Associations of histone deacetylase‐2 and histone deacetylase‐3 genes with schizophrenia in a <scp>C</scp>hinese population
Bibliographic record
Abstract
INTRODUCTION: To explore the association between histone deacetylase-2 (HDAC2) and histone deacetylase-3 (HDAC3) gene polymorphisms and schizophrenia. METHODS: A total of 208 family trios consisting of fathers, mothers and affected offspring with schizophrenia were recruited as our subjects. Four tag SNPs on HDAC2 (rs10499080, rs6568819, rs2499618 and rs13204445) and two tag SNPs on HDAC3 (rs11741808, rs2530223) genes were selected. The Mass ARRAY Assay Design software (Sequenom) was used to design amplification and allele specific extension primers. The Hardy-Weinberg equilibrium (HWE) for genotypic distributions was tested using the chi-square goodness-of-fit test. Allelic association for a single tag SNP was analyzed by using family-based association tests including the haplotype-based haplotype relative risk (HHRR) test and the transmission disequilibrium test (TDT). RESULTS: The genotypic distributions of HDAC2 SNPs rs6568819, rs2499618 and rs13204445 and HDAC3 SNPs rs11741808 and rs2530223 were all in Hardy-Weinberg equilibrium (P > 0.05). HHRR analysis revealed no associations between the SNPs and schizophrenia (P > 0.05). In addition, the TDT did not show any significant associations between HDAC2 and HDAC3 SNPs and schizophrenia (P > 0.05). DISCUSSION: HDAC2 and HDAC3 might not be associated with schizophrenia in the Chinese population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".