Integrative analysis of transcriptome, genome and disease diversity in major depr

主要疾病转录组、基因组和疾病多样性的综合分析

• 批准号: 8299726
• 负责人: ETIENNE L SIBILLE
• 金额: $ 17.1万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8299726
• 关键词: Accounting; Address; Age; Alcohols; Algorithms; Antidepressive Agents; Autopsy; Bioinformatics; Biological Markers; Biological Process; Bipolar Disorder; Brain; Brain region; Candidate Disease Gene; Cessation of life; Clinical; Cluster Analysis; Data; Detection; Developed Countries; Disease; Gene Cluster; Gene Expression; Gene Expression Profile; Genes; Genetic; Genome; Genomics; Goals; Individual; Intercept; Lead; Liquid substance; Literature; Major Depressive Disorder; Mediator of activation protein; Meta-Analysis; Methodology; Methods; Modeling; Neurobiology; Pathway Analysis; Pathway interactions; Patients; Pattern; Recurrence; Regulation; Reporting; Research; Research Personnel; Sample Size; Schizophrenia; Severity of illness; Solutions; Statistical Models; Substance abuse problem; Suicide; Systemic disease; Therapeutic; Transcript; Triplet Multiple Birth; Work; case control; data structure; design; disability; genome wide association study; improved; insight; meetings; molecular scale; novel; novel strategies; premature; sex; suicidal morbidity; transcriptomics

DESCRIPTION (provided by applicant): Major depressive disorder (MDD) is a heterogeneous illness with many clinical variables - such as sex, age, alcohol, antidepressant drug, recurrence or death by suicide -- as potential factors characterizing subtypes of MDD, making it difficult to fully understand its underlying mechanisms and heterogeneous genetic underpinnings. Many transcriptomic studies have been generated in the literature, including those from Dr. Sibille, the co-PI on this proposal. We propose to apply state-of-the-art statistical integrative analyses tailored to combine multiple MDD transcriptomic studies that will address the specific issues of case-control pairing design, confounding clinical variables and small sample size. Our study will detect novel MDD associated biomarkers, pathways and co-expression modules, and elucidate the magnitudes of transcriptomic changes attributable to substance abuse, recurrence, disease severity, age and sex. The results will enhance our understanding to MDD genetic mechanisms and lead to better and individualized therapeutic solutions. PUBLIC HEALTH RELEVANCE: Our proposed research is to develop and apply modern genomic meta-analysis methods to combine multiple major depressive disorder (MDD) transcriptomic studies that will adequately model the specific data structure of case-control pairing design, confounding clinical variables and small sample size. The goal is to detect novel MDD associated biomarkers, pathways and co-expression modules, and elucidate the magnitudes of transcriptomic changes attributable to substance abuse, recurrence, disease severity, age and sex. The results will enhance our understanding of MDD genetic mechanisms and lead to better and individualized therapeutic solutions.

描述（由申请人提供）：重度抑郁症（MDD）是一种异质性疾病，具有许多临床变量-如性别，年龄，酒精，抗抑郁药物，复发或自杀死亡-作为MDD亚型特征的潜在因素，因此难以完全理解其潜在机制和异质性遗传基础。在文献中已经产生了许多转录组学研究，包括来自Sibille博士的研究， 共同参与这项提案。我们建议应用最先进的统计综合分析，专门针对联合收割机多个MDD转录组学研究，这些研究将解决病例对照配对设计、混淆临床变量和小样本量等具体问题。我们的研究将检测新的MDD相关生物标志物，途径和共表达模块，并阐明可归因于药物滥用，复发，疾病严重程度，年龄和性别的转录组学变化的幅度。本研究结果将有助于加深我们对MDD遗传机制的理解，并为临床提供更好的个体化治疗方案。 公共卫生相关性：我们的研究目的是发展和应用现代基因组荟萃分析方法，结合联合收割机多重抑郁症（MDD）转录组学研究，充分模拟病例对照配对设计的特定数据结构，混淆临床变量和小样本量。目的是检测新的MDD相关生物标志物、途径和共表达模块，并阐明可归因于药物滥用、复发、疾病严重程度、年龄和性别的转录组学变化的幅度。这些结果将增强我们对MDD遗传机制的理解，并导致更好的个性化治疗方案。