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Record W2602197298 · doi:10.1093/brain/awx076

Heterozygous PINK1 p.G411S in rapid eye movement sleep behaviour disorder

2017· letter· en· W2602197298 on OpenAlexaff
Ziv Gan‐Or, Jennifer A. Ruskey, Dan Spiegelman, Isabelle Arnulf, Yves Dauvilliers, Birgit Högl, Christelle Monaca-Charley, Ronald B. Postuma, Jacques Montplaisir, Guy A. Rouleau

Bibliographic record

VenueBrain · 2017
Typeletter
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de MontréalMontreal General HospitalMcGill UniversityCanadian Sleep & Circadian NetworkMontreal Neurological Institute and Hospital
FundersMichael J. Fox Foundation for Parkinson's Research
KeywordsPINK1Eye movementRapid eye movement sleepSleep (system call)PsychologyNeuroscienceAudiologyNeurological disorderREM sleep behavior disorderMedicinePhysical medicine and rehabilitationCentral nervous system diseasePolysomnographyInternal medicineParkinson's diseaseElectroencephalographyParkin

Abstract

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Sir, In a recent study published in Brain, Puschmann et al. (2016) strengthened the suggested association between the heterozygous PINK1 mutation p.G411S and Parkinson’s disease. They performed a meta-analysis showing that patients with Parkinson’s disease had a pooled odds ratio (OR) of 2.89 for carrying the p.G411S mutation. Furthermore, the authors demonstrated that the p.G411S mutation led to reduced kinase activity and interfered with ubiquitin phosphorylation by PINK1 (Puschmann et al., 2016). Biallelic mutations in PINK1 are well-established causes of autosomal-recessive early onset Parkinson’s disease, and account for 3.7% of patients with early onset Parkinson’s disease (Kilarski et al., 2012). However, the role of heterozygous PINK1 mutations in Parkinson’s disease is still not clear. In a study of rare PINK1 variants, the carrier frequencies in Parkinson’s disease patients and controls were 1.8% and 1.5%, respectively, and a meta-analysis with previous studies resulted in non-significant OR of 1.62 (Marongiu et al., 2008). Other studies, however, suggested that heterozygous PINK1 variants may indeed increase the risk of developing Parkinson’s disease (Abou-Sleiman et al., 2006; Toft et al., 2007). Forest plot of the effect of the PINK1 p.G411S mutation in Parkinson’s disease and RBD. A forest plot including data from eight previously published populations, which included data on carriers of PINK p.G411S in other patients and controls. As this mutation was shown to have a functional effect, which is not random and should be similar across all carriers, a fixed-effect model was used. When usinga random-effect model, a marginal P-value of 0.06 was achieved. The heterogeneity was not significant across the different populations (Tarone’s test for heterogeneity P = 0.62), further suggesting the fit of a fixed-effect model. The average coverage of the probe covering the PINK1 p.G411S across all samples was >300×. Four carriers were identified: two RBD patients (0.6%) and two controls (0.2%, P > 0.05, Fisher’s exact test). Although not statistically significant, these frequencies have similar effect size and directionality as those reported by Puschmann et al. (2016). The age at diagnosis of RBD of the two patients was 65 and 69 years, and while the first patient had not yet converted to an overt synucleinopathy, the second patient was diagnosed with Parkinson’s disease at the age of 70. As RBD can be considered as a synucleinopathy in progress (Postuma et al., 2015), the current data could be added to the meta-analysis from Puschmann et al., which used studies that identified carriers of the PINK1 p.G411S mutation in patients or controls (Abou-Sleiman et al., 2006; Zadikoff et al., 2006; Ishihara-Paul et al., 2008; Brooks et al., 2009; Puschmann et al., 2016). Adding our data, the OR for having the PINK1 p.G411S mutation was 2.71 (95% confidence interval 1.21–6.03, P = 0.016, Tarone’s test for heterogeneity P = 0.62, Fig. 1). Our results provide some additional support for the association between the PINK1 p.G411S mutation and Parkinson’s disease. There are conflicting reports on synucleinopathy and Lewy bodies in PINK1-associated Parkinson’s disease, as some post-mortem studies identified Lewy bodies in PINK1-associated Parkinson’s disease while others did not (Samaranch et al., 2010; Takanashi et al., 2016). The identification of two carriers of the PINK1 p.G411S mutation among the RBD cohort, who have de facto early-stage synucleinopathy, may suggest that PINK1, at least in some cases, may be associated with α-synuclein pathology. Of note, although the ORs calculated here and in previous studies are higher than those typically seen in genome-wide association studies of Parkinson’s disease (Nalls et al., 2014), the overall effect of the p.G411S mutation is still very small. If the life-time risk for Parkinson’s disease is 1–3%, and if the OR represents the risk for Parkinson’s disease, carriers of the PINK1 p.G411S mutation have more than a 90% chance of never developing Parkinson’s disease. Considering this reduced penetrance with the low frequency of this variant (allele frequency of 0.002 in the ExAC database, http://exac.broadinstitute.org/), it seems that the role of this variant in Parkinson’s disease is minor. However, it is important to note that other PINK1 mutations may also contribute to Parkinson’s disease in the heterozygous form. A previous meta-analysis of various rare PINK1 mutations suggested that they do not confer increased risk for Parkinson’s disease (Marongiu et al., 2008); however, collapsing all rare PINK1 variants together into a single meta-analysis is based on the hidden assumption that different rare PINK1 variants carry the same effect on risk for Parkinson’s disease. It is more likely that different variants have different effects on Parkinson’s disease risk, as occur in other Parkinson’s disease-related genes, such as GBA (Gan-Or et al., 2015a) and LRRK2 (Gan-Or et al., 2015a). While a mutation such as p.G411S may indeed be a risk variant, other PINK1 mutations may have no effect on Parkinson’s disease risk, or the opposite effect (i.e. they may be protective), and pooling them together may hide the effects of specific variants. Studies of individual mutations and more advanced analysis methods that take into account different effect directions of individual variants should be performed to better study the role of heterozygous PINK1 mutations in Parkinson’s disease. We would like to thank the individuals who participated in this study. We thank Stephanie Strong, Simon C. Warby, Claire S. Leblond, Ambra Stefani, Patrick A. Dion, Alex Desautels, Jean-François Gagnon, Cynthia Bourassa, Jay P. Ross, Sandra Laurent, Helene Catoire, Pascale Hince and Vessela Zaharieva for their assistance. This study was funded by a grant from the Michael J. Fox Foundation.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0130.006
Insufficient payload (model declined to judge)0.0070.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.281
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2017
Admission routes1
Has abstractyes

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