P.053 Whole-genome sequencing identified a frameshift mutation at LMNB1 in a family with early-onset dystonia
Bibliographic record
Abstract
Background: Dystonia is a hyperkinetic condition that produces abnormal movements or postures. Its diagnostic procedure is often challenging and time consuming. Genetic testing provides an effective approach for diagnosis, but currently only very few dystonia genes have been identified. We propose that studying early-onset forms of dystonia with the use of whole-genome sequencing (WGS) will improve the identification of dystonia-relevant genes and mutations. Methods: We performed deep WGS using the Illumina HiSeq X technology in a mother-proband pair with dystonia. The mother has generalized dystonia (age of onset: 15) and the proband has myoclonic dystonia (age of onset: 11). Results: No pathogenic mutation was identified in any of the known dystonia genes. However, we identified a rare heterozygous frameshift mutation (p.K342fs*7) at LMNB1 that was shared between the mother and the proband. Duplication of LMNB1 is known to cause Adult-onset Demyelinating Leukodystrophy. A heterozygous deletion of LMNB1 has been reported in a patient with microcephaly and global developmental disorder. Conclusions: Further characterization of phenotypes in the participants and their family members is needed to confirm the relationship between mutation in LMNB1 and dystonia. This work provides a proof-of-principle that novel disease-relevant genes can potentially be identified using the proposed approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".