Integration of brain imaging with genomic and epigenomic data

脑成像与基因组和表观基因组数据的整合

• 批准号: 9115715
• 负责人: VINCE D CALHOUN
• 金额: $ 51.49万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9115715
• 关键词: Accounting; Algorithm Design; Behavior; Biological; Biological Markers; Biomedical Engineering; Bipolar Disorder; Brain; Brain imaging; Characteristics; Classification; Code; Communities; Computer Simulation; Computer software; Computing Methodologies; Correlation Studies; Data; Data Correlations; Databases; Detection; Diagnosis; Dimensions; Discrimination; Disease; Disease Management; Exhibits; Functional Magnetic Resonance Imaging; Genes; Genetic; Genetic Epistasis; Genetic Markers; Genetic Variation; Genetic study; Genomic approach; Genomics; Gifts; Goals; Health; Image; Image Analysis; Individual; Interdisciplinary Study; Knowledge; Mental disorders; Methodology; Methods; Modality; Modeling; Pathway Analysis; Property; Psychiatrist; Research; Research Personnel; Sampling; Schizophrenia; Scientist; Software Tools; Subgroup; Symptoms; Techniques; Testing; Translating; Variant; Work; accurate diagnosis; base; bioimaging; biomarker identification; data integration; data modeling; disease diagnosis; epigenomics; genomic biomarker; genomic data; imaging biomarker; imaging genetics; imaging modality; improved; innovation; interest; nervous system disorder; neuroimaging; novel; open source; outcome forecast; personalized care; repository; risk variant; software development; success; tool; web portal

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.

描述（由申请人提供）：该项目的目标是开发用于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，以便更好地识别多种精神疾病，如精神分裂症（SC），单极（UD）和双相（BI）障碍。影像遗传学是一种新兴的技术，它将影像学和基因组学方法相结合，以探索遗传变异与脑功能和行为之间的关系。虽然它有望为疾病诊断和预后提供更好和更强大的方法，但该领域面临着几个主要挑战：1）首先，目前大多数成像遗传学研究集中在成对数据相关和整合;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)第二，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被纳入现有的综合模型。3)最后，图像遗传数据的分析存在高维问题，样本数往往明显少于特征数。这些问题的解决需要通过考虑这些多尺度和多模态数据的具体特征，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（Calhoun博士），统计遗传学家（Deng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，在过去的几年里，他们富有成效和创造性地开发了许多用于融合成像和基因组数据的数据集成方法。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，特别是整合上位性因素，以识别生物标志物，从中可以确定风险基因。 更好地检测; 3）将检测到的生物标志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源稀疏模型的数据集成工具箱。 该项目将对临床上隐藏的亚组（例如，SC，UD，BI），通过考虑多尺度成像基因组数据的特定特征和先验知识的结合，采用创新和综合的范式。这将给目前对这些精神疾病的诊断带来变革性的变化（例如，主要基于成像症状，这通常是不准确的），有望个性化和最佳治疗。开发的方法和工具也适用于许多其他神经和精神疾病。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。