47 Genomic Studies of Reproductive Performance in American Mink
Bibliographic record
Abstract
Abstract Improving reproductive efficiency is one of the main breeding goals in mink farming. Understanding the genetics of reproductive performance is essential for the development of effective breeding programs in mink. The objectives of this study are to 1) estimate the phenotypic and genetic parameters for litter sizes (LS), mortality rate at birth. (MB) and gestation-length (GL) traits; 2) perform genome-wide association studies (GWAS) for these reproductive traits; 3) implement GWAS results in the selection of mink for reproductive performance; 4) explore the potential for genomic selection in mink. The detailed reproductive performance on 3,500 female mink has been collected at the Canadian Center for Fur Animal Research at Dalhousie University (Truro, NS, Canada), in which, 1,000 of them will be genotyped with Affymetrix 50k SNP panel. A series of univariate and bivariate analyses were implemented in ASREML software to estimate the genetic and phenotypic parameters. Heritability estimates (±SE) were low-to-moderate, ranged from 0.06±0.02 for total number born to 0.23±0.03 for GL. High positive genetic correlations (±SE) were observed between LS traits, ranged from 0.59±0.18 to 0.85±0.11. There was a moderate genetic correlation (±SE) between MB and total number of kits born (0.46±0.15). However, MB had a favorable strong negative genetic correlation (±SE) with the number of weaned kits (–0.75±0.16). These estimated genetic parameters can be incorporated into Canadian mink breeding programs. Considering the low-to-moderate heritability of reproduction traits, the availability of the mink reference genome and genotyping panel will provide opportunities to accelerate mink breeding through genomics. The results of this project will contribute significantly to the current genetic knowledge of reproductive traits and identify the opportunities for genetic improvement through the application of genomics. The overall project aim is to develop a cost-effective, low-density panel of markers for the implementation of genomic selection for reproductive performance in mink.
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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".