Variations in rumen microbiota and host genome impacted feed efficiency in goat breeds
Bibliographic record
Abstract
Introduction Improving feed efficiency (FE) is a significant goal in animal breeding programs. Variations in FE and its relationship with rumen microbiota remain poorly understood across different goat breeds. Methods This study assessed the influence of the rumen microbiome and host genome on FE in 10 Shami (SH) goats and 10 Zaraibi (ZA) goats, all of which were fed the same diet. The animals were genotyped using the Illumina 65KSNP chip arrays v2, and their rumen bacteria and archaea were investigated using PCR-amplicon sequencing of the 16S rRNA gene. Results The results showed that the ZA goats exhibited higher FE than the SH goats (p < 0.05) and a greater relative abundance (p < 0.05) of rumen bacterial groups that are involved in the degradation of cellulose and hemicelluloses, such as Bacteroidota and Fibrobacterota, along with genera such as Prevotella, Lachnospiraceae AC2044 group, Lachnospiraceae NK3A20 group, and Succiniclasticum, which are linked to carbohydrate and nitrogen metabolism pathways. In addition, breed differences were found to affect the proportions of milk fatty acids. The association analysis identified 26 genome windows containing several putative candidate genes, such as TMEM241, AP4S1, FTO, HYAL2, BBS2, CD52, CRYBG2, PIGV, WDTC1, EEF1A2, GBA2, FNIP1, ACSL6, STARD10, VPS26B, ACAD8, GLB1L3, NRN1L, LCAT, and SLC7A6. These genes contributed to FE traits in Egyptian goats, as they are involved in obesity, metabolism, and the transport of energy, vitamins, fatty acids, proteins, and lipids through diverse biological pathways. Discussion This study suggests that specific genetic markers and rumen microbial traits could be used to identify high-efficient individuals in Egyptian goat breeds, and improving breeding strategies for FE.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".