Quantitative trait locus and computational mapping identifies Kcnj9 (GIRK3) as a candidate gene affecting analgesia from multiple drug classes
Bibliographic record
Abstract
AIMS: Interindividual differences in analgesic drug response complicate the clinical management of pain. We aimed to identify genetic factors responsible for variable sensitivity to analgesic drugs of disparate neurochemical classes. METHODS AND RESULTS: Quantitative trait locus mapping in 872 (C57BL/6x129P3)F2 mice was used to identify genetic factors contributing to variability in the analgesic effect of opioid (morphine), alpha2-adrenergic (clonidine), and cannabinoid (WIN55,212-2) drugs against thermal nociception. A region on distal chromosome 1 showing significant linkage to analgesia from all three drugs was identified. Computational (in silico) genetic analysis of analgesic responses measured in a panel of inbred strains identified a haplotype block within this region containing the Kcnj9 and Kcnj10 genes, encoding the Kir3.3 (GIRK3) and Kir4.1 inwardly rectifying potassium channel subunits. The genes are differentially expressed in the midbrain periaqueductal gray of 129P3 versus C57BL/6 mice, owing to cis-acting genetic elements. The potential role of Kcnj9 was confirmed by the demonstration that knockout mice have attenuated analgesic responses. CONCLUSION: A single locus is partially responsible for the genetic mediation of pain inhibition, and genetic variation associated with the potassium channel gene, Kcnj9, is a prime candidate for explaining the variable response to these analgesic drugs.
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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 | 0.000 |
| 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) | 0.001 | 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".