# Identification of Risk Genes Supplement

> **NIH NIH K08** · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · 2022 · $122,189

## Abstract

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.

## Key facts

- **NIH application ID:** 10598757
- **Project number:** 3K08AG054727-05S1
- **Recipient organization:** FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
- **Principal Investigator:** Yun Freudenberg-Hua
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $122,189
- **Award type:** 3
- **Project period:** 2017-08-01 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598757

## Citation

> US National Institutes of Health, RePORTER application 10598757, Identification of Risk Genes Supplement (3K08AG054727-05S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10598757. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*