Correlation Analysis of CXCL10, FOS, HOXC13, and WNT4 Gene Polymorphisms with Key Economic Traits—Initial Population Screening for Jiangnan Cashmere Goats
Bibliographic record
Abstract
Background/Objectives: The Jiangnan cashmere goat is a newly developed national cashmere goat breed in China, and the genetic stability of its traits is the core of breeding work. Methods: This study used 353 Jiangnan cashmere goats as research subjects. Descriptive statistics were performed on the key economic traits of the experimental population. Polymorphisms in the CXCL10, FOS, HOXC13, and WNT4 genes were detected using multiplex PCR. The correlation between single-nucleotide polymorphism (SNP) loci and key economic traits was analyzed using the least squares variance method in SAS 9.4 software. Results: A total of 14 SNP loci were detected in the four genes, of which 5 were in the CXCL10 gene. Three SNPs were detected in the FOS, HOXC13, and WNT4 genes. SNP3, SNP4, SNP6, SNP10, SNP11, SNP12, SNP13, and SNP14 were in Hardy–Weinberg equilibrium. The results of the correlation analysis showed that SNP9 of the HOXC13 gene was significantly correlated with birth weight (BW) and mean fiber diameter (MFD), SNP10 of the HOXC13 gene was significantly correlated with yearling weight (YW), and SNP14 of the WNT4 gene was significantly correlated with birth weight (BW) (p < 0.05). Conclusions: The results of this study provide molecular markers for cashmere goat breeding and an experimental basis for accelerating the cultivation of new strains, which is conducive to further optimizing the economic traits of the Jiangnan cashmere goat and ensuring the stable inheritance of its economic traits through molecular breeding.
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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 | 0.000 |
| 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".