PSXI-4 Genome-Wide association study for muscle visual score in Nellore cattle raised in pasture-based systems
Bibliographic record
Abstract
Abstract Visual scoring traits have been proposed as an alternative to evaluate body composition of Zebu cattle near the slaughter season when phenotyping technologies are not available. Considering the increased demand for high-quality animal protein in developing countries, there is a need to genetically improve body muscle (MUSC) in Zebu cattle (Bos taurus indicus), especially in animals raised in pasture-based systems. Therefore, our main objectives were to estimate genetic parameters, perform a genome-wide association study based on the single-step GBLUP approach (ssGWAS), and identify candidate genes and metabolic pathways related to MUSC in Nellore cattle. A total of 20,808 Nellore animals born between 2009 and 2018 were visually score at 18 months of age and 2,775 of these animals were also genotyped using the GGP-Indicus 35K SNP panel (33,247 SNPs after quality control). Heritability was estimated based on the REML approach and the model included the effects of age at measurement as covariable and the contemporary group (farm, birth season, management group and sex). The ssGWAS was performed using the BLUPF90 family programs. The identification of candidate genes was performed through the Ensembl database incorporated in the BioMart tool. MUSC is heritable (0.38) and can be improved through selection. Nineteen genomic regions (explaining 38.12% of the total additive genetic variance) located on BTA1, BTA7, BTA9, BTA16, and BTA21 and harboring 19 candidate genes were identified. The main genes identified were SEMA6A, TIAM2, UNC5A, and UIMC1, which are related to the metabolism of energy, growth, homeostasis and axonogenesis, and therefore, muscle development. These findings contribute to a better understanding of the molecular mechanisms over the gene expression of muscle visual score in Nellore cattle, and the polymorphisms located in these genes can be incorporated in commercial genotyping platforms to improve the accuracy of imputation and genomic evaluations for body and carcass traits.
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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 | 0.001 |
| 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) | 0.003 | 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".