美国耶鲁大学Mark Gerstein等研究人员合作，通过可穿戴设备的数字表型与人工智能结合表征精神障碍，并识别遗传关联。该项研究成果于2024年12月19日在线发表在《细胞》杂志上。
研究人员分析了来自青少年大脑认知发展（ABCD）研究的可穿戴设备和遗传数据。利用超过250个可穿戴设备衍生的特征作为数字表型，研究人员展示了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障碍的青少年。
为了将数字表型与潜在的遗传学关联起来，研究人员展示了如何将它们用于单变量和多变量的全基因组关联研究（GWAS）。
通过这种方法，研究人员识别了16个显著的遗传位点和37个与精神障碍相关的基因，包括ELFN1和ADORA3，并证明了连续的、可穿戴设备衍生的特征比传统的病例对照GWAS更具检测力。总体而言，研究人员展示了可穿戴技术如何帮助揭示行为与遗传学之间的新联系。
据了解，精神障碍受到遗传和环境因素的影响。然而，研究这些障碍受到精确表征人类行为的限制。新技术，如可穿戴传感器，显示出克服这些限制的潜力，因为它们以定量和无偏的方式测量异质性行为。
附：英文原文
Title: Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations
Author: Jason J. Liu, Beatrice Borsari, Yunyang Li, Susanna X. Liu, Yuan Gao, Xin Xin, Shaoke Lou, Matthew Jensen, Diego Garrido-Martín, Terril L. Verplaetse, Garrett Ash, Jing Zhang, Matthew J. Girgenti, Walter Roberts, Mark Gerstein
Issue&Volume: 2024-12-19
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.
DOI: 10.1016/j.cell.2024.11.012
Source: https://www.cell.com/cell/abstract/S0092-8674(24)01329-1