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

Schormair, Barbara ; Zhao, Chen; Bell, Steven ; Didriksen, Maria ; Nawaz, Muhammad S.; Schandra, Nathalie; Stefani, Ambra ; Högl, Birgit; Dauvilliers, Yves; Bachmann, Cornelius G.; Kemlink, David; Sonka, Karel; Paulus, Walter; Trenkwalder, Claudia; Oertel, Wolfgang H.; Hornyak, Magdolna; Teder-Laving, Maris; Metspalu, Andres; Hadjigeorgiou, Georgios M. ; Polo, Olli; Fietze, Ingo; Ross, Owen A. ; Wszolek, Zbigniew K. ; Ibrahim, Abubaker; Bergmann, Melanie; Kittke, Volker ; Harrer, Philip; Dowsett, Joseph ; Chenini, Sofiene; Ostrowski, Sisse Rye ; Sørensen, Erik; Erikstrup, Christian ; Pedersen, Ole B. ; Topholm Bruun, Mie ; Nielsen, Kaspar R.; Butterworth, Adam S. ; Soranzo, Nicole ; Ouwehand, Willem H. ; Roberts, David J.; Danesh, John; Burchell, Brendan; Furlotte, Nicholas A.; Nandakumar, Priyanka; Bonnefond, Amélie; Potier, Louis; Earley, Christopher J.; Ondo, William G.; Xiong, Lan; Desautels, Alex; Perola, Markus; Vodicka, Pavel; Dina, Christian ; Stoll, Monika ; Franke, Andre ; Lieb, Wolfgang ; Stewart, Alexandre F. R. ; Shah, Svati H.; Gieger, Christian ; Peters, Annette ; Rye, David B.; Rouleau, Guy A.; Berger, Klaus; Stefansson, Hreinn; Ullum, Henrik; Stefansson, Kari; Hinds, David A. ; Di Angelantonio, Emanuele; Oexle, Konrad und Winkelmann, Juliane (2024): Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction. In: Nature Genetics, Vol. 56, No. 6: pp. 1090-1099

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.

• Item Type: Journal article
• Faculties: Medicine > Institute for Medical Information Processing, Biometry and Epidemiology; Medicine > Munich Cluster for Systems Neurology (SyNergy); Medicine > Medical Center of the University of Munich > Neurological Clinic and Polyclinic with Friedrich Baur Institute
• Subjects: 600 Technology > 610 Medicine and health
• ISSN: 1061-4036
• Language: English
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198