Microsatellite DNA Analysis of Genetic Diversity and Parentage Testing in Popular Dog Breeds in India
Bibliographic record
Abstract
Background: Microsatellite DNA sequencing has emerged as an important method for determining genetic variety and parentage in domesticated species, including popular dog breeds. The role of microsatellite markers helps in understanding the genetic landscape of dog breeds in India, where the growing popularity of particular breeds has generated worries about inbreeding and loss of genetic variety. Methods: For the parentage testing in canine microsatellite length, polymorphism markers were used to check the efficacy of the markers. In the current study 5 fluorescently labeled 12 SSR markers were used to check the use of the markers in popular owned-dog breeds (Labrador, German Shepherd, Pug, Mudhol Hound, Tibetan Mastiff, Gaddi dog, Beagle, Belgian Malinois, Pointer, and Cane Corso). The number of alleles, heterozygosity, polymorphism information content and probability of exclusion were determined for all the markers to check the effectiveness of the markers. Result: The analysis utilized a panel of microsatellite markers to evaluate genetic variation among several breeds including Punjab, Haryana, Himachal Pradesh and Karnataka. The mean number of alleles per locus ranged from 5 to 29 and the effective number ranged from 3.6 to 15.2. The expected heterozygosity was greater than 0.73. The population inbreeding coefficient (FIS) demonstrated that there was no inbreeding in the breeds studied. The polymorphism information content and the probability of the exclusion values were greater than 0.65. The combined probability of exclusion for all the breeds was (2.82E-12) 0.99999995. The findings revealed that the 12 particular microsatellite markers chosen for paternity testing demonstrated substantial exclusion probability for determining parentage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".