Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour

Se, Jones; VTv, Hees; Dr, Mazzotti; Marques‐Vidal, Pedro; Sabia, Séverine; Av, der Spek; Dashti, Hassan S.; Engmann, Jorgen; Kocevska, Desana; Tyrrell, Jess; Rn, Beaumont; Hillsdon, Melvyn; Ks, Ruth; Ma, Tuke; Yaghootkar, Hanieh; Sharp, Seth A.; Yao, Jie; Jw, Harrison; Freathy, Rachel M.; Luik, Annemarie I.; Amin, Najaf; Jm, Lane; Saxena, Richa; Rutter, Martin K.; Tiemeier, Henning; Kutalik, Zoltán; Tm, Frayling; Weedon, Michael N.; Gehrman, Phil; Wood, Andrew R.

doi:10.1101/303925

Cited by 16 publications

(21 citation statements)

References 66 publications

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“…The analysis presented in this paper will facilitate feasible large-scale population research on sleep and physical activity. In addition to the proof of validity as provided in this paper additional support for the credibility of the algorithm was found in our separate study (non-peer reviewed preprint on bioRxiv) identifying genome wide associations with sleep parameters derived from our algorithm in UK Biobank, replicating signals previously associated with self-reported sleep duration and chronotype [27][28][29][30][31][32][33][34] . Our algorithm can be applied to data from the three most widely used accelerometer brands: Actigraph, Axivity, and GENEActiv, and is available as part of open source R package GGIR (https://cran.rproject.org/web/packages/GGIR/).…”

Section: Discussionsupporting

confidence: 75%

Estimating sleep parameters using an accelerometer without sleep diary

Hees

Sabia²,

Jones

et al. 2018

Preprint

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Wrist worn raw-data accelerometers are used increasingly in large scale population research.We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPTwindow), was compared against sleep diary in 3752 participants (range=60-82years) and polysomnography in sleep clinic patients (N=28) and in healthy good sleepers (N=22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.

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Section: Discussionsupporting

confidence: 75%

Estimating sleep parameters using an accelerometer without sleep diary

Hees

Sabia²,

Jones

et al. 2018

Preprint

Self Cite

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“…Correlations between accelerometer measures have been published previously. [31] Correlations were generally weak, ranging from r=-0.001 (between accelerometer-measured L5 timing and accelerometer-measured sleep duration) to r=-0.32 (between self-report frequent insomnia and self-report short sleep duration). It is also worth noting that correlations were weak between self-reported and accelerometer-measured sleep duration ( r =0.15).…”

Section: Resultsmentioning

confidence: 99%

“…It is also worth noting that correlations were weak between self-reported and accelerometer-measured sleep duration ( r =0.15). This may reflect that accelerometer data in the UK biobank were collected between 2 and 9 years (mean 5 years)[31] after baseline, when self-reported sleep measures were assessed. It may also reflect self-reports of global sleep duration (vs daily self-reported) can be influenced by distress/affect[47].…”

Section: Resultsmentioning

confidence: 99%

“…Genome-wide association studies (GWAS) were previously performed for seven self-reported measures of habitual sleep patterns including chronotype[28], sleep duration[29], long sleep duration[29], short sleep duration[29], frequent insomnia[30], excessive daytime sleepiness and daytime napping. GWAS have also been performed for three accelerometer-measured measures of sleep including timing of the least active 5 hours of the day (L5 timing), nocturnal sleep duration and sleep fragmentation[31]. Note that the assessment of accelerometer-derived sleep for up to 7 days per individual in UK Biobank was performed, on average, five years after the self-report sleep data were collected.…”

Section: Methodsmentioning

confidence: 99%

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Is disrupted sleep a risk factor for Alzheimer’s disease? Evidence from a two-sample Mendelian randomization analysis

Anderson

Richmond

Jones

et al. 2019

Preprint

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INTRODUCTIONIt is established that Alzheimer’s disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD.METHODSMendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness) on AD risk.RESULTSOverall, there was little evidence that sleep traits affect the risk of AD. There was some evidence to suggest that self-reported daytime napping was associated with lower AD risk (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.50 to 0.99). Some other sleep traits (accelerometer-measured eveningness and sleep duration, and self-reported daytime sleepiness) had ORs for AD risk of a similar magnitude to daytime napping, but were less precisely estimated.DISCUSSONOur findings provide tentative evidence that daytime napping may reduce AD risk. However, findings should be replicated using independent samples.

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“…The accelerometer dataset that we acquired in September of 2017 consists of data in the activity count format summarized every fivesecond epoch from 103,706 participants. We applied similar quality control procedures as other accelerometer studies 78 . We excluded individuals with flagged data problems, poor wear time, poor calibration, recorded interrupted periods, or inability to calibrate activity data on the device worn itself requiring the use of other data.…”

Section: Datamentioning

confidence: 99%

Automated Feature Extraction from Population Wearable Device Data Identified Novel Loci Associated with Sleep and Circadian Rhythms

Li¹,

Zhao

2020

Preprint

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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 -8including 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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Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour

Cited by 16 publications

References 66 publications

Estimating sleep parameters using an accelerometer without sleep diary

Estimating sleep parameters using an accelerometer without sleep diary

Is disrupted sleep a risk factor for Alzheimer’s disease? Evidence from a two-sample Mendelian randomization analysis

Automated Feature Extraction from Population Wearable Device Data Identified Novel Loci Associated with Sleep and Circadian Rhythms

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