Rare copy number variations affecting the synaptic gene DMXL2 in neurodevelopmental disorders
Bibliographic record
Abstract
BACKGROUND: Ultra-rare genetic variants, including non-recurrent copy number variations (CNVs) affecting important dosage-sensitive genes, are important contributors to the etiology of neurodevelopmental disorders (NDDs). Pairing family-based whole-genome sequencing (WGS) with detailed phenotype data can enable novel gene associations in NDDs. METHODS: We performed WGS of six members from a three-generation family, where three individuals each had a spectrum of features suggestive of a NDD. CNVs and sequence-level variants were identified and further investigated in disease and control databases. RESULTS: We identified a novel 252-kb deletion at 15q21 that overlaps the synaptic gene DMXL2 and the gene GLDN. The microdeletion segregated in NDD-affected individuals. Additional rare inherited and de novo sequence-level variants were found that may also be involved, including a missense change in GRIK5. Multiple CNVs and loss-of-function sequence variants affecting DMXL2 were discovered in additional unrelated individuals with a range of NDDs. CONCLUSIONS: Disruption of DMXL2 may predispose to NDDs including autism spectrum disorder. The robust interpretation of private variants requires a multifaceted approach that incorporates multigenerational pedigrees and genome-wide and population-scale data.
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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 | 0.001 |
| 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".