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

项目总结/文摘

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.