德国环境健康研究中心Barbara Schormair研究组对不安腿综合征(RLS)患者进行的全基因组荟萃分析，为深入了解该疾病的遗传结构、疾病生物学和预测发病风险提供了见解。该研究成果发表在2024年6月5日出版的国际学术期刊《自然—遗传学》上。

据介绍，高达10%的老年人患有不安腿综合征。诊断延迟和治疗不足阻碍了他们的身体健康。

为了推进RLS发病预测并找到新的治疗切入点，研究人员对116,647名RLS患者（病例）和1,546,466名欧洲对照者进行了全基因组关联研究荟萃分析。性别特异性荟萃分析显示，两种性别之间的遗传易感性基本重叠（rg=0.96）。基因座注释优先考虑了谷氨酸受体1和4等可用药基因，孟德尔随机分析表明RLS是糖尿病的一个因果风险因素。

结合遗传和非遗传信息的机器学习方法在风险预测方面表现最佳（曲线下面积(AUC)=0.82-0.91）。总之，该研究确定了药物开发和再利用的靶点，优先考虑了RLS与相关并发症和风险因素之间的潜在因果关系，并表明非线性相互作用可能是预测RLS发病风险的相关因素。

附：英文原文

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

Author: Schormair, Barbara, Zhao, Chen, Bell, Steven, Didriksen, Maria, Nawaz, Muhammad S., Schandra, Nathalie, Stefani, Ambra, Hgl, 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, Srensen, 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, 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

Issue&Volume: 2024-06-05

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.

DOI: 10.1038/s41588-024-01763-1

Source: https://www.nature.com/articles/s41588-024-01763-1