A quantitative trait locus for variation in dopamine metabolism mapped in a primate model using reference sequences from related species
Bibliographic record
Abstract
Non-human primates (NHP) provide crucial research models. Their strong similarities to humans make them particularly valuable for understanding complex behavioral traits and brain structure and function. We report here the genetic mapping of an NHP nervous system biologic trait, the cerebrospinal fluid (CSF) concentration of the dopamine metabolite homovanillic acid (HVA), in an extended inbred vervet monkey (Chlorocebus aethiops sabaeus) pedigree. CSF HVA is an index of CNS dopamine activity, which is hypothesized to contribute substantially to behavioral variations in NHP and humans. For quantitative trait locus (QTL) mapping, we carried out a two-stage procedure. We first scanned the genome using a first-generation genetic map of short tandem repeat markers. Subsequently, using >100 SNPs within the most promising region identified by the genome scan, we mapped a QTL for CSF HVA at a genome-wide level of significance (peak logarithm of odds score >4) to a narrow well delineated interval (<10 Mb). The SNP discovery exploited conserved segments between human and rhesus macaque reference genome sequences. Our findings demonstrate the potential of using existing primate reference genome sequences for designing high-resolution genetic analyses applicable across a wide range of NHP species, including the many for which full genome sequences are not yet available. Leveraging genomic information from sequenced to nonsequenced species should enable the utilization of the full range of NHP diversity in behavior and disease susceptibility to determine the genetic basis of specific biological and behavioral traits.
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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 | 0.000 |
| 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 | 0.001 |
| 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".