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.

中文翻译：

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使用 AI 对可穿戴设备进行数字表型分析，表征精神疾病并识别遗传关联


精神疾病受遗传和环境因素的影响。然而，他们的研究受到精确描述人类行为的限制的阻碍。可穿戴传感器等新技术有望克服这些限制，因为它们以定量和无偏见的方式测量异构行为。在这里，我们分析了来自青少年大脑认知发展 （ABCD） 研究的可穿戴和遗传数据。利用 >250 可穿戴设备衍生的特征作为数字表型，我们表明可解释的 AI 框架可以比以前更准确地客观地对患有精神疾病的青少年进行分类。为了将数字表型与潜在的遗传学联系起来，我们展示了如何将它们用于单变量和多变量全基因组关联研究 （GWAS）。这样做，我们确定了 16 个重要的遗传位点和 37 个精神病学相关基因，包括 ELFN1 和 ADORA3，证明连续的可穿戴衍生特征比传统的病例对照 GWAS 具有更大的检测能力。总的来说，我们展示了可穿戴技术如何帮助揭示行为和遗传学之间的新联系。