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Record W4412162315 · doi:10.1101/2025.07.05.24312261

Digenic inheritance of mutations in <i>SPG7</i> and <i>AFG3L2</i> causes motor neuron and cerebellar disorders

2025· preprint· en· W4412162315 on OpenAlexafffund
Mehrdad A. Estiar, Eric Yu, Parizad Varghaei, Jay P. Ross, Setareh Ashtiani, Andrew N. Bayne, Giulia Coarelli, Dagmar Timmann, Thomas Klockgether, Danique Beijer, David Mengel, Marie Coutelier, Patrick A. Dion, Oksana Suchowersky, Claire Ewenczyk, Cyril Goizet, Giovanni Stévanin, Michael A. van Es, Ammar Al‐Chalabi, Stephan Züchner, Matthis Synofzik, Jan H. Veldink, Jean‐François Trempe, Alexandra Dürr, Guy A. Rouleau, Ziv Gan‐Or

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsUniversity of AlbertaAlberta Children's HospitalMcGill UniversityUniversity of OttawaMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchParkinson Canada
KeywordsInheritance (genetic algorithm)Motor neuronGeneticsMedicineBiologyNeuroscienceGene

Abstract

fetched live from OpenAlex

Abstract Background Biallelic SPG7 mutations cause one of the most common forms of hereditary spastic paraplegia (HSP). Several reports have suggested that heterozygous SPG7 variants may also play a role in HSP, but also in amyotrophic lateral sclerosis (ALS). However, it remains controversial whether heterozygous SPG7 mutations are pathogenic on their own, or if other mechanisms are at play. We recently provided evidence for non-Mendelian inheritance in spastic paraplegia 7 (SPG7), as heterozygous carriers of SPG7 mutations often also carried mutations in other disease-related genes, more frequently than expected by chance. AFG3L2 encodes for the AFG3 Like Matrix AAA Peptidase Subunit 2 protein, which creates a proteolytic complex with SPG7-encoded protein paraplegin. In this study, we aimed to examine whether digenic heterozygous mutations in SPG7 and AFG3L2 can lead to a spectrum of neurodegenerative disorders. Methods We first analyzed genome and exome sequencing data of 7,515 unrelated individuals including 5,108 motor neuron disorder (MND) and ataxia patients and 2,407 controls. We next analyzed an additional 18,748 exome data from rare disease cohorts to further examine the occurrence of variants in SPG7 and AFG3L2 . Results Among the first 5,108 MND and ataxia patients, we identified a total of 10 patients, 8 of whom were unrelated, carried potentially pathogenic variants in both SPG7 and AFG3L2 , in contrast to none in 2,407 unrelated controls. Further analysis of the 18,748 additional patients with rare disease, as well as a comprehensive literature review, identified 7 more patients, 6 of whom were unrelated, had digenic mutations in SPG7 and AFG3L2 . In the two families we identified, digenic mutations in SPG7 and AFG3L2 perfectly segregated with the disease. The 17 patients reported here exhibited predominant signs of motor neuron and cerebellar involvement. Conclusions Our findings demonstrate that digenic inheritance of concurrent heterozygous mutations in SPG7 and AFG3L2 may cause motor neuron and cerebellar disorders. Screening of the entire SPG7 and AFG3L2 genes in genetically undiagnosed cases of MND and spastic ataxia may help to increase the diagnostic yield.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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