Rare mutations in N-methyl-D-aspartate glutamate receptors in autism spectrum disorders and schizophrenia
Bibliographic record
Abstract
Pharmacological, genetic and expression studies implicate N-methyl-D-aspartate (NMDA) receptor hypofunction in schizophrenia (SCZ). Similarly, several lines of evidence suggest that autism spectrum disorders (ASD) could be due to an imbalance between excitatory and inhibitory neurotransmission. As part of a project aimed at exploring rare and/or de novo mutations in neurodevelopmental disorders, we have sequenced the seven genes encoding for NMDA receptor subunits (NMDARs) in a large cohort of individuals affected with SCZ or ASD (n=429 and 428, respectively), parents of these subjects and controls (n=568). Here, we identified two de novo mutations in patients with sporadic SCZ in GRIN2A and one de novo mutation in GRIN2B in a patient with ASD. Truncating mutations in GRIN2C, GRIN3A and GRIN3B were identified in both subjects and controls, but no truncating mutations were found in the GRIN1, GRIN2A, GRIN2B and GRIN2D genes, both in patients and controls, suggesting that these subunits are critical for neurodevelopment. The present results support the hypothesis that rare de novo mutations in GRIN2A or GRIN2B can be associated with cases of sporadic SCZ or ASD, just as it has recently been described for the related neurodevelopmental disease intellectual disability. The influence of genetic variants appears different, depending on NMDAR subunits. Functional compensation could occur to counteract the loss of one allele in GRIN2C and GRIN3 family genes, whereas GRIN1, GRIN2A, GRIN2B and GRIN2D appear instrumental to normal brain development and function.
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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 | 0.000 |
| 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) | 0.001 | 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".