Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson's disease
Aaron G Day-Williams,
David J Stone,
International Parkinson’s Disease Genomics Consortium (IPDGC),
Andrew B. Singleton,
Mike A Nalls,
Posted 05 Oct 2018
bioRxiv DOI: 10.1101/427385 (published DOI: 10.1136/jmedgenet-2019-106283)
Posted 05 Oct 2018
Background: Improper randomization in clinical trials can result in the failure of the trial to meet its primary end-point. The last ~10 years have revealed that common and rare genetic variants are an important disease factor and sometimes account for a substantial portion of disease risk variance. However, the burden of common genetic risk variants is not often considered in the randomization of clinical trials and can therefore lead to additional unwanted variance between trial arms. We simulated clinical trials to estimate false negative and false positive rates and investigated differences in single variants and mean genetic risk scores (GRS) between trial arms to investigate the potential effect of genetic variance on clinical trial outcomes at different sample sizes. Methods: Single variant and genetic risk score analyses were conducted in a clinical trial simulation environment using data from 5851 Parkinson's Disease patients as well as two simulated virtual cohorts based on public data. The virtual cohorts included a GBA variant cohort and a two variant interaction cohort. Data was resampled at different sizes (n=200-5000 for the Parkinson's Disease cohort) and (n=50-800 and n=50-2000 for virtual cohorts) for 1000 iterations and randomly assigned to the two arms of a trial. False negative and false positive rates were estimated using simulated clinical trials, and percent difference in genetic risk score and allele frequency was calculated to quantify disparity between arms. Results: Significant genetic differences between the two arms of a trial are found at all sample sizes. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 10% of iterations had a statistically significant difference between trial arms in polygenic risk score mean or variance. For significant iterations at sample size 200, the average percent difference for mean GRS between trial arms was 130.87%, decreasing to 29.87% as sample size reached 5000. By using simulated drug effects, we found that unbalanced genetics with an effect on the chosen measurable clinical outcome can result in high false negative rates among trials, especially at small sample sizes. At a sample size of n=50 and a targeted drug effect of -0.5 points in UPDRS per year, we found 35.0% of trials resulted in false negatives. In the GBA only simulations we see an average 18.86% difference in GRS scores between trial arms at n=50, decreasing to 3.09% as sample size reaches 2000. Balancing patients by genotype reduced mean percent difference in GRS between arms to 8.16% for the main cohort and 2.00% for the GBA cohort at n=200. Conclusions: Our data support the hypothesis that within genetically unmatched clinical trials, particularly those below 1000 participants, heterogeneity could confound true therapeutic effects as expected. This is particularly important in the changing environment of drug approvals. Clinical trials should undergo pre-trial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials. Clinical trial arms should be balanced on genetic risk variants, as well as cumulative variant distributions represented by GRS, in order to ensure the maximum reduction in trial arm disparities. The reduction in variance after balancing allows smaller sample sizes to be utilized without risking the large disparities between trial arms witnessed in typical randomized trials. As the cost of genotyping will likely be far less than greatly increasing sample size, genetically balancing trial arms can lead to more cost-effective clinical trials as well as better outcomes.
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