medRxivGenetic & Genomic MedicinePreprint
Shared Genetic Architecture Between Parkinsons Disease and Sleep-Related Traits Implicates the MAPT Locus on Chromosome 17
Aura Aguilar-RoldániD, Miguel E. RenteríaiD, Luis M. García‐MaríniD
Abstract
Parkinsons disease (PD) is a neurodegenerative disorder characterised by both motor and non-motor symptoms. Among the latter, sleep disturbances are particularly common and include insomnia, obstructive sleep apnoea, excessive daytime sleepiness, restless legs syndrome, and REM sleep behaviour disorder. In this study, we investigated the shared genetic architecture between PD and sleep-related traits to uncover biological pathways that may underpin this relationship. We analysed genome-wide association study (GWAS) summary statistics for PD ([~]31,700 cases, [~]18,600 proxy cases, [~]1.4 million controls) and eight self-reported sleep-related traits (each with n > 300,000): ease of getting up, chronotype (morningness), napping, insomnia, obstructive sleep apnoea, snoring, daytime dozing, and sleep duration. Genetic correlations were estimated using LD score regression, and GWAS-Pairwise analysis was used to identify genomic segments harbouring shared causal variants. We then mapped these variants to protein-coding genes using MAGMA. We observed a significant genome-wide genetic correlation between PD and daytime dozing (P < 0.05). At the local level, six genomic regions contained shared variants. A single locus on chromosome 17 contributed the majority of mapped protein-coding genes, including ARHGAP27, PLEKHM1, CRHR1, and MAPT, which are implicated in neurodegeneration and circadian rhythm regulation. These findings suggest that the MAPT locus, beyond its established role in PD, may also contribute to sleep-wake regulation via shared biological pathways, including tau pathology, stress response, and chromatin remodelling. Our results highlight sleep disturbances as a potential early marker of, or risk factor for, Parkinsons disease susceptibility.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
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