Empowering Personalized Medicine: Integrating Imaging, Genetics, and Biomarkers

描述（由申请人提供）：本项目，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机制”。通过汇集神经影像学、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，以结合来自3D神经影像学（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（如生理生物标志物、表观遗传学数据等）的各种生物医学数据。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）来提高疾病的诊断和预后。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情况下最有帮助。为了发现和测试高维度量（如图像和基因组）之间的关系，我们使用了新的概念进行数据约简，如惩罚回归（弹性网）、自适应分层聚类、贝叶斯网络和支持向量机。为了避免目前单独测试单个基因影响的工作的局限性，我们将基因表达网络的分析扩展到图像，将疾病的迹象与其遗传基础和所有可用的生物标志物联系起来。目标1使发现调节疾病测量的遗传变异（在GWAS、全外显子组和全基因组测序中发现）成为可能。我们将使用压缩编码模型来发现和验证哪组遗传变异会影响多维图像（例如，共同注册的MRI和PET， DTI）。我们将在新样本和可控测试数据中使用k-fold交叉验证和独立重复来验证我们的预测。Aim 2使用加权基因共表达网络分析（WGCNA）将我们的工作从单个性状扩展到整个3D图像数据库（MRI/PET）。我们的框架将融合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，

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.