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Empowering Personalized Medicine: Integrating Imaging, Genetics, and Biomarkers

赋能个性化医疗：整合影像、遗传学和生物标志物

基本信息

批准号：
8659510
负责人：
Giovanni Coppola
金额：
$ 39.48万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-05-01 至 2016-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8659510
关键词：
Address Affect Alzheimer&apos s Disease Amyloid Autistic Disorder Bayesian Modeling Behavioral Biological Biological Markers Bipolar Disorder Brain Brain imaging Clinical Clinical Research Code Cognitive Complex Computational algorithm Data Data Set Databases Diagnosis Diagnostic Disease Equation Frontotemporal Dementia Functional Magnetic Resonance Imaging Funding Future Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Genetic Markers Genetic Variation Genetic screening method Genome Genomics Genotype Goals Image Individual Informatics Letters Link Machine Learning Magnetic Resonance Imaging Mathematics Measures Medicine Mental Depression Mental disorders Methods Methylation Modality Modeling Molecular Profiling National Center for Research Resources Nerve Degeneration Neurologic Neurosciences Outcome Pathway Analysis Pathway interactions Patients Persons Phenotype Physiological Positron-Emission Tomography Predictive Value Publishing Research Personnel Resources Running Sampling Schizophrenia Site Structure Testing Three-Dimensional Image Validation Variant Weight Work base computer based statistical methods data reduction disease diagnosis disorder risk empowered endophenotype epigenetic marker exome experience fluorodeoxyglucose positron emission tomography genetic variant genome sequencing genome wide association study improved insight mathematical algorithm neurodegenerative dementia neuroimaging neuropsychiatry novel outcome forecast prognostic success tool trait treatment response web-accessible

项目摘要

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.

描述（由申请人提供）：该项目“赋能个性化医疗：整合影像、遗传学和生物标志物”，响应 RFA-MH-12-020，题为“整合多维数据以探索精神障碍的潜在机制”。通过汇集神经影像、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，以结合来自 3D 神经影像（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（例如生理生物标志物、表观遗传数据等）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）来改善疾病的诊断和预后。在此过程中，新颖的数学工具将自动发现哪些生物标志物在不同情况下最有帮助。为了发现和测试极高维测量（例如图像和基因组）之间的关系，我们使用新的数据缩减概念，例如惩罚回归（弹性网络）、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 促进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鉴定）。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，共同配准的 MRI 和 PET、DTI）。我们将在新样本和可控测试数据中使用 k 倍交叉验证和独立复制来验证我们的预测。目标 2 使用加权基因共表达网络分析 (WGCNA) 将我们的工作从单一性状扩展到 3D 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、功能磁共振成像）。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的附加预测价值，以实现个性化预后和诊断。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项工作的影响，我们和我们的合作者将在一系列神经精神疾病的现有和新数据集上测试我们的工具，包括额颞叶痴呆、阿尔茨海默病、精神分裂症、双相情感障碍和自闭症（参见支持信）。所有工具都将被传播并链接到可通过网络访问的数据库，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。