# Genomic determinants of sleep traits as risk and protective factors for Alzheimer's disease

> **NIH NIH R03** · TRANSLATIONAL GENOMICS RESEARCH INST · 2022 · $192,000

## Abstract

PROJECT SUMMARY
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease in the United States and there are
no effective treatments or cure. The detection of modifiable protective or risk factors can improve the possibility
of intervention through life-style habits focused to reduce the disease risk or elevate disease protection. Sleep
disorders and disturbances have recently been recognized as risk factors for AD according to evidence from
epidemiological studies as well as associations with specific AD neuropathological hallmarks such as plaques
and tangles in the brain. However, the causal relationship between sleep disorders and disturbances and AD
has not been well established.
 In this secondary data analysis proposal, we aim to study the causal effects of sleep traits on AD using
large publicly available genomics datasets including the UK Biobank (UKB), the AD Genetic Consortium
(ADGC), and others. We will use a bioinformatics workflow consisting of innovative analytical methods
designed to shed light on the causal relationship and identify specific genomics factors involved. The project
will be carried out as follows:
1) We will leverage large-scale genome-wide association studies (GWAS) conducted on sleep traits to
 prioritize genes using a method (transcriptome-wide association study - TWAS) capable of detecting
 phenotype-associated genes under genetic control and simultaneously related to changes in gene
 expression. Then, AD RNA profiling studies will be analyzed using pseudotime algorithms, extracting latent
 temporal information and ordering the samples according to disease progression. Genes identified in this
 step (showing a high correlation with the disease progression and previously detected in the TWAS) will be
 further investigated by Mendelian randomization to assess the causal relationship between sleep traits
 (exposure) and AD (outcome).
2) A second independent analysis will be conducted by Mendelian randomization, prioritizing variants by
 statistical significance from the large scale GWAS conducted on sleep traits and assessing the causal
 relationship with AD. Additionally, a recently developed algorithm (latent causal variable method) will be
 applied as well to detect causal relationships between sleep traits and AD.
This analytical workflow and the large size of the cohorts included will provide us with the statistical power to
identify modifiable risk and protective factors to demonstrate a causal relationship with AD.

## Key facts

- **NIH application ID:** 10453007
- **Project number:** 1R03AG073906-01A1
- **Recipient organization:** TRANSLATIONAL GENOMICS RESEARCH INST
- **Principal Investigator:** Ignazio Stefano Piras
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $192,000
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10453007

## Citation

> US National Institutes of Health, RePORTER application 10453007, Genomic determinants of sleep traits as risk and protective factors for Alzheimer's disease (1R03AG073906-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10453007. Licensed CC0.

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