The Parkinson's Disease DNA Variant Browser

Parkinson's disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism and 90 common genetic risk factors. Large-scale next-generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease-causing mutations. In most instances, however, the results remain quite limited and rather preliminary, in large part because of an inability of any single group to validate findings in a large independent series of sequenced patients. We present here the Parkinson's Disease Sequencing Browser: a Shiny-based web application that presents comprehensive summary-level frequency data from multiple large-scale genotyping and sequencing projects. The data is aggregated and involves a total of 102,127 participants, including 30,103 PD cases (including 1,650 proxy cases) and 72,024 controls. Our aim is to assist researchers on their search for PD-risk genes and variant candidates with an easily accessible and open summary-level genomic data browser for the PD research community, https://pdgenetics.shinyapps.io/VariantBrowser/.

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Parkinson's disease risk genes act in glia to control neuronal α-synuclein toxicity

Neurobiology of Disease ◽

10.1016/j.nbd.2021.105482 ◽

2021 ◽

pp. 105482

Author(s):

Abby L. Olsen ◽

Mel B. Feany

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Disease Risk ◽

Risk Genes

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Human Activity Recognition from Sensor-Based Large-Scale Continuous Monitoring of Parkinson’s Disease Patients

2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) ◽

10.1109/chase.2017.87 ◽

2017 ◽

Cited By ~ 11

Author(s):

Wei-Yi Cheng ◽

Alf Scotland ◽

Florian Lipsmeier ◽

Timothy Kilchenmann ◽

Liping Jin ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Activity Recognition ◽

Human Activity ◽

Continuous Monitoring ◽

Large Scale ◽

Human Activity Recognition

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Large-Scale Continuous Mobility Monitoring of Parkinson’s Disease Patients Using Smartphones

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Wireless Mobile Communication and Healthcare ◽

10.1007/978-3-319-98551-0_2 ◽

2018 ◽

pp. 12-19 ◽

Cited By ~ 3

Author(s):

Wei-Yi Cheng ◽

Florian Lipsmeier ◽

Andrew Creigh ◽

Alf Scotland ◽

Timothy Kilchenmann ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Large Scale

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Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice

The Journal of the Acoustical Society of America ◽

10.1121/1.5100272 ◽

2019 ◽

Vol 145 (5) ◽

pp. 2871-2884 ◽

Cited By ~ 4

Author(s):

Siddharth Arora ◽

Ladan Baghai-Ravary ◽

Athanasios Tsanas

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Large Scale ◽

Screening Tool ◽

Population Screening ◽

Scale Population

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P.042 Associations of pain and depression with marital status in patients diagnosed with Parkinson’s disease

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2016.146 ◽

2016 ◽

Vol 43 (S2) ◽

pp. S31-S31

Author(s):

A Rana ◽

I Abdullah ◽

AM Qureshi ◽

A Mumtaz ◽

M A. Rana

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Marital Status ◽

Depression Scale ◽

Brief Pain Inventory ◽

Pain Interference ◽

Control Group ◽

Depression Severity ◽

Single Group ◽

Depression Prevalence

Background: Depression and pain are significant clinical problems that are comorbid with Parkinson’s disease (PD). However, the relationship of these variables with the marital status of patients with PD has not been explored in previous studies. The goal of this study was to assess the possible relationship between depression prevalence, depression severity, and pain interference with the marital status of the sufferers of PD. Methods: This study included 40 patients and 40 healthy control participants who were assessed for depression prevalence and pain interference using The Hospital Anxiety and Depression Scale and the Brief Pain Inventory, respectively. Results: When compared to the control groups, the PD (Single) group was found to have the highest prevalence of depression, followed by the PD (Married) group whereas the Control (Single) group was found to have a higher prevalence than the Control (Married) group (P<0.0001). A main effect was found on depression severity (P<0.0001), but no significant differences were observed between the PD groups. Lastly, PD (Single) patients had significantly greater pain interference scores than the PD (Married) patients (P<0.05) with no other significant case-control or control-control group differences. Conclusions: Patient-spouse relationship may have a mitigating effect on patient outcomes of depression prevalence and pain interference.

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Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

Journal of The Royal Society Interface ◽

10.1098/rsif.2010.0456 ◽

2010 ◽

Vol 8 (59) ◽

pp. 842-855 ◽

Cited By ~ 128

Author(s):

Athanasios Tsanas ◽

Max A. Little ◽

Patrick E. McSharry ◽

Lorraine O. Ramig

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Signal Processing ◽

Symptom Severity ◽

Large Scale ◽

Rating Scale ◽

Signal Processing Algorithm ◽

Wide Range ◽

Signal Processing Algorithms ◽

Processing Algorithms

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

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