Genes and Neural Cell Types Influencing Reading and the Overlap with Neurodevelopmental Disorders
Bibliographic record
Abstract
Although a breadth of research has been conducted on Reading Disabilities (RD), the genes, molecular mechanisms, and cell types involved in its etiology remain to be elucidated. Understanding these processes is complicated by the frequent overlap between RD and other neurodevelopmental disorders. The purpose of this dissertation was to identify genes and neural cell types involved in the development of RD, while considering the genotypic/phenotypic overlap with comorbid disorders. To that end, genome-wide association studies (GWAS) and polygenic risk scores (PRS) were used to identify genes associated with word reading and examine shared genetic etiology with neurodevelopment/psychiatric disorders. We identified two novel loci for word reading, with top SNPs in or near ARHGAP23 (n=624) and the CCNT1/LINC00935 region (n=4430) (p~10-7) (significant by gene-based analysis (p~10-6)). We also identified significant genetic overlap for word reading and intelligence, word reading and educational attainment, and word reading and attention deficit hyperactivity disorder (ADHD) (threshold for significance=7.14x10−3) and observed shared genes between word reading and Autism Spectrum Disorder (ASD) and language. We observed genes involved in neuronal migration among top results. To increase power to identify genes for word reading, we performed Hypothesis-Driven GWAS in a larger sample. SNPs in DOCK7, CDH4 (n~26,000), and intergenic between BTG3-C21orf91 (n=4152) showed significant association. Top SNPs were eQTLs/sQTLs, providing hints to the molecular mechanism of risk for these genes. We also found that genes previously implicated in ASD cumulatively and significantly contributed to word reading (n=624). We hypothesized the link between RD-ASD was through deficits in language systems. To determine neural cell types, we used Linkage Disequilibrium Score Regression to look for cell enrichment in our GWAS results and correlated traits -- as determined by PRS. We demonstrated enrichment of adult excitatory neurons in word reading; adult excitatory neurons in ADHD; and adult and fetal excitatory neurons, inhibitory neurons, astrocytes, and oligodendrocytes in educational attainment and cognitive ability. This thesis contributed novel genes and neural cell types to RD, as well as an understanding of the shared overlap between its comorbid disorders. It can inform future functional studies examining the molecular mechanisms of reading.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".