Genetic, Genomic, and Imaging Biomarkers in Degenerative Dementia

描述（由研究者提供）：该申请涉及广泛挑战领域05（比较有效性研究）和特定挑战主题05- ag -103（衰老相关疾病和条件包括神经退行性疾病的早期诊断和进展的成像和液体生物标志物）。在与加州大学旧金山分校记忆与衰老中心（UCSF-MAC）的合作中，我们一直在收集-作为正在进行的研究的一部分-从一大群退行性痴呆患者和对照组的外周血中收集DNA， RNA和基因表达数据。该系列具有非常好的特征，具有大量的临床，神经心理学和影像学数据。我们的方法是基于这样的假设，即与退行性痴呆相关的遗传成分反映在外周组织，如外周血中，并且可以通过基因表达研究捕获，作为遗传关联研究的中间表型。我们的团队和加州大学洛杉矶分校的其他团队正在开发新的数据分析方法（iWGCNA），它为整合遗传标记数据、基因表达数据和复杂表型提供了一个强大的框架。针对NIH确定的挑战领域和特定挑战主题，我们建议：1)获取大量退行性痴呆患者和对照组的基因分型数据，其中基因表达、纵向成像和神经心理学数据已经可用，并开发生物标记集和分类器；2)基于同一受试者的影像数据构建分类器；3)比较遗传/基因组分类器和成像分类器，构建一个复合分类器，并评估两种不同信息来源一起使用的性能是否优于单独使用。我们期望一个复合的、多模态的生物标记集将比单独使用任何一种方法表现得更好，并且将成为第一个基于这些生物标记的痴呆症多模态分类器。这项工作的总体目标是确定替代生物标志物，以便建立一个诊断分类器，这将是宝贵的早期和准确的诊断，以及临床试验的样本分层。该项目利用我们与UCSF-MAC的合作，通过在世界上最大和最具特征的AD/FTLD系列中通过其他长期资助机制获得的表型和遗传数据增加了一个新的维度。由于大多数表型、基因表达和成像数据已经可用，因此只需要相对较少的努力和成本，就可以用基因分型数据扩展该队列的遗传特征，这为该Challenge Grant机制提供了独特的机会和利基。这里提出的工作已经准备好开始，并将立即创造就业机会，并与《复苏法案》的目标保持一致。

DESCRIPTION (provided by investigator): This application addresses the Broad Challenge Area 05 (Comparative Effectiveness Research) and specific Challenge Topic 05-AG-103 (Imaging and Fluid Biomarkers for Early Diagnosis and Progression of Aging- related Diseases and Conditions including Neurodegenerative Diseases). In collaboration with the Memory and Aging Center at the University of California San Francisco (UCSF-MAC), we have been collecting - as part of ongoing studies - DNA, RNA, and gene expression data from peripheral blood in a large cohort of patients with degenerative dementia and controls. This series is extremely well characterized, with a large body of clinical, neuropsychological, and imaging data. Our approach is based on the hypothesis that the genetic component associated with degenerative dementia is reflected in peripheral tissues, such as peripheral blood, and can be captured by gene expression studies to be used as an intermediate phenotype for genetic association studies. Our group and others at UCLA are developing novel data analysis methods (iWGCNA), which provide a powerful framework for integrating genetic marker data, gene expression data, and complex phenotypes. Responding to the Challenge Areas and specific Challenge Topics identified by the NIH we propose to: 1) obtain genotyping data on a large series of patients with degenerative dementia and controls, where gene expression and longitudinal imaging and neuropsychological data are already available, and develop a biomarker set and classifier; 2) to build a classifier based on imaging data collected on the same subjects; 3) to compare the genetic/genomic and the imaging classifiers, and build a composite classifier and assess whether performance using the two different sources of information together is better than either alone. We expect that a composite, multi-modal biomarker set will perform better than either method alone, and will be the first multi-modal classifier in dementia, based solely on these biomarkers. The overarching goal of this work is to identify surrogate biomarkers in order to build a diagnostic classifier that would be invaluable in early and accurate diagnosis, as well as for sample stratification for clinical trials. This project leverages our collaboration with the UCSF-MAC by adding a new dimension to the phenotypic and genetic data obtained via other long-term funding mechanisms in one of the largest and best-characterized AD/FTLD series in the world. Since most of the phenotype, gene expression, and imaging data are already available, only a relatively marginal effort and cost is required to extend the genetic characterization of this cohort with genotyping data, providing a unique opportunity and niche for this Challenge Grant mechanism. The work proposed here is ready to begin, and will result in immediate job creation and retention consistent with the goals of the Recovery Act. PUBLIC HEALTH RELEVANCE: Identification of biomarkers enriching diagnostic, prognostic, and therapeutic capabilities is an important goal in dementia. We propose to build a molecular classifier based on peripheral blood samples and imaging data from demented patients. This would be a valuable tool for biomarker identification, improved patients classification, therapy evaluation, and to further our understanding of disease pathophysiology.