Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction

23andMe Research Team, D.E.S.I.R. study group

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82–0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

Original languageEnglish
Pages (from-to)1090-1099
Number of pages10
JournalNature Genetics
Volume56
Issue number6
DOIs
Publication statusPublished - Jun 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

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