High Frequency of Pathogenic Rearrangements in <i>SPG11</i> and Extensive Contribution of Mutational Hotspots and Founder Alleles
Bibliographic record
Abstract
Biallelic loss-of-function mutations in SPG11 cause a wide spectrum of recessively inherited, neurodegenerative disorders including hereditary spastic paraplegia (HSP), amyotrophic lateral sclerosis, and Charcot-Marie-Tooth disease. By comprehensive screening of three large cohorts of HSP index patients, we identified 83 alleles with “small” mutations and 13 alleles that carry large genomic rearrangements. Including relevant data from previous studies, we estimate that copy number variants (CNVs) account for ∼19% of pathogenic SPG11 alleles. The breakpoints for all novel and some previously reported CNVs were determined by long-range PCR and sequencing. This revealed several Alu-associated recombination hotspots. We also found evidence for additional mutational mechanisms, including for a two-step event in which an Alu retrotransposition preceded the actual rearrangement. Apparently independent samples with identical breakpoints were analyzed by microsatellite PCRs. The resulting haplotypes suggested the existence of two rearrangement founder alleles. Our findings widen the spectra of mutations and mutational mechanisms in SPG11, underscore the pivotal role played by Alus, and are of high diagnostic relevance for a wide spectrum of clinical phenotypes including the most frequent form of recessive HSP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".