PSXII-10 Development of a 70k Snp Genotyping Array for American Mink (<i>Neogale vison</i>)
Bibliographic record
Abstract
Abstract Mink is an important fur animal species; however, little is known about the genomics of complex traits in mink. The development of single nucleotide polymorphism (SNP) array has accelerated genomics application in animal breeding by facilitating genotyping and associated analyses. The current project aimed to design a medium-density SNP array in American mink for genomic studies and selection in this species. For this purpose, whole genome sequence with 30x coverage was generated for 100 individuals from the Canadian Centre for Fur Animal Research (CCFAR) at the Dalhousie Faculty of Agriculture (Truro, NS, Canada) and Millbank Fur Farm (Rockwood, ON, Canada). Variant calling was done using Samtools and GATK pipelines. A total of 8,373,854 bi-allelic SNPs identified by both pipelines were used for an initial selection of 91K SNPs for submission to the Affymetrix scoring system. After filtering, 62,375 SNPs were selected for probe design and production of an Axiom Mink Genotyping Array 70K. This 70K SNP array was used to genotype 2,973 mink for validation. After quality control of genotypes, 47,159 SNPs were classified as high-resolution SNPs, and among them 24,237 were polymorphic. A preliminary genome-wide association study of 1,356 mink for body length at harvest identified a SNP (AX-647623051) significantly associated (P < 1.26 × 10-5) with the trait. In conclusion, this 70K mink SNP array could be used for genomic studies and potentially as a starting point for genomic selection programs in American 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".