Identification of Risk Genes by Comparing Whole Genome Sequences of Alzheimer's Disease Patients and Cognitively Healthy Centenarians

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Project Summary/Abstract Late onset Alzheimer’s Disease (AD) is a common and devastating disease with a high heritability. Identification of risk genes for AD has the potential to further our understanding of disease mechanism and modifying factors, thus lead to development of effective treatments. Despite the identification of common genetic risk variants in more than 20 genes associated with AD, most of the heritability remains unexplained. This may be due to the presence of many rare risks variants, which cannot be identified in genome wide association studies. Therefore evaluating the impact of rare genetic variants is required. Compared to a heterogeneous population, conducting genetic studies in a homogeneous founder population such as the Ashkenazi Jews (AJ) reduces statistical noise, thereby increases statistical power. Furthermore, compared to age-matched controls who may still develop AD at a later age, cognitively healthy centenarians may be viewed as true controls for AD. The rapid advance of next generation sequencing technologies now makes it possible to comprehensively analyze whole genome data. In an ongoing separate project, whole genome sequencing data of 400 AD patients and 200 cognitively healthy centenarians and 200 age-matched controls, all of AJ ancestry, are being ascertained. Dr. Yun Freudenberg-Hua is a physician-scientist with expert knowledge in clinical geriatric psychiatry who has a keen interest in advancing genetics for AD. This award will provide her with protected research time to gain expertise on (1) gene regulation and epigenetics, (2) developing candidate pathways and gene sets informed by AD biology, and (3) novel computational and statistical methods for rare variants burden and risk allele interaction. She will accomplish these goals with a cross-disciplinary team of mentors. The goal of this proposal is to test the hypothesis that rare functional variants are enriched in specific pathways or gene sets among AD patients, and that these rare variant effects depend on the genetic background of common variants. In addition to annotating coding variants, we will prioritize non-coding variants according to their potential regulatory impact on gene expression and epigenetic remodeling. We will identify pathways and gene sets that are enriched for coding and non-coding variants for AD in the whole genome data set of our AJ case/control cohort. First, we will perform rare variant burden analysis across pre-defined candidate gene sets based on biological networks with focus on immune system pathways (Aim1); next, we will investigate the interaction between polygenic risk scores predicted by common risk variants across specific gene sets and rare variant burden for AD (Aim2); finally, we will replicate significant findings by integrating the results with data from other publicly available sequencing projects (Aim3). Elucidating rare genetic risk variants for AD in specific pathways will generate knowledge that can be translated into improvement of AD diagnosis and development of therapeutic agents. The data and infrastructure generated in this project should allow Dr. Freudenberg-Hua to compete for R01 funding to implement translational genomics into clinical diagnosis and management of dementia.