Phylogeographic and genetic diversity analysis through genome-wide SNPs in indigenous and exotic canine breeds owned in India
Bibliographic record
Abstract
The present study aimed to assess genetic diversity within six distinct dog breeds (Labrador Retriever, German Shepherd, Pug, Mudhol Hound, Tibetan Mastiff, and Gaddi) sampled from four different Indian states: Punjab, Himachal Pradesh, Haryana, and Karnataka. The research employed double digest restriction-site associated-DNA-genotyping by sequencing (ddRAD-GBS) analysis followed by next-generation sequencing (NGS), specifically using Illumina 150 bp paired-end sequencing to analyze the genetic makeup of fifty canine samples. A total of 3,56,461 SNP loci were identified across the samples, with 75811 high-quality SNPs selected. R programming and Linux-bash coding were employed to conduct population genetic and phylogenetic analyses including genetic distance, construction of phylogenetic trees, principal component analysis, and population structure assessments to distinguish the dog populations based on their historical origins. The results revealed there was a close genetic similarity between samples collected from Punjab and Himachal Pradesh, followed by Karnataka, and Haryana. It is inferred that the divergent dog breeds harbor distinct genetic uniqueness based on specific geographical regions. The findings offer valuable insights for future research involving allele/gene identification through genome-wide association studies (GWAS) and marker-assisted selection (MAS), enhancing genetic improvement in breeding programs. The study expands understanding of the genetic structure of popular indigenous and exotic canine germplasm. This research represents the first study on molecular-level characterization of the native Gaddi dog and Mudhol Hound breed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.001 | 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".