Meta-analysis identifies genetic loci and biomarkers for restless legs syndrome in over 1.3 million individuals

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medRxiv Published May 23, 2026 Medically reviewed May 23, 2026 Study authors: Jing, X.; Wang, Z.; Williams, J.; Zhao, J.; Ma, K.; Fu, X.; Ren, J.; Chen, O.; Kang, H.; Lin, J.; Ya… DOI ↗ By Dr. Julia Lee, PhD · Oncology, Genomics & Drug Development

Key Takeaway

Note that a meta-analysis of over 1.3 million individuals identified 15 novel loci and over 100 associated factors for restless legs syndrome.

This meta-analysis utilizes genome-wide association analyses, phenome-wide association analysis, and brain MRI association analyses to investigate restless legs syndrome. The research population consists of more than 1.3 million individuals of European ancestry drawn from the UK Biobank and All of Us cohorts. The primary outcomes focus on identifying novel RLS-associated loci and linking risk signals to specific biological pathways.

The analysis successfully identified 15 novel RLS-associated loci. Additionally, the study found that over 100 diseases, lifestyle factors, and biomarkers are significantly associated with the disorder. These results allow for the localization of genetic effects to specific neural tissues and the characterization of brain structure and connectivity with the condition.

The authors note that this work is a meta-analysis rather than a primary trial, meaning it synthesizes existing genetic and observational data. Safety data, including adverse events, serious adverse events, and tolerability, were not reported in the source material. Consequently, clinical recommendations regarding medication use or direct patient management cannot be derived from this genetic evidence alone.

Study Details

Study typeMeta analysis

EvidenceLevel 1

PublishedMay 2026

View Original Abstract ↓

Restless legs syndrome (RLS) is a common sleep-related movement disorder, affecting approximately 4% of adults worldwide, with a substantially higher prevalence in individuals of European ancestry, reaching 5-13%. However, its genetic architecture and pathophysiology remain poorly understood. Here, we conducted a large-scale meta-analysis of more than 1.3 million individuals of European ancestry to explored the shared and ancestry-specific genetic architectures of RLS. We performed the first genome-wide association analyses of RLS in the UK Biobank and All of Us cohorts, leading to the identification of 15 novel RLS-associated loci. Using an updated variant-to-gene mapping and enrichment framework, we linked RLS risk signals to coherent neurodevelopmental and synaptic programs, with spatial transcriptomics analyses further localizing these genetic effects to specific neural tissues. In addition, we performed the largest and most comprehensive phenome-wide association analysis of RLS to date, identifying over 100 diseases, lifestyle factors, and biomarkers significantly associated with RLS, thereby delineating its broad comorbidity spectrum. Finally, we conducted the largest brain MRI association analyses for RLS, providing further insight into how brain structure and connectivity are associated with the disorder. Together, these genome- and phenome-wide analyses clarify the genetic architecture, pathophysiology, and comorbidity landscape of restless legs syndrome.