NIH Toolbox performance of persons with Parkinson's disease according to GBA1 and STN-DBS status.

OBJECTIVE: Mutations in the glucocerebrosidase (GBA1) gene and subthalamic nucleus deep brain stimulation (STN-DBS) are independently associated with cognitive dysfunction in persons with Parkinson's disease (PwP). We hypothesized that PwP with both GBA1 mutations and STN-DBS are at greater risk of cognitive dysfunction than PwP with only GBA1 mutations or STN-DBS, or neither. In this study, we determined the pattern of cognitive dysfunction in PwP based on GBA1 mutation status and STN-DBS treatment. METHODS: PwP who are GBA1 mutation carriers with or without DBS (GBA1+DBS+, GBA1+DBS-), and noncarriers with or without DBS (GBA1-DBS+, GBA1-DBS-) were included. Using the NIH Toolbox, cross-sectional differences in response inhibition, processing speed, and episodic memory were compared using analysis of variance with adjustment for relevant covariates. RESULTS: Data were available for 9 GBA1+DBS+, 14 GBA1+DBS-, 17 GBA1-DBS+, and 26 GBA1-DBS- PwP. In this cross-sectional study, after adjusting for covariates, we found that performance on the Flanker test (measure of response inhibition) was lower in GBA1+DBS+ PwP compared with GBA1-DBS+ PwP (P = 0.030). INTERPRETATION: PwP who carry GBA1 mutations and have STN-DBS have greater impaired response inhibition compared with PwP with STN-DBS but without GBA1 mutations. Longitudinal data, including preoperative scores, are required to definitively determine whether GBA1 mutation carriers respond differently to STN-DBS, particularly in the domain of response inhibition.

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.