Broad Scale Genomic Analysis to Find Genes Associated with Depression Under Stres

大规模基因组分析寻找与压力下抑郁症相关的基因

• 批准号: 9317292
• 负责人: SRIJAN SEN
• 金额: $ 28.84万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9317292
• 关键词: Accounting; Affect; American; Antidepressive Agents; Architecture; Awareness; Biological; Candidate Disease Gene; Chronic; Chronic stress; Code; Complex; Data; Data Set; Depressed mood; Development; Disease; Effectiveness; Environmental Risk Factor; Epidemiology; Etiology; Frequencies; Genes; Genetic; Genetic Polymorphism; Genome; Genomic approach; Genomics; Goals; Health and Retirement Study; Heart Diseases; Individual; Inherited; Internships; Investigation; Life Stress; Longitudinal cohort; Major Depressive Disorder; Medical; Mental Depression; Mental disorders; Methodology; Methods; Modeling; Pathway interactions; Patients; Pattern; Phenotype; Physicians; Public Health; Research Design; Retrospective Studies; Risk; Sampling; Stress; Study Subject; Testing; Time; Training; Untranslated RNA; Variant; Work; World Health Organization; base; case control; cohort; depressive symptoms; design; disability; disorder risk; epidemiology study; exome; experience; falls; genetic variant; genome wide association study; genome-wide; improved; innovation; novel; predictive of treatment response; prospective; public health relevance; stressor

DESCRIPTION (provided by applicant): Despite hundreds of linkage and association studies, including eight recent case-control genome-wide association studies (GWAS), there has been limited progress in identifying specific genes associated with depression. Epidemiological evidence indicates that life stress is a key factor in the etiology of depression. Indeed, there is growing recognition that accounting for stress facilitates the identification of genes important in the development of depression. Because of methodological limitations however, gene x stress studies have been limited from utilizing the broad-scale genomic approaches that have been successful in identifying genes in other complex disorders. Our long-term goal is to elucidate the pathophysiological architecture underlying depression to facilitate the development of improved treatments. Our objective in this application is to identify genetic variants associated with the development of depression under stress by utilizing medical internship as a model. The power and effectiveness of traditional gene x stress interaction studies have been compromised by the following study design limitations: 1) substantial variation in the type and intensity of stress between subjects 2) retrospective design and 3) loss of power due to tests of statistical interaction. Designing methods to overcome these limitations has been difficult because the onset of chronic stress is difficult to predict beforehand and the type of stress encountered by individuals varies greatly. 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 a dramatic increase in depressive symptoms. We hypothesize that both common and rare SNPs from across the genome will interact with internship stress to impact depressive symptom phenotypes. To test this hypothesis, we propose the following three specific aims: 1) identify longitudinal patterns of depressive symptoms under internship stress and factors associated with the depressive symptom patterns, 2) identify common and rare, functional genetic variants associated with depressive symptoms and depressive symptom trajectories during internship stress using cutting edge GWAS and Exome chip analysis and 3) assess whether significant associations with depressive symptoms in the intern sample replicate in other depression samples. Our approach is innovative because it takes advantage of a naturally occurring stress to overcome limitations of existing studies and allows us to perform a broad-scale, longitudinal cohort gene x stress study. This project is significant because it has the potential to identify key genetic factors involved in depression under stress, an advance that holds promises in predicting treatment response and identifying novel targets for antidepressant development.

描述(由申请人提供)：尽管有数百项连锁和关联研究，包括最近的8项病例对照全基因组关联研究(GWAS)，但在识别与抑郁症相关的特定基因方面进展有限。流行病学证据表明，生活压力是抑郁症病因中的一个关键因素。事实上，有 越来越多的人认识到，解释应激有助于识别在 抑郁症的发展。然而，由于方法学的限制，基因x应激研究在利用广泛的基因组方法方面受到了限制，这些方法已经成功地在其他复杂疾病中识别了基因。我们的长期目标是阐明抑郁症潜在的病理生理结构，以促进改进治疗的发展。我们在这项应用中的目标是通过利用医学实习作为模式来识别与应激下抑郁症的发展相关的基因变异。传统的基因x压力交互作用研究的威力和有效性受到以下研究设计限制的影响：1)受试者之间应激类型和强度的显著差异；2)回溯性设计；3)统计交互作用测试造成的威力损失。设计克服这些限制的方法一直很困难，因为慢性应激的开始很难事先预测，而且个人遇到的应激类型差异很大。医学实习是专业医生培训的第一年，它呈现了一种独特的情况，在这种情况下，我们可以前瞻性地预测统一的慢性应激源的出现和抑郁症状的急剧增加。我们假设，来自整个基因组的常见和罕见的SNP都会与实习压力相互作用，影响抑郁症状的表型。为了验证这一假设，我们提出了以下三个具体目标：1)确定实习压力下抑郁症状的纵向模式和与抑郁症状模式相关的因素；2)使用尖端的GWAS和Exome芯片分析，确定与抑郁症状和实习压力期间抑郁症状轨迹相关的常见和罕见的功能性遗传变量；3)评估与实习样本中的抑郁症状的显著关联是否在其他抑郁样本中重复。我们的方法是创新的，因为它利用自然产生的压力来克服现有研究的局限性，并允许我们进行广泛的纵向队列基因x压力研究。该项目意义重大，因为它有可能确定与压力下的抑郁症有关的关键遗传因素，这一进展有望在预测治疗反应和确定抗抑郁药物开发的新靶点方面取得进展。