Nature genetics · 2024

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

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

Аннотация

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.

Предметные рубрики MeSH

Restless Legs Syndrome Humans Genome-Wide Association Study Genetic Predisposition to Disease Risk Factors Female Male Polymorphism, Single Nucleotide Mendelian Randomization Analysis Machine Learning

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

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Аннотация

Предметные рубрики MeSH