GWAS by Subtraction to Disentangle RBD Genetic Background from α-Synucleinopathies

TL;DR

This study explores whether idiopathic REM sleep behavior disorder (iRBD) has a unique genetic background or is an early sign of neurodegeneration linked to α-synucleinopathies.

Contribution

The study introduces a GWAS-by-subtraction approach using GenomicSEM to separate iRBD's genetic signature from shared α-synucleinopathy risk.

Findings

The SNCA locus is a key genetic regulator of iRBD susceptibility.

iRBD has a partially distinct genetic signature but overlaps with α-synucleinopathy traits.

Neuroanatomical associations may indicate future neurodegeneration risk.

Abstract

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia and abnormal behaviors occurring during REM sleep. Idiopathic RBD (iRBD) is recognized as the strongest prodromal hallmark of α-synucleinopathies, with an established conversion rate to a neurodegenerative condition that reaches up to 96.6% at 15 years of follow-up. Moreover, RBD-converters display a more severe clinical trajectory compared to those that do not present with RBD. However, the extent to which iRBD represents a distinct genetic entity or an early manifestation of neurodegeneration remains unclear. To address this, we applied Genomic Structural Equation Modeling (GenomicSEM) using a GWAS-by-subtraction approach to disentangle the genetic architecture of iRBD from the shared genomic liability across α-synucleinopathies. Our findings highlight the SNCA locus as a key genetic regulator of iRBD susceptibility. While iRBD exhibits a partially distinct genetic signature, residual genomic overlap with neurodegenerative traits suggests that its genetic architecture exists along a continuum of α-synucleinopathy risk. In this scenario, the associations with neuroanatomical correlates may serve as early indicators of a trajectory toward future neurodegeneration. These findings provide a framework for identifying biomarkers that could aid in disease stratification and risk prediction, potentially improving early intervention strategies.

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