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

2016 ◽  
Vol 31 (4) ◽  
pp. 487-490 ◽  
Author(s):  
Lasse Pihlstrøm ◽  
Kristina Rebekka Morset ◽  
Espen Grimstad ◽  
Valeria Vitelli ◽  
Mathias Toft
Author(s):  
Alison Hall ◽  
Samuel R. Weaver ◽  
Lindsey J. Compton ◽  
Winston D. Byblow ◽  
Ned Jenkinson ◽  
...  

Brain ◽  
2019 ◽  
Vol 143 (1) ◽  
pp. 234-248 ◽  
Author(s):  
Cornelis Blauwendraat ◽  
Xylena Reed ◽  
Lynne Krohn ◽  
Karl Heilbron ◽  
Sara Bandres-Ciga ◽  
...  

Abstract Parkinson’s disease is a genetically complex disorder. Multiple genes have been shown to contribute to the risk of Parkinson’s disease, and currently 90 independent risk variants have been identified by genome-wide association studies. Thus far, a number of genes (including SNCA, LRRK2, and GBA) have been shown to contain variability across a spectrum of frequency and effect, from rare, highly penetrant variants to common risk alleles with small effect sizes. Variants in GBA, encoding the enzyme glucocerebrosidase, are associated with Lewy body diseases such as Parkinson’s disease and Lewy body dementia. These variants, which reduce or abolish enzymatic activity, confer a spectrum of disease risk, from 1.4- to >10-fold. An outstanding question in the field is what other genetic factors that influence GBA-associated risk for disease, and whether these overlap with known Parkinson’s disease risk variants. Using multiple, large case-control datasets, totalling 217 165 individuals (22 757 Parkinson’s disease cases, 13 431 Parkinson’s disease proxy cases, 622 Lewy body dementia cases and 180 355 controls), we identified 1691 Parkinson’s disease cases, 81 Lewy body dementia cases, 711 proxy cases and 7624 controls with a GBA variant (p.E326K, p.T369M or p.N370S). We performed a genome-wide association study and analysed the most recent Parkinson’s disease-associated genetic risk score to detect genetic influences on GBA risk and age at onset. We attempted to replicate our findings in two independent datasets, including the personal genetics company 23andMe, Inc. and whole-genome sequencing data. Our analysis showed that the overall Parkinson’s disease genetic risk score modifies risk for disease and decreases age at onset in carriers of GBA variants. Notably, this effect was consistent across all tested GBA risk variants. Dissecting this signal demonstrated that variants in close proximity to SNCA and CTSB (encoding cathepsin B) are the most significant contributors. Risk variants in the CTSB locus were identified to decrease mRNA expression of CTSB. Additional analyses suggest a possible genetic interaction between GBA and CTSB and GBA p.N370S induced pluripotent cell-derived neurons were shown to have decreased cathepsin B expression compared to controls. These data provide a genetic basis for modification of GBA-associated Parkinson’s disease risk and age at onset, although the total contribution of common genetics variants is not large. We further demonstrate that common variability at genes implicated in lysosomal function exerts the largest effect on GBA associated risk for disease. Further, these results have implications for selection of GBA carriers for therapeutic interventions.


2018 ◽  
Author(s):  
Hampton Leonard ◽  
Cornelis Blauwendraat ◽  
Lynne Krohn ◽  
Faraz Faghri ◽  
Hirotaka Iwaki ◽  
...  

SummaryBackgroundImproper 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.MethodsSingle 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.FindingsSignificant 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. 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 36.71% for the main cohort and 2.00% for the GBA cohort at n = 200. When adding a drug effect to the simulations, 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 discovered 33.9% of trials resulted in false negatives.InterpretationsOur 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.


Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1535-P
Author(s):  
RACHEL G. MILLER ◽  
TINA COSTACOU ◽  
SUNA ONENGUT-GUMUSCU ◽  
WEI-MIN CHEN ◽  
STEPHEN S. RICH ◽  
...  

Author(s):  
Sara R. Rashkin ◽  
Evadnie Rampersaud ◽  
Guolian Kang ◽  
Kenneth I. Ataga ◽  
Jane S. Hankins ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.


2011 ◽  
Vol 258 (S2) ◽  
pp. 311-315 ◽  
Author(s):  
Jürgen Winkler ◽  
Reinhard Ehret ◽  
Thomas Büttner ◽  
Ulrich Dillmann ◽  
Wolfgang Fogel ◽  
...  

JAMA ◽  
2016 ◽  
Vol 316 (17) ◽  
pp. 1825
Author(s):  
Marcus R. Munafò ◽  
Kate Tilling ◽  
George Davey Smith

Sign in / Sign up

Export Citation Format

Share Document