Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms
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...
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| Published in | PLoS genetics Vol. 16; no. 10; p. e1009089 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
19.10.2020
Public Library of Science (PLoS) |
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| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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<5x10.sup.-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. 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. While wearable devices have been increasingly used in research for objective and continuous activity monitoring, how to effectively extract sleep and rest-activity circadian rhythm features remains the major obstacle for researchers, especially in population studies where labeled outcome data such as sleep diaries are unavailable and thus existing supervised methods cannot be applied. Here, we developed unsupervised feature extraction methods based on machine learning without the need for labeled outcome data. We applied the methods to population wearable device data to extract sleep and circadian features, and we further identified novel associated loci and the key roles of the central nervous system and the metabolic system. The findings are essential for understanding the underlying shared genetic architecture of sleep and circadian rhythms with physical and mental health, and the proposed methods can largely expand and promote the use of wearable device data in population and genetic studies. 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. 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.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. |
| Audience | Academic |
| Author | Zhao, Hongyu Li, Xinyue |
| AuthorAffiliation | 1 School of Data Science, City University of Hong Kong, Hong Kong, China 2 Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America Stanford University School of Medicine, UNITED STATES 4 Department of Genetics, Yale University School of Medicine, New Haven, CT, United States of America 3 Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America |
| AuthorAffiliation_xml | – name: 1 School of Data Science, City University of Hong Kong, Hong Kong, China – name: 4 Department of Genetics, Yale University School of Medicine, New Haven, CT, United States of America – name: 3 Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States of America – name: Stanford University School of Medicine, UNITED STATES – name: 2 Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America |
| Author_xml | – sequence: 1 givenname: Xinyue orcidid: 0000-0003-1972-9021 surname: Li fullname: Li, Xinyue – sequence: 2 givenname: Hongyu orcidid: 0000-0003-1195-9607 surname: Zhao fullname: Zhao, Hongyu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33075057$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.2147/NSS.S34838 10.1038/ncomms10448 10.1186/s12966-020-00938-3 10.1007/s13311-012-0145-6 10.1038/tp.2016.171 10.1007/s00424-011-1041-3 10.1093/ije/dyv080 10.1161/CIRCRESAHA.117.312086 10.2147/nedt.2006.2.4.513 10.1097/YCO.0000000000000292 10.1002/jcsm.12171 10.1371/journal.pone.0169649 10.1038/nrendo.2014.78 10.1371/journal.pmed.1001779 10.1016/j.cell.2016.10.042 10.1111/j.1467-9868.2005.00503.x 10.5664/jcsm.7228 10.1038/nprot.2008.211 10.1249/MSS.0b013e31820513be 10.1038/s41366-018-0120-3 10.1038/s41588-017-0009-4 10.1038/ng.3951 10.1186/s12876-019-0945-9 10.1111/j.2517-6161.1996.tb02080.x 10.1371/journal.pone.0167472 10.1038/ncomms10889 10.1159/000491808 10.1186/s13229-017-0137-9 10.1210/er.2016-1083 10.1111/nyas.13143 10.1016/j.sleh.2014.12.010 10.1038/mp.2015.218 10.1111/j.1365-2869.2008.00706.x 10.1214/aoms/1177699147 10.1111/j.1365-2869.2007.00581.x 10.1038/ng.3708 10.1038/s41467-019-09576-1 10.1093/sleep/17.3.201 10.1016/j.jad.2018.09.003 10.1016/j.ncl.2012.08.011 10.1086/504639 10.1002/gepi.22032 10.1038/s41588-019-0345-7 10.1249/MSS.0000000000001435 10.5993/AJHB.39.4.3 10.1016/S1474-4422(17)30327-7 10.1371/journal.pone.0119752 10.1038/ng.3749 10.1038/ng.3211 10.1007/s11325-006-0064-z 10.4314/ahs.v15i2.40 10.1161/CIRCGENETICS.112.964619 10.3109/07420528.2011.565895 10.1038/s41467-019-08917-4 10.1038/s41588-018-0059-2 10.1093/sleep/15.5.461 10.1371/journal.pone.0097263 10.1016/j.cell.2012.04.031 10.1038/nature13595 10.1186/s13742-015-0047-8 10.1214/aoms/1177697196 10.1093/cercor/bht101 10.1002/ajmg.b.32349 10.1038/s41598-018-26174-1 10.7554/eLife.03351 10.1177/0748730414557634 10.1007/s11920-013-0418-8 10.1038/ng.3404 10.1371/journal.pone.0142533 10.1534/genetics.118.301479 10.1016/j.sleep.2016.05.001 10.1002/ajmg.b.32168 10.1038/s41467-018-07743-4 10.1371/journal.pgen.1001308 10.1371/journal.pgen.1002171 10.1371/journal.pgen.1005378 10.1002/art.40051 10.1007/s00125-016-3908-5 10.1016/j.autneu.2019.01.007 10.1007/s11239-008-0240-z 10.1016/j.ajhg.2017.11.001 10.1038/nature14177 10.1093/nar/gkn721 10.2174/15672050113109990134 10.3109/09540261.2014.911149 10.1093/hmg/ddu250 |
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| Copyright | COPYRIGHT 2020 Public Library of Science 2020 Li, Zhao. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Li, Zhao 2020 Li, Zhao |
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| References | L Zhu (pgen.1009089.ref006) 2012; 30 Q Tu (pgen.1009089.ref075) 2017; 29 YC Klimentidis (pgen.1009089.ref073) 2018; 42 Schizophrenia Working Group of the Psychiatric Genomics C (pgen.1009089.ref049) 2014; 511 C Bycroft (pgen.1009089.ref079) 2017 P Turley (pgen.1009089.ref047) 2018; 50 C Chen (pgen.1009089.ref041) 2018; 43 MS Ryan (pgen.1009089.ref087) 1973; 61 M Zornoza-Moreno (pgen.1009089.ref005) 2011; 28 SE Jones (pgen.1009089.ref084) 2018 SA Prince (pgen.1009089.ref076) 2020; 17 MA Kjellberg (pgen.1009089.ref061) 2014; 9 RJ Cole (pgen.1009089.ref011) 1992; 15 C Herold (pgen.1009089.ref032) 2016; 21 U Hodgson (pgen.1009089.ref066) 2006; 79 HK Finucane (pgen.1009089.ref053) 2015; 47 JE Huffman (pgen.1009089.ref027) 2015; 10 RK Bogan (pgen.1009089.ref069) 2006; 2 B Schormair (pgen.1009089.ref029) 2017; 16 R Tibshirani (pgen.1009089.ref092) 1996 CM Schroeder (pgen.1009089.ref065) 2014; 3 WJ Astle (pgen.1009089.ref035) 2016; 167 MK Hyun (pgen.1009089.ref057) 2019; 19 Y Hu (pgen.1009089.ref051) 2016; 7 H Liu (pgen.1009089.ref037) 2009; 28 EM Wijsman (pgen.1009089.ref024) 2011; 7 M Akiyama (pgen.1009089.ref039) 2017; 49 QS Li (pgen.1009089.ref046) 2016; 6 J Tilmanne (pgen.1009089.ref013) 2009; 18 DW Esliger (pgen.1009089.ref082) 2011; 43 MT Smith (pgen.1009089.ref009) 2018; 14 AF Pardinas (pgen.1009089.ref050) 2018; 50 SE Jones (pgen.1009089.ref089) 2018 M Willetts (pgen.1009089.ref015) 2018; 8 RN Eppinga (pgen.1009089.ref052) 2016; 48 JB Choi (pgen.1009089.ref038) 2006; 10 M Hirshkowitz (pgen.1009089.ref088) 2015; 1 A Doherty (pgen.1009089.ref014) 2018; 9 KG Baron (pgen.1009089.ref008) 2014; 26 A Castello (pgen.1009089.ref064) 2012; 149 AR Wood (pgen.1009089.ref030) 2016; 59 MJ Thorpy (pgen.1009089.ref090) 2012; 9 W Huang da (pgen.1009089.ref068) 2009; 4 MN McDonald (pgen.1009089.ref023) 2017; 8 FS Luyster (pgen.1009089.ref003) 2012; 35 X Li (pgen.1009089.ref059) 2016; 22 HS Dashti (pgen.1009089.ref016) 2019; 10 AE Locke (pgen.1009089.ref031) 2015; 518 S Murat (pgen.1009089.ref058) 2015; 15 CC Chang (pgen.1009089.ref094) 2015; 4 JM Lane (pgen.1009089.ref019) 2017; 49 C Sudlow (pgen.1009089.ref078) 2015; 12 SE Jones (pgen.1009089.ref017) 2019; 10 TW Winkler (pgen.1009089.ref026) 2015; 11 J Philippe (pgen.1009089.ref071) 2015; 30 TJ Hoffmann (pgen.1009089.ref025) 2018; 210 BK Bulik-Sullivan (pgen.1009089.ref018) 2015; 47 EM Byrne (pgen.1009089.ref044) 2013; 162B TA Hargens (pgen.1009089.ref070) 2013; 5 Q Lu (pgen.1009089.ref096) 2017; 101 G Hemani (pgen.1009089.ref097) 2018 GD Potter (pgen.1009089.ref007) 2016; 37 R Sterniczuk (pgen.1009089.ref004) 2013; 10 JH Wu (pgen.1009089.ref028) 2013; 6 JM Lane (pgen.1009089.ref022) 2016; 7 Autism Spectrum Disorders Working Group of The Psychiatric Genomics C (pgen.1009089.ref034) 2017; 8 LE Baum (pgen.1009089.ref086) 1970; 41 VT van Hees (pgen.1009089.ref010) 2015; 10 J Bowden (pgen.1009089.ref098) 2015; 44 JT Heinzman (pgen.1009089.ref045) 2019; 243 PR Jansen (pgen.1009089.ref021) 2018 KD Pruitt (pgen.1009089.ref067) 2009; 37 SL Pulit (pgen.1009089.ref095) 2017; 41 H Zou (pgen.1009089.ref091) 2005; 67 M Hokama (pgen.1009089.ref063) 2014; 24 X Li (pgen.1009089.ref093) 2019 JH Oh (pgen.1009089.ref074) 2016; 1380 X Li (pgen.1009089.ref077) 2020 KL Gamble (pgen.1009089.ref054) 2014; 10 FS Goes (pgen.1009089.ref033) 2015; 168 X Li (pgen.1009089.ref043) 2019 J Fernandez-Mendoza (pgen.1009089.ref001) 2017; 30 J Winkelmann (pgen.1009089.ref020) 2011; 7 R Saxena (pgen.1009089.ref048) 2017; 69 J Trinder (pgen.1009089.ref055) 2012; 463 S Carbon (pgen.1009089.ref062) 2018 P van der Harst (pgen.1009089.ref040) 2018; 122 M Zhan (pgen.1009089.ref042) 2014; 23 J Fernandez-Mendoza (pgen.1009089.ref002) 2013; 15 A Sadeh (pgen.1009089.ref012) 1994; 17 AV Rowlands (pgen.1009089.ref083) 2018; 50 EE Benarroch (pgen.1009089.ref056) 2019; 218 A Doherty (pgen.1009089.ref080) 2017; 12 Y Hu (pgen.1009089.ref060) 2019; 51 G Vandewalle (pgen.1009089.ref072) 2007; 16 T White (pgen.1009089.ref081) 2016; 11 PD Loprinzi (pgen.1009089.ref036) 2015; 39 LE Baum (pgen.1009089.ref085) 1966; 37 |
| References_xml | – volume: 5 start-page: 27 year: 2013 ident: pgen.1009089.ref070 article-title: Association between sleep disorders, obesity, and exercise: a review publication-title: Nat Sci Sleep doi: 10.2147/NSS.S34838 – volume: 7 start-page: 10448 year: 2016 ident: pgen.1009089.ref051 article-title: GWAS of 89,283 individuals identifies genetic variants associated with self-reporting of being a morning person publication-title: Nat Commun doi: 10.1038/ncomms10448 – volume: 17 start-page: 31 issue: 1 year: 2020 ident: pgen.1009089.ref076 article-title: A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis publication-title: Int J Behav Nutr Phys Act doi: 10.1186/s12966-020-00938-3 – volume: 9 start-page: 687 issue: 4 year: 2012 ident: pgen.1009089.ref090 article-title: Classification of sleep disorders publication-title: Neurotherapeutics doi: 10.1007/s13311-012-0145-6 – start-page: 303925 year: 2018 ident: pgen.1009089.ref084 article-title: Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour publication-title: bioRxiv – volume: 6 start-page: e889 issue: 9 year: 2016 ident: pgen.1009089.ref046 article-title: Analysis of 23andMe antidepressant efficacy survey data: implication of circadian rhythm and neuroplasticity in bupropion response publication-title: Transl Psychiatry doi: 10.1038/tp.2016.171 – volume: 463 start-page: 161 issue: 1 year: 2012 ident: pgen.1009089.ref055 article-title: Sleep and cardiovascular regulation publication-title: Pflugers Arch doi: 10.1007/s00424-011-1041-3 – volume: 44 start-page: 512 issue: 2 year: 2015 ident: pgen.1009089.ref098 article-title: Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression publication-title: Int J Epidemiol doi: 10.1093/ije/dyv080 – volume: 122 start-page: 433 issue: 3 year: 2018 ident: pgen.1009089.ref040 article-title: Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease publication-title: Circ Res doi: 10.1161/CIRCRESAHA.117.312086 – volume: 2 start-page: 513 issue: 4 year: 2006 ident: pgen.1009089.ref069 article-title: Effects of restless legs syndrome (RLS) on sleep publication-title: Neuropsychiatr Dis Treat doi: 10.2147/nedt.2006.2.4.513 – volume: 30 start-page: 56 issue: 1 year: 2017 ident: pgen.1009089.ref001 article-title: The insomnia with short sleep duration phenotype: an update on it's importance for health and prevention publication-title: Curr Opin Psychiatry doi: 10.1097/YCO.0000000000000292 – volume: 8 start-page: 428 issue: 3 year: 2017 ident: pgen.1009089.ref023 article-title: Body mass index change in gastrointestinal cancer and chronic obstructive pulmonary disease is associated with Dedicator of Cytokinesis 1 publication-title: J Cachexia Sarcopenia Muscle doi: 10.1002/jcsm.12171 – volume: 12 start-page: e0169649 issue: 2 year: 2017 ident: pgen.1009089.ref080 article-title: Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study publication-title: PLoS One doi: 10.1371/journal.pone.0169649 – volume: 10 start-page: 466 issue: 8 year: 2014 ident: pgen.1009089.ref054 article-title: Circadian clock control of endocrine factors publication-title: Nat Rev Endocrinol doi: 10.1038/nrendo.2014.78 – volume: 12 start-page: e1001779 issue: 3 year: 2015 ident: pgen.1009089.ref078 article-title: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age publication-title: PLoS Med doi: 10.1371/journal.pmed.1001779 – volume: 167 start-page: 1415 issue: 5 year: 2016 ident: pgen.1009089.ref035 article-title: The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease publication-title: Cell doi: 10.1016/j.cell.2016.10.042 – volume: 67 start-page: 301 issue: 2 year: 2005 ident: pgen.1009089.ref091 article-title: Regularization and variable selection via the elastic net publication-title: Journal of the Royal Statistical Society: Series B (Statistical Methodology) doi: 10.1111/j.1467-9868.2005.00503.x – volume: 14 start-page: 1209 issue: 7 year: 2018 ident: pgen.1009089.ref009 article-title: 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 publication-title: J Clin Sleep Med doi: 10.5664/jcsm.7228 – volume: 4 start-page: 44 issue: 1 year: 2009 ident: pgen.1009089.ref068 article-title: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources publication-title: Nat Protoc doi: 10.1038/nprot.2008.211 – volume: 43 start-page: 1085 issue: 6 year: 2011 ident: pgen.1009089.ref082 article-title: Validation of the GENEA Accelerometer publication-title: Med Sci Sports Exerc doi: 10.1249/MSS.0b013e31820513be – volume: 42 start-page: 1161 issue: 6 year: 2018 ident: pgen.1009089.ref073 article-title: Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE publication-title: Int J Obes (Lond) doi: 10.1038/s41366-018-0120-3 – volume: 50 start-page: 229 issue: 2 year: 2018 ident: pgen.1009089.ref047 article-title: Multi-trait analysis of genome-wide association summary statistics using MTAG publication-title: Nat Genet doi: 10.1038/s41588-017-0009-4 – volume: 49 start-page: 1458 issue: 10 year: 2017 ident: pgen.1009089.ref039 article-title: Genome-wide association study identifies 112 new loci for body mass index in the Japanese population publication-title: Nat Genet doi: 10.1038/ng.3951 – volume: 19 start-page: 34 issue: 1 year: 2019 ident: pgen.1009089.ref057 article-title: Association between digestive symptoms and sleep disturbance: a cross-sectional community-based study publication-title: BMC Gastroenterol doi: 10.1186/s12876-019-0945-9 – start-page: 267 year: 1996 ident: pgen.1009089.ref092 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of the Royal Statistical Society Series B (Methodological) doi: 10.1111/j.2517-6161.1996.tb02080.x – volume: 11 start-page: e0167472 issue: 12 year: 2016 ident: pgen.1009089.ref081 article-title: Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults publication-title: PLoS One doi: 10.1371/journal.pone.0167472 – volume: 7 start-page: 10889 year: 2016 ident: pgen.1009089.ref022 article-title: Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UK Biobank publication-title: Nature communications doi: 10.1038/ncomms10889 – volume: 43 start-page: 1121 issue: 4 year: 2018 ident: pgen.1009089.ref041 article-title: Association Between Thyroid-Stimulating Hormone and Renal Function: a Mendelian Randomization Study publication-title: Kidney Blood Press Res doi: 10.1159/000491808 – volume: 8 start-page: 21 year: 2017 ident: pgen.1009089.ref034 article-title: 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 publication-title: Mol Autism doi: 10.1186/s13229-017-0137-9 – volume: 37 start-page: 584 issue: 6 year: 2016 ident: pgen.1009089.ref007 article-title: Circadian Rhythm and Sleep Disruption: Causes, Metabolic Consequences, and Countermeasures publication-title: Endocr Rev doi: 10.1210/er.2016-1083 – volume: 1380 start-page: 195 issue: 1 year: 2016 ident: pgen.1009089.ref074 article-title: Gastroesophageal reflux disease: recent advances and its association with sleep publication-title: Ann N Y Acad Sci doi: 10.1111/nyas.13143 – volume: 61 start-page: 268 issue: 5 year: 1973 ident: pgen.1009089.ref087 article-title: The Viterbi Algorithm publication-title: Proc IEEE – volume: 1 start-page: 40 issue: 1 year: 2015 ident: pgen.1009089.ref088 article-title: National Sleep Foundation's sleep time duration recommendations: methodology and results summary publication-title: Sleep Health doi: 10.1016/j.sleh.2014.12.010 – volume: 21 start-page: 1608 issue: 11 year: 2016 ident: pgen.1009089.ref032 article-title: Family-based association analyses of imputed genotypes reveal genome-wide significant association of Alzheimer's disease with OSBPL6, PTPRG, and PDCL3 publication-title: Mol Psychiatry doi: 10.1038/mp.2015.218 – volume: 18 start-page: 85 issue: 1 year: 2009 ident: pgen.1009089.ref013 article-title: Algorithms for sleep–wake identification using actigraphy: a comparative study and new results publication-title: Journal of Sleep Research doi: 10.1111/j.1365-2869.2008.00706.x – volume: 37 start-page: 1554 issue: 6 year: 1966 ident: pgen.1009089.ref085 article-title: Statistical inference for probabilistic functions of finite state Markov chains publication-title: Annals of Mathematical Statistics doi: 10.1214/aoms/1177699147 – volume: 16 start-page: 148 issue: 2 year: 2007 ident: pgen.1009089.ref072 article-title: Robust circadian rhythm in heart rate and its variability: influence of exogenous melatonin and photoperiod publication-title: J Sleep Res doi: 10.1111/j.1365-2869.2007.00581.x – volume: 48 start-page: 1557 issue: 12 year: 2016 ident: pgen.1009089.ref052 article-title: Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality publication-title: Nat Genet doi: 10.1038/ng.3708 – volume: 10 start-page: 1585 issue: 1 year: 2019 ident: pgen.1009089.ref017 article-title: Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour publication-title: Nat Commun doi: 10.1038/s41467-019-09576-1 – volume: 17 start-page: 201 issue: 3 year: 1994 ident: pgen.1009089.ref012 article-title: Activity-based sleep-wake identification: an empirical test of methodological issues publication-title: Sleep doi: 10.1093/sleep/17.3.201 – volume: 243 start-page: 16 year: 2019 ident: pgen.1009089.ref045 article-title: GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort publication-title: J Affect Disord doi: 10.1016/j.jad.2018.09.003 – start-page: 1 year: 2020 ident: pgen.1009089.ref077 article-title: A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy publication-title: Chronobiology International – volume: 29 issue: 3 year: 2017 ident: pgen.1009089.ref075 article-title: Sleep disturbances in irritable bowel syndrome: a systematic review publication-title: Neurogastroenterol Motil – volume: 30 start-page: 1167 issue: 4 year: 2012 ident: pgen.1009089.ref006 article-title: Circadian rhythm sleep disorders publication-title: Neurol Clin doi: 10.1016/j.ncl.2012.08.011 – volume: 79 start-page: 149 issue: 1 year: 2006 ident: pgen.1009089.ref066 article-title: ELMOD2 is a candidate gene for familial idiopathic pulmonary fibrosis publication-title: Am J Hum Genet doi: 10.1086/504639 – volume: 41 start-page: 145 issue: 2 year: 2017 ident: pgen.1009089.ref095 article-title: Resetting the bar: Statistical significance in whole-genome sequencing-based association studies of global populations publication-title: Genet Epidemiol doi: 10.1002/gepi.22032 – volume: 51 start-page: 568 issue: 3 year: 2019 ident: pgen.1009089.ref060 article-title: A statistical framework for cross-tissue transcriptome-wide association analysis publication-title: Nat Genet doi: 10.1038/s41588-019-0345-7 – volume: 50 start-page: 257 issue: 2 year: 2018 ident: pgen.1009089.ref083 article-title: Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent? publication-title: Med Sci Sports Exerc doi: 10.1249/MSS.0000000000001435 – volume: 39 start-page: 471 issue: 4 year: 2015 ident: pgen.1009089.ref036 article-title: Sleep duration and sleep disorder with red blood cell distribution width publication-title: Am J Health Behav doi: 10.5993/AJHB.39.4.3 – volume: 16 start-page: 898 issue: 11 year: 2017 ident: pgen.1009089.ref029 article-title: Identification of novel risk loci for restless legs syndrome in genome-wide association studies in individuals of European ancestry: a meta-analysis publication-title: Lancet Neurol doi: 10.1016/S1474-4422(17)30327-7 – volume: 10 start-page: e0119752 issue: 3 year: 2015 ident: pgen.1009089.ref027 article-title: Modulation of genetic associations with serum urate levels by body-mass-index in humans publication-title: PLoS One doi: 10.1371/journal.pone.0119752 – start-page: 7 year: 2018 ident: pgen.1009089.ref097 article-title: The MR-Base platform supports systematic causal inference across the human phenome publication-title: Elife – volume: 49 start-page: 274 issue: 2 year: 2017 ident: pgen.1009089.ref019 article-title: Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits publication-title: Nature genetics doi: 10.1038/ng.3749 – volume: 47 start-page: 291 issue: 3 year: 2015 ident: pgen.1009089.ref018 article-title: LD Score regression distinguishes confounding from polygenicity in genome-wide association studies publication-title: Nat Genet doi: 10.1038/ng.3211 – volume: 10 start-page: 155 issue: 3 year: 2006 ident: pgen.1009089.ref038 article-title: Does obstructive sleep apnea increase hematocrit? publication-title: Sleep Breath doi: 10.1007/s11325-006-0064-z – volume: 15 start-page: 621 issue: 2 year: 2015 ident: pgen.1009089.ref058 article-title: Assessment of subjective sleep quality in iron deficiency anaemia publication-title: Afr Health Sci doi: 10.4314/ahs.v15i2.40 – start-page: 166298 year: 2017 ident: pgen.1009089.ref079 article-title: Genome-wide genetic data on~ 500,000 UK Biobank participants publication-title: BioRxiv – volume: 6 start-page: 171 issue: 2 year: 2013 ident: pgen.1009089.ref028 article-title: 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 publication-title: Circ Cardiovasc Genet doi: 10.1161/CIRCGENETICS.112.964619 – volume: 28 start-page: 330 issue: 4 year: 2011 ident: pgen.1009089.ref005 article-title: Assessment of circadian rhythms of both skin temperature and motor activity in infants during the first 6 months of life publication-title: Chronobiol Int doi: 10.3109/07420528.2011.565895 – volume: 10 start-page: 1100 issue: 1 year: 2019 ident: pgen.1009089.ref016 article-title: Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates publication-title: Nature Communications doi: 10.1038/s41467-019-08917-4 – volume: 35 start-page: 727 issue: 6 year: 2012 ident: pgen.1009089.ref003 article-title: Boards of Directors of the American Academy of Sleep M, the Sleep Research S. Sleep: a health imperative publication-title: Sleep – volume: 50 start-page: 381 issue: 3 year: 2018 ident: pgen.1009089.ref050 article-title: Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection publication-title: Nat Genet doi: 10.1038/s41588-018-0059-2 – volume: 15 start-page: 461 issue: 5 year: 1992 ident: pgen.1009089.ref011 article-title: Automatic sleep/wake identification from wrist activity publication-title: Sleep doi: 10.1093/sleep/15.5.461 – volume: 9 start-page: e97263 issue: 5 year: 2014 ident: pgen.1009089.ref061 article-title: Alternation in the glycolipid transfer protein expression causes changes in the cellular lipidome publication-title: PLoS One doi: 10.1371/journal.pone.0097263 – volume: 149 start-page: 1393 issue: 6 year: 2012 ident: pgen.1009089.ref064 article-title: Insights into RNA biology from an atlas of mammalian mRNA-binding proteins publication-title: Cell doi: 10.1016/j.cell.2012.04.031 – volume: 511 start-page: 421 issue: 7510 year: 2014 ident: pgen.1009089.ref049 article-title: Biological insights from 108 schizophrenia-associated genetic loci publication-title: Nature doi: 10.1038/nature13595 – volume: 4 start-page: 7 year: 2015 ident: pgen.1009089.ref094 article-title: Second-generation PLINK: rising to the challenge of larger and richer datasets publication-title: Gigascience doi: 10.1186/s13742-015-0047-8 – year: 2019 ident: pgen.1009089.ref043 article-title: Penalized Selection of Periodicities Characterizes the Consolidation of Sleep-Wake Circadian Rhythms During Early Childhood Development publication-title: Submitted – volume: 41 start-page: 164 issue: 1 year: 1970 ident: pgen.1009089.ref086 article-title: A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains publication-title: Annals of Mathematical Statistics doi: 10.1214/aoms/1177697196 – volume: 24 start-page: 2476 issue: 9 year: 2014 ident: pgen.1009089.ref063 article-title: Altered expression of diabetes-related genes in Alzheimer's disease brains: the Hisayama study publication-title: Cereb Cortex doi: 10.1093/cercor/bht101 – volume: 168 start-page: 649 issue: 8 year: 2015 ident: pgen.1009089.ref033 article-title: Genome-wide association study of schizophrenia in Ashkenazi Jews publication-title: Am J Med Genet B Neuropsychiatr Genet doi: 10.1002/ajmg.b.32349 – volume: 8 start-page: 7961 issue: 1 year: 2018 ident: pgen.1009089.ref015 article-title: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants publication-title: Sci Rep doi: 10.1038/s41598-018-26174-1 – year: 2019 ident: pgen.1009089.ref093 publication-title: PML: Penalized Multi-Band Learning for Circadian Rhythm Analysis Using Actigraphy – volume: 3 start-page: e03351 year: 2014 ident: pgen.1009089.ref065 article-title: A Ras-like domain in the light intermediate chain bridges the dynein motor to a cargo-binding region publication-title: Elife doi: 10.7554/eLife.03351 – year: 2018 ident: pgen.1009089.ref062 publication-title: Gene Ontology Data Archive – volume: 30 start-page: 76 issue: 2 year: 2015 ident: pgen.1009089.ref071 article-title: Thyroid circadian timing: roles in physiology and thyroid malignancies publication-title: J Biol Rhythms doi: 10.1177/0748730414557634 – volume: 15 start-page: 418 issue: 12 year: 2013 ident: pgen.1009089.ref002 article-title: Insomnia and its impact on physical and mental health publication-title: Curr Psychiatry Rep doi: 10.1007/s11920-013-0418-8 – volume: 47 start-page: 1228 issue: 11 year: 2015 ident: pgen.1009089.ref053 article-title: Partitioning heritability by functional annotation using genome-wide association summary statistics publication-title: Nat Genet doi: 10.1038/ng.3404 – volume: 10 start-page: e0142533 issue: 11 year: 2015 ident: pgen.1009089.ref010 article-title: A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer publication-title: PLoS One doi: 10.1371/journal.pone.0142533 – start-page: 214973 year: 2018 ident: pgen.1009089.ref021 article-title: Genome-wide Analysis of Insomnia (N = 1,331,010) Identifies Novel Loci and Functional Pathways publication-title: bioRxiv – volume: 210 start-page: 499 issue: 2 year: 2018 ident: pgen.1009089.ref025 article-title: A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci publication-title: Genetics doi: 10.1534/genetics.118.301479 – volume: 22 start-page: 75 year: 2016 ident: pgen.1009089.ref059 article-title: Brain iron deficiency in idiopathic restless legs syndrome measured by quantitative magnetic susceptibility at 7 tesla publication-title: Sleep Med doi: 10.1016/j.sleep.2016.05.001 – volume: 162B start-page: 439 issue: 5 year: 2013 ident: pgen.1009089.ref044 article-title: A genome-wide association study of sleep habits and insomnia publication-title: Am J Med Genet B Neuropsychiatr Genet doi: 10.1002/ajmg.b.32168 – volume: 9 start-page: 5257 issue: 1 year: 2018 ident: pgen.1009089.ref014 article-title: GWAS identifies 14 loci for device-measured physical activity and sleep duration publication-title: Nature Communications doi: 10.1038/s41467-018-07743-4 – volume: 7 start-page: e1001308 issue: 2 year: 2011 ident: pgen.1009089.ref024 article-title: Genome-wide association of familial late-onset Alzheimer's disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE publication-title: PLoS Genet doi: 10.1371/journal.pgen.1001308 – volume: 7 start-page: e1002171 issue: 7 year: 2011 ident: pgen.1009089.ref020 article-title: Genome-wide association study identifies novel restless legs syndrome susceptibility loci on 2p14 and 16q12. 1 publication-title: PLoS genetics doi: 10.1371/journal.pgen.1002171 – volume: 11 start-page: e1005378 issue: 10 year: 2015 ident: pgen.1009089.ref026 article-title: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study publication-title: PLoS Genet doi: 10.1371/journal.pgen.1005378 – volume: 69 start-page: 976 issue: 5 year: 2017 ident: pgen.1009089.ref048 article-title: A Multinational Arab Genome-Wide Association Study Identifies New Genetic Associations for Rheumatoid Arthritis publication-title: Arthritis Rheumatol doi: 10.1002/art.40051 – volume: 59 start-page: 1214 issue: 6 year: 2016 ident: pgen.1009089.ref030 article-title: Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively publication-title: Diabetologia doi: 10.1007/s00125-016-3908-5 – volume: 218 start-page: 54 year: 2019 ident: pgen.1009089.ref056 article-title: Control of the cardiovascular and respiratory systems during sleep publication-title: Auton Neurosci doi: 10.1016/j.autneu.2019.01.007 – volume: 28 start-page: 46 issue: 1 year: 2009 ident: pgen.1009089.ref037 article-title: Effects of sleep and sleep deprivation on blood cell count and hemostasis parameters in healthy humans publication-title: J Thromb Thrombolysis doi: 10.1007/s11239-008-0240-z – volume: 101 start-page: 939 issue: 6 year: 2017 ident: pgen.1009089.ref096 article-title: A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics publication-title: Am J Hum Genet doi: 10.1016/j.ajhg.2017.11.001 – volume: 518 start-page: 197 issue: 7538 year: 2015 ident: pgen.1009089.ref031 article-title: Genetic studies of body mass index yield new insights for obesity biology publication-title: Nature doi: 10.1038/nature14177 – volume: 37 start-page: D32 year: 2009 ident: pgen.1009089.ref067 article-title: NCBI Reference Sequences: current status, policy and new initiatives publication-title: Nucleic Acids Res doi: 10.1093/nar/gkn721 – volume: 10 start-page: 767 issue: 7 year: 2013 ident: pgen.1009089.ref004 article-title: Sleep disturbance is associated with incident dementia and mortality publication-title: Curr Alzheimer Res doi: 10.2174/15672050113109990134 – volume: 26 start-page: 139 issue: 2 year: 2014 ident: pgen.1009089.ref008 article-title: Circadian misalignment and health publication-title: Int Rev Psychiatry doi: 10.3109/09540261.2014.911149 – volume: 23 start-page: 5505 issue: 20 year: 2014 ident: pgen.1009089.ref042 article-title: Genome-wide association study identifies a novel susceptibility gene for serum TSH levels in Chinese populations publication-title: Hum Mol Genet doi: 10.1093/hmg/ddu250 – start-page: 303941 year: 2018 ident: pgen.1009089.ref089 article-title: Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease publication-title: BioRxiv |
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| SubjectTerms | Accelerometers Actigraphy - methods Biobanks Biology and Life Sciences Body mass index Central nervous system Chromosomes Circadian rhythm Circadian Rhythm - genetics Circadian Rhythm - physiology Circadian rhythms Diaries Electronic data processing Exercise Female Gene loci Genetic aspects Genetic factors Genetic Predisposition to Disease Genome-wide association studies Genome-Wide Association Study Genomes Humans Identification and classification Insomnia Learning algorithms Machine learning Male Markov Chains Medicine and Health Sciences Mental disorders Metabolism Middle Aged Neurological diseases Phenotypes Physical activity Physical sciences Population Population genetics Population studies Quantitative trait loci Sleep Sleep - genetics Sleep - physiology Sleep and wakefulness Sleep disorders Sleep Wake Disorders - genetics Sleep Wake Disorders - pathology Wearable computers Wearable Electronic Devices |
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| Title | Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms |
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