Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction.
Authors
Barbara Schormair, Chen Zhao, Steven Bell, Maria Didriksen, Muhammad S Nawaz, Nathalie Schandra, Ambra Stefani, Birgit Högl, Yves Dauvilliers, Cornelius G Bachmann, David Kemlink, Karel Sonka, Walter Paulus, Claudia Trenkwalder, Wolfgang H Oertel, Magdolna Hornyak, Maris Teder-Laving, Andres Metspalu, Georgios M Hadjigeorgiou, Olli Polo, Ingo Fietze, Owen A Ross, Zbigniew K Wszolek, Abubaker Ibrahim, Melanie Bergmann, Volker Kittke, Philip Harrer, Joseph Dowsett, Sofiene Chenini, Sisse Rye Ostrowski, Erik Sørensen, Christian Erikstrup, Ole B Pedersen, Mie Topholm Bruun, Kaspar R Nielsen, Adam S Butterworth, Nicole Soranzo, Willem H Ouwehand, David J Roberts, John Danesh, Brendan Burchell, Nicholas A Furlotte, Priyanka Nandakumar, Christopher J Earley, William G Ondo, Lan Xiong, Alex Desautels, Markus Perola, Pavel Vodicka, Christian Dina, Monika Stoll, Andre Franke, Wolfgang Lieb, Alexandre F R Stewart, Svati H Shah, Christian Gieger, Annette Peters, David B Rye, Guy A Rouleau, Klaus Berger, Hreinn Stefansson, Henrik Ullum, Kari Stefansson, David A Hinds, Emanuele Di Angelantonio, Konrad Oexle, Juliane Winkelmann
Year of publication
2024Journal
NAT GENETVolume
56Issue
6Abstract
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.