Identification of Risk Genes Supplement

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Abstract Late onset Alzheimer’s Disease (AD) is a common and devastating disease with a high estimated heritability. Identification of risk genes for AD has the potential to further our understanding of disease mechanism and modifying factors, thus leading to development of effective treatments. Despite the identification of common genetic risk variants in more than 40 genes associated with AD, much of the heritability remains unexplained. This may be due to the presence of many rare risk variants, which cannot be identified in genome wide association studies. Therefore, evaluating the impact of rare genetic variants is required through genome sequencing. Compared to a heterogeneous population, conducting genetic studies in a genetically homogeneous founder population of Ashkenazi Jewish (AJ) ancestry reduces statistical noise, thereby increasing statistical power. Furthermore, compared to typical controls aged 60s to 80s, who may develop AD at a later age, cognitively healthy centenarians represent true controls for AD. Thus, comparing whole genome sequencing data of AD cases and centenarian controls of AJ ancestry could offer insights on high impact rare variants in AD. Dr. Yun Freudenberg-Hua is a physician data scientist with expert knowledge in clinical geriatric psychiatry and clinical dementia phenotypes. She has a keen interest in genetics for AD and the ability to analyze genetic data. The extension of the K08 award will provide her with protected research time to complete her ongoing analysis of whole genome sequencing data, which was disrupted by the COVID-19 pandemic. During the extension period, she will identify putative functional variants by integrating 1) knowledge of functional coding variants, 2) selection of functional non-coding variants, and 3) gene sets involved in AD by applying novel bioinformatics and statistical methods. She will accomplish these goals under the mentorship of Dr. Alison Goate and continue her plan to apply for independent NIH funding. The goal is to test the hypothesis that rare functional variants are enriched in specific gene sets among AD patients. Putative functional coding and non-coding variants will be included for aggregation analysis by applying a novel machine-learning method REGENIE. We will expand the analysis to include AJ whole genome subsets from the Alzheimer’s Disease Sequencing Project. The rare genetic risk variants highlighted in specific pathways can be translated into both AD prediction and development of therapeutic agents. The data generated in this project should allow Dr. Freudenberg-Hua to compete for R01 funding to investigate the multidimensional interplay between genetic risks, biomarkers, and complex clinical phenotypes of dementia and to identify factors that are modifiable.