Genome-Wide Association Analysis of Growth Traits in Hu Sheep
Bibliographic record
Abstract
(1) Background: The Hu sheep is a renowned breed characterized by high reproduction, year-round estrus, and resistance to high humidity and temperature conditions. However, the breed exhibits lower growth rates and meat yields, which necessitate improvements through selective breeding. The integration of molecular markers in sheep breeding programs has the potential to enhance growth performance, reduce breeding cycles, and increase meat production. Currently, the applications of SNP chips for genotyping in conjunction with genome-wide association studies (GWAS) have become a prevalent approach for identifying candidate genes associated with economically significant traits in livestock. (2) Methods: To pinpoint candidate genes influencing growth traits in Hu sheep, we recorded the birth weight, weaning weight, and weights at 3, 4, 5, 6, and 7 months for a total of 567 Hu sheep, and genotyping was performed using the Ovine 40K SNP chip. (3) Results: Through GWAS analysis and KEGG pathway enrichment, we identified three candidate genes associated with birth weight (CAMK2B, CACNA2D1, and CACNA1C). Additionally, we found two candidate genes linked to weaning weight (FGF9 and BMPR1B), with CACNA2D1 also serving as a shared gene between birth weight and weaning weight traits. Furthermore, we identified eight candidate genes related to monthly weight (FIGF, WT1, KCNIP4, JAK2, WWP1, PLCL1, GPRIN3, and CCSER1). (4) Conclusion: Our findings revealed a total of 13 candidate genetic markers that can be utilized for molecular marker-assisted selection, aiming to improve meat production in sheep breeding 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 | 0.001 | 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".