Host and rumen microbiome contributions to feed efficiency traits in Holstein cows
Bibliographic record
Abstract
It is now widely accepted that dairy cow performance is influenced by both the host genome and rumen microbiome composition. The contributions of the genome and the microbiome to the phenotypes of interest are quantified by heritability ( h 2 ) and microbiability ( m 2 ), respectively. However, if the genome and microbiome are included in the model, then the heritability reflects only the contribution of the direct genetic effects quantified as direct heritability ( h d 2 ) , and the holobiont effect reflects the joint action of the genome and the microbiome, quantified as the holobiability ( ho 2 ). The objectives of this study were to estimate h 2 , h d 2 , m 2 , and ho 2 for dry matter intake, milk energy, and residual feed intake; and to evaluate the predictive ability of different models, including genome, microbiome, and their interaction. Data consisted of feed efficiency records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. Three kernel models were fit to each trait: one with only the genomic effect (model G), one with the genomic and microbiome effects (model GM), and one with the genomic, microbiome, and interaction effects (model GMO). The model GMO, or holobiont model, showed the best goodness-of-fit. The h d 2 estimates were always 10 to 15% lower than h 2 estimates for all traits, suggesting a mediated genetic effect through the rumen microbiome, and m 2 estimates were moderate for all traits, and up to 26% for milk energy. The ho 2 was greater than the sum of h d 2 and m 2 , suggesting that the genome-by-microbiome interaction had a sizeable effect on feed efficiency. Kernel models fitting the rumen microbiome, i.e., models GM and GMO, showed larger predictive correlations and smaller prediction bias than the model G. These findings reveal a moderate contribution of the rumen microbiome to feed efficiency traits in lactating Holstein cows and strongly suggest that the rumen microbiome mediates part of the host genetic effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".