Hereditary monogenic health disorders in canine breeds in relation to breeding
Bibliographic record
Abstract
This thesis deals with problematic of prevalence of single-gene disorders for various dog breeds. The goal was to discover the groups of dog breed with the highest prevalence of single-gene disorders and discover which of these disorders tends to affects most of the dog breeds. We have evaluated 23 closely related groups of dog breeds (Parkerová et al. 2017) with the appearance of some single-gene disorder as we predicted based on our analysis based on OMIA database dated to year 2018-2020. The analysis showed us that the Neuronal Ceroid Lipofuscinosis (NCL) and Progressive Rod-Cone Degeneration (PRCD) affecting most of the dog breeds. We also discovered that the genetically closely related dog breed group 20T, which contains 16 dog breeds, suffers from over 49 types of single-gene disorders. We also discovered that the Border Collie is the dog breed with the highest single-gene disorder prevalence in previously mentioned group 20T. This dog breed is affected by 9 disorders at least. In our analysis the dog breed Labrador Retriever and German Shepherd are suffering from most types of single-gene disorder (more than 15). These dog breeds are one of the most common dog breeds in Czech Republic. This finding supports the hypothesis that the popular dog breeds tends to have better mapped genome and are subjects of testing more often. This thesis provides the characteristic of selected dog breeds. The genetic specifications also contains the recommended breeding method parameters for the minimization of disorders in population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".