Association of the Polygenic Risk Score with the Incidence Risk of Parkinson’s Disease and Cerebrospinal Fluid α-Synuclein in a Chinese Cohort

2019 ◽  
Vol 36 (3) ◽  
pp. 515-522 ◽  
Author(s):  
Wei-Wei Li ◽  
Dong-Yu Fan ◽  
Ying-Ying Shen ◽  
Fa-Ying Zhou ◽  
Yang Chen ◽  
...  
2020 ◽  
Vol 35 (5) ◽  
pp. 774-780 ◽  
Author(s):  
Hirotaka Iwaki ◽  
Cornelis Blauwendraat ◽  
Mary B. Makarious ◽  
Sara Bandrés‐Ciga ◽  
Hampton L. Leonard ◽  
...  

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Sungjae Kim ◽  
Jong-Yeon Shin ◽  
Nak-Jung Kwon ◽  
Chang-Uk Kim ◽  
Changhoon Kim ◽  
...  

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.


2021 ◽  
Vol 14 ◽  
Author(s):  
Maria I. Maraki ◽  
Alexandros Hatzimanolis ◽  
Niki Mourtzi ◽  
Leonidas Stefanis ◽  
Mary Yannakoulia ◽  
...  

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.


2020 ◽  
Author(s):  
Yingnan Han ◽  
Erin Teeple ◽  
Srinivas Shankara ◽  
Mahdiar Sadeghi ◽  
Cheng Zhu ◽  
...  

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.


2015 ◽  
Vol 11 (7S_Part_6) ◽  
pp. P279-P279
Author(s):  
Eva Louwersheimer ◽  
Steffen Wolfsgruber ◽  
Ana Espinosa ◽  
Andre Lacour ◽  
Stefanie Heilman ◽  
...  

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