Dense‐map genome scan for dyslexia supports loci at 4q13, 16p12, 17q22; suggests novel locus at 7q36
Bibliographic record
Abstract
Analysis of genetic linkage to dyslexia was performed using 133,165 array-based SNPs genotyped in 718 persons from 101 dyslexia-affected families. Results showed five linkage peaks with lod scores >2.3 (4q13.1, 7q36.1-q36.2, 7q36.3, 16p12.1, and 17q22). Of these five regions, three have been previously implicated in dyslexia (4q13.1, 16p12.1, and 17q22), three have been implicated in attention-deficit hyperactivity disorder (ADHD, which highly co-occurs with dyslexia; 4q13.1, 7q36.3, 16p12.1) and four have been implicated in autism (a condition characterized by language deficits; 7q36.1-q36.2, 7q36.3, 16p12.1, and 17q22). These results highlight the reproducibility of dyslexia linkage signals, even without formally significant lod scores, and suggest dyslexia predisposing genes with relatively major effects and locus heterogeneity. The largest lod score (2.80) occurred at 17q22 within the MSI2 gene, involved in neuronal stem cell lineage proliferation. Interestingly, the 4q13.1 linkage peak (lod 2.34) occurred immediately upstream of the LPHN3 gene, recently reported both linked and associated with ADHD. Separate analyses of larger pedigrees revealed lods >2.3 at 1-3 regions per family; one family showed strong linkage (lod 2.9) to a known dyslexia locus (18p11) not detected in our overall data, demonstrating the value of analyzing single large pedigrees. Association analysis identified no SNPs with genome-wide significance, although a borderline significant SNP (P = 6 × 10(-7)) occurred at 5q35.1 near FGF18, involved in laminar positioning of cortical neurons during development. We conclude that dyslexia genes with relatively major effects exist, are detectable by linkage analysis despite genetic heterogeneity, and show substantial overlapping predisposition with ADHD and autism.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".