Genome‐wide association study of word reading: Overlap with risk genes for neurodevelopmental disorders
Bibliographic record
Abstract
Abstract Reading disabilities (RD) are the most common neurocognitive disorder, affecting 5% to 17% of children in North America. These children often have comorbid neurodevelopmental/psychiatric disorders, such as attention deficit/hyperactivity disorder (ADHD). The genetics of RD and their overlap with other disorders is incompletely understood. To contribute to this, we performed a genome‐wide association study (GWAS) for word reading. Then, using summary statistics from neurodevelopmental/psychiatric disorders, we computed polygenic risk scores (PRS) and used them to predict reading ability in our samples. This enabled us to test the shared aetiology between RD and other disorders. The GWAS consisted of 5.3 million single nucleotide polymorphisms (SNPs) and two samples; a family‐based sample recruited for reading difficulties in Toronto (n = 624) and a population‐based sample recruited in Philadelphia [Philadelphia Neurodevelopmental Cohort (PNC)] (n = 4430). The Toronto sample SNP‐based analysis identified suggestive SNPs (P ~ 5 × 10−7) in the ARHGAP23 gene, which is implicated in neuronal migration/axon pathfinding. The PNC gene‐based analysis identified significant associations (P < 2.72 × 10−6) for LINC00935 and CCNT1, located in the region of the KANSL2/CCNT1/LINC00935/SNORA2B/SNORA34/MIR4701/ADCY6 genes on chromosome 12q, with near significant SNP‐based analysis. PRS identified significant overlap between word reading and intelligence (R2 = 0.18, P = 7.25 × 10−181), word reading and educational attainment (R2 = 0.07, P = 4.91 × 10−48) and word reading and ADHD (R2 = 0.02, P = 8.70 × 10−6; threshold for significance = 7.14 × 10−3). Overlap was also found between RD and autism spectrum disorder (ASD) as top‐ranked genes were previously implicated in autism by rare and copy number variant analyses. These findings support shared risk between word reading, cognitive measures, educational outcomes and neurodevelopmental disorders, including ASD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".