Utilitarian selection signatures co-localized with copy number variation regions in Indian goat breeds revealed through whole-genome re-sequencing
Bibliographic record
Abstract
This study investigates the selection signatures of 11 Indigenous goat breeds from diverse eco-topographies of India, using whole-genome re-sequencing data from 103 individuals. We identified population-wide copy number variation regions (as well as selection signatures through a variance-stabilizing transformation approach for utility traits. A total of 32 711 polymorphic sites were analyzed, revealing 327 significant and 32 highly significant signatures under selection. Key genes identified in selection signatures include GHR, PLAG1, and MTOR, which play crucial roles in growth, development, and reproductive traits across different utility groups. Notable reproduction-related genes such as ITPR3, ESRRG, and SOX6 were found to be associated with fertility, hormone regulation, and reproductive system. Network analysis revealed ESR1 as a central hub gene forming significant interactions with RUNX2, HDAC2, and BCL2, indicating its vital role in muscle development and metabolism. The MTOR signaling pathway emerged as another crucial hub, connecting with DEPDC5 and SESN1, suggesting its importance in nutrient sensing and metabolic regulation for production traits. Gene ontology analysis of the selection signatures revealed pathways for functional categories between meat, milk, and fiber-producing breeds, reflecting the genetic architecture underlying their specialized phenotypes. Identified selection signatures and hub genes can be used in marker-assisted and genomic selection to improve growth, reproduction, and adaptability in indigenous goats, aiding precision breeding and conservation programs.
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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 | 0.001 |
| 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 | 0.000 |
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".