Analysis of treatment-resistant schizophrenia and 384 markers from candidate genes
Bibliographic record
Abstract
The treatment of patients with schizophrenia who fail to respond to antipsychotics is a major challenge and the proportion of treatment-resistant patients is estimated to be 20 to 40%. There are few genetic association studies that have compared resistant versus non-resistant schizophrenic patients; however, many genetic association studies focusing on antipsychotic response have been published. This contribution investigates the genetics of treatment-resistant schizophrenia, testing 384 candidate gene loci related to the neurobiology of the disease. First, we identified a subgroup of treatment-resistant patients in a sample of 240 schizophrenia patients using the American Psychiatric Association criteria and then we genotyped all patients using a custom Illumina Bead Chip comprising of 384 single nucleotide polymorphisms. We screened all markers for nominal significance and for statistical significance after multiple-testing correction. The most significant single nucleotide polymorphism was the rs2152324 marker in the NALCN gene (P=0.004); however, after the FDR correction, the P-value was not significant. Our analysis of 384 markers across candidate genes did not indicate any robust association with treatment-resistant schizophrenia. However, this phenotype can be assessed retrospectively in cross-sectional studies and these preliminary results point out the importance of choosing alternative phenotypes in psychiatric pharmacogenetics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".