198 Discovery of key functional SNP markers associated with feed efficiency in beef cattle
Bibliographic record
Abstract
Abstract Beef production contributes to approximately 2.4% of the total Greenhouse Gas emissions in Canada. Improving beef cattle feed efficiency (FE) may lead to improved energy partitioning and resource use, thereby improving the sustainability of the beef industry. Our objective was to identify differentially expressed key regulatory genes and associated novel functional SNP markers linked to FE in Canadian beef cattle using RNA-Sequencing (RNA-Seq) of rumen tissue collected from 48 beef cattle [n = 16 Angus, n = 16 Charolais, n = 16 Kinsella (Composite Hybrid including Angus, Charolais, Galloway, Hereford, Holstein, Brown Swiss, and Simmental)] selected for extreme FE phenotypes. In total, 11 key regulatory genes (MYH1, MYL2, MYLPF, TNNC2, EIF4B, RHOD, TCEANC, CKM, ENSBTAG00000040518, SERPINB2, and USP43) were significantly differentially expressed (DE) between extreme Residual Feed Intake (RFI) groups (low-RFI n = 8, high-RFI n = 8 per breed) using CLC Genomics Workbench (FDR < 0.05; |FC| >2). Using an optimized RNA-Seq variant calling pipeline using STAR and BCFtools, a total of [total (unique to low-RFI, unique to high-RFI)] 75 (36, 39), 78 (42, 36), and 53 (35, 18) uniquely fixed functional SNPs were located within coding regions of these 11 functional candidate genes, in low- and high-RFI animals in the Angus, Charolais, and Kinsella breeds, respectively. Considering all functional SNPs uniquely identified in low- or high-RFI groups for all breed comparisons, the majority of SNPs were identified in MYH1, EIF4B, and SERPINB2 genes, which function together in metabolically demanding biological processes (P < 0.05) related to muscle contraction, muscle system, and muscle filament function processes. Additionally, EIF4B and SERPINB2 genes were found to have a role in signaling pathways that coordinate cell growth and immune function, respectively. Using RNA-Seq to identify key regulatory genes and associated functional SNPs linked to FE may uncover important genetic markers that influence the regulation of FE in beef cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".