Genome‐wide selection signal analysis of Australian Boer goat reveals artificial selection imprinting on candidate genes related to muscle development
Bibliographic record
Abstract
Summary As one of the best‐known commercial goat breeds in the world, Boer goat has undergone long‐term artificial selection for nearly 100 years, and its excellent growth rate and meat production performance have attracted considerable worldwide attention. Herein, we used single nucleotide polymorphisms (SNPs) called from the whole‐genome sequencing data of 46 Australian Boer goats to detect polymorphisms and identify genomic regions related to muscle development in comparison with those of 81 non‐specialized meat goat individuals from Europe, Africa, and Asia. A total of 13 795 202 SNPs were identified, and the whole‐genome selective signal screen with aπratio of nucleotide diversity (πcase/πcontrol) and pairwise fixation index (FST) was analyzed. Finally, we identified 1741 candidate selective windows based on the top 5% threshold of both parameters; here, 449 candidate genes were only found in 727 of these regions. A total of 433 genes out of the 449 genes obtained were annotated to 2729 gene ontology terms, of which 51 were directly linked to muscle development (e.g., muscle organ development, muscle cell differentiation) by 30 candidate genes (e.g.,JAK2,KCNQ1,PDE5A,PDLIM5,TBX5). In addition, 246 signaling pathways were annotated by 178 genes, and two pathways related to muscle contraction, including vascular smooth muscle contraction (ADCY7,PRKCB,PLA2G4E,ROCK2) and cardiac muscle contraction (CACNA2D3,CASQ2,COX6B1), were identified. The results could improve the current understanding of the genetic effects of artificial selection on the muscle development of goat. More importantly, this study provides valuable candidate genes for future breeding of goats.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".