Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Journal: Cell
Published Date: Dec 19, 2024

Abstract

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitations in that they measure heterogeneous behavior in a quantitative and unbiased fashion. Here, we analyze wearable and genetic data from the Adolescent Brain Cognitive Development (ABCD) study. Leveraging >250 wearable-derived features as digital phenotypes, we show that an interpretable AI framework can objectively classify adolescents with psychiatric disorders more accurately than previously possible. To relate digital phenotypes to the underlying genetics, we show how they can be employed in univariate and multivariate genome-wide association studies (GWASs). Doing so, we identify 16 significant genetic loci and 37 psychiatric-associated genes, including ELFN1 and ADORA3, demonstrating that continuous, wearable-derived features give greater detection power than traditional case-control GWASs. Overall, we show how wearable technology can help uncover new linkages between behavior and genetics.

Authors

  • Jason J Liu
    Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
  • Beatrice Borsari
    Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
  • Yunyang Li
    Department of Computer Science, Yale University, New Haven, CT 06520, United States.
  • Susanna X Liu
    Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Xin Xin
  • Shaoke Lou
    Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
  • Matthew Jensen
    Bioinformatics and Genomics Graduate Program of the Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
  • Diego Garrido-Martín
    Department of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Barcelona 08028, Spain.
  • Terril L Verplaetse
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.
  • Garrett Ash
    Section of General Internal Medicine, Yale University School of Medicine, New Haven, CT 06511, USA; Center for Pain, Research, Informatics, Medical Comorbidities and Education Center (PRIME), VA Connecticut Healthcare System, West Haven, CT 06516, USA; Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT 06511, USA.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Matthew J Girgenti
    Department of Psychiatry, School of Medicine, Yale University, New Haven, CT 06520, USA.
  • Walter Roberts
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA; Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT 06511, USA. Electronic address: walter.roberts@yale.edu.
  • Mark Gerstein
    Program of Computational Biology and Bioinformatics and Department of Molecular Biophysics and Biochemistry and Department of Computer Science, Yale University, New Haven, CT 06511, USA.

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