The genetic variations in SAP97 gene and the risk of schizophrenia in the Chinese Han population: a further study
Bibliographic record
Abstract
BACKGROUND AND METHODS: Based on our previous discovery that SAP97 rs3915512 polymorphism significantly affects the cognitive function of schizophrenia, we further genotyped the other 12 single-nucleotide polymorphisms (SNPs) capturing the known common haplotype variations of this gene in a sample including 1014 patients with schizophrenia and 1078 matched controls. RESULTS: There were no significant differences in the distribution of genotypes and alleles of the 12 SNPs of SAP97 between the patients and the controls (all P > 0.05). But, in the evaluation of the phenotypic effects of these SNPs on the patients' clinical symptoms and cognitive functions. While patients with minor allele in the rs9843659 polymorphism had higher N5 (difficulty in abstract thinking) scores than that with the main genotype (P = 0.002, Pcor = 0.014), the patients with minor allele in the rs6805920, rs4916461 and rs7638423 had lower verbal memory scores (P = 0.003, 0.003, 0.001, Pcor = 0.021, 0.021, 0.007, respectively) and the P values of these SNPs were still significant after the Bonferroni correction. CONCLUSION: Our data are further to indicate that the SAP97 gene polymorphisms may affect neurocognitive function especially verbal memory and the first to suggest that the SAP97 rs9843659 polymorphism may influence abstract thinking of schizophrenic patients in the southern Han 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".