Abstract
Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes. The underlying pathophysiological processes and causal relationships of insomnia with disease are poorly understood. Here we identified 57 loci for self-reported insomnia symptoms in the UK Biobank (nâ=â453,379) and confirmed their effects on self-reported insomnia symptoms in the HUNT Study (nâ=â14,923 cases and 47,610 controls), physician-diagnosed insomnia in the Partners Biobank (nâ=â2,217 cases and 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (nâ=â83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis and of genes expressed in multiple brain regions, skeletal muscle, and adrenal glands. Evidence of shared genetic factors was found between frequent insomnia symptoms and restless legs syndrome, aging, and cardiometabolic, behavioral, psychiatric, and reproductive traits. Evidence was found for a possible causal link between insomnia symptoms and coronary artery disease, depressive symptoms, and subjective well-being.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 /Â 30Â days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout



Similar content being viewed by others
Data availability
GWAS summary statistics are available at the Sleep Disorders Knowledge Portal data download page (http://sleepdisordergenetics.org/informational/data/).
References
Morin, C. M. & Benca, R. Chronic insomnia. Lancet 379, 1129â1141 (2012).
Morin, C. M. et al. Insomnia disorder. Nat. Rev. Dis. Primers. 1, 15026 (2015).
Hoevenaar-Blom, M. P., Spijkerman, A. M. W., Kromhout, D., van den Berg, J. F. & Verschuren, W. M. M. Sleep duration and sleep quality in relation to 12-year cardiovascular disease incidence: the MORGEN study. Sleep 34, 1487â1492 (2011).
Riemann, D. et al. European guideline for the diagnosis and treatment of insomnia. J. Sleep Res. 26, 675â700 (2017).
Qaseem, A., Barry, M. J. & Kansagara, D. Clinical guidelines committee of the american college of physicians. nonpharmacologic versus pharmacologic treatment of adult patients with major depressive disorder: a clinical practice guideline from the american college of physicians. Ann. Intern. Med. 164, (350â359 (2016).
Sateia, M. J., Buysse, D. J., Krystal, A. D., Neubauer, D. N. & Heald, J. L. Clinical practice guideline for the pharmacologic treatment of chronic insomnia in adults: an American Academy of Sleep Medicine clinical practice guideline. J. Clin. Sleep Med. 13, 307â349 (2017).
Kapil, V., Green, J. L., Le Lait, C., Wood, D. M. & Dargan, P. I. Misuse of benzodiazepines and Z-drugs in the UK. Br. J. Psychiatry. 205, 407â408 (2014).
Naylor, E. et al. The circadian clock mutation alters sleep homeostasis in the mouse. J. Neurosci. 20, 8138â8143 (2000).
Cirelli, C. The genetic and molecular regulation of sleep: from fruit flies to humans. Nat. Rev. Neurosci. 10, 549â560 (2009).
Allada, R., Cirelli, C. & Sehgal, A. Molecular mechanisms of sleep homeostasis in flies and mammals. Cold Spring Harb. Perspect. Biol. 9, a027730 (2017).
Pimentel, D. et al. Operation of a homeostatic sleep switch. Nature 536, 333â337 (2016).
Funato, H. et al. Forward-genetics analysis of sleep in randomly mutagenized mice. Nature. 539, 378â383 (2016).
Chung, S. et al. Identification of preoptic sleep neurons using retrograde labelling and gene profiling. Nature 545, 477â481 (2017).
Lind, M. J. & Gehrman, P. R. Genetic pathways to insomnia. Brain Sci. 6, E64 (2016).
Lane, J. M. 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, 274â281 (2017).
Hammerschlag, A. R. et al. Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Nat. Genet. 49, 1584â1592 (2017).
Wain, L. V. et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. Lancet Respir. Med. 3, 769â781 (2015).
Vahia, V. N. Diagnostic and statistical manual of mental disorders 5: a quick glance. Indian J. Psychiatry 55, 220â223 (2013).
Benjamins, J. S. et al. Insomnia heterogeneity: characteristics to consider for data-driven multivariate subtyping. Sleep Med. Rev. 36, 71â81 (2017).
Krokstad, S. et al. Cohort profile: the hunt study, Norway. Int. J. Epidemiol. 42, 968â977 (2013).
Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337â343 (2015).
Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102âR110 (2015). R1.
Duclot, F. & Kabbaj, M. The role of early growth response 1 (EGR1) in brain plasticity and neuropsychiatric disorders. Front. Behav. Neurosci. 11, 35 (2017).
Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210â216 (2018).
Winkelman, J. W. et al. Increased rostral anterior cingulate cortex volume in chronic primary insomnia. Sleep 36, 991â998 (2013).
Lamparter, D., Marbach, D., Rueedi, R., Kutalik, Z. & Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLOS Comput. Biol. 12, e1004714 (2016).
Vetrivelan, R., Qiu, M.-H., Chang, C. & Lu, J. Role of basal ganglia in sleep-wake regulation: neural circuitry and clinical significance. Front. Neuroanat. 4, 145 (2010).
Jansen, P.R. et al. Genome-wide analysis of insomnia (N=1,331,010) identifies novel loci and functional pathways. Preprint at https://www.biorxiv.org/content/10.1101/214973v2 (2018).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245â252 (2016).
Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284â290 (2015).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228â1235 (2015).
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291â295 (2015).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90âW97 (2016).
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).
Schormair, B. et al. Identification of novel risk loci for restless legs syndrome in genome-wide association studies in individuals of European ancestry: a meta-analysis. Lancet Neurol. 16, 898â907 (2017).
Stavropoulos, N. & Young, M. W. insomniac and Cullin-3 regulate sleep and wakefulness in Drosophila. Neuron 72, 964â976 (2011).
Livingston, W. S. et al. Improved sleep in military personnel is associated with changes in the expression of inflammatory genes and improvement in depression symptoms. Front. Psychiatry 6, 59 (2015).
Freeman, A. A. H., Mandilaras, K., Missirlis, F. & Sanyal, S. An emerging role for Cullin-3 mediated ubiquitination in sleep and circadian rhythm: insights from Drosophila. Fly (Austin) 7, 39â43 (2013).
Anafi, R. C. et al. Sleep is not just for the brain: transcriptional responses to sleep in peripheral tissues. BMC Genomics 14, 362 (2013).
Trotti, L. M. Restless legs syndrome and sleep-related movement disorders. Continuum (Minneap. Minn.) 23, 1005â1016 (2017).
Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48, 803â810 (2016).
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272â279 (2017).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236â1241 (2015).
Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658â665 (2013).
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512â525 (2015).
Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian Randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304â314 (2016).
Javaheri, S. & Redline, S. Insomnia and risk of cardiovascular disease. Chest 152, 435â444 (2017).
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).
Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018).
Smith, D. J. et al. Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants. PLoS One 8, e75362 (2013).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203â209 (2018).
OâConnell, J. et al. Haplotype estimation for biobank-scale data sets. Nat. Genet. 48, 817â820 (2016).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559â575 (2007).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369â375 (2012). S1âS3.
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190â2191 (2010).
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
R Development Core Team. R: a Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018)
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).
Acknowledgements
This research was conducted by using the UK Biobank Resource (UK Biobank applications 6818 and 9072). We would like to thank the participants and researchers from the UK Biobank who contributed or collected data. This work was supported by 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.S.), NHLBI R35 35HL135818 (S.R. and R.S), R01DK105072 (R.S. and F.S.), T32HL007567 (J.M.L.), K01HL136884 (J.M.L.), and HG003054 (H.W.), The MGH Research Scholar Fund (R.S.), The University of Manchester (Research Infrastructure Fund), the Wellcome Trust (salary support for D.W.R. and A.S.L.), UK Medical Research Council MC_UU_12013/5 (D.A.L.), UK Medical Research Council MC_UU_00011/6 (D.A.L.), and UK National Institute of Health Research NF-SI-0611-10196 (D.A.L.). A.R.W. and T.M.F. are supported by a European Research Council grant (SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC). S.E.J. is funded by the Medical Research Council (MR/M005070/1). M.N.W. is supported by a Wellcome Trust Institutional Strategic Support Award (WT097835MF). The following groups provided summary statistics to LDHub and MR-base: ADIPOGen (Adiponectin Genetics Consortium), C4D (Coronary Artery Disease Genetics Consortium), CARDIoGRAM (Coronary Artery Disease Genome-wide Replication and Meta-analysis), CKDGen (Chronic Kidney Disease Genetics Consortium), dbGAP (Database of Genotypes and Phenotypes), DIAGRAM (Diabetes Genetics Replication and Meta-analysis), ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis), EAGLE (Early Genetics & Lifecourse Epidemiology Eczema Consortium, excluding 23andMe), EGG (Early Growth Genetics Consortium), GABRIEL (a multidisciplinary study to identify the genetic and environmental causes of asthma in the European community), GCAN (Genetic Consortium for Anorexia Nervosa), GEFOS (Genetic Factors for Osteoporosis Consortium), GIANT (Genetic Investigation of Anthropometric Traits), GIS (Genetics of Iron Status Consortium), GLGC (Global Lipids Genetics Consortium), GPC (Genetics of Personality Consortium), GUGC (Global Urate and Gout Consortium), HaemGen (Haemotological and Platelet Traits Genetics Consortium), HRgene (Heart Rate Consortium), IIBDGC (International Inflammatory Bowel Disease Genetics Consortium), ILCCO (International Lung Cancer Consortium), IMSGC (International Multiple Sclerosis Genetic Consortium), MAGIC (Meta-analyses of Glucose and Insulin-related Traits Consortium), MESA (Multi-ethnic Study of Atherosclerosis), PGC (Psychiatric Genomics Consortium), Project MinE consortium, ReproGen (Reproductive Genetics Consortium), SSGAC (Social Science Genetics Association Consortium), TAG (Tobacco and Genetics Consortium), TRICL (Transdisciplinary Research in Cancer of the Lung Consortium), and UK Biobank. The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration among the HUNT Research Centre (Faculty of Medicine, NTNU, Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority, and Norwegian Institute of Public Health. We are grateful for the contributions from H. Zhang and H. M. Kang. We also acknowledge the support given to us by the Genotyping core and J. Chen. The K.G. Jebsen center for genetic epidemiology is financed by Stiftelsen Kristian Gerhard Jebsen, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), and the Central Norway Regional Health Authority. B.B. and L.B.S. received research grants from The Liaison Committee for education, research and innovation in central Norway. We thank the International EU-RLS-GENE Consortium and KORA for providing RLS GWAS data.
Author information
Authors and Affiliations
Consortia
Contributions
The study was designed by J.M.L., S.E.J., A.R.W., H.S.D., V.T.V.H., K.H., B.B., L.B.S., B.S.W., K.G.A., H.W., S.G.A., A.S.L., D.W.R., T.M.F., M.N.W., D.A.L., M.K.R., and R.S. J.M.L., S.E.J., A.R.W., H.S.D., V.T.V.H., C.Z., J.B.N., J.-A.Z., M.H., R.N.B., J.T., K.G.A., H.W., Y.S., K.P., S.P., J.W.W., T.M.F., D.A.L., M.K.R., M.N.W., and R.S. participated in acquisition, analysis, and/or interpretation of data. J.M.L., H.S.D., B.B., L.B.S., H.W., and R.S. wrote the manuscript, and all coauthors 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.
Corresponding author
Ethics declarations
Competing interests
J.W.W. is a consultant for Merck and Flex Pharma. He receives royalties from UpToDate. He has received speaker fees and travel support from Otsuka. He has received research grants from UCB Pharma, NeuroMetrix, NIMH, the RLS Foundation, and Luitpold Pharma. F.A.J.L.S. has received lecture fees from Bayer HealthCare, Sentara HealthCare, Philips, Vanda Pharmaceuticals, and Pfizer. D.A.L. has received funding from Medtronic and Roche Diagnostics for biomarker research unrelated to this study. M.K.R. has acted as a consultant for GlaxoSmithKline, Novo Nordisk, Roche, and Merck Sharp & Dohme (MSD), and also participated in advisory-board meetings on their behalf. M.K.R. has received lecture fees from MSD and grant support from Novo Nordisk, MSD, and GlaxoSmithKline.
Additional information
Publisherâs note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Figures 1â7 and Supplementary Note
Supplementary Tables
Supplementary Tables 1â20
Rights and permissions
About this article
Cite this article
Lane, J.M., Jones, S.E., Dashti, H.S. et al. Biological and clinical insights from genetics of insomnia symptoms. Nat Genet 51, 387â393 (2019). https://doi.org/10.1038/s41588-019-0361-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-019-0361-7
This article is cited by
-
Genetic associations between orexin genes and phenotypes related to behavioral regulation in humans, including substance use
Molecular Psychiatry (2025)
-
Characterizing Genetic-Predisposed Proteins Involving Insomnia by Integrating Genome-Wide Association Study Summary Statistics
Molecular Neurobiology (2025)
-
Genome-wide association analysis of composite sleep health scores in 413,904 individuals
Communications Biology (2025)
-
Causal relationships between neuropsychiatric disorders and nonalcoholic fatty liver disease: A bidirectional Mendelian randomization study
BMC Gastroenterology (2024)
-
The role of hypertension in the relationship between leisure screen time, physical activity and migraine: a 2-sample Mendelian randomization study
The Journal of Headache and Pain (2024)