# Quantitative Genetic Models for Exploring Missing Heritability of Alzheimer's Disease

> **NIH NIH RF1** · EMORY UNIVERSITY · 2020 · $2,863,784

## Abstract

Project Summary/Abstract
Alzheimer's disease (AD) is a devastating disorder that causes relentlessly progressive loss of memory and
cognition. AD affects 5.4 million people in the United States and its cost to society is estimated at $180 billion
annually. Current treatment options offer some symptomatic benefit, but do not alter the course of this disorder.
Thus, there is a great interest in uncovering novel genetic AD-risk loci that can potentially lead to new drug
treatment targets as well as identify individuals at early risk for AD for such future drug studies. Several
genome-wide association studies (GWAS) of AD have been conducted to search for risk loci, with many such
studies collecting a broad range of phenotypic, transcriptomic, and proteomic data both to enhance gene
detection as well as gain molecular insight into mapped genes. However, existing well-powered GWAS have
had limited success in identifying such risk loci. Here, we propose novel quantitative tools to improve the
performance of gene mapping for AD that leverages two important observations from the multitude of GWAS
analyses of AD performed to date. First, the genetic origins for AD involve potentially thousands (or even tens
of thousands) of trait loci. Second, family-based estimates of heritability for AD tend to be ~50% greater than
heritability estimated from GWAS SNP data. While larger GWAS samples may fill this heritability gap, we
argue a substantial portion of genetic variance for AD is likely due to non-additive effects that include higher
order interactions. Combining existing knowledge that a large number of loci affect AD with the concept that a
substantial portion of genetic variance in AD liability is due to higher-order interactions, we propose novel
quantitative tools to improve our understanding of the genetic basis of this debilitating disorder. We will explore
plausible quantitative-genetics models of the non-additive contributions to genetic variance as well as create
novel tests for detecting interactions that jointly analyze multiple AD-related phenotypes together for improved
performance. In Aim 1, we will explore plausible genetic models that have potential to yield significant
differences in AD heritability estimates between family-based and GWAS approaches due to higher-order
interactions (Aim 1a) and then, under such models, develop a multi-phenotype LD score regression that allows
for testing such interactions in GWAS data (Aim 1b). In Aim 2, we propose novel tools for gene mapping in
GWAS that leverage multiple AD-related phenotypes to identify specific SNPs across the genome that interact
with other (latent) factors to help explain higher-order interaction effects (Aim 2a). We further will scan for
stereotypical outcomes of those interactions on related phenotypes, including studying correlated changes in
gene or protein expression within the brain as a function of disease status or other variables of interest (Aim
2b). Upon creating and valida...

## Key facts

- **NIH application ID:** 10144547
- **Project number:** 1RF1AG071170-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** DAVID Joseph CUTLER
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $2,863,784
- **Award type:** 1
- **Project period:** 2020-09-11 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10144547

## Citation

> US National Institutes of Health, RePORTER application 10144547, Quantitative Genetic Models for Exploring Missing Heritability of Alzheimer's Disease (1RF1AG071170-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10144547. Licensed CC0.

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