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. 2021 Nov 23;12(12):1859.
doi: 10.3390/genes12121859.

Validity and Prognostic Value of a Polygenic Risk Score for Parkinson's Disease

Sebastian Koch  1 , Björn-Hergen Laabs  2 , Meike Kasten  3   4 , Eva-Juliane Vollstedt  4 , Jos Becktepe  5 , Norbert Brüggemann  4   6 , Andre Franke  7 , Ulrike M Krämer  6 , Gregor Kuhlenbäumer  5 , Wolfgang Lieb  8 , Brit Mollenhauer  9   10 , Miriam Neis  6   11 , Claudia Trenkwalder  10   12 , Eva Schäffer  5 , Tatiana Usnich  4 , Michael Wittig  7 , Christine Klein  4 , Inke R König  2 , Katja Lohmann  4 , Michael Krawczak  1 , Amke Caliebe  1
Affiliations

Validity and Prognostic Value of a Polygenic Risk Score for Parkinson's Disease

Sebastian Koch et al. Genes (Basel). .

Abstract

Idiopathic Parkinson's disease (PD) is a complex multifactorial disorder caused by the interplay of both genetic and non-genetic risk factors. Polygenic risk scores (PRSs) are one way to aggregate the effects of a large number of genetic variants upon the risk for a disease like PD in a single quantity. However, reassessment of the performance of a given PRS in independent data sets is a precondition for establishing the PRS as a valid tool to this end. We studied a previously proposed PRS for PD in a separate genetic data set, comprising 1914 PD cases and 4464 controls, and were able to replicate its ability to differentiate between cases and controls. We also assessed theoretically the prognostic value of the PD-PRS, i.e., its ability to predict the development of PD in later life for healthy individuals. As it turned out, the PD-PRS alone can be expected to perform poorly in this regard. Therefore, we conclude that the PD-PRS could serve as an important research tool, but that meaningful PRS-based prognosis of PD at an individual level is not feasible.

Keywords: Parkinson’s disease; genetic risk; polygenic risk score; prognostic value; replication; validation.

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Conflict of interest statement

C.K. serves as a medical advisor for genetic testing reports in the field of movement disorders and dementia, but excluding Parkinson’s disease, to Centogene and as a member of the Scientific Advisory Board of Retromer Therapeutics. N.B. has previously served as a consultant for Centogene GmbH. The other authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure A1
Figure A1
Identification of population outliers by PCA drawing upon 1000Genomes data. White circles represent polygonal circle approximations around European samples of the 1000Genomes project. The thick black line marks the union set, the thinner line marks the final boundary. Dots representing our samples are colored according to their inclusion in or exclusion from the study. Samples were excluded if they were outside the boundary. PC: principal component, PCA: principal component analysis.
Figure A2
Figure A2
PCA plots after quality control. (A) Plot of the first two PCs from the 1000Genomes supra populations and the samples of this study. Our study samples were plotted on top, therefore obscuring part of the European samples from the 1000Genomes project. (B) Plot of the first two PCs from the cohorts included in our study (Table A1). PC: principal component, PCA: principal component analysis.
Figure 1
Figure 1
PD-PRS in PD cases and controls. (A) Density of PD-PRS in cases and controls. (B) ROC curve for PD-PRS as a predictor of case-control status. PRS: polygenic risk score, PD: Parkinson’s disease, ROC: receiver operating characteristic.
Figure 2
Figure 2
Disease OR for the 2nd to 10th deciles of the PD-PRS distribution among controls. (1st decile used as reference). Vertical bars demarcate 95% confidence intervals. OR: odds ratio, PD: Parkinson’s disease, PRS: polygenic risk score.
Figure 3
Figure 3
PD-PRS in early and late onset cases. (A) Density of PD-PRS in the 1st and 4th AAO quartile of cases. (B) ROC curve for PD-PRS as a predictor of 1st vs 4th AAO quartile. AAO: age-at-onset, PRS: polygenic risk score, PD: Parkinson’s disease, ROC: receiver operating characteristic.
Figure 4
Figure 4
Influence of individual SNPs upon PD-PRS performance. For each of the 1743 PD-PRS SNPs, the AUC was calculated after removing the SNP from the PRS. SNPs were color-coded as either genome-wide significant in a meta-GWAS [2] (blue), as ‘most relevant’ in the present study (red), both of the former (black) or none of the former (yellow). SNP: single nucleotide polymorphism, PD: Parkinson’s disease, PRS: polygenic risk score, AUC: area under ROC curve, ROC: receiver operating characteristic, GWAS: genome-wide association study.
Figure 5
Figure 5
Prognostic value of PD-PRS. (A) Sensitivity and specificity of PD-PRS for the optimal threshold were determined by maximizing a weighted Youden index. The relative costs of false negative vs false positive results varied from 1 to 5. (B) ppv and npv were calculated from the costs-based sensitivity and specificity and the residual lifetime incidence (see Methods and Table A3) in 10 age groups. PRS: polygenic risk score, PD: Parkinson’s disease, ppv: positive predictive value, npv: negative predictive value.
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