Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption.
Adult
Aged
Aged, 80 and over
Coffee
/ adverse effects
Cohort Studies
CpG Islands
DNA Methylation
Epigenesis, Genetic
Epigenome
Female
Gene Knockdown Techniques
Genome-Wide Association Study
Humans
Liver
/ enzymology
Male
Middle Aged
Phosphoglycerate Dehydrogenase
/ antagonists & inhibitors
Risk Factors
Tea
/ adverse effects
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
14 05 2021
14 05 2021
Historique:
received:
15
04
2020
accepted:
26
03
2021
entrez:
15
5
2021
pubmed:
16
5
2021
medline:
4
6
2021
Statut:
epublish
Résumé
Coffee and tea are extensively consumed beverages worldwide which have received considerable attention regarding health. Intake of these beverages is consistently linked to, among others, reduced risk of diabetes and liver diseases; however, the mechanisms of action remain elusive. Epigenetics is suggested as a mechanism mediating the effects of dietary and lifestyle factors on disease onset. Here we report the results from epigenome-wide association studies (EWAS) on coffee and tea consumption in 15,789 participants of European and African-American ancestries from 15 cohorts. EWAS meta-analysis of coffee consumption reveals 11 CpGs surpassing the epigenome-wide significance threshold (P-value <1.1×10
Identifiants
pubmed: 33990564
doi: 10.1038/s41467-021-22752-6
pii: 10.1038/s41467-021-22752-6
pmc: PMC8121846
doi:
Substances chimiques
Coffee
0
Tea
0
Phosphoglycerate Dehydrogenase
EC 1.1.1.95
Types de publication
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2830Subventions
Organisme : NHLBI NIH HHS
ID : N01HC85080
Pays : United States
Organisme : Wellcome Trust
ID : 217065/Z/19/Z
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/S020845/1
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : N01HC85082
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC55222
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201700002I
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC85086
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC25195
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201700003I
Pays : United States
Organisme : Medical Research Council
ID : G9815508
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : HHSN268200800007C
Pays : United States
Organisme : Medical Research Council
ID : MR/R023484/1
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : R01 AG023629
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00011/5
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : HHSN268201800001C
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL080295
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_19009
Pays : United Kingdom
Organisme : NCATS NIH HHS
ID : UL1 TR001881
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL120393
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201200036C
Pays : United States
Organisme : NHLBI NIH HHS
ID : RC2 HL102419
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS087541
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL103612
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201700001I
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_12013_2
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : HHSN268201700004I
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL085251
Pays : United States
Organisme : NHLBI NIH HHS
ID : K22 HL135075
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201500001I
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_15018
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : N01HC85079
Pays : United States
Organisme : NHLBI NIH HHS
ID : N01HC85083
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL105756
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201700005I
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL130114
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL087652
Pays : United States
Organisme : Medical Research Council
ID : MR/L01632X/1
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : N01HC85081
Pays : United States
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/T019980/1
Pays : United Kingdom
Références
Landais, E. 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 10, 725 (2018).
pmcid: 6024313
doi: 10.3390/nu10060725
National Research Council Committee on, D. & Health. https://doi.org/10.17226/1222 (1989).
Temple, J. L. et al. The safety of ingested caffeine: a comprehensive review. Front. Psychiatry 8, 80 (2017).
pubmed: 28603504
pmcid: 5445139
doi: 10.3389/fpsyt.2017.00080
Bunker, M. L. & McWilliams, M. Caffeine content of common beverages. J. Am. Diet. Assoc. 74, 28–32 (1979).
pubmed: 762339
doi: 10.1016/S0002-8223(21)39775-9
Rein, M. J. et al. Bioavailability of bioactive food compounds: a challenging journey to bioefficacy. Br. J. Clin. Pharm. 75, 588–602 (2013).
doi: 10.1111/j.1365-2125.2012.04425.x
Ferruzzi, M. G. The influence of beverage composition on delivery of phenolic compounds from coffee and tea. Physiol. Behav. 100, 33–41 (2010).
pubmed: 20138903
doi: 10.1016/j.physbeh.2010.01.035
Fuller, M. & Rao, N. Z. The effect of time, roasting temperature, and grind size on caffeine and chlorogenic acid concentrations in cold brew coffee. Sci. Rep. 7, https://doi.org/10.1038/s41598-017-18247-4 (2017).
Gross, G., Jaccaud, E. & Huggett, A. C. Analysis of the content of the diterpenes cafestol and kahweol in coffee brews. Food Chem. Toxicol. 35, 547–554 (1997).
pubmed: 9225012
doi: 10.1016/S0278-6915(96)00123-8
Nieber, K. The impact of coffee on health. Planta Med. 83, 1256–1263 (2017).
pubmed: 28675917
doi: 10.1055/s-0043-115007
Khan, N. & Mukhtar, H. Tea and health: studies in humans. Curr. Pharm. Des. 19, 6141–6147 (2013).
pubmed: 23448443
pmcid: 4055352
doi: 10.2174/1381612811319340008
van Dieren, S. et al. Coffee and tea consumption and risk of type 2 diabetes. Diabetologia 52, 2561–2569 (2009).
pubmed: 19727658
doi: 10.1007/s00125-009-1516-3
Bohn, S. K., Ward, N. C., Hodgson, J. M. & Croft, K. D. Effects of tea and coffee on cardiovascular disease risk. Food Funct. 3, 575–591 (2012).
pubmed: 22456725
doi: 10.1039/c2fo10288a
Alferink, L. J. M. et al. Coffee and herbal tea consumption is associated with lower liver stiffness in the general population: the Rotterdam study. J. Hepatol. 67, 339–348 (2017).
pubmed: 28578837
doi: 10.1016/j.jhep.2017.03.013
Heath, R. D., Brahmbhatt, M., Tahan, A. C., Ibdah, J. A. & Tahan, V. Coffee: the magical bean for liver diseases. World J. Hepatol. 9, 689–696 (2017).
pubmed: 28596816
pmcid: 5440772
doi: 10.4254/wjh.v9.i15.689
Loftfield, E. et al. Association of coffee drinking with mortality by genetic variation in caffeine metabolism: findings from the UK Biobank. JAMA Intern. Med. 178, 1086–1097 (2018).
pubmed: 29971434
pmcid: 6143111
doi: 10.1001/jamainternmed.2018.2425
Jee, S. H. et al. Coffee consumption and serum lipids: a meta-analysis of randomized controlled clinical trials. Am. J. Epidemiol. 153, 353–362 (2001).
pubmed: 11207153
doi: 10.1093/aje/153.4.353
Hamilton, J. P. Epigenetics: principles and practice. Dig. Dis. 29, 130–135 (2011).
pubmed: 21734376
pmcid: 3134032
doi: 10.1159/000323874
Alegria-Torres, J. A., Baccarelli, A. & Bollati, V. Epigenetics and lifestyle. Epigenomics 3, 267–277 (2011).
pubmed: 22122337
doi: 10.2217/epi.11.22
Weinhold, B. Epigenetics: the science of change. Environ. Health Perspect. 114, A160–A167 (2006).
pubmed: 16507447
pmcid: 1392256
doi: 10.1289/ehp.114-a160
Anderson, O. S., Sant, K. E. & Dolinoy, D. C. Nutrition and epigenetics: an interplay of dietary methyl donors, one-carbon metabolism and DNA methylation. J. Nutr. Biochem. 23, 853–859 (2012).
pubmed: 22749138
pmcid: 3405985
doi: 10.1016/j.jnutbio.2012.03.003
Ek, W. E. et al. Tea and coffee consumption in relation to DNA methylation in four European cohorts. Hum. Mol. Genet. 26, 3221–3231 (2017).
pubmed: 28535255
pmcid: 6455036
doi: 10.1093/hmg/ddx194
Chuang, Y. H. et al. Coffee consumption is associated with DNA methylation levels of human blood. Eur. J. Hum. Genet. 25, 608–616 (2017).
pubmed: 28198392
pmcid: 5437893
doi: 10.1038/ejhg.2016.175
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).
pubmed: 31427789
doi: 10.1038/s41588-019-0481-0
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
pubmed: 29184056
pmcid: 5705698
doi: 10.1038/s41467-017-01261-5
Ma, J. et al. A peripheral blood DNA methylation signature of hepatic fat reveals a potential causal pathway for non-alcoholic fatty liver disease. Diabetes https://doi.org/10.2337/db18-1193 (2019).
Ruhl, C. E. & Everhart, J. E. Coffee and tea consumption are associated with a lower incidence of chronic liver disease in the United States. Gastroenterology 129, 1928–1936 (2005).
pubmed: 16344061
doi: 10.1053/j.gastro.2005.08.056
Saab, S., Mallam, D., Cox, G. A. 2nd & Tong, M. J. Impact of coffee on liver diseases: a systematic review. Liver Int 34, 495–504 (2014).
pubmed: 24102757
doi: 10.1111/liv.12304
Nano, J. et al. Epigenome-Wide Association Study identifies methylation sites associated with liver enzymes and hepatic steatosis. Gastroenterology 153, 1096-+ (2017).
pubmed: 28624579
doi: 10.1053/j.gastro.2017.06.003
Wahl, S. et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541, 81–86 (2017).
pubmed: 28002404
doi: 10.1038/nature20784
Treutlein, J. et al. Genetic contribution to alcohol dependence: Investigation of a heterogeneous german sample of individuals with alcohol dependence, chronic alcoholic pancreatitis, and alcohol-related cirrhosis. Genes 8, 183 (2017).
pmcid: 5541316
doi: 10.3390/genes8070183
Tabassum, R. et al. Genetic architecture of human plasma lipidome and its link to cardiovascular disease. Nat. Commun. 10, 4329 (2019).
pubmed: 31551469
pmcid: 6760179
doi: 10.1038/s41467-019-11954-8
Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).
pubmed: 24097068
pmcid: 3838666
doi: 10.1038/ng.2797
Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).
pubmed: 19060906
doi: 10.1038/ng.291
Zhang, Y. et al. F2RL3 methylation in blood DNA is a strong predictor of mortality. Int. J. Epidemiol. 43, 1215–1225 (2014).
pubmed: 24510982
pmcid: 4258765
doi: 10.1093/ije/dyu006
Cole, J. W. & Xu, H. (Am Heart Assoc, 2015).
Sim, W.-C. et al. Downregulation of PHGDH expression and hepatic serine level contribute to the development of fatty liver disease. Metabolism 102, 154000 (2020).
pubmed: 31678070
doi: 10.1016/j.metabol.2019.154000
Coffee et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol. Psychiatry 20, 647–656 (2015).
doi: 10.1038/mp.2014.107
Larigot, L., Juricek, L., Dairou, J. & Coumoul, X. AhR signaling pathways and regulatory functions. Biochim. Open 7, 1–9 (2018).
pubmed: 30003042
pmcid: 6039966
doi: 10.1016/j.biopen.2018.05.001
Vu, A. T. et al. Polycyclic aromatic hydrocarbons in the mainstream smoke of popular U.S. cigarettes. Chem. Res Toxicol. 28, 1616–1626 (2015).
pubmed: 26158771
pmcid: 4540633
doi: 10.1021/acs.chemrestox.5b00190
Houessou, J. K. et al. Effect of roasting conditions on the polycyclic aromatic hydrocarbon content in ground Arabica coffee and coffee brew. J. Agric Food Chem. 55, 9719–9726 (2007).
pubmed: 17941690
doi: 10.1021/jf071745s
Chavan, H. & Krishnamurthy, P. Polycyclic aromatic hydrocarbons (PAHs) mediate transcriptional activation of the ATP binding cassette transporter ABCB6 gene via the aryl hydrocarbon receptor (AhR). J. Biol. Chem. 287, 32054–32068 (2012).
pubmed: 22761424
pmcid: 3442536
doi: 10.1074/jbc.M112.371476
Joehanes, R. et al. Epigenetic signatures of cigarette smoking. Circ. Cardiovasc. Genet. 9, 436–447 (2016).
pubmed: 27651444
pmcid: 5267325
doi: 10.1161/CIRCGENETICS.116.001506
Philibert, R. A., Beach, S. R. H., Lei, M.-K. & Brody, G. H. Changes in DNA methylation at the aryl hydrocarbon receptor repressor may be a new biomarker for smoking. Clin. Epigenet. 5, 19 (2013).
doi: 10.1186/1868-7083-5-19
Bjorngaard, J. H. et al. Heavier smoking increases coffee consumption: findings from a Mendelian randomization analysis. Int. J. Epidemiol. 46, 1958–1967 (2017).
pubmed: 29025033
pmcid: 5837196
doi: 10.1093/ije/dyx147
Fu, Q. et al. Protease-activated receptor 4: a critical participator in inflammatory response. Inflammation 38, 886–895 (2015).
pubmed: 25120239
doi: 10.1007/s10753-014-9999-6
Arlt, A. & Schäfer, H. Role of the immediate early response 3 (IER3) gene in cellular stress response, inflammation and tumorigenesis. Eur. J. Cell Biol. 90, 545–552 (2011).
pubmed: 21112119
doi: 10.1016/j.ejcb.2010.10.002
Gratio, V., Walker, F., Lehy, T., Laburthe, M. & Darmoul, D. Aberrant expression of proteinase‐activated receptor 4 promotes colon cancer cell proliferation through a persistent signaling that involves Src and ErbB‐2 kinase. Int. J. Cancer 124, 1517–1525 (2009).
pubmed: 19058300
doi: 10.1002/ijc.24070
Khandanpour, C. et al. A variant allele of Growth Factor Independence 1 (GFI1) is associated with acute myeloid leukemia. Blood. J. Am. Soc. Hematol. 115, 2462–2472 (2010).
Leger, A. J., Covic, L. & Kuliopulos, A. Protease-activated receptors in cardiovascular diseases. Circulation 114, 1070–1077 (2006).
pubmed: 16952995
doi: 10.1161/CIRCULATIONAHA.105.574830
Parmar, P. et al. Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adults. EBioMedicine 38, 206–216 (2018).
pubmed: 30442561
pmcid: 6306313
doi: 10.1016/j.ebiom.2018.10.066
Ding, M., Bhupathiraju, S. N., Satija, A., van Dam, R. M. & Hu, F. B. Long-term coffee consumption and risk of cardiovascular disease: a systematic review and a dose-response meta-analysis of prospective cohort studies. Circulation 129, 643–659 (2014).
pubmed: 24201300
doi: 10.1161/CIRCULATIONAHA.113.005925
Arab, L. Epidemiologic evidence on coffee and cancer. Nutr. Cancer 62, 271–283 (2010).
pubmed: 20358464
doi: 10.1080/01635580903407122
Yu, H. et al. Epigenome-wide association study identifies Behcet’s disease-associated methylation loci in Han Chinese. Rheumatology 58, 1574–1584 (2019).
pubmed: 30863869
doi: 10.1093/rheumatology/kez043
Xia, P. et al. Polymorphisms in ESR1 and FLJ43663 are associated with breast cancer risk in the Han population. Tumor Biol. 35, 2187–2190 (2014).
doi: 10.1007/s13277-013-1289-7
Seto, E. & Yoshida, M. Erasers of histone acetylation: the histone deacetylase enzymes. Cold Spring Harb. Perspect. Biol. 6, a018713 (2014).
pubmed: 24691964
pmcid: 3970420
doi: 10.1101/cshperspect.a018713
Penrod, R. D. et al. Novel role and regulation of HDAC4 in cocaine-related behaviors. Addict. Biol. 23, 653–664 (2018).
pubmed: 28635037
doi: 10.1111/adb.12522
Kumar, A. et al. Chromatin remodeling is a key mechanism underlying cocaine-induced plasticity in striatum. Neuron 48, 303–314 (2005).
pubmed: 16242410
doi: 10.1016/j.neuron.2005.09.023
Camilo, C. et al. Genome-wide DNA methylation profile in the peripheral blood of cocaine and crack dependents. Braz. J. Psychiatry 41, 485–493 (2019).
pubmed: 31116258
pmcid: 6899365
doi: 10.1590/1516-4446-2018-0092
Petersen, A. K. et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum. Mol. Genet. 23, 534–545 (2014).
pubmed: 24014485
doi: 10.1093/hmg/ddt430
Casiglia, E., Spolaore, P., Inocchio, G. & Ambrosio, B. Unexpected effects of coffee consumption on liver enzymes. Eur. J. Epidemiol. 9, 293–297 (1993).
pubmed: 8104822
doi: 10.1007/BF00146266
Bravi, F., Bosetti, C., Tavani, A., Gallus, S. & La Vecchia, C. Coffee reduces risk for hepatocellular carcinoma: an updated meta-analysis. Clin. Gastroenterol. Hepatol. 11, 1413–1421. e1411 (2013).
pubmed: 23660416
doi: 10.1016/j.cgh.2013.04.039
Liu, F. et al. Coffee consumption decreases risks for hepatic fibrosis and cirrhosis: a meta-analysis. PLoS ONE 10, e0142457 (2015).
pubmed: 26556483
pmcid: 4640566
doi: 10.1371/journal.pone.0142457
Cornelis, M. C. & Munafo, M. R. Mendelian randomization studies of coffee and caffeine consumption. Nutrients 10, 1343 (2018).
pmcid: 6213346
doi: 10.3390/nu10101343
Kennedy, O. J. et al. Coffee consumption and kidney function: a Mendelian Randomization Study. Am. J. Kidney Dis. 5, 753–761 (2020).
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).
pubmed: 26050253
pmcid: 4469799
doi: 10.1093/ije/dyv080
Pierce, B. L. & Burgess, S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am. J. Epidemiol. 178, 1177–1184 (2013).
pubmed: 23863760
pmcid: 3783091
doi: 10.1093/aje/kwt084
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).
pubmed: 30275531
pmcid: 6521726
doi: 10.1038/s41588-018-0222-9
Shin, S. Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).
pubmed: 24816252
pmcid: 4064254
doi: 10.1038/ng.2982
Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).
pubmed: 21886157
doi: 10.1038/nature10354
John, J., Kodama, T. & Siegel, J. M. Caffeine promotes glutamate and histamine release in the posterior hypothalamus. Am. J. Physiol. Regul. Integr. Comp. Physiol. 307, R704–R710 (2014).
pubmed: 25031227
pmcid: 4166758
doi: 10.1152/ajpregu.00114.2014
Matoba, N. et al. GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits. Nat. Hum. Behav. 4, 308–316 (2020).
pubmed: 31959922
doi: 10.1038/s41562-019-0805-1
Barfield, R. T. et al. Accounting for population stratification in DNA methylation studies. Genet. Epidemiol. 38, 231–241 (2014).
pubmed: 24478250
pmcid: 4090102
doi: 10.1002/gepi.21789
Liu, C. et al. A DNA methylation biomarker of alcohol consumption. Mol. Psychiatry 23, 422–433 (2018).
pubmed: 27843151
doi: 10.1038/mp.2016.192
Ma, J. et al. Whole blood DNA methylation signatures of diet are associated with cardiovascular disease risk factors and all-cause mortality. Circ. Genom. Precis. Med. 4, e002766 (2020).
Bennett, D. A., Landry, D., Little, J. & Minelli, C. Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology. BMC Med. Res. Methodol. 17, 146 (2017).
pubmed: 28927376
pmcid: 5606038
doi: 10.1186/s12874-017-0421-6
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 5 cohorts. Circulation-Cardiovasc. Genet. 2, 73–U128 (2009).
doi: 10.1161/CIRCGENETICS.108.829747
Elliott, P. et al. The Airwave Health Monitoring Study of police officers and staff in Great Britain: rationale, design and methods. Environ. Res. 134, 280–285 (2014).
pubmed: 25194498
doi: 10.1016/j.envres.2014.07.025
Fraser, A. et al. Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int. J. Epidemiol. 42, 97–110 (2013).
pubmed: 22507742
doi: 10.1093/ije/dys066
Low, M., Stegmaier, C., Ziegler, H., Rothenbacher, D. & Brenner, H. Epidemiological investigations of the chances of preventing, recognizing early and optimally treating chronic diseases in an elderly population (ESTHER study). Dtsch. Med. Wochenschr. 129, 2643–2647 (2004).
pubmed: 15578318
Kannel, W. B., Feinleib, M., Mcnamara, P. M., Garrison, R. J. & Castelli, W. P. Investigation of coronary heart-disease in families—Framingham Offspring Study. Am. J. Epidemiol. 110, 281–290 (1979).
pubmed: 474565
doi: 10.1093/oxfordjournals.aje.a112813
Wichmann, H. E., Gieger, C., Illig, T. & Grp, M. K. S. KORA-gen—Resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen 67, S26–S30 (2005).
pubmed: 16032514
doi: 10.1055/s-2005-858226
Ikram, M. A. et al. The Rotterdam Study: 2018 update on objectives, design and main results. Eur. J. Epidemiol. 32, 807–850 (2017).
pubmed: 29064009
pmcid: 5662692
doi: 10.1007/s10654-017-0321-4
Moayyeri, A., Hammond, C. J., Hart, D. J. & Spector, T. D. The UK adult twin registry (TwinsUK Resource). Twin Res. Hum. Genet. 16, 144–149 (2013).
pubmed: 23088889
doi: 10.1017/thg.2012.89
Aric, I. The atherosclerosis risk in communit (aric) stui) y: Design and objectwes. Am. J. Epidemiol. 129, 687–702 (1989).
doi: 10.1093/oxfordjournals.aje.a115184
Fried, L. P. et al. The cardiovascular health study: design and rationale. Ann. Epidemiol. 1, 263–276 (1991).
pubmed: 1669507
doi: 10.1016/1047-2797(91)90005-W
Bingham, S. & Riboli, E. Diet and cancer—the European prospective investigation into cancer and nutrition. Nat. Rev. Cancer 4, 206–215 (2004).
pubmed: 14993902
doi: 10.1038/nrc1298
Yang, T. O., Crowe, F., Cairns, B. J., Reeves, G. K. & Beral, V. Tea and coffee and risk of endometrial cancer: cohort study and meta-analysis. Am. J. Clin. Nutr. 101, 570–578 (2015).
pubmed: 25733642
pmcid: 4340062
doi: 10.3945/ajcn.113.081836
Sandoval, J. et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6, 692–702 (2011).
pubmed: 21593595
doi: 10.4161/epi.6.6.16196
Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinforma. 13, 86 (2012).
doi: 10.1186/1471-2105-13-86
Van der Most, P. J., Kupers, L. K., Snieder, H. & Nolte, I. QCEWAS: automated quality control of results of epigenome-wide association studies. Bioinformatics 33, 1243–1245 (2017).
pubmed: 28119308
Chen, Y.-A. et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8, 203–209 (2013).
pubmed: 23314698
pmcid: 3592906
doi: 10.4161/epi.23470
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
pubmed: 20616382
pmcid: 2922887
doi: 10.1093/bioinformatics/btq340
Magi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinforma. 11, 288 (2010).
doi: 10.1186/1471-2105-11-288
Galperin, M. Y., Fernandez-Suarez, X. M. & Rigden, D. J. The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes. Nucleic Acids Res. 45, D1–D11 (2017).
pubmed: 28053160
doi: 10.1093/nar/gkw1188
Kwok, M. K., Leung, G. M. & Schooling, C. M. Habitual coffee consumption and risk of type 2 diabetes, ischemic heart disease, depression and Alzheimer’s disease: a Mendelian randomization study. Sci. Rep. 6, 36500 (2016).
pubmed: 27845333
pmcid: 5109212
doi: 10.1038/srep36500
Huan, T. et al. Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat. Commun. 10, 1–14 (2019).
doi: 10.1038/s41467-019-12228-z
Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).
pubmed: 29686387
pmcid: 6083837
doi: 10.1038/s41588-018-0099-7
Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).
pubmed: 28398548
pmcid: 5510723
doi: 10.1093/ije/dyx034