Validity and Prognostic Value of a Polygenic Risk Score for Parkinson's Disease.
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ABSTRACT:
SUBMITTER: Koch S
PROVIDER: S-EPMC8700849 | biostudies-literature | 2021 Nov
REPOSITORIES: biostudies-literature
Publications
Validity and Prognostic Value of a Polygenic Risk Score for Parkinson's Disease.
Koch Sebastian S Koch Sebastian S Laabs Björn-Hergen BH Kasten Meike M Vollstedt Eva-Juliane EJ Becktepe Jos J Brüggemann Norbert N Franke Andre A Krämer Ulrike M UM Kuhlenbäumer Gregor G Lieb Wolfgang W Mollenhauer Brit B Neis Miriam M Trenkwalder Claudia C Schäffer Eva E Usnich Tatiana T Wittig Michael M Klein Christine C König Inke R IR Lohmann Katja K Krawczak Michael M Caliebe Amke A
Genes 20211123 12
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 P ...[more]
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Project description:BackgroundThe use of a polygenic risk score (PRS) to improve risk prediction of coronary heart disease (CHD) events has been demonstrated to have clinical utility in the general adult population. However, the prognostic value of a PRS for CHD has not been examined specifically in older populations of individuals aged ≥70 years, who comprise a distinct high-risk subgroup. The objective of this study was to evaluate the predictive value of a PRS for incident CHD events in a prospective cohort of older individuals without a history of cardiovascular events.MethodsWe used data from 12 792 genotyped, healthy older individuals enrolled into the ASPREE trial (Aspirin in Reducing Events in the Elderly), a randomized double-blind placebo-controlled clinical trial investigating the effect of daily 100 mg aspirin on disability-free survival. Participants had no previous history of diagnosed atherothrombotic cardiovascular events, dementia, or persistent physical disability at enrollment. We calculated a PRS (meta-genomic risk score) consisting of 1.7 million genetic variants. The primary outcome was a composite of incident myocardial infarction or CHD death over 5 years.ResultsAt baseline, the median population age was 73.9 years, and 54.9% were female. In total, 254 incident CHD events occurred. When the PRS was added to conventional risk factors, it was independently associated with CHD (hazard ratio, 1.24 [95% CI, 1.08-1.42], P=0.002). The area under the curve of the conventional model was 70.53 (95% CI, 67.00-74.06), and after inclusion of the PRS increased to 71.78 (95% CI, 68.32-75.24, P=0.019), demonstrating improved prediction. Reclassification was also improved, as the continuous net reclassification index after adding PRS to the conventional model was 0.25 (95% CI, 0.15-0.28).ConclusionA PRS for CHD performs well in older people and improves prediction over conventional cardiovascular risk factors. Our study provides evidence that genomic risk prediction for CHD has clinical utility in individuals aged 70 years and older. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01038583.
| S-EPMC8847323 | biostudies-literature
Project description:BackgroundParkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders.ObjectivesWe aimed to develop and validate a polygenic hazard score model in sporadic PD.MethodsUsing a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls.ResultsA polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups.ConclusionsWe demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
| S-EPMC9843635 | biostudies-literature
Project description:Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.
| S-EPMC10864389 | biostudies-literature
Project description:BackgroundDeep brain stimulation (DBS) is a well-established treatment option for select patients with Parkinson's Disease (PD). However, response to DBS varies, therefore, the ability to predict who will have better outcomes can aid patient selection. Some PD-related monogenic mutations have been reported among factors that influence response to DBS. However, monogenic disease accounts for only a minority of patients with PD. The polygenic risk score (PRS) is an indication of cumulative genetic risk for disease. The PRS in PD has also been correlated with age of onset and symptom progression, but it is unknown whether correlations exist between PRS and DBS response. Here, we performed a pilot study to look for any such correlation.MethodsWe performed a retrospective analysis of 33 PD patients from the NIH PD Clinic and 13 patients from the Parkinson's Progression Markers Initiative database who had genetic testing and underwent bilateral subthalamic nucleus DBS surgery and clinical follow-up. A PD-specific PRS was calculated for all 46 patients based on the 90 susceptibility variants identified in the latest PD genome-wide association study. We tested associations between PRS and pre- and post-surgery motor and cognitive measures using multiple regression analysis for up to two years after surgery.ResultsChanges in scores on the Beck Depression Inventory (BDI) were not correlated with PRS when derived from all susceptibility variants, however, when removing pathogenic and high-risk carriers from the calculation, higher PRS was significantly associated with greater reduction in BDI score at 3 months and with similar trend 24 months after DBS. PRS was not a significant predictor of Unified Parkinson's Disease Rating Scale, Dementia Rating Scale, or phenomic and semantic fluency outcomes at 3- and 24-months after DBS surgery.ConclusionsThis exploratory study suggests that PRS may predict degree of improvement in depressive symptoms after DBS, though was not predictive of motor and other cognitive outcomes after DBS. Additionally, PRS may be most relevant in predicting DBS outcomes in patients lacking pathogenic or high-risk PD variants. However, this was a small preliminary study and response to DBS treatment is multifactorial, therefore, more standardized high-powered studies are needed.
| S-EPMC10071605 | biostudies-literature
Project description:Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.
| S-EPMC8564367 | biostudies-literature
Project description:BackgroundLow-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson's disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.ResultsUsing eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered.ConclusionsOverall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.
| S-EPMC8403377 | biostudies-literature
Project description:BackgroundAlthough the leucine-rich repeat kinase 2 p.G2019S mutation has been demonstrated to be a strong risk factor for PD, factors that contribute to penetrance among carriers, other than aging, have not been well identified.ObjectivesTo evaluate whether a cumulative genetic risk identified in the recent genome-wide study is associated with penetrance of PD among p.G2019S mutation carriers.MethodsWe included p.G2019S heterozygote carriers with European ancestry in three genetic cohorts in which the mutation carriers with and without PD were selectively recruited. We also included the carriers from two data sets: one from a case-control setting without selection of mutation carriers and the other from a population sampling. Associations between polygenic risk score constructed from 89 variants reported recently and PD were tested and meta-analyzed. We also explored the interaction of age and PRS.ResultsAfter excluding eight homozygotes, 833 p.G2019S heterozygote carriers (439 PD and 394 unaffected) were analyzed. Polygenic risk score was associated with a higher penetrance of PD (odds ratio: 1.34; 95% confidence interval: [1.09, 1.64] per +1 standard deviation; P = 0.005). In addition, associations with polygenic risk score and penetrance were stronger in the younger participants (main effect: odds ratio 1.28 [1.04, 1.58] per +1 standard deviation; P = 0.022; interaction effect: odds ratio 0.78 [0.64, 0.94] per +1 standard deviation and + 10 years of age; P = 0.008).ConclusionsOur results suggest that there is a genetic contribution for penetrance of PD among p.G2019S carriers. These results have important etiological consequences and potential impact on the selection of subjects for clinical trials. © 2020 International Parkinson and Movement Disorder Society.
| S-EPMC8975556 | biostudies-literature
Project description:Background: Asthma is common chronic inflammatory disease of the airways with a heterogenous clinical presentation. Individual differences in asthma susceptibility remain poorly understood, although genetics is thought to play a major role. Aim: To build a polygenic risk score (PRS) for asthma and determine whether predictive genetic variants can be epigenomically linked to specific pathophysiological mechanisms. Methods: PRSs were constructed using data from genome-wide association studies and performance was validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14,926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and exacerbations. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) data from 14 primary cell types, including lung epithelial cells and T lymphocytes was used for epigenomic PRS partitioning. Results: All PRSs successfully predicted risk to develop asthma and related outcomes, with the strongest predictive power (2.42 odds ratios per PRS standard deviation, area under the curve of 0.736) achieved for childhood-onset asthma. PRSs allowed for stratification of the Rotterdam Study cohort into groups at low or high risk to develop asthma. PRS partitioning using genome-wide epigenomic profiles identified 5 clusters of variants within gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways. Conclusions: PRSs can predict whether individuals in a Dutch cohort developed asthma and asthma-related phenotypes, which is most effective for childhood-onset asthma. Importantly, we show that PRS partitioning based on epigenomics data dissects a genetic risk score into blocks of regulatory variants with differential predictive power, which likely represent distinct genetically driven disease pathways. These findings have potential implications for personalized risk mitigation and treatment strategies.
2024-07-03 | GSE245569 | GEO
Project description:Polygenic risk scores (PRS) summarize genetic liability to a disease at the individual level, and the aim is to use them as biomarkers of disease and poor outcomes in real-world clinical practice. To date, few studies have assessed the prognostic value of PRS relative to standards of care. Schizophrenia (SCZ), the archetypal psychotic illness, is an ideal test case for this because the predictive power of the SCZ PRS exceeds that of most other common diseases. Here, we analyzed clinical and genetic data from two multi-ethnic cohorts totaling 8,541 adults with SCZ and related psychotic disorders, to assess whether the SCZ PRS improves the prediction of poor outcomes relative to clinical features captured in a standard psychiatric interview. For all outcomes investigated, the SCZ PRS did not improve the performance of predictive models, an observation that was generally robust to divergent case ascertainment strategies and the ancestral background of the study participants.
| S-EPMC8446329 | biostudies-literature
Project description:IntroductionRecent clinical trials are considering inclusion of more than just apolipoprotein E (APOE) ε4 genotype as a way of reducing variability in analysis of outcomes.MethodsCase-control data were used to compare the capacity of age, sex, and 58 Alzheimer's disease (AD)-associated single nucleotide polymorphisms (SNPs) to predict AD status using several statistical models. Model performance was assessed with Brier scores and tenfold cross-validation. Genotype and sex × age estimates from the best performing model were combined with age and intercept estimates from the general population to develop a personalized genetic risk score, termed age, and sex-adjusted GenoRisk.ResultsThe elastic net model that included age, age x sex interaction, allelic APOE terms, and 29 additional SNPs performed the best. This model explained an additional 19% of the heritable risk compared to APOE genotype alone and achieved an area under the curve of 0.747.DiscussionGenoRisk could improve the risk assessment of individuals identified for prevention studies.
| S-EPMC8485054 | biostudies-literature