Rapid and reliable detection of exon rearrangements in various movement disorders genes by multiplex ligation‐dependent probe amplification
Bibliographic record
Abstract
Because of the occurrence of different types of mutations, comprehensive genetic testing for Parkinson's disease (PD), dopa-responsive dystonia (DRD), and myoclonus-dystonia (M-D) should include screening for small sequence changes and for large exonic rearrangements in disease-associated genes. In diagnostic and research settings, the latter is frequently omitted or performed by laborious and expensive quantitative real-time PCR (qPCR). Our study aimed to evaluate the utility of a novel method, multiplex ligation-dependent probe amplification (MLPA), in molecular diagnostics of movement disorders. We have analyzed, by MLPA, genomic DNA from 21 patients affected with PD, DRD, or M-D, in which the presence of exon rearrangement(s) (n = 20) or of a specific point mutation (detectable by MLPA, n = 1) had been established previously by qPCR or sequencing. In parallel, we have studied, in a blinded fashion, DNA from 49 patients with an unknown mutational status. Exon rearrangements were evident in 20 samples with previously established mutations; in the 21st sample the known specific point mutation was detected. We conclude that MLPA represents a reliable method for large-scale and cost-effective gene dosage screening of various movement disorders genes. This finding reaches far beyond a simple technical advancement and has two major implications: (1) By improving the availability of comprehensive genetic testing, it supports clinicians in the establishment of a genetically defined diagnosis; (2) By enabling gene dosage testing of several genes simultaneously, it significantly facilitates the mutational analysis of large patient and control populations and thereby constitutes the prerequisite for meaningful phenotype-genotype correlations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".