Twin Modeling of Individual Symptoms of Depression and Generalized Anxiety: A Symptom Network Approach

抑郁症和广泛性焦虑个体症状的孪生模型：症状网络方法

• 批准号: 10065869
• 负责人: Carter Funkhouser
• 金额: $ 4.04万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-16 至 2022-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10065869
• 关键词: Adult; Anxiety; Anxiety Disorders; Area; Behavior; Complex; Computer Analysis; Computing Methodologies; Data; Depressed mood; Depressive disorder; Development; Disease; Distress; Educational workshop; Environment; Environmental Risk Factor; Etiology; Fatigue; Fellowship; Generalized Anxiety Disorder; Genetic; Genetic Risk; Genetic study; Goals; Impairment; Individual; Intervention; Knowledge; Laboratory Study; Literature; Maintenance; Major Depressive Disorder; Measures; Mediating; Mental Depression; Mentorship; Methods; Modeling; National Institute of Mental Health; Neurosciences; Pathway interactions; Phenotype; Prevention; Proxy; Psychiatric Diagnosis; Psychopathology; Psychotic Disorders; Recurrence; Research; Research Personnel; Risk; Risk Factors; Sampling; Science; Sex Differences; Sleeplessness; Source; Substance Use Disorder; Symptoms; Testing; Training; Twin Multiple Birth; Twin Studies; United States National Institutes of Health; Universities; Virginia; Visit; Woman; career; causal model; comorbidity; cost; depressive symptoms; dual diagnosis; generalized anxiety; genetic risk factor; genetic variant; innovation; large datasets; meetings; men; network models; novel; pediatric trauma; polygenic risk score; psychogenetics; risk sharing; skills; stressor; theories

Project Summary/Abstract The goals of this fellowship are to further develop the applicant's knowledge and skills in advanced computational methods (i.e., twin modeling and network modeling), the comorbidity of Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD), and basic neuroscience and genetics. In line with these goals, a cornerstone of the applicant's training will be the statistical and theoretical training he receives through workshops, coursework, annual visits to Virginia Commonwealth University (VCU) to meet with Drs. Gillespie and Neale, regular sponsor and co-sponsor meetings, and professional development activities. The project will serve as the applicant's dissertation and help him pursue his goal of becoming an independent investigator who uses advanced computational analyses of large datasets and multi-method laboratory studies to study the etiology, maintenance, and recurrence of phenotypes of depression and anxiety. In addition to the skills to be gained by the applicant, the project's goals will greatly advance the understanding of MDD and GAD symptom etiology by testing the central tenet of a novel theory of psychopathology (network theory). Understanding the causes of symptoms and their covariance is critical, as different causal models have markedly different implications for intervention. Additionally, the study is consistent with the NIMH's strategic objective to define mechanisms of complex behaviors and NIH's increased emphasis on replicability. Several twin studies have estimated the genetic and environmental etiology of individual MDD symptoms. However, no studies have considered the possibility of causal relationships between symptoms as hypothesized by the network theory. The present study will therefore use a novel method - direction of causation (DoC) modeling - of twin data to (1) test putative causal relationships between individual MDD and GAD symptoms and estimate the contributions of genetics and environment to each symptom, (2) evaluate the replicability of the best fitting model in aim 1 in an independent twin sample, and (3) explore sex differences in the phenotypic causal pathways and genetic and environmental liabilities of each symptom. This project greatly extends prior studies of individual MDD symptoms by testing putatively causal pathways hypothesized by the network theory and including both MDD and GAD symptoms in the same model, which is important given their high comorbidity and potential causal relationships between symptoms of the two disorders. Mentorship for this project will be provided by experts in the areas of twin and DoC modeling, network theory and modeling, the comorbidity of depressive and anxiety disorders, and neuroscience (sponsors: Shankman, Gillespie, and Fried; OSCs: Neale, Roitman). This fellowship will not only be an important step in the applicant's research career, but the proposed study's primary objective of testing different causal models of symptom co-occurrence and quantifying the genetic and environmental contributions to each symptom will have important implications for the prevention and treatment of MDD and GAD symptoms and for theories of symptom etiology.

项目概要/摘要 该奖学金的目标是进一步发展申请人的高级知识和技能 计算方法（即双胞胎建模和网络建模），重度抑郁症的共病 疾病（MDD）和广泛性焦虑症（GAD），以及基础神经科学和遗传学。符合 为了实现这些目标，申请人接受的统计和理论培训将是其培训的基石 通过研讨会、课程作业、每年访问弗吉尼亚联邦大学 (VCU) 与 Drs 会面。 Gillespie 和 Neale、定期发起人和联合发起人会议以及专业发展活动。这 该项目将作为申请人的论文，帮助他实现成为独立的目标 使用大型数据集和多方法实验室研究的高级计算分析的研究者 研究抑郁和焦虑表型的病因、维持和复发。除了 申请人将获得的技能，该项目的目标将极大地促进对 MDD 的理解和 通过测试精神病理学新理论（网络理论）的中心原则来研究广泛性焦虑症（GAD）症状的病因。 了解症状的原因及其协方差至关重要，因为不同的因果模型具有 对干预的影响明显不同。此外，该研究与 NIMH 的战略一致 NIH 的目标是定义复杂行为的机制，并且越来越重视可复制性。 几项双胞胎研究评估了个体 MDD 的遗传和环境病因 症状。然而，没有研究考虑过症状之间因果关系的可能性： 网络理论的假设。因此，本研究将使用一种新颖的方法——方向 因果关系 (DoC) 建模 - 双胞胎数据 (1) 测试个体 MDD 和 GAD 症状并估计遗传和环境对每种症状的贡献，(2) 评估 目标 1 中最佳拟合模型在独立双胞胎样本中的可复制性，以及 (3) 探索性别差异 每种症状的表型因果途径以及遗传和环境因素。这个工程大大 通过测试假设的因果路径，扩展了先前对个体 MDD 症状的研究 网络理论，将 MDD 和 GAD 症状纳入同一模型中，这一点很重要，因为它们 两种疾病的症状之间的高合并症和潜在因果关系。为此提供辅导 项目将由双胞胎和 DoC 建模、网络理论和建模、 抑郁症和焦虑症的共病以及神经科学（发起人：Shankman、Gillespie 和 Fried； OSC：Neale、Roitman）。该奖学金不仅将是申请人研究生涯中的重要一步， 但拟议研究的主要目标是测试症状共现的不同因果模型和 量化遗传和环境对每种症状的影响将对 MDD 和 GAD 症状的预防和治疗以及症状病因学理论。