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Rare variation contributing to Alzheimer's disease risk

PI
Yokoyama, Jennifer
DAR ID
10050
Initial Approval Date
February 19, 2019
Current DAR Status
Active
PI Institution
University of California, San Francisco
PI Institution Country
United States
PI Institution Type
Non-Profit
Identification and characterization of genetic modifiers of risk for Alzheimer’s disease (AD) is paramount to development of a deeper understanding of AD pathogenesis as well as the identification of biomarkers and drug targets. The proposed research seeks to identify novel rare variants that could modulate an individual’s risk for developing sporadic late-onset or early-onset AD, and validate rare variants identified and characterized by our group that contribute to AD risk. In particular, we will focus on characterizing the contributions of changes in repetitive sequences within the coding regions of genes to AD risk. This study will also combine datasets from this project with whole genome or exome sequencing data generated by the University of California, San Francisco from individuals diagnosed with atypical or early-onset (< 65 years of age at diagnosis) AD to identify genetic risk factors unique to these less common forms of AD.
We will use ADSP data in conjunction with existing data from our research center to characterize genetic variation that influence a person’s risk for developing Alzheimer’s disease. After establishing a set of candidate variants, we will functionally characterize their biological effect using cell and biochemical assays. Identification and characterization of Alzheimer’s disease risk modifiers will not only enhance our understanding of disease pathogenesis, but may also facilitate identification of therapeutic targets and biomarkers for preventing Alzheimer’s disease.

Reported Publications (2)

Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.

  1. Cross-Ancestry Polygenic Risk Scores Enhance Alzheimer's Disease Risk Prediction in Multiethnic Cohorts. Okorie M, et al. medRxiv : the preprint server for health sciences. 2025. PMID: 41282686
  2. The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes. Andrews SJ, et al. medRxiv : the preprint server for health sciences. 2025. PMID: 38903124