Friday, April 19, 2024
4/19/2024
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.
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