Rare copy number variants are an important cause of epileptic encephalopathies
Bibliographic record
Abstract
OBJECTIVE: Rare copy number variants (CNVs)--deletions and duplications--have recently been established as important risk factors for both generalized and focal epilepsies. A systematic assessment of the role of CNVs in epileptic encephalopathies, the most devastating and often etiologically obscure group of epilepsies, has not been performed. METHODS: We evaluated 315 patients with epileptic encephalopathies characterized by epilepsy and progressive cognitive impairment for rare CNVs using a high-density, exon-focused, whole-genome oligonucleotide array. RESULTS: We found that 25 of 315 (7.9%) of our patients carried rare CNVs that may contribute to their phenotype, with at least one-half being clearly or likely pathogenic. We identified 2 patients with overlapping deletions at 7q21 and 2 patients with identical duplications of 16p11.2. In our cohort, large deletions were enriched in affected individuals compared to controls, and 4 patients harbored 2 rare CNVs. We screened 2 novel candidate genes found within the rare CNVs in our cohort but found no mutations in our patients with epileptic encephalopathies. We highlight several additional novel candidate genes located in CNV regions. INTERPRETATION: Our data highlight the significance of rare CNVs in the epileptic encephalopathies, and we suggest that CNV analysis should be considered in the genetic evaluation of these patients. Our findings also highlight novel candidate genes for further study.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".