GRIN2B mediates susceptibility to intelligence quotient and cognitive impairments in developmental dyslexia
Bibliographic record
Abstract
OBJECTIVE(S): Developmental dyslexia (DD) is a complex heritable condition associated with impairments in multiple neurocognitive domains. Substantial heritability has been reported for DD and related phenotypes, and candidate genes have been identified. Recently, a candidate gene for human cognitive processes, that is, GRIN2B, has been found to be associated significantly with working memory in a German DD sample. In this study, we explored the contribution of six GRIN2B markers to DD and key DD-related phenotypes by association analyses in a sample of Italian nuclear families. Moreover, we assessed potential gene-by-environment interactions on DD-related phenotypes. MATERIALS AND METHODS: We carried out a family-based association study to determine whether the GRIN2B gene influences both DD as a categorical trait and its related cognitive traits in a large cohort of 466 Italian nuclear families ascertained through a proband affected by DD. Moreover, we tested the role of the selected GRIN2B markers and a set of commonly described environmental moderators using a test for G×E interaction in sib pair-based association analysis of quantitative traits in 178 Italian nuclear families. RESULTS: Evidence for a significant association was found with the categorical diagnosis of DD, performance intelligence quotient, phonemic elision, and auditory short-term memory. No significant gene-by-environment effects were found. CONCLUSION: Our results add further evidence in support of GRIN2B contributing toward DD and deficits in DD. More specifically, our data support the view that GRIN2B influences DD as a categorical trait and its related quantitative phenotypes, thus shedding further light on the etiologic basis and the phenotypic complexity of this disorder.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".