Literature DB >> 31784483

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

Mike A Nalls1,2, Ziv Gan-Or3,4, Hampton Leonard5,2, Cornelis Blauwendraat5, Lynne Krohn6, Faraz Faghri5,7, Hirotaka Iwaki5,8, Glen Ferguson9, Aaron G Day-Williams10, David J Stone10, Andrew B Singleton5.   

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. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  clinical genetics; genetics; parkinson-s disease

Mesh:

Year:  2019        PMID: 31784483      PMCID: PMC8474559          DOI: 10.1136/jmedgenet-2019-106283

Source DB:  PubMed          Journal:  J Med Genet        ISSN: 0022-2593            Impact factor:   6.318


  31 in total

1.  The NCBI dbGaP database of genotypes and phenotypes.

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Journal:  Nat Genet       Date:  2007-10       Impact factor: 38.330

2.  ApoE genotyping as a progression-rate biomarker in phase II disease-modification trials for Alzheimer's disease.

Authors:  D J Stone; C Molony; C Suver; E E Schadt; W Z Potter
Journal:  Pharmacogenomics J       Date:  2009-12-08       Impact factor: 3.550

Review 3.  Heterogeneity of leucine-rich repeat kinase 2 mutations: genetics, mechanisms and therapeutic implications.

Authors:  Iakov N Rudenko; Mark R Cookson
Journal:  Neurotherapeutics       Date:  2014-10       Impact factor: 7.620

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Journal:  Neurosci Lett       Date:  2017-08-19       Impact factor: 3.046

Review 5.  A cumulative genetic risk score predicts progression in Parkinson's disease.

Authors:  Lasse Pihlstrøm; Kristina Rebekka Morset; Espen Grimstad; Valeria Vitelli; Mathias Toft
Journal:  Mov Disord       Date:  2016-02-08       Impact factor: 10.338

6.  Differential effects of severe vs mild GBA mutations on Parkinson disease.

Authors:  Ziv Gan-Or; Idan Amshalom; Laura L Kilarski; Anat Bar-Shira; Mali Gana-Weisz; Anat Mirelman; Karen Marder; Susan Bressman; Nir Giladi; Avi Orr-Urtreger
Journal:  Neurology       Date:  2015-02-04       Impact factor: 9.910

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Journal:  Nature       Date:  2016-08-18       Impact factor: 49.962

10.  The glucocerobrosidase E326K variant predisposes to Parkinson's disease, but does not cause Gaucher's disease.

Authors:  Raquel Duran; Niccolo E Mencacci; Aikaterini V Angeli; Maryam Shoai; Emma Deas; Henry Houlden; Atul Mehta; Derralynn Hughes; Timothy M Cox; Patrick Deegan; Anthony H Schapira; Andrew J Lees; Patricia Limousin; Paul R Jarman; Kailash P Bhatia; Nicholas W Wood; John Hardy; Tom Foltynie
Journal:  Mov Disord       Date:  2012-12-05       Impact factor: 10.338

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  15 in total

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4.  Combining biomarkers for prognostic modelling of Parkinson's disease.

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5.  Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study.

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Journal:  Lancet Digit Health       Date:  2022-03-24

Review 6.  The genetic architecture of Parkinson's disease.

Authors:  Cornelis Blauwendraat; Mike A Nalls; Andrew B Singleton
Journal:  Lancet Neurol       Date:  2019-09-11       Impact factor: 44.182

Review 7.  Precision medicine in Parkinson's disease patients with LRRK2 and GBA risk variants - Let's get even more personal.

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8.  Penetrance of Parkinson's Disease in LRRK2 p.G2019S Carriers Is Modified by a Polygenic Risk Score.

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9.  Parkinson's disease determinants, prediction and gene-environment interactions in the UK Biobank.

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10.  Genetic modifiers of risk and age at onset in GBA associated Parkinson's disease and Lewy body dementia.

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Journal:  Brain       Date:  2020-01-01       Impact factor: 13.501

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