Mobile Technology to Identify Behavioral Mechanisms Linking Genetic Variation and Depression

项目摘要/摘要 大规模的全基因组关联研究已经确定遗传变异与 严重的抑郁症。为了将这一进步转化为改进的诊断、监测和治疗，一个关键的 下一步是阐明将相关基因变异与抑郁症联系起来的行为机制。 不幸的是，已经确定相关变异的大规模研究通常采用单一的 时间点和有限的表型评估不适合研究基因和 抑郁症，一种慢性多模式疾病。我们的长期目标是阐明 架构，以促进改进治疗方法的发展。我们的目标是 父母R01的应用是为了了解基因变异如何与抑郁症的发展相关 发挥他们的作用。医学实习是专业医生培训的第一年，呈现出一种独特的 在这种情况下，我们可以前瞻性地预测一种统一的慢性应激源的开始，并遵循 出现抑郁症状。我们发现，抑郁症的发病率从4%急剧上升 实习前为26%，实习期间为26%。我们的实习生队列是密切监测 抑郁症的发展，将最新的移动医疗技术作为跟踪这些人的工具- 时间，客观的衡量标准。对于目前的授权期，我们提出了以下三个具体目标：1) 确定源自移动数据元素的数据驱动功能，这些功能可预测情绪变化的短期风险 以及抑郁发作。2)找出与压力下的抑郁有关的基因变异。3)识别 抑郁症相关基因变异与客观抑郁症移动性的关系 功能。到目前为止，赠款期间一直很有成效，主要是从研究中收集的数据 发表在《新英格兰医学杂志》、《英国医学杂志》、《内科医学年鉴》上论文的数据来源， 《自然人类行为》、《美国精神病学杂志》、《生物精神病学》、《NPJ数字医学》等 其他日记。不幸的是，新冠肺炎疫情以多种方式打乱了这项研究，并推迟了 完成各项目标。随着大学关闭和儿童保育的减少，我们的可用性和带宽 临床招聘和分析人员大幅减少。同样，随着新冠肺炎的负担 在培训医生的招聘人数方面，我们的招聘速度也受到了影响。此外， 作为当前R01通过的一部分提出的基因分型因新冠肺炎而推迟了9个月 密歇根测序中心的相关延误。本补编旨在为以下人员提供资金 避免艰辛，让学习目标得以完成。

Project Summary/Abstract Large scale genome-wide association studies have identified genetic variation definitively associated with major depression. To translate this advancement into improved diagnosis, monitoring, and treatment, a critical next step is to elucidate the behavioral mechanisms linking the implicated genetic variation with depression. Unfortunately, the large-scale studies that have identified associated variants have typically employed single time point and limited phenotypic assessments that are not suited to study mechanisms linking genes and depression, a chronic multi-modal disease. Our long-term goal is to elucidate the pathophysiological architecture underlying depression to facilitate the development of improved treatments. Our objective in the parent R01 application is to understand how genetic variants associated with the development of depression exert their effect. Medical internship, the first year of professional physician training, presents a unique situation in which we can prospectively predict the onset of a uniform, chronic stressor and follow the development of depressive symptoms. We have found that rates of depression increase dramatically, from 4% prior to internship to 26% during internship year. Our intern cohort is an ideal population to closely monitor the development of depression with recent mobile health technology as a tool to follow these individuals in real- time, with objective measures. For the current grant period, we proposed the following three specific aims: 1) Identify data driven features, derived from mobile data elements, that predict short-term risk for mood changes and depressive episodes. 2) Identify genetic variants associated with depression under stress. 3) Identify relationship between depression-associated genetic variation and objective depression-associated mobile features. The grant period has been highly productive to date, with data collected from the study as primary data source for papers published in New England Journal of Medicine, BMJ, Annals of Internal Medicine, Nature Human Behavior, American Journal of Psychiatry, Biological Psychiatry, NPJ Digital Medicine among other journals. Unfortunately, the COVID-19 pandemic disrupted this study in multiple ways and has delayed completion of the Aims. With university shutdowns and loss of childcare, the availability and bandwidth for our clinical recruitment and analysis staff were substantially reduced. Similarly, with the burden COVID-19 placed on our recruitment population of training physicians, our recruitment rate was also compromised. Further, the genotyping proposed as part of the current R01 through was delayed by 9 months because of COVID-19 related delays at the Michigan Sequencing Core. This supplement seeks to provide funding for personnel to avoid hardship and allow completion of the study aims.