Empowering Personalized Medicine: Integrating Imaging, Genetics, and Biomarkers

描述（由申请人提供）：该项目，授权个性化医疗：整合成像，遗传学和生物标志物，响应RFA-MH-12-020，题为整合多维数据，探索精神障碍的机制。通过汇集神经成像、遗传学和数学方面的专家，我们计划创建一个先进的便携式框架，联合收割机来自3D神经成像（MRI、淀粉样蛋白/FDG-PET）、基因表达网络、全基因组关联研究（GWAS）和其他多维数据（例如，生理学生物标志物、表观遗传学数据等）。我们的总体目标是通过结合多层次的生物信息（个性化医疗）来改善疾病的诊断和预后。在这样做的过程中，新的数学工具将自动发现哪些生物标志物在不同的背景下最有帮助。为了发现和测试非常高维的度量（如图像和基因组）之间的关系，我们使用了新的概念来减少数据，如惩罚回归（弹性网），自适应层次聚类，贝叶斯网络和支持向量机。为了避免当前独立测试个体基因效应的工作的局限性，我们将基因表达网络的分析扩展到图像，将疾病体征与其遗传基础和所有可用的生物标志物联系起来。目标1授权发现遗传变异（在GWAS、全外显子组和全基因组测序中鉴定），这些变异调节疾病的测量。我们将使用压缩编码模型来发现和验证哪些遗传变异集影响多维图像（例如，共同注册的MRI & PET、DTI）。我们将使用k折交叉验证和新样本和可控测试数据中的独立重复来验证我们的预测。目的2扩展我们的工作，使用加权基因共表达网络分析（WGCNA）从单个性状的三维图像（MRI/PET）的整个数据库。我们的框架将合并GWAS，eQTL分析和表达表型分析，但将广泛适用于任何未来的高通量生物信息（例如甲基化谱， DTI、fMRI）。在目标3中，我们将量化基因分型、基因表达谱和多模式神经影像学对个性化预后和诊断的预测价值。例如，哪些生物标志物（基因表达，CSF，MRI）在哪些情况下最有用？为了最大限度地发挥这一努力的影响，我们和我们的合作者将测试我们的工具对现有的和新的数据集从一系列神经精神疾病，包括额颞叶痴呆症，阿尔茨海默氏病，精神分裂症，双相情感障碍和自闭症（见支持信）。所有工具都将被传播并链接到网络访问数据库，这些数据库存储并方便访问高通量遗传，基因组和成像数据集。 公共卫生相关性：我们的项目通过整合不同类型的患者数据，包括神经影像学，基因表达谱，疾病诊断，进展和治疗反应的认知和行为标志物，改善了对患者结局的诊断和预测。为了解决这些复杂的数据类型，我们开发了新的机器学习，网络分析和数据库方法。我们的个性化医疗方法将帮助研究人员研究和评估神经和精神疾病，如阿尔茨海默病，额颞叶痴呆症，精神分裂症，双相情感障碍和自闭症。

DESCRIPTION (provided by applicant): This project, Empowering Personalized Medicine: Integrating Imaging, Genetics and Biomarkers, responds to RFA-MH-12-020, entitled Integrating Multi-Dimensional Data to Explore Mechanisms Underlying Mental Disorders. By bringing together experts in neuroimaging, genetics, and mathematics, we plan to create an advanced, portable framework to combine diverse biomedical data from 3D neuroimaging (MRI, amyloid/FDG-PET), gene expression networks, genome-wide association studies (GWAS), and other multidimensional data (e.g., physiological biomarkers, epigenetic data, etc.). Our overall goal is to improve diagnosis and prognosis of disease by combining multiple levels of biological information (personalized medicine). In doing so, novel mathematical tools will automatically discover which biomarkers are most helpful in different contexts. To discover and test relationships between very high-dimensional measures (such as images and genomes), we use novel concepts for data reduction such as penalized regression (elastic nets), adaptive hierarchical clustering, Bayesian networks, and support vector machines. Avoiding the limitations of current work that tests individual gene effects independently, we extend the analysis of gene expression networks to images, to relate signs of disease to their genetic underpinnings and to all available biomarkers. Aim 1 empowers discovery genetic variants (identified in GWAS, whole-exome and whole-genome sequencing) that modulate measures of disease. We will use compressive coding models to discover and verify which sets of genetic variants affect multidimensional images (e.g., co-registered MRI & PET, DTI). We will verify our predictions using k-fold cross-validation and independent replications in new samples and controllable test data. Aim 2 extends our work using weighted gene co-expression network analysis (WGCNA) from single traits to entire databases of 3D images (MRI/PET). Our framework will merge GWAS, eQTL analysis, and expression-phenotype analysis but will be broadly applicable to any future high-throughput biological information (e.g. methylation profiles, DTI, fMRI). In Aim 3, we will quantify the added predictive value derivable from genotyping, gene expression profiling, and multimodal neuroimaging for personalized prognosis and diagnosis. For example, which biomarkers (gene expression, CSF, MRI) are most useful in which cases? To maximize impact of this effort, we and our collaborators will test our tools on existing and new datasets from a range of neuropsychiatric disorders including frontotemporal dementia, Alzheimer's disease, schizophrenia, bipolar disorder, and autism (see Support Letters). All tools will be disseminated and linked to web-accessible databases that store and ease access to high-throughput genetic, genomic, and imaging datasets. PUBLIC HEALTH RELEVANCE: Our project improves diagnosis and predictions of patient outcomes by integrating diverse types of patient data including neuroimaging, gene expression profiles, cognitive, and behavioral markers of disease diagnosis, progression, and treatment response. To tackle these complex data types, we develop novel machine learning, network analysis, and database methods. Our personalized medicine approach will help researchers study and evaluate neurological and psychiatric conditions such as Alzheimer's disease, frontotemporal dementia, schizophrenia, bipolar disorder, and autism.