A Predictive Model for Parkinson's Disease Reveals Candidate Gene Sets for Progression Subtype

Saya R. Dennis, Tanya Simuni, Yuan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Parkinson's Disease (PD) is the second most common neurodegenerative disease in the United States, and is characterized by the progressive decline of motor and non-motor symptoms. The progression rate and manifestation of PD is highly heterogenous, and the underlying etiology for this heterogeneity remains elusive. Although some studies have identified risk genes associated with the development of PD, it is unknown whether the patient genome influences the progression pattern of PD in any way. In this study, we used the whole-exome sequencing data of PD patients from the Parkinson's Disease Progression Marker Initiative (PPMI) to examine whether an individual's genetic profile is associated with their progression pattern of PD. We used the three distinct progression subtypes defined by Zhang et al. as the outcome variable, and trained logistic regression and support vector classifiers. Our best performing model achieved an area under the receiver operating characteristic curve of 0.69 on the test set, indicating that the genetic profile of a PD patient appears to have some relationship with their likely disease course. We then interpreted our trained model by performing Gene Set Enrichment Analysis on the sets of genes with high model coefficients for each progression subtype. The results showed enrichment for genes related to olfactory signaling in Subtype I and III, and an enrichment for genes related to protein glycosylation and glycation for Subtype I and II. Overall, our findings suggests a connection between an individual's genetic profile and PD progression subtype, and calls for controlled research studies to further examine the relationship between the implicated gene sets and each progression subtype.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages417-420
Number of pages4
ISBN (Electronic)9781728162157
DOIs
StatePublished - Dec 16 2020
Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
Duration: Dec 16 2020Dec 19 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/TerritoryKorea, Republic of
CityVirtual, Seoul
Period12/16/2012/19/20

Keywords

  • Parkinson's Disease
  • genetic profile
  • germline variants
  • machine learning
  • progression subtype

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Medicine (miscellaneous)
  • Health Informatics

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