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

基本信息

批准号：
8573528
负责人：
SRIJAN SEN
金额：
$ 46.74万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
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/8573528
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 Epidemiologic Studies Epidemiology Etiology Frequencies Functional RNA Genes Genetic Genetic Polymorphism Genome Genomics Goals Health Heart Diseases Individual Inherited Internships Investigation Life Stress Major Depressive Disorder Medical Mental Depression Mental disorders Methods Modeling Pathway interactions Patients Pattern Phenotype Physicians Public Health Research Design Retirement Retrospective Studies Risk Sampling Stress Testing Time Training Variant Work World Health Organization base case control cohort depressive symptoms design disability disorder risk exome experience falls genetic variant genome wide association study genome-wide improved innovation novel prospective public health relevance stressor treatment response

项目摘要

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，包括最近的八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的关键因素。确实，有人们越来越认识到，考虑压力有助于识别重要的基因抑郁症的发展。然而，由于方法学上的限制，基因 x 应激研究在利用大规模基因组方法方面受到限制，而这些方法已经成功地鉴定了其他复杂疾病中的基因。我们的长期目标是阐明抑郁症的病理生理学结构，以促进改进治疗方法的开发。我们在此应用中的目标是通过利用医学实习作为模型来识别与压力下抑郁症发展相关的遗传变异。传统基因 x 压力相互作用研究的功效和有效性受到以下研究设计限制的影响：1) 受试者之间压力类型和强度的巨大差异 2) 回顾性设计和 3) 由于统计相互作用测试而导致功效损失。设计克服这些限制的方法一直很困难，因为慢性压力的发作很难事先预测，而且个体遇到的压力类型差异很大。医学实习是专业医生培训的第一年，它提供了一种独特的情况，在这种情况下，我们可以前瞻性地预测一致的、慢性的压力源的发作和抑郁症状的急剧增加。我们假设整个基因组中常见和罕见的 SNP 都会与实习压力相互作用，从而影响抑郁症状表型。为了检验这一假设，我们提出以下三个具体目标：1）识别实习压力下抑郁症状的纵向模式以及与抑郁症状模式相关的因素，2）识别与抑郁症状和抑郁症状相关的常见和罕见的功能性遗传变异使用尖端的 GWAS 和外显子组芯片分析来确定实习压力期间的轨迹，3) 评估实习生样本中与抑郁症状的显着关联是否会在其他抑郁样本中复制。我们的方法是创新的，因为它利用自然发生的压力来克服现有研究的局限性，并使我们能够进行大规模、纵向队列基因 x 压力研究。该项目意义重大，因为它有可能识别出压力下抑郁症的关键遗传因素，这一进展有望预测治疗反应和确定抗抑郁药物开发的新靶标。