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

描述(由申请人提供)：与精神疾病的生物学原因有关的数据量呈指数级增长。整合所有这些研究的结果可以通过增加发现生物标记物的统计能力来促进心理健康研究，减少因特定平台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发现的信心。由于数据集成可能涉及不同的数据集和生物关系，它也有相当大的潜力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。这项拟议的工作建立在Van den Oord(VCU)和Sullivan(UNC)实验室的长期合作和互补专业知识的基础上。其结果是一种高度连贯、严格和创新的数据集成战略，旨在提高对精神分裂症、双相情感障碍、严重抑郁障碍和自闭症潜在的独特和共享疾病机制的理解。具体地说：(1)我们将使用的数据源是深入的、全面的，并且是为精神病学量身定做的；(2)我们开发了Mind Package(基于数学的异类数据集成)，它基于一个严格的数学框架，允许以有意义的和统计上最优的方式进行数据集成。Mind并不完全依赖于模拟，而是使用了一项大型的独立重复研究对Mind进行了经验性测试，该研究表明它确定了否则需要更多样本或遗传标记的生物标记物；(3)随着我们开发的方法可以在所有可用数据集中对几乎任何类型的生物关系执行独立测试，可以在非常大的样本中研究Diseae的发病机制；以及(4)我们将通过SLEP(Sullivan Lab Evience Project)向高级用户提供所有结果和软件，并为最终用户提供用户友好的实施方式。拟议项目的成功完成将使最终用户能够随时获得用于数据整合的复杂工具，(B)使高级用户能够根据他们的选择调整和使用这些资源，(C)对更好地了解精神疾病的潜在疾病机制作出重要的、有重大影响的贡献。

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.