Adult expression of a 3q13.31 microdeletion
Bibliographic record
Abstract
BACKGROUND: The emerging 3q13.31 microdeletion syndrome appears to encompass diverse neurodevelopmental conditions. However, the 3q13.31 deletion is rare and few adult cases have yet been reported. We examined a cohort with schizophrenia (n = 459) and adult control subjects (n = 26,826) using high-resolution microarray technology for deletions and duplications at the 3q13.31 locus. RESULTS: We report on the extended adult phenotype associated with a 3q13.31 microdeletion in a 41-year-old male proband with schizophrenia and a nonverbal learning disability. He was noted to have a speech impairment, delayed motor skills, and other features consistent with the 3q13.31 microdeletion syndrome. The 2.06 Mb deletion overlapped two microRNAs and seven RefSeq genes, including GAP43, LSAMP, DRD3, and ZBTB20. No overlapping 3q13.31 deletions or duplications were identified in control subjects. CONCLUSIONS: Later-onset conditions like schizophrenia are increasingly associated with rare copy number variations and associated genomic disorders like the 3q13.31 microdeletion syndrome. Detailed phenotype information across the lifespan facilitates genotype-phenotype correlations, accurate genetic counselling, and anticipatory care.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".