Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms
Autoři:
Xinyue Li aff001; Hongyu Zhao aff002
Působiště autorů:
School of Data Science, City University of Hong Kong, Hong Kong, China
aff001; Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America
aff002; Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America
aff003; Department of Genetics, Yale University School of Medicine, New Haven, CT, United States of America
aff004
Vyšlo v časopise:
Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms. PLoS Genet 16(10): e32767. doi:10.1371/journal.pgen.1009089
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009089
Souhrn
Wearable devices have been increasingly used in research to provide continuous physical activity monitoring, but how to effectively extract features remains challenging for researchers. To analyze the generated actigraphy data in large-scale population studies, we developed computationally efficient methods to derive sleep and activity features through a Hidden Markov Model-based sleep/wake identification algorithm, and circadian rhythm features through a Penalized Multi-band Learning approach adapted from machine learning. Unsupervised feature extraction is useful when labeled data are unavailable, especially in large-scale population studies. We applied these two methods to the UK Biobank wearable device data and used the derived sleep and circadian features as phenotypes in genome-wide association studies. We identified 53 genetic loci with p<5×10−8 including genes known to be associated with sleep disorders and circadian rhythms as well as novel loci associated with Body Mass Index, mental diseases and neurological disorders, which suggest shared genetic factors of sleep and circadian rhythms with physical and mental health. Further cross-tissue enrichment analysis highlights the important role of the central nervous system and the shared genetic architecture with metabolism-related traits and the metabolic system. Our study demonstrates the effectiveness of our unsupervised methods for wearable device data when additional training data cannot be easily acquired, and our study further expands the application of wearable devices in population studies and genetic studies to provide novel biological insights.
Klíčová slova:
Circadian oscillators – Circadian rhythms – Genetic loci – Genetics of disease – Genome-wide association studies – Hidden Markov models – Physical activity – Sleep
Zdroje
1. Fernandez-Mendoza J. The insomnia with short sleep duration phenotype: an update on it's importance for health and prevention. Curr Opin Psychiatry. 2017;30(1):56–63. doi: 10.1097/YCO.0000000000000292 27764017
2. Fernandez-Mendoza J, Vgontzas AN. Insomnia and its impact on physical and mental health. Curr Psychiatry Rep. 2013;15(12):418. doi: 10.1007/s11920-013-0418-8 24189774
3. Luyster FS, Strollo PJ Jr, Zee PC, Walsh JK, Boards of Directors of the American Academy of Sleep M, the Sleep Research S. Sleep: a health imperative. Sleep. 2012;35(6):727–34. doi: 10.5665/sleep.1846 22654183
4. Sterniczuk R, Theou O, Rusak B, Rockwood K. Sleep disturbance is associated with incident dementia and mortality. Curr Alzheimer Res. 2013;10(7):767–75. doi: 10.2174/15672050113109990134 23905991
5. Zornoza-Moreno M, Fuentes-Hernandez S, Sanchez-Solis M, Rol MA, Larque E, Madrid JA. Assessment of circadian rhythms of both skin temperature and motor activity in infants during the first 6 months of life. Chronobiol Int. 2011;28(4):330–7. doi: 10.3109/07420528.2011.565895 21539424
6. Zhu L, Zee PC. Circadian rhythm sleep disorders. Neurol Clin. 2012;30(4):1167–91. doi: 10.1016/j.ncl.2012.08.011 23099133
7. Potter GD, Skene DJ, Arendt J, Cade JE, Grant PJ, Hardie LJ. Circadian Rhythm and Sleep Disruption: Causes, Metabolic Consequences, and Countermeasures. Endocr Rev. 2016;37(6):584–608. doi: 10.1210/er.2016-1083 27763782
8. Baron KG, Reid KJ. Circadian misalignment and health. Int Rev Psychiatry. 2014;26(2):139–54. doi: 10.3109/09540261.2014.911149 24892891
9. Smith MT, McCrae CS, Cheung J, Martin JL, Harrod CG, Heald JL, et al. Use of Actigraphy for the Evaluation of Sleep Disorders and Circadian Rhythm Sleep-Wake Disorders: An American Academy of Sleep Medicine Systematic Review, Meta-Analysis, and GRADE Assessment. J Clin Sleep Med. 2018;14(7):1209–30. doi: 10.5664/jcsm.7228 29991438
10. van Hees VT, Sabia S, Anderson KN, Denton SJ, Oliver J, Catt M, et al. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. PLoS One. 2015;10(11):e0142533. doi: 10.1371/journal.pone.0142533 26569414
11. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep. 1992;15(5):461–9. doi: 10.1093/sleep/15.5.461 1455130
12. Sadeh A, Sharkey KM, Carskadon MA. Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep. 1994;17(3):201–7. doi: 10.1093/sleep/17.3.201 7939118
13. Tilmanne J, Urbain J, Kothare MV, Wouwer AV, Kothare SV. Algorithms for sleep–wake identification using actigraphy: a comparative study and new results. Journal of Sleep Research. 2009;18(1):85–98. doi: 10.1111/j.1365-2869.2008.00706.x 19250177
14. Doherty A, Smith-Byrne K, Ferreira T, Holmes MV, Holmes C, Pulit SL, et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nature Communications. 2018;9(1):5257. doi: 10.1038/s41467-018-07743-4 30531941
15. Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Sci Rep. 2018;8(1):7961. doi: 10.1038/s41598-018-26174-1 29784928
16. 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. Nature Communications. 2019;10(1):1100. doi: 10.1038/s41467-019-08917-4 30846698
17. Jones SE, van Hees VT, Mazzotti DR, Marques-Vidal P, Sabia S, van der Spek A, et al. Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour. Nat Commun. 2019;10(1):1585. doi: 10.1038/s41467-019-09576-1 30952852
18. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–5. doi: 10.1038/ng.3211 25642630
19. Lane JM, Liang J, Vlasac I, Anderson SG, Bechtold DA, Bowden J, et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nature genetics. 2017;49(2):274. doi: 10.1038/ng.3749 27992416
20. Winkelmann J, Czamara D, Schormair B, Knauf F, Schulte EC, Trenkwalder C, et al. Genome-wide association study identifies novel restless legs syndrome susceptibility loci on 2p14 and 16q12. 1. PLoS genetics. 2011;7(7):e1002171. doi: 10.1371/journal.pgen.1002171 21779176
21. Jansen PR, Watanabe K, Stringer S, Skene N, Bryois J, Hammerschlag AR, et al. Genome-wide Analysis of Insomnia (N = 1,331,010) Identifies Novel Loci and Functional Pathways. bioRxiv. 2018:214973.
22. Lane JM, Vlasac I, Anderson SG, Kyle SD, Dixon WG, Bechtold DA, et al. Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UK Biobank. Nature communications. 2016;7:10889. doi: 10.1038/ncomms10889 26955885
23. McDonald MN, Won S, Mattheisen M, Castaldi PJ, Cho MH, Rutten E, et al. Body mass index change in gastrointestinal cancer and chronic obstructive pulmonary disease is associated with Dedicator of Cytokinesis 1. J Cachexia Sarcopenia Muscle. 2017;8(3):428–36. doi: 10.1002/jcsm.12171 28044437
24. Wijsman EM, Pankratz ND, Choi Y, Rothstein JH, Faber KM, Cheng R, et al. Genome-wide association of familial late-onset Alzheimer's disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE. PLoS Genet. 2011;7(2):e1001308. doi: 10.1371/journal.pgen.1001308 21379329
25. Hoffmann TJ, Choquet H, Yin J, Banda Y, Kvale MN, Glymour M, et al. A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci. Genetics. 2018;210(2):499–515. doi: 10.1534/genetics.118.301479 30108127
26. Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, Chu S, et al. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study. PLoS Genet. 2015;11(10):e1005378. doi: 10.1371/journal.pgen.1005378 26426971
27. Huffman JE, Albrecht E, Teumer A, Mangino M, Kapur K, Johnson T, et al. Modulation of genetic associations with serum urate levels by body-mass-index in humans. PLoS One. 2015;10(3):e0119752. doi: 10.1371/journal.pone.0119752 25811787
28. Wu JH, Lemaitre RN, Manichaikul A, Guan W, Tanaka T, Foy M, et al. Genome-wide association study identifies novel loci associated with concentrations of four plasma phospholipid fatty acids in the de novo lipogenesis pathway: results from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Circ Cardiovasc Genet. 2013;6(2):171–83. doi: 10.1161/CIRCGENETICS.112.964619 23362303
29. Schormair B, Zhao C, Bell S, Tilch E, Salminen AV, Putz 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. 2017;16(11):898–907. doi: 10.1016/S1474-4422(17)30327-7 29029846
30. Wood AR, Tyrrell J, Beaumont R, Jones SE, Tuke MA, Ruth KS, et al. Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively. Diabetologia. 2016;59(6):1214–21. doi: 10.1007/s00125-016-3908-5 26961502
31. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. doi: 10.1038/nature14177 25673413
32. Herold C, Hooli BV, Mullin K, Liu T, Roehr JT, Mattheisen M, et al. Family-based association analyses of imputed genotypes reveal genome-wide significant association of Alzheimer's disease with OSBPL6, PTPRG, and PDCL3. Mol Psychiatry. 2016;21(11):1608–12. doi: 10.1038/mp.2015.218 26830138
33. Goes FS, McGrath J, Avramopoulos D, Wolyniec P, Pirooznia M, Ruczinski I, et al. Genome-wide association study of schizophrenia in Ashkenazi Jews. Am J Med Genet B Neuropsychiatr Genet. 2015;168(8):649–59. doi: 10.1002/ajmg.b.32349 26198764
34. Autism Spectrum Disorders Working Group of The Psychiatric Genomics C. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol Autism. 2017;8:21. doi: 10.1186/s13229-017-0137-9 28540026
35. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell. 2016;167(5):1415–29 e19. doi: 10.1016/j.cell.2016.10.042 27863252
36. Loprinzi PD. Sleep duration and sleep disorder with red blood cell distribution width. Am J Health Behav. 2015;39(4):471–4. doi: 10.5993/AJHB.39.4.3 26018095
37. Liu H, Wang G, Luan G, Liu Q. Effects of sleep and sleep deprivation on blood cell count and hemostasis parameters in healthy humans. J Thromb Thrombolysis. 2009;28(1):46–9. doi: 10.1007/s11239-008-0240-z 18597046
38. Choi JB, Loredo JS, Norman D, Mills PJ, Ancoli-Israel S, Ziegler MG, et al. Does obstructive sleep apnea increase hematocrit? Sleep Breath. 2006;10(3):155–60. doi: 10.1007/s11325-006-0064-z 16770648
39. Akiyama M, Okada Y, Kanai M, Takahashi A, Momozawa Y, Ikeda M, et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat Genet. 2017;49(10):1458–67.
40. van der Harst P, Verweij N. Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease. Circ Res. 2018;122(3):433–43.
41. Chen C, Xia F, Chen Y, Zhang K, Cheng J, Li Q, et al. Association Between Thyroid-Stimulating Hormone and Renal Function: a Mendelian Randomization Study. Kidney Blood Press Res. 2018;43(4):1121–30. doi: 10.1159/000491808 30016786
42. Zhan M, Chen G, Pan CM, Gu ZH, Zhao SX, Liu W, et al. Genome-wide association study identifies a novel susceptibility gene for serum TSH levels in Chinese populations. Hum Mol Genet. 2014;23(20):5505–17. doi: 10.1093/hmg/ddu250 24852370
43. Li X, Kane M, Zhang Y, Sun W, Song Y, Dong S, et al. Penalized Selection of Periodicities Characterizes the Consolidation of Sleep-Wake Circadian Rhythms During Early Childhood Development. Submitted. 2019.
44. Byrne EM, Gehrman PR, Medland SE, Nyholt DR, Heath AC, Madden PA, et al. A genome-wide association study of sleep habits and insomnia. Am J Med Genet B Neuropsychiatr Genet. 2013;162B(5):439–51. doi: 10.1002/ajmg.b.32168 23728906
45. Heinzman JT, Hoth KF, Cho MH, Sakornsakolpat P, Regan EA, Make BJ, et al. GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort. J Affect Disord. 2019;243:16–22. doi: 10.1016/j.jad.2018.09.003 30219690
46. Li QS, Tian C, Seabrook GR, Drevets WC, Narayan VA. Analysis of 23andMe antidepressant efficacy survey data: implication of circadian rhythm and neuroplasticity in bupropion response. Transl Psychiatry. 2016;6(9):e889. doi: 10.1038/tp.2016.171 27622933
47. Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet. 2018;50(2):229–37.
48. Saxena R, Plenge RM, Bjonnes AC, Dashti HS, Okada Y, Gad El Haq W, et al. A Multinational Arab Genome-Wide Association Study Identifies New Genetic Associations for Rheumatoid Arthritis. Arthritis Rheumatol. 2017;69(5):976–85. doi: 10.1002/art.40051 28118524
49. Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–7. doi: 10.1038/nature13595 25056061
50. Pardinas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018;50(3):381–9. doi: 10.1038/s41588-018-0059-2 29483656
51. Hu Y, Shmygelska A, Tran D, Eriksson N, Tung JY, Hinds DA. GWAS of 89,283 individuals identifies genetic variants associated with self-reporting of being a morning person. Nat Commun. 2016;7:10448. doi: 10.1038/ncomms10448 26835600
52. Eppinga RN, Hagemeijer Y, Burgess S, Hinds DA, Stefansson K, Gudbjartsson DF, et al. Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality. Nat Genet. 2016;48(12):1557–63. doi: 10.1038/ng.3708 27798624
53. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47(11):1228–35.
54. Gamble KL, Berry R, Frank SJ, Young ME. Circadian clock control of endocrine factors. Nat Rev Endocrinol. 2014;10(8):466–75. doi: 10.1038/nrendo.2014.78 24863387
55. Trinder J, Waloszek J, Woods MJ, Jordan AS. Sleep and cardiovascular regulation. Pflugers Arch. 2012;463(1):161–8. doi: 10.1007/s00424-011-1041-3 22038322
56. Benarroch EE. Control of the cardiovascular and respiratory systems during sleep. Auton Neurosci. 2019;218:54–63. doi: 10.1016/j.autneu.2019.01.007 30890349
57. Hyun MK, Baek Y, Lee S. Association between digestive symptoms and sleep disturbance: a cross-sectional community-based study. BMC Gastroenterol. 2019;19(1):34. doi: 10.1186/s12876-019-0945-9 30782128
58. Murat S, Ali U, Serdal K, Suleyman D, Ilknur P, Mehmet S, et al. Assessment of subjective sleep quality in iron deficiency anaemia. Afr Health Sci. 2015;15(2):621–7. doi: 10.4314/ahs.v15i2.40 26124812
59. Li X, Allen RP, Earley CJ, Liu H, Cruz TE, Edden RAE, et al. Brain iron deficiency in idiopathic restless legs syndrome measured by quantitative magnetic susceptibility at 7 tesla. Sleep Med. 2016;22:75–82. doi: 10.1016/j.sleep.2016.05.001 27544840
60. Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet. 2019;51(3):568–76. doi: 10.1038/s41588-019-0345-7 30804563
61. Kjellberg MA, Backman AP, Ohvo-Rekila H, Mattjus P. Alternation in the glycolipid transfer protein expression causes changes in the cellular lipidome. PLoS One. 2014;9(5):e97263.
62. Carbon S, Mungall C. Gene Ontology Data Archive. 2018.
63. Hokama M, Oka S, Leon J, Ninomiya T, Honda H, Sasaki K, et al. Altered expression of diabetes-related genes in Alzheimer's disease brains: the Hisayama study. Cereb Cortex. 2014;24(9):2476–88. doi: 10.1093/cercor/bht101 23595620
64. Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, et al. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell. 2012;149(6):1393–406. doi: 10.1016/j.cell.2012.04.031 22658674
65. Schroeder CM, Ostrem JM, Hertz NT, Vale RD. A Ras-like domain in the light intermediate chain bridges the dynein motor to a cargo-binding region. Elife. 2014;3:e03351. doi: 10.7554/eLife.03351 25272277
66. Hodgson U, Pulkkinen V, Dixon M, Peyrard-Janvid M, Rehn M, Lahermo P, et al. ELMOD2 is a candidate gene for familial idiopathic pulmonary fibrosis. Am J Hum Genet. 2006;79(1):149–54. doi: 10.1086/504639 16773575
67. Pruitt KD, Tatusova T, Klimke W, Maglott DR. NCBI Reference Sequences: current status, policy and new initiatives. Nucleic Acids Res. 2009;37(Database issue):D32–6. doi: 10.1093/nar/gkn721 18927115
68. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. doi: 10.1038/nprot.2008.211 19131956
69. Bogan RK. Effects of restless legs syndrome (RLS) on sleep. Neuropsychiatr Dis Treat. 2006;2(4):513–9. doi: 10.2147/nedt.2006.2.4.513 19412499
70. Hargens TA, Kaleth AS, Edwards ES, Butner KL. Association between sleep disorders, obesity, and exercise: a review. Nat Sci Sleep. 2013;5:27–35. doi: 10.2147/NSS.S34838 23620691
71. Philippe J, Dibner C. Thyroid circadian timing: roles in physiology and thyroid malignancies. J Biol Rhythms. 2015;30(2):76–83.
72. Vandewalle G, Middleton B, Rajaratnam SM, Stone BM, Thorleifsdottir B, Arendt J, et al. Robust circadian rhythm in heart rate and its variability: influence of exogenous melatonin and photoperiod. J Sleep Res. 2007;16(2):148–55.
73. Klimentidis YC, Raichlen DA, Bea J, Garcia DO, Wineinger NE, Mandarino LJ, et al. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes (Lond). 2018;42(6):1161–76.
74. Oh JH. Gastroesophageal reflux disease: recent advances and its association with sleep. Ann N Y Acad Sci. 2016;1380(1):195–203. doi: 10.1111/nyas.13143 27391766
75. Tu Q, Heitkemper MM, Jarrett ME, Buchanan DT. Sleep disturbances in irritable bowel syndrome: a systematic review. Neurogastroenterol Motil. 2017;29(3).
76. Prince SA, Cardilli L, Reed JL, Saunders TJ, Kite C, Douillette K, et al. A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2020;17(1):31. doi: 10.1186/s12966-020-00938-3 32131845
77. Li X, Zhang Y, Jiang F, Zhao H. A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy. Chronobiology International. 2020;1–14.
78. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, 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. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779 25826379
79. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. Genome-wide genetic data on~ 500,000 UK Biobank participants. BioRxiv. 2017:166298.
80. Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, et al. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. PLoS One. 2017;12(2):e0169649. doi: 10.1371/journal.pone.0169649 28146576
81. White T, Westgate K, Wareham NJ, Brage S. Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults. PLoS One. 2016;11(12):e0167472. doi: 10.1371/journal.pone.0167472 27936024
82. Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA Accelerometer. Med Sci Sports Exerc. 2011;43(6):1085–93. doi: 10.1249/MSS.0b013e31820513be 21088628
83. Rowlands AV, Mirkes EM, Yates T, Clemes S, Davies M, Khunti K, et al. Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent? Med Sci Sports Exerc. 2018;50(2):257–65. doi: 10.1249/MSS.0000000000001435 28976493
84. Jones SE, van Hees VT, Mazzotti DR, Marques-Vidal P, Sabia S, van der Spek A, et al. Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour. bioRxiv. 2018:303925.
85. Baum LE, Petrie T. Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics. 1966;37(6):1554–63.
86. Baum LE, Petrie T, Soules G, Weiss N. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. Annals of Mathematical Statistics. 1970;41(1):164–71.
87. Ryan MS, Nudd GR. The Viterbi Algorithm. Proc IEEE. 1973;61(5):268–78.
88. Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National Sleep Foundation's sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40–3. doi: 10.1016/j.sleh.2014.12.010 29073412
89. 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 new insights into circadian rhythms in humans and links to disease. BioRxiv. 2018:303941.
90. Thorpy MJ. Classification of sleep disorders. Neurotherapeutics. 2012;9(4):687–701. doi: 10.1007/s13311-012-0145-6 22976557
91. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2005;67(2):301–20.
92. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological). 1996:267–88.
93. Li X, Kane M. PML: Penalized Multi-Band Learning for Circadian Rhythm Analysis Using Actigraphy 2019 [Available from: https://CRAN.R-project.org/package=PML.
94. 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. doi: 10.1186/s13742-015-0047-8 25722852
95. Pulit SL, de With SA, de Bakker PI. Resetting the bar: Statistical significance in whole-genome sequencing-based association studies of global populations. Genet Epidemiol. 2017;41(2):145–51. doi: 10.1002/gepi.22032 27990689
96. Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, et al. A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics. Am J Hum Genet. 2017;101(6):939–64. doi: 10.1016/j.ajhg.2017.11.001 29220677
97. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7.
98. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. doi: 10.1093/ije/dyv080 26050253
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