Whole-genome comparative analysis reveals genetic mechanisms of disease resistance and heat tolerance of tropical <i>Bos indicus</i> cattle breeds
Bibliographic record
Abstract
Bos indicus cattle breeds have been naturally selected for thousands of years for disease resistance and thermo-tolerance. However, the genetic mechanisms underlying these specific inherited characteristics must be elucidated. Hence, in this study, a whole-genome comparative analysis of the Bos indicus cattle breeds Kangayam, Tharparkar, Sahiwal, Red Sindhi, and Hariana of the Indian subcontinent was conducted. Genetic variant identification analysis revealed 155 851 012 SNPs and 10 062 805 InDels in the mapped reads across all Bos indicus cattle breeds. The functional annotation of 17 252 genes that comprised both SNPs and InDels, with high functional impact on proteins, was carried out. The functional annotation results revealed the pathways involved in the innate immune response, including toll-like receptors, retinoic acid-inducible gene I-like receptors, NOD-like receptors, Jak-STAT signaling pathways, and non-synonymous variants in the candidate immune genes. We also identified several pathways involved in the heat shock response, hair and skin properties, oxidative stress response, osmotic stress response, thermal sweating, feed intake, metabolism, and non-synonymous variants in the candidate thermo-tolerant genes. These pathways and genes directly or indirectly contribute to the disease resistance and thermo-tolerance adaptations of Bos indicus cattle breeds.
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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 | 0.000 |
| 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 | 0.000 |
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".