Genome-wide association study for mammary structure in Canadian Angus cows
Bibliographic record
Abstract
Functional and enduring mammary structure is pivotal for producer profitability, and animal health and welfare in beef production. Genetic evaluations for teat and udder score in Canadian Angus cattle have previously been developed. The aim of this study was to identify genomic regions associated with teat and udder structure in Canadian Angus cows thereby enhancing knowledge of the biological architecture of these traits. Thus, we performed a weighted single-step genome wide association study (WssGWAS) to identify candidate genes for teat and udder score in 1,582 Canadian Angus cows typed with the GeneSeek® Genomic Profiler Bovine 130K SNP array. Genomically enhanced estimated breeding values (GEBVs) were converted to SNP marker effects using unequal variances for markers to calculate weights for each SNP over three iterations. At the genome wide level, we detected windows of 20 consecutive SNPs that explained more than 0.5% of the variance observed in these traits. A total of 35 and 28 windows were identified for teat and udder score, respectively, with two SNP windows in common for both traits. Using Ensembl, the SNP windows were used to search for candidate genes and quantitative trait loci (QTL). A total of 94 and 71 characterized genes were identified in the regions for teat and udder score, respectively. Of these, 7 genes were common for both traits. Gene network and enrichment analysis, using Ingenuity Pathway Analysis (IPA), signified key pathways unique to each trait. Genes of interest were associated with immune response and wound healing, adipose tissue development and morphology, and epithelial and vascular development and morphology. Genetic architecture from this GWAS confirms that teat and udder score are distinct, polygenic traits involving varying and complex biological pathways, and that genetic selection for improved teat and udder score is possible.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".