# Identification of genetic modifiers of Alzheimer's disease in multiethnic cohorts

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2021 · $377,897

## Abstract

Project Summary/Abstract
 We have developed a novel and powerful statistical approach to identify genetic variants associated with
age-at-onset (AAO) or time-to-event traits. Here, we propose to identify genetic modifiers of AAO of Alzheimer's
disease (AD) through genome-wide association testing in two large data sets representing different ancestries,
followed by replication studies in independent data sets.
 The proposed studies improve upon previous work in three areas. First and foremost, we introduce a novel
statistical approach that evaluates variation in AAO of AD as a censored trait. By analyzing AAO as a quantitative
censored trait, we also improve the power of our study relative to case-control studies as the quantitative trait is
more informative. We will maximize that information by reducing the genetic and phenotypic variation caused by
known sources (ex., APOE genotype and population structure). Secondly, we improve upon previous work by
incorporating data sets representing diverse ancestries and adequately adjusting for both population structure
and relatedness within the data. The more-distant relationships between populations involve more recombination
between variants, resulting in smaller shared haplotypes and therefore more precise estimates of the location of
AAO modifiers. Furthermore, many of the known AD risk loci are ancestry-informative, varying significantly in
frequency across human populations. As the power to detect association increases with allele frequency, we
improve the power of our study by studying diverse populations. Lastly, we will be able to more precisely locate
AAO modifiers by incorporating denser marker data through imputation. Imputation is a cost-effective strategy
for obtaining sequence-level genotype data from microarray data. Recent advances in imputation methods and
the establishment of large and diverse sequence-based reference panels have made it possible to accurately
impute variants with frequencies as low as 0.1%, ensuring that we capture much of the polymorphic variation
within these data sets. By identifying genetic modifiers of AAO, we will provide further insight into the biology of
AD and nominate additional therapeutic targets.

## Key facts

- **NIH application ID:** 10219135
- **Project number:** 5R01AG059737-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** ELIZABETH ELOYCE BLUE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $377,897
- **Award type:** 5
- **Project period:** 2018-08-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10219135

## Citation

> US National Institutes of Health, RePORTER application 10219135, Identification of genetic modifiers of Alzheimer's disease in multiethnic cohorts (5R01AG059737-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10219135. Licensed CC0.

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