Polygenic risk scores in neurological disorders: restless legs syndrome as a translational model.

Abstract

Polygenic risk scores (PRS) aggregate the effects of common genetic variants into a single metric of disease predisposition. Many neurological disorders exhibit a polygenic architecture, thereby providing a rationale for the application of PRS in risk prediction, biological subtyping, and stratification of patients to inform clinical decision-making. Here, we use restless legs syndrome (RLS) as an informative translational model to discuss both opportunities and current constraints of PRS use in neurology. RLS has a well-characterized polygenic component with 164 GWAS risk loci, a PRS with moderate case-control discrimination (AUC 0.73) when used alone, but showing potential for higher performance (AUC 0.82-0.91) in machine-learning models incorporating non-genetic variables. We discuss how multi-omics integration, PRS-based clinical subgrouping, and rare variant penetrance modification can advance PRS development and application in RLS and contextualize these developments within the wider landscape of PRS in neurological disorders.