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）：与精神病生物学原因有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增加统计能力来找到生物标志物，减少由于平台特定的技术错误引起的错误发现，并在多个证据指向相同的生物学因素时提高对发现的信心，从而提高心理健康研究。由于数据整合可能涉及异质数据集和生物关系，因此它也具有相当大的潜力，可以通过阐明生物学因素共同行动的更广泛的环境来提高我们对疾病机制的理解。 这项工作提出的是基于范登·奥德（VCU）和沙利文（UNC）实验室的长期合作和互补专业知识。结果是一种高度连贯，严格和创新的数据整合策略，旨在提高对精神分裂症，躁郁症，重度抑郁症和自闭症的独特和共享疾病机制的理解。具体来说：（1）我们将使用的数据来源是深层，全面且针对精神病学量身定制的； （2）我们基于严格的数学框架，开发了思维软件包（基于数学上基于数学的集成），该框架允许以有意义且统计上最佳的方式集成数据。使用大型独立复制研究对Mind进行了经验测试，而不是仅仅依靠模拟，而是表明它确定了生物标志物，否则这些生物标志物将需要更多的样本或遗传标记。 （3）由于我们开发了可以在所有可用数据集中对任何类型的生物关系进行独立测试的方法，因此可以在很大的样本中研究脉冲机制； （4）我们将通过SLEP（Sullivan Lab证据项目）和用于最终用户的用户友好实施来提供所有结果和软件。 拟议项目的成功完成将（a）允许最终用户准备访问复杂的数据集成工具，（b）允许权力用户​​适应他们选择的使用这些资源，以及（c）提出重要的，高影响力的贡献，以更好地了解精神病患者的疾病机制。

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。

• DOI: 10.1093/schbul/sbv182
• 发表时间: 2016
• 期刊: Schizophrenia bulletin
• 影响因子: 6.6
• 作者: vandenOord,EdwinJCG;Clark,ShaunnaL;Xie,LinYing;Shabalin,AndreyA;Dozmorov,MikhailG;Kumar,Gaurav;SwedishSchizophreniaConsortium;Vladimirov,VladimirI;Magnusson,PatrikKE;Aberg,KarolinaA
• 通讯作者: Aberg,KarolinaA

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

• DOI: 10.1038/tp.2014.96
• 发表时间: 2014-10-07
• 期刊: Translational psychiatry
• 影响因子: 6.8
• 作者: Kim Y;Xia K;Tao R;Giusti-Rodriguez P;Vladimirov V;van den Oord E;Sullivan PF
• 通讯作者: Sullivan PF