Integration of brain imaging with genomic and epigenomic data

描述（由申请人提供）：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志物的综合方法，以便更好地识别精神分裂症（SC），单极（UD）和双相（BI）等多种精神疾病。成像遗传学是一门新兴的技术，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关系。尽管它有望为疾病诊断和预后提供更好、更强大的方法，但该领域仍面临几个主要挑战：1)首先，目前大多数成像遗传学研究都集中在成对数据的相关性和整合上；其他重要的遗传因素，如表观基因组学和遗传相互作用（上位性）尚未纳入。2)其次，多尺度成像遗传学数据通常表现出特定的特征，如相互关联，但这种先验知识尚未纳入现有的整合模型。3)最后，成像遗传数据分析存在高维问题，样本数量总是明显少于特征数量。这些问题的解决需要考虑到这些多尺度和多模态数据的具体特征，从而在计算模型中进行范式转换。我们的多学科研究团队由成像科学家（Dr. Calhoun）、统计遗传学家（Dr. Deng）、生物医学工程师和生物成像信息学家（Dr. Wang）和精神病学家（Dr. Pearson）组成，在过去的几年中，他们在开发一些用于融合成像和基因组数据的数据集成方法方面进行了富有成效和创造性的工作。在我们初步成功的基础上，我们将实现以下具体目标：1)研究多重成像和基因组数据之间的相关性，以检测上位因子或相互作用网络；2)整合多尺度成像和基因组数据，特别是结合上位因子，以识别生物标志物，从中可以识别风险基因

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.