Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson's disease

Abstract

Background: Classical randomisation of clinical trial patients creates a source of genetic variance that may be contributing to the high failure rate seen in neurodegenerative disease trials. Our objective was to quantify genetic difference between randomised trial arms and determine how imbalance can affect trial outcomes.

Methods: 5851 patients with Parkinson's disease of European ancestry data and two simulated virtual cohorts based on public data were used. Data were resampled at different sizes for 1000 iterations and randomly assigned to the two arms of a simulated trial. False-negative and false-positive rates were estimated using simulated clinical trials, and per cent difference in genetic risk score (GRS) and allele frequency was calculated to quantify variance between arms.

Results: 5851 patients with Parkinson's disease (mean (SD) age, 61.02 (12.61) years; 2095 women (35.81%)) as well as simulated patients from virtually created cohorts were used in the study. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 5%-6% of iterations had a statistically significant difference between trial arms in mean GRS. For significant iterations, the average per cent difference for mean GRS between trial arms was 130.87%, 95% CI 120.89 to 140.85 (n=200). Glucocerebrocidase (GBA) gene-only simulations see an average 18.86%, 95% CI 18.01 to 19.71 difference in GRS scores between trial arms (n=50). When adding a drug effect of -0.5 points in MDS-UPDRS per year at n=50, 33.9% of trials resulted in false negatives.

Conclusions: Our data support the hypothesis that within genetically unmatched clinical trials, genetic heterogeneity could confound true therapeutic effects as expected. Clinical trials should undergo pretrial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials.

Keywords: clinical genetics; genetics; parkinson-s disease.

Figure 1.. Analysis Workflow

A visualization of the analysis process. This workflow was followed for every chosen sample size and repeated for 1000 iterations.

Figure 2.

(A) Average per cent difference in GRS between trial groups (significant iterations). (B) Average per cent difference between GBA trial groups (significant iterations). (C) Total number of significant variants per sample size/lines are fit to relationships using locally weighted polynomial regression models (LOESS) and CIs are shown in grey. Residual SE and span values were obtained from the LOESS models and significance and R2 values were obtained from polynomial regression models. (A) The relationship between per cent difference in between-group variance and sample size is shown. Significant iterations were determined by Z-score cut-off values of ±1.96 (p<0.05). Span=1.4; residual SE=7.946; adjusted R2=0.928; p=0.0006. (B) The relationship between per cent difference in between-group variance and sample size is shown for the GBA cohort. Significant iterations were determined by Z-score cut-off values of ±1.96 (p<0.05). Span=1.17; residual SE=2.379; adjusted R2=0.794; p=0.0032. (C) The relationship between the total number of times a variant was a significant predictor and sample size is shown. Significance for prediction was determined by a binomial generalised linear model with a legit link function (p<0.05). Span=1.4; residual SE=57.52; adjusted R2=0.585; p=0.097. GRS, genetic risk score.