Protocadherin α (PCDHA) as a novel susceptibility gene for autism
Bibliographic record
Abstract
BACKGROUND: Synaptic dysfunction has been shown to be involved in the pathogenesis of autism. We hypothesized that the protocadherin α gene cluster (PCDHA), which is involved in synaptic specificity and in serotonergic innervation of the brain, could be a suitable candidate gene for autism. METHODS: We examined 14 PCDHA single nucleotide polymorphisms (SNPs) for genetic association with autism in DNA samples of 3211 individuals (841 families, including 574 multiplex families) obtained from the Autism Genetic Resource Exchange. RESULTS: Five SNPs (rs251379, rs1119032, rs17119271, rs155806 and rs17119346) showed significant associations with autism. The strongest association (p < 0.001) was observed for rs1119032 (z score of risk allele G = 3.415) in multiplex families; SNP associations withstand multiple testing correction in multiplex families (p = 0.041). Haplotypes involving rs1119032 showed very strong associations with autism, withstanding multiple testing corrections. In quantitative transmission disequilibrium testing of multiplex fam - ilies, the G allele of rs1119032 showed a significant association (p = 0.033) with scores on the Autism Diagnostic Interview-Revised (ADI-R)_D (early developmental abnormalities). We also found a significant difference in the distribution of ADI-R_A (social interaction) scores between the A/A, A/G and G/G genotypes of rs17119346 (p = 0.002). LIMITATIONS: Our results should be replicated in an independent population and/or in samples of different racial backgrounds. CONCLUSION: Our study provides strong genetic evidence of PCDHA as a potential candidate gene for 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".