Mouse and Human Genetic Analyses Associate Kalirin with Ventral Striatal Activation during Impulsivity and with Alcohol Misuse
Bibliographic record
Abstract
Impulsivity is associated with a spectrum of psychiatric disorders including drug addiction. To investigate genetic associations with impulsivity and initiation of drug taking, we took a two-step approach. First, we identified genes whose expression level in prefrontal cortex, striatum and accumbens were associated with impulsive behavior in the 5-choice serial reaction time task across 10 BXD recombinant inbred (BXD RI) mouse strains and their progenitor C57BL/6J and DBA2/J strains. Behavioral data were correlated with regional gene expression using GeneNetwork (www.genenetwork.org), to identify 44 genes whose probability of association with impulsivity exceeded a false discovery rate of < 0.05. We then interrogated the IMAGEN database of 1423 adolescents for potential associations of SNPs in human homologs of those genes identified in the mouse study, with brain activation during impulsive performance in the Monetary Incentive Delay task, and with novelty seeking scores from the Temperament and Character Inventory, as well as alcohol experience. There was a significant overall association between the human homologs of impulsivity-related genes and percentage of premature responses in the MID task and with fMRI BOLD-response in ventral striatum (VS) during reward anticipation. In contrast, no significant association was found between the polygenic scores and anterior cingulate cortex activation. Univariate association analyses revealed that the G allele (major) of the intronic SNP rs6438839 in the KALRN gene was significantly associated with increased VS activation. Additionally, the A-allele (minor) of KALRN intronic SNP rs4634050, belonging to the same haplotype block, was associated with increased frequency of binge drinking.
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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 | 0.000 | 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) | 0.004 | 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".