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

2020 ◽  
Vol 35 (5) ◽  
pp. 774-780 ◽  
Author(s):  
Hirotaka Iwaki ◽  
Cornelis Blauwendraat ◽  
Mary B. Makarious ◽  
Sara Bandrés‐Ciga ◽  
Hampton L. Leonard ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson’s disease

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Sungjae Kim ◽  
Jong-Yeon Shin ◽  
Nak-Jung Kwon ◽  
Chang-Uk Kim ◽  
Changhoon Kim ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Risk Score ◽  
Genotype Imputation ◽  
Polygenic Risk Score ◽  
Array Data ◽  
Polygenic Risk ◽  
Low Pass ◽  
Low Coverage ◽  
Genotype Array

Abstract Background Low-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. Results Using 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. Conclusions Overall, 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.

scholarly journals Association of the Polygenic Risk Score With the Probability of Prodromal Parkinson’s Disease in Older Adults

2021 ◽  
Vol 14 ◽  
Author(s):  
Maria I. Maraki ◽  
Alexandros Hatzimanolis ◽  
Niki Mourtzi ◽  
Leonidas Stefanis ◽  
Mary Yannakoulia ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Risk Score ◽  
Strong Relationship ◽  
Population Based ◽  
Community Dwelling ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Common Genetic Variants ◽  
Research Criteria

Several studies have investigated the association of the Parkinson’s disease (PD) polygenic risk score (PRS) with several aspects of well-established PD. We sought to evaluate the association of PRS with the prodromal stage of PD. We calculated PRS in a longitudinal sample (n = 1120) of community dwelling individuals ≥ 65 years from the HELIAD (The Hellenic Longitudinal Investigation of Aging and Diet) study in order to evaluate the association of this score with the probability of prodromal PD or any of the established risk and prodromal markers in MDS research criteria, using regression multi-adjusted models. Increases in PRS estimated from GWAS summary statistics’ ninety top SNPS with p < 5 × 10–8 was associated with increased odds of having probable/possible prodromal PD (i.e., ≥ 30% probability, OR = 1.033, 95%CI: 1.009–1.057 p = 0.006). From the prodromal PD risk markers, significant association was found between PRS and global cognitive deficit exclusively (p = 0.003). To our knowledge, our study is the first population based study investigating the association between PRS scores and prodromal markers of Parkinson’s disease. Our results suggest a strong relationship between the accumulation of many common genetic variants, as measured by PRS, and cognitive deficits.

scholarly journals Genome-Wide Polygenic Risk Score Identifies Individuals at Elevated Parkinson’s Disease Risk

2020 ◽  
Author(s):  
Yingnan Han ◽  
Erin Teeple ◽  
Srinivas Shankara ◽  
Mahdiar Sadeghi ◽  
Cheng Zhu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Risk Score ◽  
Age Of Onset ◽  
Polygenic Risk Score ◽  
List Type ◽  
Polygenic Risk ◽  
Genome Wide ◽  
A Genome ◽  
Dopamine Signaling

SUMMARYParkinson’s Disease (PD) is the second most common and fastest-growing neurological disorder. Polygenic Risk Scores (PRS) using hundreds to thousands of PD-associated variants support polygenic heritability. Here, for the first time, we apply a genome-wide polygenic risk score approach using 6.2 million variants to compute a PD genome-wide polygenic risk score (PD-GPRS) via the LDPred algorithm. PD-GPRS validation and testing used Accelerating Medicines Partnership – Parkinson’s Disease (AMP-PD) and FinnGen Consortia genomic data from 1,654 PD Cases and 79,123 Controls. PD odds for the top 8%, 2.5%, and 1% of PD-GPRS were three-, four-, and seven times greater compared with lower percentiles, respectively (p<1e-10). PD age of onset and MDS-UPDRS motor scores also differed by PD-GPRS decile. Enrichment for phagosome related, dopamine signaling, immune related, and neuronal signaling pathways was found for genes nearest high PD-GPRS variants identified by MAF analysis. PD-GPRS offers a promising screening tool to identify high-risk individuals for preventive lifestyle or new drug therapy trials.In BriefIn Han and Teeple et al., Parkinson’s Disease inherited risk is quantified by a genome-wide polygenic risk score (PD-GPRS) approach using 6.2 million variants and data from 80,777 individuals. For the top 2.5% and 1% of PD-GPRS, individuals had five- and seven-fold greater odds of PD, respectively. PD-GPRS was found to be associated with overall PD risk, earlier age of onset, and MDS-UPDRS motor scores. Genes nearest to variants observed at higher frequencies among high-GPRS individuals are enriched for PD-implicated pathways.HIGHLIGHTS-Parkinson’s Disease genome-wide polygenic risk score (PD-GPRS) calculated from 6.2 million variants identifies individuals with inherited clinically significant increased neurodegeneration risk.-Top percentile PD-GPRS individuals were found to have up to seven-fold greater odds of PD and earlier age at PD diagnosis.-PD-GPRS scores correlated with all-subjects cohort mean MDS-UPDRS motor scores.-Pathway analysis of genes adjacent to frequently occurring variants in the high PD-GPRS population identified polygenic risk contributions for variations in PD-implicated pathways including dopamine signaling, immune responses, and autophagy pathways.

scholarly journals Validity and Prognostic Value of a Polygenic Risk Score for Parkinson’s Disease

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1859
Author(s):  
Sebastian Koch ◽  
Björn-Hergen Laabs ◽  
Meike Kasten ◽  
Eva-Juliane Vollstedt ◽  
Jos Becktepe ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Prognostic Value ◽  
Later Life ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Data Sets ◽  
Data Set ◽  
Polygenic Risk ◽  
Individual Level

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.

scholarly journals Genome-wide association studies of LRRK2 modifiers of Parkinson's disease

2020 ◽  
Author(s):  
Dongbing Lai ◽  
Babak Alipanahi ◽  
Pierre Fontanillas ◽  
Tae-Hwi Schwantes-An ◽  
Jan Aasly ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Age At Onset ◽  
Genome Wide Association ◽  
P Value ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genome Wide

Objective: The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age-at-onset of Parkinson's disease. Methods: We performed the first genome-wide association study of penetrance and age-at-onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non-cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age-at-onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genome-wide association study of Parkinson's disease was able to explain variability in penetrance and age-at-onset in LRRK2 mutation carriers. Results: A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; P-value=2.5E-08, beta=1.27, SE=0.23, risk allele: C) met genome-wide significance for the penetrance model. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: P-value=1.1E-07; age-at-onset top variant: P-value=9.3E-07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age-at-onset. Interpretation: This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations.

scholarly journals Interaction-based feature selection algorithm outperforms polygenic risk score in predicting Parkinson's Disease status

2021 ◽  
Author(s):  
Joern E. Klinger ◽  
Charles N. J. Ravarani ◽  
Hannes A. Baukmann ◽  
Justin L. Cope ◽  
Erwin P. Boettinger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gene Interactions ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

Polygenic risk scores (PRS) aggregating results from genome-wide association studies are state of the art to predict the susceptibility to complex traits or diseases. 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.

scholarly journals Large-scale pathway-specific polygenic risk, transcriptomic community networks and functional inferences in Parkinson disease

2020 ◽  
Author(s):  
S Bandres-Ciga ◽  
S Saez-Atienzar ◽  
JJ Kim ◽  
MB Makarious ◽  
F Faghri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Parkinson Disease ◽  
Gene Expression Data ◽  
Large Scale ◽  
Polygenic Risk Score ◽  
Expression Data ◽  
Polygenic Risk ◽  
Cellular Context

ABSTRACTPolygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinson’s Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinson’s disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

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