0311 Joint association analysis of additive and non-additive genomic effects for growth and carcass traits of beef cattle
Bibliographic record
Abstract
The biological dominance effects of genes have been suggested as one of the genetic mechanism explaining heterosis. We performed a joint association analysis using genotypes from Illumina BovineSNP50 (50K) BeadChip to evaluate the contributions of additive and dominance genomic effects to the variance of growth and carcass traits in beef cattle and to identify genomic regions that potentially harbor genes or quantitative trait loci underlying the variation. A total of 6794 multi-breed and crossbred beef cattle with phenotype and 50K genotypes were used. Traits studied included birth weight (BWT), weaning weight (WWT), pre-weaning daily gain (PDG), average daily gain (ADG), yearling weight (YWT), hot carcass weight (HCW), back fat thickness (BFT), rib-eye area (REA), marbling score (MS), lean meat yield (LMY) and yield grade (YG). Additive and dominance genomic relationships were created based on 42,610 single nucleotide polymorphism (SNP) markers that passed the quality control. The model used accounted for fixed contemporary group effects (herd, year, data source, and sex), covariates of genomic breed composition, age of dam, weaning age, age at start of feedlot test, and slaughter age, and random maternal and maternal permanent effect depending on the trait analyzed. A single SNP analysis that partitions the SNP effects into additive and dominance components was used for genome-wide association. The proportions of total phenotypic variance explained by additive and dominance effects for the studied traits are presented in Table 1. After applying a false discovery rate at a 5% significance ly, for the additive component. For the dominance component, three SNPs (rs11level, a total of 66, 20, 2, 36, 66, 22, 9, 15, 10, and 3 SNPs were significantly associated with BWT, WWT, PDG, ADG, YWT, HCW, BFT, REA, LMY, and YG, respectively, for the additive component. For the dominance component, three SNPs (rs110564527, rs110361335, and rs41663796) and one SNP (rs43624164) were significantly associated with MS and WWT, respectively. The SNP rs110361335 located on chromosome 4 was found to be within islet cell autoantigen 1 (ICA1) gene which is involved in insulin regulation. In addition, SNP rs43624164 on chromosome 10 found to be near the gene ribosomal protein L10-like (RPL10L) had significant additive and dominance effects on WWT. Although, the proportions of phenotypic variance explained by dominance were moderate for growth traits with known heterosis effects, the results of this study suggest that dominance effects are polygenic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".