A common genetic variant in <i>FOXP2</i> is associated with language‐based learning (dis)abilities: Evidence from two Italian independent samples
Bibliographic record
Abstract
Language-based Learning Disabilities (LLDs) encompass a group of complex, comorbid, and developmentally associated deficits in communication. Language impairment and developmental dyslexia (DD) represent the most recognized forms of LLDs. Substantial genetic correlations exist between language and reading (dis)abilities. Common variants in the FOXP2 gene were consistently associated with language- and reading-related neuropsychological and neuroanatomical phenotypes. We tested the effect of a FOXP2 common variant, that is, rs6980093 (A/G), on quantitative measures of language and reading in two independent Italian samples: a population-based cohort of 699 subjects (3-11 years old) and a sample of 572 children with DD (6-18 years old). rs6980093 modulates expressive language in the general population sample, with an effect on fluency scores. In the DD sample, the variant showed an association with the accuracy in the single word reading task. rs6980093 shows distinct genetic models of association in the two cohorts, with a dominant effect of the G allele in the general population sample and heterozygote advantage in the DD cohort. We provide preliminary evidence that rs6980093 associates with language and reading (dis)abilities in two independent Italian cohorts. rs6980093 is an intronic SNP, suggesting that it (or a linked variant) modulates phenotypic association via regulation of FOXP2 expression. Because FOXP2 brain expression is finely regulated, both temporally and spatially, it is possible that the two alleles at rs6980093 differentially modulate expression levels in a developmental stage- or brain area-specific manner. This might help explaining the heterozygote advantage effect and the different genetic models in the two cohorts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".