to diseases and mortality: a review of observational and Mendelian randomization studies including cardiometabolic diseases, cancer, gallstones and other diseases. Eur J Nutr. 2022;61:573–87.

    Article  PubMed  Google Scholar 

  107. Verweij KJH, Treur JL, Vink JM. Investigating causal associations between use of nicotine, alcohol, caffeine and cannabis: a two-sample bidirectional Mendelian randomization study. Addiction. 2018;113:1333–38.

    Article  PubMed  Google Scholar 

  108. Ware JJ, Tanner JA, Taylor AE, Bin Z, Haycock P, Bowden J, et al. Does coffee consumption impact on heaviness of smoking? Addiction. 2017;112:1842–53.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Bjørngaard JH, Nordestgaard AT, Taylor AE, Treur JL, Gabrielsen ME, Munafò MR, et al. Heavier smoking increases coffee consumption: findings from a Mendelian randomization analysis. Int J Epidemiol. 2017;46:1958–67.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Sanchez-Roige S, Palmer AA. Emerging phenotyping strategies will advance our understanding of psychiatric genetics. Nat Neurosci. 2020;23:475–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Jee HJ, Lee SG, Bormate KJ, Jung YS. Effect of caffeine consumption on the risk for neurological and psychiatric disorders: sex differences in human. Nutrients. 2020;12:3080.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Nehlig A. Interindividual differences in caffeine metabolism and factors driving caffeine consumption. Pharm Rev. 2018;70:384–411.

    Article  CAS  PubMed  Google Scholar 

  113. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9:224.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank the research participants and employees of 23andMe for making this work possible. The following members of the 23andMe Research Team contributed to this study: Stella Aslibekyan, Adam Auton, Elizabeth Babalola, Robert K. Bell, Jessica Bielenberg, Katarzyna Bryc, Emily Bullis, Daniella Coker, Gabriel Cuellar Partida, Devika Dhamija, Sayantan Das, Teresa Filshtein, Kipper Fletez-Brant, Will Freyman, Karl Heilbron, Pooja M. Gandhi, Karl Heilbron, Barry Hicks, David A. Hinds, Ethan M. Jewett, Yunxuan Jiang, Katelyn Kukar, Keng-Han Lin, Maya Lowe, Jey C. McCreight, Matthew H. McIntyre, Steven J. Micheletti, Meghan E. Moreno, Joanna L. Mountain, Priyanka Nandakumar, Elizabeth S. Noblin, Jared O’Connell, Aaron A. Petrakovitz, G. David Poznik, Morgan Schumacher, Anjali J. Shastri, Janie F. Shelton, Jingchunzi Shi, Suyash Shringarpure, Vinh Tran, Joyce Y. Tung, Xin Wang, Wei Wang, Catherine H. Weldon, Peter Wilton, Alejandro Hernandez, Corinna Wong, Christophe Toukam Tchakouté. We would also like to thank The Externalizing Consortium for sharing the GWAS summary statistics of externalizing. The Externalizing Consortium: Principal Investigators: Danielle M. Dick, Philipp Koellinger, K. Paige Harden, Abraham A. Palmer. Lead Analysts: Richard Karlsson Linnér, Travis T. Mallard, Peter B. Barr, Sandra Sanchez-Roige. Significant Contributors: Irwin D. Waldman. The Externalizing Consortium has been supported by the National Institute on Alcohol Abuse and Alcoholism (R01AA015416-administrative supplement), and the National Institute on Drug Abuse (R01DA050721). Additional funding for investigator effort has been provided by K02AA018755, U10AA008401, P50AA022537, as well as a European Research Council Consolidator Grant (647648 EdGe to Koellinger). The content is solely the responsibility of the authors and does not necessarily represent the official views of the above funding bodies. The Externalizing Consortium would like to thank the following groups for making the research possible: 23andMe, Add Health, Vanderbilt University Medical Center’s BioVU, Collaborative Study on the Genetics of Alcoholism (COGA), the Psychiatric Genomics Consortium’s Substance Use Disorders working group, UK10K Consortium, UK Biobank, and Philadelphia Neurodevelopmental Cohort.

Funding

MVJ, SBB, and SSR are supported by funds from the California Tobacco-Related Disease Research Program (TRDRP; Grant Number T29KT0526 and T32IR5226). SBB and AAP were also supported by P50DA037844. BKP, JJM, and SSR are supported by NIH/NIDA DP1DA054394. HHAT is funded through a Natural Science and Engineering Research Council PGS-D scholarship and Canadian Institutes of Health Research (CIHR) Fellowship (#491556). JYK is supported by a CIHR Canada Research Chair in Translational Neuropsychopharmacology. LKD is supported by R01 MH113362. NSCK is funded through an Interdisciplinary Research Fellowship in NeuroAIDs (Grant Number R25MH081482) and an NIH/NIAAA Loan Repayment Program (L40AA031140). JM is funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The datasets used for the PheWAS and LabWAS analyses described were obtained from Vanderbilt University Medical Center’s BioVU which is supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH funded Shared Instrumentation Grant S10RR025141; and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/. PheWAS and LabWAS analyses used CTSA (SD, Vanderbilt Resources). This project was supported by the National Center for Research Resources, Grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06.

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SSR and AAP conceived the idea. PF and SLE contributed formal analyses and curation of 23andMe data. HHAT contributed to formal analyses, investigation, and data visualization. BP, AA, and NSCK contributed to formal data analysis and data visualization. JJM and LVR contributed to formal analyses. JM contributed to data visualization. HHAT and SSR wrote the manuscript. All authors reviewed and edited the manuscript.

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Correspondence to Sandra Sanchez-Roige.

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Competing interests

HHAT is on the Neuropsychopharmacology Special Projects Team. PF, the 23andMe Research Team, and SLE are employees of 23andMe, Inc., and PF and SLE hold stock or stock options in 23andMe. AAP is on the scientific advisory board of Vivid Genomics for which he receives stock options. The remaining authors have nothing to disclose.

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Thorpe, H.H.A., Fontanillas, P., Pham, B.K. et al. Genome-wide association studies of coffee intake in UK/US participants of European ancestry uncover cohort-specific genetic associations. Neuropsychopharmacol. (2024). https://doi.org/10.1038/s41386-024-01870-x

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