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
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