The genetic basis and improvement of feed efficiency in lactating Holstein dairy cattle
Bibliographic record
Abstract
Improvement of feed utilization of the dairy cow through genetic selection may be a solution to increasing the environmental and economic sustainability of the dairy industry. Understanding the genetic basis of feed efficiency is imperative such that selection strategies can be optimized. The objective of this dissertation was to characterize the genetic architecture of feed efficiency, explore strategies for predicting genetic merit for feed efficiency, and consider the impact that selection for feed efficiency could have on related traits. Feed efficiency-related phenotypes and genotypes were collected on 4,916 cows from the United States, Canada, the Netherlands, and the United Kingdom. Residual feed intake (RFI) was chosen as the measure of feed efficiency. A genome-wide association study was performed separately for primiparous and multiparous cows, and genetic correlations were estimated with phenotypes in the two parity groups considered as separate traits. Results from these analyses suggested that RFI is a highly polygenic trait and has a genetic basis that is distinct from production traits and differs between primiparous and multiparous cows. Beta-3 adrenergic receptor (ADRB3) and leptin (LEP) were identified as candidate genes for RFI in primiparous and multiparous cows, respectively. Because many loci explained genetic variation of RFI, genomic prediction strategies were explored such that genetic markers across the genome could be utilized to estimate breeding values for animals. Results indicated that the accuracy of prediction was lower for RFI than related traits that in combination could be explored as predictors of feed efficiency. On a subset of cows, surface body temperature as measured by thermal imaging was explored as an indicator trait and considered as an alternative strategy for use in the estimation of genetic merit. A positive relationship between rear leg temperature and RFI was established, and surface temperature was moderately heritable, but the percentage of variation in RFI explained by surface temperature and the confidence in genetic correlation between RFI and surface body temperature were weak. In a final study, the possibility that improved feed efficiency may inadvertently favor cows that mobilize body tissue in early lactation was explored. Feed efficient cows when defined as RFI carried more body condition throughout lactation and body condition loss was not different between feed efficient and inefficient cows. In conclusion, implementation of selection strategies in conjunction with the consideration of adverse effects may be valuable to improve the feed efficiency of dairy cows.
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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 | 0.000 |
| 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".