Integration of brain imaging with genomic and epigenomic data

描述(申请人提供)：该项目的目标是开发从多尺度基因组和成像数据中检测生物标记物的综合方法，以便更好地识别多种精神疾病，如精神分裂症(SC)、单相(UD)和双相(BI)障碍。成像遗传学是一项新兴的技术，它将成像和基因组方法结合起来，探索遗传变异与大脑功能和行为之间的联系。尽管它有望为疾病的诊断和预后提供更好、更强大的方法，但该领域正面临着几个主要挑战：1)目前的成像遗传学研究大多集中在成对数据的关联和整合上，其他重要的遗传因素，如表观基因组学和遗传互作(上位性)还没有被纳入。2)其次，多尺度成像遗传学数据往往表现出特定的特征，如相互关联，但这种先验知识尚未被纳入现有的综合模型中。3)最后，成像遗传数据的分析存在高维问题，样本数目总是明显少于特征数目。这些问题的解决需要通过考虑这些多尺度和多模式数据的具体特征来改变计算模型的范式。我们的多学科研究团队由成像科学家(Calhoun博士)、统计遗传学家(邓博士)、生物医学工程师和生物成像信息学家(Wang博士)以及精神病学家(Pearson博士)组成，在过去几年中富有成效和创造性地开发了多种数据集成方法，用于融合成像和基因组数据。在我们初步成功的基础上，我们将实现以下具体目标：1)研究多种成像和基因组数据之间的相关性，以检测上位性因素或相互作用网络；2)整合多尺度成像和基因组数据，特别是结合上位性因素，以识别生物标记，从中可以识别危险基因 更好地检测；3)将检测到的生物标记物应用于目前基于症状且经常被误诊的多种精神疾病的分类；以及4)开发并向广泛的研究社区传播基于开放源码稀疏模型的数据集成工具箱。该项目将通过考虑多尺度成像基因组数据的具体特征和纳入先验知识，以创新和综合的范式对临床隐蔽亚群(如SC、UD、BI)进行更准确的分类方面产生重大影响。这将给这些精神疾病的当前诊断带来变革性的变化(例如，主要基于通常不准确的成像症状)，有望实现个性化和最佳治疗。开发的方法和工具也适用于许多其他神经和精神障碍。通过向研究界传播开发的软件工具，该项目将产生广泛和持续的影响。

DESCRIPTION (provided by applicant): The goal of this project is to develop integrative approaches for the detection of biomarkers from multiscale genomic and imaging data, so that multiple mental illnesses such as schizophrenia (SC), Unipolar (UD) and bipolar (BI) disorder can be better identified. Imaging genetics is an emerging technique, which integrates imaging and genomic approaches to explore the association between genetic variations and brain functions and behaviors. Although it promises a better and more powerful approach for disease diagnosis and prognosis, the field is facing several major challenges: 1) First, most of current imaging genetics studies focus on pair-wise data correlation and integration; other important genetic factors such as epigenomics and genetic interactions (epistasis) have not been incorporated. 2) Second, multiscale imaging genetics data often exhibit specific characteristics such as inter- correlations, but this prior knowledge has not been incorporated into existing integrative models. 3) Finally, there is a high dimensionality problem with the analysis of imaging genetic data the number of sample is always significantly less than that of features. The solution of these problems necessitates a paradigm shift in computational models by considering the specific characteristics of these multiscale and multimodal data. Our multidisciplinary research team consisting of imaging scientist (Dr. Calhoun), statistical geneticist (Dr. Deng), biomedical engineer and bioimaging informatician (Dr. Wang), and psychiatrist (Dr. Pearson) has worked productively and creatively over the past few years in developing a number of data integration methods for fusion of imaging and genomic data. Building on our initial success, we will accomplish the following specific aims: 1) to study the correlation between multiple imaging and genomic data for the detection of epistasis factors or interaction networks; 2) to integrate multiscale imaging and genomic data, especially incorporating epistasis factors, for the identification of biomarkers, from which risk genes can be better detected; 3) to apply the detected biomarkers for the classification of multiple mental illnesses that are currently based on symptoms and are often misdiagnosed; and 4) to develop and disseminate an open source sparse model based data integration toolbox to the broad research community. The project will make significant impact on more accurate classification of clinically cryptic subgroups (e.g., SC, UD, BI) with an innovative and integrative paradigm by taking into account specific features of multiscale imaging genomic data and incorporation of prior knowledge. This will bring transformative changes on the current diagnosis of these mental illnesses (e.g., primarily based on imaging symptoms, which are often inaccurate), promising for personalized and optimal treatments. The developed methodology and tools are also applicable to many other neurological and psychiatric disorders. By the dissemination of the developed software tools to the research community, the project will have a broad and sustained impact.