Polygenic risk score-based prediction for Parkinson’s disease

Abstract

AbstractParkinson’s disease (PD) is a complex neurodegenerative disorder with unclear etiology and ineffective treatments. Integrating multimodal data for PD prediction remains challenging. We analyzed data obtained from the Parkinson’s Progression Markers Initiative, using polygenic risk scores (PRS) to reflect genetic susceptibility to PD. We compared the prediction accuracy of models with PRS, demographics, clinical assessment, and biomarkers progressively integrated and investigated relationships. The SDPR-based PRS exhibited the highest prediction performance with an AUC of 0.75. Models combining PRS, demographic, and clinical variables achieved an AUC of 0.91, surpassing models without PRS and matching those with biomarkers. PRS correlated with olfactory function and Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), with its influence on PD risk dependent on gender and MDS-UPDRS. Our study illuminates PD etiology and provides a practical risk assessment framework, highlighting its omnigenic architecture, and the potential for accurate prediction using PRS and non-invasive clinical data.