# The Familial Alzheimer Sequencing (FASe) Project

> **NIH NIH U01** · WASHINGTON UNIVERSITY · 2022 · $669,813

## Abstract

Abstract
Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the
genes encoding amyloid-beta precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2).
Subsequently, the identification of these genes led to the Aβ-cascade hypothesis and recently to the
development of drugs that target that pathway. In this proposal, we will identify rare risk and protective alleles.
In a recent study, we identified a rare coding variant in TREM2 with large effect size for risk for AD, confirming
that rare coding variants play a role in the etiology of AD. We will use sequence data from families densely
affected by AD, because we hypothesize that these families are enriched for genetic risk factors. We already
have access to sequence data from 695 families (2,462 individuals), that combined with the ADSP data will
lead to a very large family-based dataset: more than 805 families and 4,512 participants. Our preliminary
results support the flexibility of this approach and strongly suggest that protective and risk variants with large
effect size will be found. The identification of those variants and genes will lead to a better understanding of the
biology of the disease.

## Key facts

- **NIH application ID:** 10470727
- **Project number:** 5U01AG058922-05
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Carlos Cruchaga
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $669,813
- **Award type:** 5
- **Project period:** 2018-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470727

## Citation

> US National Institutes of Health, RePORTER application 10470727, The Familial Alzheimer Sequencing (FASe) Project (5U01AG058922-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10470727. Licensed CC0.

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