Evaluation of allele frequencies of inherited disease genes in subgroups of American Quarter Horses
Bibliographic record
Abstract
OBJECTIVE: To estimate allele frequencies of the hyperkalaemic periodic paralysis (HYPP), lethal white foal syndrome (LWFS), glycogen branching enzyme deficiency (GBED), hereditary equine regional dermal asthenia (HERDA), and type 1 polysaccharide storage myopathy (PSSM) genes in elite performance subgroups of American Quarter Horses (AQHs). DESIGN: Prospective genetic survey. ANIMALS: 651 elite performance AQHs, 200 control AQHs, and 180 control American Paint Horses (APHs). PROCEDURES: Elite performance AQHs successful in 7 competitive disciplines (barrel racing, cutting, halter, racing, reining, western pleasure, and working cow horse) were geno- typed for 5 disease-causing alleles. Age-matched control AQHs and APHs were used to establish comparative whole-breed estimates of allele frequencies. RESULTS: Highest allele frequencies among control AQHs were for type 1 PSSM (0.055) and GBED (0.054), whereas HERDA (0.021) and HYPP (0.008) were less prevalent. Control APHs uniquely harbored LWFS (0.107) and had high prevalence of HYPP (0.025), relative to AQHs. Halter horse subgroups had significantly greater allele frequencies for HYPP (0.299) and PSSM (0.155). Glycogen branching enzyme deficiency, HERDA, and PSSM were found broadly throughout subgroups; cutting subgroups were distinct for HERDA (0.142), and western pleasure subgroups were distinct for GBED (0.132). Racing and barrel racing subgroups had the lowest frequencies of the 5 disease genes. CONCLUSIONS AND CLINICAL RELEVANCE: Accurate estimates of disease-causing alleles in AQHs and APHs may guide use of diagnostic genetic testing, aid management of genetic diseases, and help minimize production of affected foals.
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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 | 0.001 |
| 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".