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Multi-ancestry genome-wide gene–sleep interactions identify novel loci for blood pressure

Abstract

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 Pjoint < 5 × 10−8), including rs7955964 (FIGNL2/ANKRD33) that increases BP among long sleepers, and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) that increase BP among short sleepers (Pint < 5 × 10−8). Secondary ancestry-specific analysis identified another novel gene by long sleep interaction at rs111887471 (TRPC3/KIAA1109) in individuals of African ancestry (Pint = 2 × 10−6). Combined stage 1 and 2 analyses additionally identified significant gene by long sleep interactions at 10 loci including MKLN1 and RGL3/ELAVL3 previously associated with BP, and significant gene by short sleep interactions at 10 loci including C2orf43 previously associated with BP (Pint < 10−3). 2df test also identified novel loci for BP after modeling sleep that has known functions in sleep–wake regulation, nervous and cardiometabolic systems. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

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Fig. 1: Study overview.
Fig. 2: Forest plots of effects on BP in long, normal, and short sleepers at three replicated novel loci in the multi-ancestry population.

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Code availability

The URLs of genetic software and database used in this study are provided as follows: ProbABEL, https://github.com/GenABEL-Project/ProbABEL; MMAP, https://mmap.github.io; sandwich, https://github.com/cran/sandwich; METAL, http://csg.sph.umich.edu/abecasis/metal/; EasyQC, http://www.genepi-regensburg.de/easyqc; varExp, https://github.com/vincela/VarExp; HaploReg, https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php; RegulomeDB, http://www.regulomedb.org/; GTEx, https://gtexportal.org/home/; PLINK 2.0, https://www.cog-genomics.org/plink/2.0/; SNPsea, http://pubs.broadinstitute.org/mpg/snpsea/; PheGeni, https://www.ncbi.nlm.nih.gov/gap/phegeni; OMIM, https://www.omim.org; DGIdb, https://www.dgidb.org; FUMA, https://fuma.ctglab.nl. The detailed settings are described in Supplementary Methods.

References

  1. Berry JD, Dyer A, Cai X, Garside DB, Ning H, Thomas A, et al. Lifetime risks of cardiovascular disease. N Engl J Med. 2012;366:321–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Cooper RS, Luke A, Zhu X, Kan D, Adeyemo A, Rotimi C, et al. Genome scan among Nigerians linking blood pressure to chromosomes 2, 3, and 19. Hypertension. 2002;40:629–33.

    Article  CAS  PubMed  Google Scholar 

  3. Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, et al. Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the framingham heart study. Hypertension. 2000;36:477–83.

    Article  CAS  PubMed  Google Scholar 

  4. Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41:666–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. International Consortium for Blood Pressure Genome-Wide Association S, Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103–9.

    Article  Google Scholar 

  7. Ehret GB, Ferreira T, Chasman DI, Jackson AU, Schmidt EM, Johnson T, et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet. 2016;48:1171–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Liu C, Kraja AT, Smith JA, Brody JA, Franceschini N, Bis JC, et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet. 2016;48:1162–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Surendran P, Drenos F, Young R, Warren H, Cook JP, Manning AK, et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet. 2016;48:1151–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hoffmann TJ, Ehret GB, Nandakumar P, Ranatunga D, Schaefer C, Kwok PY, et al. Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat Genet. 2017;49:54–64.

    Article  CAS  PubMed  Google Scholar 

  11. Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet. 2017;49:403–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Giri A, Hellwege JN, Keaton JM, Park J, Qiu C, Warren HR, et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat Genet. 2019;51:51–62.

    Article  CAS  PubMed  Google Scholar 

  14. Franceschini N, Fox E, Zhang Z, Edwards TL, Nalls MA, Sung YJ, et al. Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. Am J Hum Genet. 2013;93:545–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhu X, Feng T, Tayo BO, Liang J, Young JH, Franceschini N, et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am J Hum Genet. 2015;96:21–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liang J, Le TH, Edwards DRV, Tayo BO, Gaulton KJ, Smith JA, et al. Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations. PLoS Genet. 2017;13:e1006728.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sung YJ, Winkler TW, de Las Fuentes L, Bentley AR, Brown MR, Kraja AT, et al. A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure. Am J Hum Genet. 2018;102:375–400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Noordam R, Bos MM, Wang H, Winkler TW, Bentley AR, Kilpelainen TO, et al. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. Nat Commun. 2019;10:5121.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Gangwisch JE. A review of evidence for the link between sleep duration and hypertension. Am J Hypertens. 2014;27:1235–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rao DC, Sung YJ, Winkler TW, Schwander K, Borecki I, Cupples LA, et al. Multiancestry study of gene-lifestyle interactions for cardiovascular traits in 610 475 Individuals from 124 cohorts: design and rationale. Circ Cardiovasc Genet. 2017;10:e001649.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Manning AK, LaValley M, Liu CT, Rice K, An P, Liu Y, et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP x environment regression coefficients. Genet Epidemiol. 2011;35:11–18.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Aulchenko YS, Struchalin MV, van Duijn CM. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinforma. 2010;11:134.

    Article  Google Scholar 

  23. Zeileis A Object-oriented computation of sandwich estimators. 2006.

  24. Grandner MA, Schopfer EA, Sands-Lincoln M, Jackson N, Malhotra A. Relationship between sleep duration and body mass index depends on age. Obes (Silver Spring). 2015;23:2491–8.

    Article  Google Scholar 

  25. Martins D, Tareen N, Pan D, Norris K. The relationship between body mass index, blood pressure and pulse rate among normotensive and hypertensive participants in the third National Health and Nutrition Examination Survey (NHANES). Cell Mol Biol (Noisy-le-Gd). 2003;49:1305–9.

    CAS  Google Scholar 

  26. Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, Magi R, et al. Quality control and conduct of genome-wide association meta-analyses. Nat Protoc. 2014;9:1192–212.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Laville V, Bentley AR, Prive F, Zhu X, Gauderman J, Winkler TW, et al. VarExp: estimating variance explained by genome-wide GxE summary statistics. Bioinformatics. 2018;34:3412–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930–934.

    Article  CAS  PubMed  Google Scholar 

  29. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.

    Article  Google Scholar 

  31. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Slowikowski K, Hu X, Raychaudhuri S. SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics. 2014;30:2496–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ramos EM, Hoffman D, Junkins HA, Maglott D, Phan L, Sherry ST, et al. Phenotype–Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Eur J Hum Genet. 2014;22:144–7.

    Article  CAS  PubMed  Google Scholar 

  34. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33:D514–517.

    Article  CAS  PubMed  Google Scholar 

  35. Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 2018;46:D1068–D1073.

    Article  CAS  PubMed  Google Scholar 

  36. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Dashti HS, Jones SE, Wood AR, Lane JM, van Hees VT, Wang H, et al. Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nat Commun. 2019;10:1100.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sanuki R, Omori Y, Koike C, Sato S, Furukawa T. Panky, a novel photoreceptor-specific ankyrin repeat protein, is a transcriptional cofactor that suppresses CRX-regulated photoreceptor genes. FEBS Lett. 2010;584:753–8.

    Article  CAS  PubMed  Google Scholar 

  39. Medzikovic L, de Vries CJM, de Waard V. NR4A nuclear receptors in cardiac remodeling and neurohormonal regulation. Trends Cardiovasc Med. 2019;29:429–37.

    Article  CAS  PubMed  Google Scholar 

  40. Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One. 2012;7:e51954.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2009;41:25–34.

    Article  CAS  PubMed  Google Scholar 

  42. Teng X, Aouacheria A, Lionnard L, Metz KA, Soane L, Kamiya A, et al. KCTD: A new gene family involved in neurodevelopmental and neuropsychiatric disorders. CNS Neurosci Ther. 2019;25:887–902.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Eder P, Probst D, Rosker C, Poteser M, Wolinski H, Kohlwein SD, et al. Phospholipase C-dependent control of cardiac calcium homeostasis involves a TRPC3-NCX1 signaling complex. Cardiovasc Res. 2007;73:111–9.

    Article  CAS  PubMed  Google Scholar 

  44. Dabertrand F, Nelson MT, Brayden JE. Ryanodine receptors, calcium signaling, and regulation of vascular tone in the cerebral parenchymal microcirculation. Microcirculation. 2013;20:307–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kashef F, Li J, Wright P, Snyder J, Suliman F, Kilic A, et al. Ankyrin-B protein in heart failure: identification of a new component of metazoan cardioprotection. J Biol Chem. 2012;287:30268–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Vicario N, Zappala A, Calabrese G, Gulino R, Parenti C, Gulisano M, et al. Connexins in the central nervous system: physiological traits and neuroprotective targets. Front Physiol. 2017;8:1060.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Andersen JL, Schroder TJ, Christensen S, Strandbygard D, Pallesen LT, Garcia-Alai MM, et al. Identification of the first small-molecule ligand of the neuronal receptor sortilin and structure determination of the receptor-ligand complex. Acta Crystallogr D Biol Crystallogr. 2014;70:451–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zheng JS, Arnett DK, Lee YC, Shen J, Parnell LD, Smith CE, et al. Genome-wide contribution of genotype by environment interaction to variation of diabetes-related traits. PLoS One. 2013;8:e77442.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K, Zhao JH, et al. LDL-cholesterol concentrations: a genome-wide association study. Lancet. 2008;371:483–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Pickrell JK, Berisa T, Liu JZ, Segurel L, Tung JY, Hinds DA. Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet. 2016;48:709–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lambert JC, Grenier-Boley B, Harold D, Zelenika D, Chouraki V, Kamatani Y, et al. Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer’s disease. Mol Psychiatry. 2013;18:461–70.

    Article  CAS  PubMed  Google Scholar 

  53. Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    Article  Google Scholar 

  54. Wang Q, Xi B, Liu M, Zhang Y, Fu M. Short sleep duration is associated with hypertension risk among adults: a systematic review and meta-analysis. Hypertens Res. 2012;35:1012–8.

    Article  PubMed  Google Scholar 

  55. Baron KG, Reid KJ. Circadian misalignment and health. Int Rev Psychiatry. 2014;26:139–54.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Douma LG, Gumz ML. Circadian clock-mediated regulation of blood pressure. Free Radic Biol Med. 2018;119:108–14.

    Article  CAS  PubMed  Google Scholar 

  57. Nikolaeva S, Pradervand S, Centeno G, Zavadova V, Tokonami N, Maillard M, et al. The circadian clock modulates renal sodium handling. J Am Soc Nephrol. 2012;23:1019–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Paillasse MR, de Medina P. The NR4A nuclear receptors as potential targets for anti-aging interventions. Med Hypotheses. 2015;84:135–40.

    Article  CAS  PubMed  Google Scholar 

  59. Xi B, He D, Zhang M, Xue J, Zhou D. Short sleep duration predicts risk of metabolic syndrome: a systematic review and meta-analysis. Sleep Med Rev. 2014;18:293–7.

    Article  PubMed  Google Scholar 

  60. Chambers BE, Clark EG, Gatz AE, Wingert RA. Kctd15 regulates nephron segment development by repressing Tfap2a activity. Development. 2020;147:dev191973.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Adeva-Andany MM, Perez-Felpete N, Fernandez-Fernandez C, Donapetry-Garcia C, Pazos-Garcia C. Liver glucose metabolism in humans. Biosci Rep. 2016;36:e00416.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Cascone T, McKenzie JA, Mbofung RM, Punt S, Wang Z, Xu C, et al. Increased Tumor Glycolysis Characterizes Immune Resistance to Adoptive T Cell Therapy. Cell Metab. 2018;27:977–87. e974

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Segovia J, Zarco N. Gas1 is a pleiotropic regulator of cellular functions: from embryonic development to molecular actions in cancer gene therapy. Mini Rev Med Chem. 2014;14:1139–47.

    Article  CAS  PubMed  Google Scholar 

  64. Zarco N, Bautista E, Cuellar M, Vergara P, Flores-Rodriguez P, Aguilar-Roblero R, et al. Growth arrest specific 1 (GAS1) is abundantly expressed in the adult mouse central nervous system. J Histochem Cytochem. 2013;61:731–48.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Jones SE, Lane JM, Wood AR, van Hees VT, Tyrrell J, Beaumont RN, et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat Commun. 2019;10:343.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 2019;139:e56–e528.

    Article  PubMed  Google Scholar 

  67. Nunes J, Jean-Louis G, Zizi F, Casimir GJ, von Gizycki H, Brown CD, et al. Sleep duration among black and white Americans: results of the National Health Interview Survey. J Natl Med Assoc. 2008;100:317–22.

    PubMed  Google Scholar 

  68. Hale L, Do DP. Racial differences in self-reports of sleep duration in a population-based study. Sleep. 2007;30:1096–103.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Barfield R, Wang H, Liu Y, Brody JA, Swenson B, Li R, et al. Epigenome-wide association analysis of daytime sleepiness in the Multi-Ethnic Study of Atherosclerosis reveals African-American-specific associations. Sleep. 2019;42:zsz101.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Lindhorst J, Alexander N, Blignaut J, Rayner B. Differences in hypertension between blacks and whites: an overview. Cardiovasc J Afr. 2007;18:241–7.

    PubMed  PubMed Central  Google Scholar 

  71. Delto CF, Heisler FF, Kuper J, Sander B, Kneussel M, Schindelin H. The LisH motif of muskelin is crucial for oligomerization and governs intracellular localization. Structure. 2015;23:364–73.

    Article  CAS  PubMed  Google Scholar 

  72. Heisler FF, Loebrich S, Pechmann Y, Maier N, Zivkovic AR, Tokito M, et al. Muskelin regulates actin filament- and microtubule-based GABA(A) receptor transport in neurons. Neuron. 2011;70:66–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Ogawa Y, Kakumoto K, Yoshida T, Kuwako KI, Miyazaki T, Yamaguchi J, et al. Elavl3 is essential for the maintenance of Purkinje neuron axons. Sci Rep. 2018;8:2722.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Jackson CL, Patel SR, Jackson WB, 2nd, Lutsey PL, Redline S. Agreement between self-reported and objectively measured sleep duration among white, black, Hispanic, and Chinese adults in the United States: multi-ethnic study of atherosclerosis. Sleep. 2018;41:zsy057.

    Article  PubMed Central  Google Scholar 

  75. Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, et al. Recommended amount of sleep for a healthy adult: a joint consensus statement of the american academy of sleep medicine and sleep research society. Sleep. 2015;38:843–4.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This project was supported by the US National Heart, Lung, and Blood Institute (NHLBI) R01HL118305. HW and SR were supported by NHLBI R35HL135818. BEC was supported by NHLBI K01HL135405. ARB was supported by the Intramural Research Program of the National Institutes of Health in 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 (1ZIAHG200362). D.v.H. was supported by the European Commission funded project HUMAN (Health-2013-INNOVATION-1-602757). The CHARGE cohorts were supported in part by NHLBI infrastructure grant HL105756. Study-specific acknowledgments can be found in the Supplementary Notes.

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HW, BEC, and JL conducted the centralized data analyses, including quality controls, meta-analyses, and post association lookups and bioinformatics. HW, RN, BEC, KS, TWW, JL, YJS, ARB, DCR, SR, and DvanH were part of the writing group and participated in study design, interpreting the data, and drafting the manuscript. All other co-authors were responsible for cohort-level data collection, cohort-level data analysis, and critical reviews of the draft paper. All authors approved the final version of the paper that was submitted to the journal.

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Correspondence to Heming Wang or Diana van Heemst.

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DOMK is a part-time research consultant at Metabolon, Inc. BMP serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. HJG has received travel grants and speakers honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag as well as research funding from Fresenius Medical Care. The remaining authors declare no competing interests.

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Wang, H., Noordam, R., Cade, B.E. et al. Multi-ancestry genome-wide gene–sleep interactions identify novel loci for blood pressure. Mol Psychiatry 26, 6293–6304 (2021). https://doi.org/10.1038/s41380-021-01087-0

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