Statistical and computational methods for integrative analysis of Alzheimer's Disease genetics

项目总结/摘要 遗传因素在阿尔茨海默病的发展中起着重要作用。虽然取得了很大进展， 尽管阿尔茨海默病遗传学研究已经取得了进展，但非编码变体的作用在很大程度上是未知的。非编码 基因组覆盖了约98%的人类基因组，包括调控何时、何地和向何处的元件。 蛋白质编码基因（如APOE）被转录。本建议的目标是， 特别是对阿尔茨海默病的全基因组测序研究的分析，以确定 罕见的非编码变体，并表征其在阿尔茨海默病发病机制中的作用。我们将实现我们的 通过创新的方法，结合全基因组测序，表观遗传技术和 多层表型数据，如成像和生物标志物。这将导致一个独特的组合， 分析非编码变异体的方法，允许缺乏天然单位（例如基因）， 测试（目标1），整合多层信息以增强功效（目标2），以及生物学 关联信号的有意义解释（目标3）。所提出的方法将适用于总共 大约20，000个完整的基因组，统一了阿尔茨海默病神经成像倡议（ADNI）， 阿尔茨海默病测序项目（ADSP），宗教秩序研究和记忆与衰老项目 （ROSMAP）和一个新建立的队列，斯坦福大学阿尔茨海默病极端表型（StEP AD）。我们期望所提出的方法的应用将显着提高我们的理解 阿尔茨海默氏病的遗传结构，关键是，提供了一套明确的，新的目标， 基因组驱动医学的发展。

Project Summary/Abstract Genetic factors play an important role in the development of Alzheimer's disease. While much progress has been made in Alzheimer’s disease genetics, the role of noncoding variants is largely unknown. The noncoding genome covers ~98% of the human genome and includes elements that regulate when, where, and to what degree protein-coding genes (e.g. APOE) are transcribed. The objective of this proposal will be to focus specifically on the analysis of whole-genome sequencing studies of Alzheimer’s disease, in order to identify rare noncoding variants and characterize their role in Alzheimer’s disease pathogenesis. We will attain our objective via an innovative approach, combining whole-genome sequencing, epigenetic technologies and multi-layered phenotypic data such as imaging and biomarkers. This will lead to a unique combination of methodologies for the analysis of noncoding variants, allowing for absence of natural units (e.g. genes) for testing (Aim 1), integration of multi-layer information for enhancing power (Aim 2), and biologically meaningful interpretation of association signals (Aim 3). The proposed methods will be applied to a total of roughly 20,000 whole genomes unifying the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Alzheimer's Disease Sequencing Project (ADSP), the Religious Orders Study and Memory and Aging Project (ROSMAP) and a newly established cohort, the Stanford Extreme Phenotypes in Alzheimer's Disease (StEP AD). We expect that the application of the proposed methods will significantly improve our understanding of the genetic architecture of Alzheimer's disease and, critically, provide a set of well-defined, novel targets for the development of genomic-driven medicine.