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

Abstract

Restless legs syndrome (RLS) affects up to 1 in 10 older adults. Their health care is impeded by delayed diagnosis and insufficient treatment options. To advance disease prediction and find novel entry points for therapy, we performed a meta-analysis of genome-wide association studies (GWAS) in 116,647 cases and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci to 164, comprising 196 independent lead SNPs, with the first sex-specific GWAS on RLS revealing largely overlapping genetic predispositions of the sexes (rg=0.96). Locus annotation prioritized druggable genes such as glutamate-receptors 1 and 4. Mendelian randomization indicated RLS as a causal risk factor of diabetes. Machine-learning approaches combining genetic and environmental information performed best in risk prediction (AUC=0.82-0.91). Our study identified targets for drug development, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up, and provided evidence that gene-environment interaction is likely highly relevant for RLS risk prediction.