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Record W3007000351 · doi:10.1111/gbb.12648

Genome‐wide association study of word reading: Overlap with risk genes for neurodevelopmental disorders

2020· article· en· W3007000351 on OpenAlexafffundabout
Kaitlyn M. Price, Karen Wigg, Yu Feng, Kirsten Blokland, Margaret Wilkinson, Gengming He, Elizabeth N. Kerr, Tasha‐Cate Carter, Sharon Guger, Maureen W. Lovett, Lisa J. Strug, Cathy L. Barr

Bibliographic record

VenueGenes Brain & Behavior · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity Health NetworkUniversity of TorontoSickKids FoundationPublic Health OntarioHospital for Sick ChildrenKrembil Foundation
FundersCanadian Institutes of Health Research
KeywordsGenome-wide association studySingle-nucleotide polymorphismDyslexiaReading disabilityPopulationNeurocognitiveGenetic associationAttention deficit hyperactivity disorderPsychologyNeurodevelopmental disorderGeneticsPsychiatryMedicineBiologyAutismGeneReading (process)CognitionGenotype

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.023
GPT teacher head0.292
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations55
Published2020
Admission routes3
Has abstractyes

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