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Original research
Excess of singleton loss-of-function variants in Parkinson’s disease contributes to genetic risk
  1. Dheeraj Reddy Bobbili1,2,
  2. Peter Banda1,
  3. Rejko Krüger3,4,5,
  4. Patrick May1
  1. 1Bioinformatics Core, Luxembourg Centre for Systems Biomedicine (LCSB), Belvaux, Luxembourg
  2. 2MeGeno S.A, Esch-sur-Alzette, Luxembourg
  3. 3Developmental and Cellular Biology, Luxembourg Centre for Systems Biomedicine (LCSB), Belvaux, Luxembourg
  4. 4Parkinson Research Clinic, Centre Hospitalier de Luxemborg (CHL), Luxembourg, Luxembourg
  5. 5Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
  1. Correspondence to Dr Patrick May, Bioinformatics, Luxembourg Centre for Systems Biomedicine, Belvaux, Luxembourg;; Dr Dheeraj Reddy Bobbili; dheeraj.bobbili{at}uni.lu

Abstract

Background Parkinson’s disease (PD) is a neurodegenerative disorder with complex genetic architecture. Besides rare mutations in high-risk genes related to monogenic familial forms of PD, multiple variants associated with sporadic PD were discovered via association studies.

Methods We studied the whole-exome sequencing data of 340 PD cases and 146 ethnically matched controls from the Parkinson’s Progression Markers Initiative (PPMI) and performed burden analysis for different rare variant classes. Disease prediction models were built based on clinical, non-clinical and genetic features, including both common and rare variants, and two machine learning methods.

Results We observed a significant exome-wide burden of singleton loss-of-function variants (corrected p=0.037). Overall, no exome-wide burden of rare amino acid changing variants was detected. Finally, we built a disease prediction model combining singleton loss-of-function variants, a polygenic risk score based on common variants, and family history of PD as features and reached an area under the curve of 0.703 (95% CI 0.698 to 0.708). By incorporating a rare variant feature, our model increased the performance of the state-of-the-art classification model for the PPMI dataset, which reached an area under the curve of 0.639 based on common variants alone.

Conclusion The main finding of this study is to highlight the contribution of singleton loss-of-function variants to the complex genetics of PD and that disease risk prediction models combining singleton and common variants can improve models built solely on common variants.

  • Parkinson-s disease
  • genetics
  • complex traits
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Footnotes

  • DRB and PB are joint first authors.

  • Contributors DRB and PM conceived the study. DRB analysed the data. PB performed machine learning. All authors interpreted the results and wrote the manuscript.

  • Funding This work was supported by grants from the Luxembourg National Research Fund (FNR) within the National Centre for Excellence in Research on Parkinson’s disease (NCER-PD to PB, RK, PM) and PEARL program (FNR/P13/6682797 to RK), the EU Joint Program-Neurodegenerative Diseases (JPND; COURAGE-PD to RK, DRB, PM), the German Research Council (DFG; KR2119/8-1 to RK) and by the European Union’s Horizon2020 research and innovation program under grant agreement no. 692320 (WIDESPREAD; CENTRE-PD to RK and PM).

  • Competing interests RK received speaker’s honoraria and travel grants from Medtronic. DRB works for MeGeno S.A. Luxembourg.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available in a public, open access repository. Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit the PPMI website (www.ppmi-info.org).

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