1/2-Cis & Trans-Data Integration to Find Mechanisms Causing Psychiatric Disorder

1/2-顺式

• 批准号: 8659512
• 负责人: EDWIN VAN DEN OORD
• 金额: $ 31.77万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8659512
• 关键词: Algorithms; Autistic Disorder; Biochemical Pathway; Bioinformatics; Biological; Biological Factors; Biological Markers; Biological databases; Bipolar Disorder; Brain; Candidate Disease Gene; Collaborations; Collection; Communication; Computer software; DNA; Data; Data Analyses; Data Collection; Data Set; Data Sources; Databases; Diagnosis; Disease; Elements; Ensure; Gene Expression; Genes; Genetic Markers; Genome; Goals; Graph; Imagery; Internet; Knowledge; Literature; Location; Major Depressive Disorder; Mental Health; Mental disorders; Metabolic Pathway; Methods; MicroRNAs; Online Mendelian Inheritance In Man; Pathway interactions; Prevention; Probability; Process; Proteins; Psychiatrist; Psychiatry; Research; Research Infrastructure; Research Personnel; Resources; Sampling; Schizophrenia; Secure; Site; Testing; Text; Update; Work; base; data integration; disease mechanisms study; experience; feeding; genome wide association study; genome-wide linkage; improved; innovation; methylome; novel; psychogenetics; simulation; statistics; theories; tool; user-friendly; web interface

DESCRIPTION (provided by applicant): The amount of data related to the biological causes of psychiatric conditions has grown exponentially. Integrating results from all these studies can advance mental health research by increasing statistical power to find biomarkers, reduce false discoveries due to platform-specific technical errors, and increase confidence in findings when multiple lines of evidence point to the same biological factors. Because data integration can involve heterogeneous datasets and biological relations, it also has a considerable potential to improve our understanding of disease mechanisms by elucidating the broader context in which biological factors co-act. This work proposed builds on the long collaboration and complementary expertise of the Van den Oord (VCU) and Sullivan (UNC) labs. The result is a highly coherent, rigorous, and innovative data integration strategy aimed at improving understanding of unique and shared disease mechanisms underlying schizophrenia, bipolar disorder, major depressive disorder, and autism. Specifically: (1) the data sources we will use are deep, comprehensive, and tailored to psychiatry; (2) we developed the MIND package (Mathematically-based Integration of heterogeNeous Data) that is based on a rigorous mathematical framework that allows data integration in a meaningful and statistically optimal fashion. Rather than relying exclusively on simulations, MIND was tested empirically using a large independent replication study showing that it identified biomarkers that would otherwise require far more samples or genetic markers; (3) as we developed methods that can perform independent tests of virtually any kind of biological relationship in all available datasets, diseae mechanisms can be studied in very large samples; and (4) we will make all results and software available via SLEP (Sullivan Lab Evidence Project) for power users and in an user- friendly implementation for end users. Successful completion of the proposed project will (a) allow end users ready access to sophisticated tools for data integration, (b) allow power users to adapt use these resources as they choose, and (c) make an important, high-impact contribution to better understand disease mechanisms underlying psychiatric disorders.

描述（由申请人提供）：与精神疾病的生物学原因相关的数据量呈指数增长。整合所有这些研究的结果可以通过提高寻找生物标志物的统计能力来推进心理健康研究，减少由于平台特定的技术错误而导致的错误发现，并在多条证据指向相同的生物因素时增加对结果的信心。由于数据集成可能涉及异构数据集和生物关系，因此它还具有通过阐明生物因素共同作用的更广泛背景来提高我们对疾病机制的理解的巨大潜力。 拟议的这项工作建立在范登奥尔德 (VCU) 和沙利文 (UNC) 实验室的长期合作和互补专业知识的基础上。其结果是一个高度连贯、严格和创新的数据整合策略，旨在增进对精神分裂症、双相情感障碍、重度抑郁症和自闭症的独特和共同疾病机制的理解。具体来说：（1）我们将使用的数据源是深入的、全面的，并且是针对精神病学量身定制的； (2) 我们开发了 MIND 包（基于数学的异构数据集成），它基于严格的数学框架，允许以有意义且统计上最佳的方式集成数据。 MIND 并没有完全依赖模拟，而是使用大型独立复制研究进行了实证测试，表明它识别出了需要更多样本或遗传标记的生物标记； （3）由于我们开发的方法可以对所有可用数据集中几乎任何类型的生物学关系进行独立测试，因此可以在非常大的样本中研究疾病机制； (4) 我们将通过 SLEP（沙利文实验室证据项目）向高级用户提供所有结果和软件，并以用户友好的方式向最终用户提供。 拟议项目的成功完成将（a）允许最终用户随时访问复杂的数据集成工具，（b）允许高级用户根据自己的选择调整使用这些资源，以及（c）为以下方面做出重要的、高影响力的贡献：更好地了解精神疾病的疾病机制。

项目成果

A Whole Methylome CpG-SNP Association Study of Psychosis in Blood and Brain Tissue.

血液和脑组织中精神病的全甲基化 CpG-SNP 关联研究。

• 发表时间: 2016-07
• 影响因子: 6.6
• 作者: van den Oord, Edwin J C G;Clark, Shaunna L;Xie, Lin Ying;Shabalin, Andrey A;Dozmorov, Mikhail G;Kumar, Gaurav;Swedish Schizophrenia Consortium;Vladimirov, Vladimir I;Magnusson, Patrik K E;Aberg, Karolina A
• 通讯作者: Aberg, Karolina A

A meta-analysis of gene expression quantitative trait loci in brain.

大脑基因表达数量性状位点的荟萃分析。

• 发表时间: 2014
• 影响因子: 6.8
• 作者: Kim, Y;Xia, K;Tao, R;Giusti;Vladimirov, V;van den Oord, E;Sullivan, P F
• 通讯作者: Sullivan, P F

Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use.

全甲基化和全基因组关联研究表明 CNTN4 与酒精使用有关。

• 发表时间: 2015-08
• 作者: Clark SL;Aberg KA;Nerella S;Kumar G;McClay JL;Chen W;Xie LY;Harada A;Shabalin AA;Gao G;Bergen SE;Hultman CM;Magnusson PK;Sullivan PF;van den Oord EJ
• 通讯作者: van den Oord EJ