Liu Jason J, Borsari Beatrice, Li Yunyang, Liu Susanna X, Gao Yuan, Xin Xin, Lou Shaoke, Jensen Matthew, Garrido-Martín Diego, Verplaetse Terril L, Ash Garrett, Zhang Jing, Girgenti Matthew J, Roberts Walter, Gerstein Mark
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.
Department of Computer Science, Yale University, New Haven, CT 06511, USA.
Cell. 2025 Jan 23;188(2):515-529.e15. doi: 10.1016/j.cell.2024.11.012. Epub 2024 Dec 19.
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.
精神疾病受遗传和环境因素影响。然而，对它们的研究因精确描述人类行为存在局限性而受阻。可穿戴传感器等新技术有望克服这些局限性，因为它们以定量且无偏差的方式测量异质性行为。在此，我们分析了来自青少年大脑认知发展（ABCD）研究的可穿戴和基因数据。利用超过250个源自可穿戴设备的特征作为数字表型，我们表明一个可解释的人工智能框架能够比以往更准确地客观分类患有精神疾病的青少年。为了将数字表型与潜在遗传学联系起来，我们展示了它们如何用于单变量和多变量全基因组关联研究（GWAS）。通过这样做，我们识别出16个显著的基因位点和37个与精神疾病相关的基因，包括ELFN1和ADORA3，这表明持续的、源自可穿戴设备的特征比传统的病例对照GWAS具有更强的检测能力。总体而言，我们展示了可穿戴技术如何有助于揭示行为与遗传学之间的新联系。
