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Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits

Abstract

Chronic sleep disturbances, associated with cardiometabolic diseases, psychiatric disorders and all-cause mortality1,2, affect 25–30% of adults worldwide3. Although environmental factors contribute substantially to self-reported habitual sleep duration and disruption, these traits are heritable4,5,6,7,8,9 and identification of the genes involved should improve understanding of sleep, mechanisms linking sleep to disease and development of new therapies. We report single- and multiple-trait genome-wide association analyses of self-reported sleep duration, insomnia symptoms and excessive daytime sleepiness in the UK Biobank (n = 112,586). We discover loci associated with insomnia symptoms (near MEIS1, TMEM132E, CYCL1 and TGFBI in females and WDR27 in males), excessive daytime sleepiness (near AROPHN1) and a composite sleep trait (near PATJ (INADL) and HCRTR2) and replicate a locus associated with sleep duration (at PAX8). We also observe genetic correlation between longer sleep duration and schizophrenia risk (rg = 0.29, P = 1.90 × 10−13) and between increased levels of excessive daytime sleepiness and increased measures for adiposity traits (body mass index (BMI): rg = 0.20, P = 3.12 × 10−9; waist circumference: rg = 0.20, P = 2.12 × 10−7).

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Figure 1: Sleep traits are phenotypically and genetically correlated.
Figure 2: Regional association plots for genome-wide significant loci.
Figure 3: Partitioning of the genetic architecture of sleep duration, insomnia symptoms and excessive daytime sleepiness across functional annotation categories.
Figure 4: Genetic architectures shared between sleep duration, insomnia symptoms or excessive daytime sleepiness and 20 behavioral and disease traits.

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References

  1. Fernandez-Mendoza, J. & Vgontzas, A.N. Insomnia and its impact on physical and mental health. Curr. Psychiatry Rep. 15, 418 (2013).

    PubMed  PubMed Central  Google Scholar 

  2. Luyster, F.S., Strollo, P.J. Jr., Zee, P.C. & Walsh, J.K. Sleep: a health imperative. Sleep 35, 727–734 (2012).

    PubMed  PubMed Central  Google Scholar 

  3. Stranges, S., Tigbe, W., Gómez-Olivé, F.X., Thorogood, M. & Kandala, N.B. Sleep problems: an emerging global epidemic? Findings from the INDEPTH WHO-SAGE study among more than 40,000 older adults from 8 countries across Africa and Asia. Sleep 35, 1173–1181 (2012).

    PubMed  PubMed Central  Google Scholar 

  4. de Castro, J.M. The influence of heredity on self-reported sleep patterns in free-living humans. Physiol. Behav. 76, 479–486 (2002).

    CAS  PubMed  Google Scholar 

  5. Evans, D.S. et al. Habitual sleep/wake patterns in the Old Order Amish: heritability and association with non-genetic factors. Sleep 34, 661–669 (2011).

    PubMed  PubMed Central  Google Scholar 

  6. Heath, A.C., Eaves, L.J., Kirk, K.M. & Martin, N.G. Effects of lifestyle, personality, symptoms of anxiety and depression, and genetic predisposition on subjective sleep disturbance and sleep pattern. Twin Res. 1, 176–188 (1998).

    CAS  PubMed  Google Scholar 

  7. Heath, A.C., Kendler, K.S., Eaves, L.J. & Martin, N.G. Evidence for genetic influences on sleep disturbance and sleep pattern in twins. Sleep 13, 318–335 (1990).

    CAS  PubMed  Google Scholar 

  8. Partinen, M., Kaprio, J., Koskenvuo, M., Putkonen, P. & Langinvainio, H. Genetic and environmental determination of human sleep. Sleep 6, 179–185 (1983).

    CAS  PubMed  Google Scholar 

  9. Wing, Y.K. et al. Familial aggregation and heritability of insomnia in a community-based study. Sleep Med. 13, 985–990 (2012).

    CAS  PubMed  Google Scholar 

  10. He, Y. et al. The transcriptional repressor DEC2 regulates sleep length in mammals. Science 325, 866–870 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Gottlieb, D.J., O'Connor, G.T. & Wilk, J.B. Genome-wide association of sleep and circadian phenotypes. BMC Med. Genet. 8 (Suppl. 1), S9 (2007).

    PubMed  PubMed Central  Google Scholar 

  12. Gottlieb, D.J. et al. Novel loci associated with usual sleep duration: the CHARGE Consortium Genome-Wide Association Study. Mol. Psychiatry 20, 1232–1239 (2015).

    CAS  PubMed  Google Scholar 

  13. Byrne, E.M. et al. A genome-wide association study of sleep habits and insomnia. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 162B, 439–451 (2013).

    PubMed  Google Scholar 

  14. Allebrandt, K.V. et al. A KATP channel gene effect on sleep duration: from genome-wide association studies to function in Drosophila. Mol. Psychiatry 18, 122–132 (2013).

    CAS  PubMed  Google Scholar 

  15. Gehrman, P.R., Keenan, B.T., Byrne, E.M. & Pack, A.I. Genetics of sleep disorders. Psychiatr. Clin. North Am. 38, 667–681 (2015).

    PubMed  Google Scholar 

  16. Sehgal, A. & Mignot, E. Genetics of sleep and sleep disorders. Cell 146, 194–207 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Lane, J.M. et al. Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UK Biobank. Nat. Commun. 7, 10889 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Jones, S.E. et al. Genome-wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLoS Genet. 12, e1006125 (2016).

    PubMed  PubMed Central  Google Scholar 

  19. Hu, Y. et al. GWAS of 89,283 individuals identifies genetic variants associated with self-reporting of being a morning person. Nat. Commun. 7, 10448 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Pemberton, R. & Fuller Tyszkiewicz, M.D. Factors contributing to depressive mood states in everyday life: a systematic review. J. Affect. Disord. 200, 103–110 (2016).

    PubMed  Google Scholar 

  21. Foral, P., Knezevich, J., Dewan, N. & Malesker, M. Medication-induced sleep disturbances. Consult Pharm. 26, 414–425 (2011).

    PubMed  Google Scholar 

  22. Rosenberg, R.P. Clinical assessment of excessive daytime sleepiness in the diagnosis of sleep disorders. J. Clin. Psychiatry 76, e1602 (2015).

    PubMed  Google Scholar 

  23. Gonnissen, H.K. et al. Sleep duration, sleep quality and body weight: parallel developments. Physiol. Behav. 121, 112–116 (2013).

    CAS  PubMed  Google Scholar 

  24. Kurant, E. et al. Dorsotonals/homothorax, the Drosophila homologue of meis1, interacts with extradenticle in patterning of the embryonic PNS. Development 125, 1037–1048 (1998).

    CAS  PubMed  Google Scholar 

  25. Casares, F. & Mann, R.S. Control of antennal versus leg development in Drosophila. Nature 392, 723–726 (1998).

    CAS  PubMed  Google Scholar 

  26. Hisa, T. et al. Hematopoietic, angiogenic and eye defects in Meis1 mutant animals. EMBO J. 23, 450–459 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Davidson, S., Miller, K.A., Dowell, A., Gildea, A. & Mackenzie, A. A remote and highly conserved enhancer supports amygdala specific expression of the gene encoding the anxiogenic neuropeptide substance-P. Mol. Psychiatry 11 323, 410–421 (2006).

    CAS  Google Scholar 

  28. Oh-hashi, K., Naruse, Y., Amaya, F., Shimosato, G. & Tanaka, M. Cloning and characterization of a novel GRP78-binding protein in the rat brain. J. Biol. Chem. 278, 10531–10537 (2003).

    CAS  PubMed  Google Scholar 

  29. Erhardt, A. et al. Replication and meta-analysis of TMEM132D gene variants in panic disorder. Transl. Psychiatry 2, e156 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Sklar, P. et al. Whole-genome association study of bipolar disorder. Mol. Psychiatry 13, 558–569 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Edwards, A.C. et al. Genome-wide association study of comorbid depressive syndrome and alcohol dependence. Psychiatr. Genet. 22, 31–41 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Han, K.E. et al. Pathogenesis and treatments of TGFBI corneal dystrophies. Prog. Retin. Eye Res. 50, 67–88 (2016).

    CAS  PubMed  Google Scholar 

  33. Bradfield, J.P. et al. A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci. PLoS Genet. 7, e1002293 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Patry, M. et al. βig-h3 represses T-cell activation in type 1 diabetes. Diabetes 64, 4212–4219 (2015).

    CAS  PubMed  Google Scholar 

  35. Han, B. et al. TGFBI (βIG-H3) is a diabetes-risk gene based on mouse and human genetic studies. Hum. Mol. Genet. 23, 4597–4611 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Poelmans, G., Buitelaar, J.K., Pauls, D.L. & Franke, B. A theoretical molecular network for dyslexia: integrating available genetic findings. Mol. Psychiatry 16, 365–382 (2011).

    CAS  PubMed  Google Scholar 

  37. Dalal, J. et al. Translational profiling of hypocretin neurons identifies candidate molecules for sleep regulation. Genes Dev. 27, 565–578 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Yelin-Bekerman, L. et al. Hypocretin neuron–specific transcriptome profiling identifies the sleep modulator Kcnh4a. eLife 4, e08638 (2015).

    PubMed  PubMed Central  Google Scholar 

  39. Mackiewicz, M. et al. Macromolecule biosynthesis: a key function of sleep. Physiol. Genomics 31, 441–457 (2007).

    CAS  PubMed  Google Scholar 

  40. Takahama, K. et al. Pan-neuronal knockdown of the c-Jun N-terminal kinase (JNK) results in a reduction in sleep and longevity in Drosophila. Biochem. Biophys. Res. Commun. 417, 807–811 (2012).

    CAS  PubMed  Google Scholar 

  41. Farh, K.K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    CAS  PubMed  Google Scholar 

  42. Ward, L.D. & Kellis, M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44 D1, D877–D881 (2016).

    CAS  PubMed  Google Scholar 

  43. Hamdan, F.F. et al. De novo mutations in FOXP1 in cases with intellectual disability, autism, and language impairment. Am. J. Hum. Genet. 87, 671–678 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Fan, Y., Newman, T., Linardopoulou, E. & Trask, B.J. Gene content and function of the ancestral chromosome fusion site in human chromosome 2q13–2q14.1 and paralogous regions. Genome Res. 12, 1663–1672 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Fan, Y., Linardopoulou, E., Friedman, C., Williams, E. & Trask, B.J. Genomic structure and evolution of the ancestral chromosome fusion site in 2q13–2q14.1 and paralogous regions on other human chromosomes. Genome Res. 12, 1651–1662 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, J., Duncan, D., Shi, Z. & Zhang, B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 41, W77–W83 (2013).

    PubMed  PubMed Central  Google Scholar 

  47. Cade, B.E. et al. Common variants in DRD2 are associated with sleep duration: the CARe consortium. Hum. Mol. Genet. 25, 167–179 (2016).

    CAS  PubMed  Google Scholar 

  48. Loh, P.R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Finucane, H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhu, X. et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am. J. Hum. Genet. 96, 21–36 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Mignot, E. Sleep, sleep disorders and hypocretin (orexin). Sleep Med. 5 (Suppl. 1), S2–S8 (2004).

    PubMed  Google Scholar 

  53. Thompson, M.D., Xhaard, H., Sakurai, T., Rainero, I. & Kukkonen, J.P. OX1 and OX2 orexin/hypocretin receptor pharmacogenetics. Front. Neurosci. 8, 57 (2014).

    PubMed  PubMed Central  Google Scholar 

  54. Herring, W.J. et al. Suvorexant in patients with insomnia: results from two 3-month randomized controlled clinical trials. Biol. Psychiatry 79, 136–148 (2016).

    CAS  PubMed  Google Scholar 

  55. Shieh, B.H. & Niemeyer, B. A novel protein encoded by the InaD gene regulates recovery of visual transduction in Drosophila. Neuron 14, 201–210 (1995).

    CAS  PubMed  Google Scholar 

  56. Peirson, S.N. et al. Microarray analysis and functional genomics identify novel components of melanopsin signaling. Curr. Biol. 17, 1363–1372 (2007).

    CAS  PubMed  Google Scholar 

  57. Bécamel, C. et al. The serotonin 5-HT2A and 5-HT2C receptors interact with specific sets of PDZ proteins. J. Biol. Chem. 279, 20257–20266 (2004).

    PubMed  Google Scholar 

  58. Sharpley, A.L., Elliott, J.M., Attenburrow, M.J. & Cowen, P.J. Slow wave sleep in humans: role of 5-HT2A and 5-HT2C receptors. Neuropharmacology 33, 467–471 (1994).

    CAS  PubMed  Google Scholar 

  59. Rosenberg, R. et al. APD125, a selective serotonin 5-HT2A receptor inverse agonist, significantly improves sleep maintenance in primary insomnia. Sleep 31, 1663–1671 (2008).

    PubMed  PubMed Central  Google Scholar 

  60. Winkelmann, J. et al. Genome-wide association study of restless legs syndrome identifies common variants in three genomic regions. Nat. Genet. 39, 1000–1006 (2007).

    CAS  PubMed  Google Scholar 

  61. Xiong, L. et al. MEIS1 intronic risk haplotype associated with restless legs syndrome affects its mRNA and protein expression levels. Hum. Mol. Genet. 18, 1065–1074 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Schulte, E.C. et al. Targeted resequencing and systematic in vivo functional testing identifies rare variants in MEIS1 as significant contributors to restless legs syndrome. Am. J. Hum. Genet. 95, 85–95 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Spieler, D. et al. Restless legs syndrome–associated intronic common variant in Meis1 alters enhancer function in the developing telencephalon. Genome Res. 24, 592–603 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Moore, H. IV et al. Periodic leg movements during sleep are associated with polymorphisms in BTBD9, TOX3/BC034767, MEIS1, MAP2K5/SKOR1, and PTPRD. Sleep 37, 1535–1542 (2014).

    PubMed  PubMed Central  Google Scholar 

  65. Winkelmann, J. et al. Genome-wide association study identifies novel restless legs syndrome susceptibility loci on 2p14 and 16q12.1. PLoS Genet. 7, e1002171 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Allen, R.P., Barker, P.B., Horská, A. & Earley, C.J. Thalamic glutamate/glutamine in restless legs syndrome: increased and related to disturbed sleep. Neurology 80, 2028–2034 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Byrne, E.M., Gehrman, P.R., Trzaskowski, M., Tiemeier, H. & Pack, A.I. Genetic correlation analysis suggests association between increased self-reported sleep duration in adults and schizophrenia and type 2 diabetes. Sleep 39, 1853–1857 (2016).

    PubMed  PubMed Central  Google Scholar 

  69. Wulff, K., Dijk, D.J., Middleton, B., Foster, R.G. & Joyce, E.M. Sleep and circadian rhythm disruption in schizophrenia. Br. J. Psychiatry 200, 308–316 (2012).

    PubMed  PubMed Central  Google Scholar 

  70. Poulin, J. et al. Sleep habits in middle-aged, non-hospitalized men and women with schizophrenia: a comparison with healthy controls. Psychiatry Res. 179, 274–278 (2010).

    PubMed  Google Scholar 

  71. Chouinard, S., Poulin, J., Stip, E. & Godbout, R. Sleep in untreated patients with schizophrenia: a meta-analysis. Schizophr. Bull. 30, 957–967 (2004).

    PubMed  Google Scholar 

  72. Hattersley, A.T. & Tooke, J.E. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet 353, 1789–1792 (1999).

    CAS  PubMed  Google Scholar 

  73. Horikoshi, M. et al. New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nat. Genet. 45, 76–82 (2013).

    CAS  PubMed  Google Scholar 

  74. Ananthakrishnan, A.N. et al. Sleep duration affects risk for ulcerative colitis: a prospective cohort study. Clin. Gastroenterol. Hepatol. 12, 1879–1886 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. Tasali, E., Leproult, R., Ehrmann, D.A. & Van Cauter, E. Slow-wave sleep and the risk of type 2 diabetes in humans. Proc. Natl. Acad. Sci. USA 105, 1044–1049 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Nedeltcheva, A.V. & Scheer, F.A. Metabolic effects of sleep disruption, links to obesity and diabetes. Curr. Opin. Endocrinol. Diabetes Obes. 21, 293–298 (2014).

    PubMed  PubMed Central  Google Scholar 

  77. Vgontzas, A.N. et al. Obesity without sleep apnea is associated with daytime sleepiness. Arch. Intern. Med. 158, 1333–1337 (1998).

    CAS  PubMed  Google Scholar 

  78. Bixler, E.O. et al. Excessive daytime sleepiness in a general population sample: the role of sleep apnea, age, obesity, diabetes, and depression. J. Clin. Endocrinol. Metab. 90, 4510–4515 (2005).

    CAS  PubMed  Google Scholar 

  79. Swanson, J.M. The UK Biobank and selection bias. Lancet 380, 110 (2012).

    PubMed  Google Scholar 

  80. Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).

    PubMed  Google Scholar 

  81. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed  PubMed Central  Google Scholar 

  82. Allen, N.E., Sudlow, C., Peakman, T. & Collins, R. UK Biobank. UK Biobank data: come and get it. Sci. Transl. Med. 6, 224ed4 (2014).

    PubMed  Google Scholar 

  83. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    PubMed  PubMed Central  Google Scholar 

  84. O'Connell, J. et al. Haplotype estimation for biobank-scale data sets. Nat. Genet. 48, 817–820 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Loh, P.R., Palamara, P.F. & Price, A.L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    CAS  PubMed  Google Scholar 

  87. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  88. Chang, C.C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

  89. International HapMap Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

  90. Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  92. Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This research has been conducted using the UK Biobank Resource under application number 6818. We would like to thank the participants and researchers from the UK Biobank who contributed or collected data. This work was supported by US NIH grants R01DK107859 (R.S.), R21HL121728 (R.S.), F32DK102323 (J.M.L.), R01HL113338 (J.M.L., S.R. and R.S.), R01DK102696 (R.S. and F.A.J.L.S.), R01DK105072 (R.S. and F.A.J.L.S.), T32HL007567 (J.L.) and HG003054 (X.Z.), the University of Manchester (Research Infrastructure Fund), the Wellcome Trust (salary support for D.W.R. and A.L.) and UK Medical Research Council MC_UU_12013/5 (D.A.L.). Data on glycemic traits have been contributed by MAGIC investigators and were downloaded from http://www.magicinvestigators.org/. Data on coronary artery disease and myocardial infarction have been contributed by CARDIoGRAMplusC4D investigators and were downloaded from http://www.cardiogramplusc4d.org/. We thank the International Genomics of Alzheimer's Project (IGAP) for providing summary results data for these analyses.

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J.M.L., M.K.R. and R.S. designed the study. J.M.L., J.L., I.V. and R.S. performed genetic analyses. J.M.L. and R.S. wrote the manuscript, and all co-authors helped interpret data and reviewed and edited the manuscript, before approving its submission. R.S. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Richa Saxena.

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Lane, J., Liang, J., Vlasac, I. et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat Genet 49, 274–281 (2017). https://doi.org/10.1038/ng.3749

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