A panel of microsatellite markers for genetic diversity and parentage analysis of dog breeds in Pakistan.
Bibliographic record
Abstract
A molecular genetics tool comprised of a panel of 15 microsatellite markers was developed and used to investigate parentage and breed characterization of two most kept breeds of dogs including German shepherd and Labrador retriever in Pakistan. Blood samples of 20 dog families (10 from each breed) were collected from Army dog Breeding Training Center and School, Rawalpindi, Pakistan and Kennel Club of Pakistan. Genomic DNA was extracted by standard inorganic protocol. Microsatellite markers with high Polymorphism Information Content (PIC) and He (Hetrozygosity) values were selected and optimized into four multiplexes . Amplification reactions were followed by genotyping in 7% non-denaturing polyacrylamide gel electrophoresis (PAGE). Parentage analysis of 20 families using this panel of microsatellite markers was 100% successful. Average values of Polymorphism Information Content (PIC), Heterozygosity (He) and Combined Power of Exclusion (CPE) combined for both of the breeds were found to be 0.724, 0.6345 and 0.9998 respectively. Moreover, deviation from Hardy-Weinberg equilibrium equation was observed moderately for both dog breeds. Allelic frequencies for majority of the microsatellite markers between both dog breeds were clearly distinct. This study demonstrated the panel of 15 microsatellite markers could effectively validate parentage and breed characterization in dogs.
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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 | 0.001 |
| 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) | 0.001 | 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".