Genomic retained heterosis effects on fertility and lifetime productivity in beef heifers
Bibliographic record
Abstract
This study evaluated the effects of three genomic indicators of heterosis on female fertility and lifetime productivity, and quantified changes over 11 production cycles in a crossbred cow herd. Pedigree-based breed composition (pBC) was determined and used to calculate retained heterozygosity for 412 replacement heifers born from 2004 to 2014 at the Lacombe Research and Development Centre (AB, Canada). Heifers were followed as cows over 1050 mating opportunities, 11 production cycles, and five parities. Heifers and their sires (51) were genotyped and these genotypes were used to predict each animal’s genomic breed composition (gBC) and three genomic indicators of heterosis: (1) retained heterozygosity (RHETg), (2) heterozygous proportion (H), and (3) retained heterosis (RHg). Correlations between pedigree and genomic breed fractions for Angus, Hereford, and Charolais were high (r p = 0.74–0.94; P < 0.001). Genomic indicators of heterosis were highly related (r p = 0.61 for RHETg vs. H; 0.71 for RHg vs. H; 0.96 for RHETg vs. RHg; P < 0.001). Each 10% change in RHETg resulted in 51 ± 20 d longer survival (P = 0.011) in the herd and 35.7 ± 15.2 kg more (P = 0.019) calf wean weight per cow exposed to breeding when summed over five parities. These differences resulted in an extra $161 per heifer in a year. Optimizing heterosis using genomic tools can be very beneficial for the cow herd if applied correctly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".