Haplotype combination of <i>SREBP-1c</i> gene sequence variants is associated with growth traits in cattle
Bibliographic record
Abstract
The aim of this study was to examine the association of the SREBP-1c polymorphism with growth traits in cattle breeds. Five sequence variants (SVs) were identified within the bovine sterol regulatory element-binding protein-1c gene (SREBP-1c), using DNA sequencing, PCR, PCR–RFLP, and forced PCR–RFLP methods. These polymorphisms include three missense mutations (SV1, SV4, and SV5) in exons 7, 9, and 12, a silent mutation (SV3) in exon 9, and a large deletion (SV2) in intron 7. Overall, we report the validation of polymorphisms within the bovine SREBP-1c gene, and the haplotype variability and extent of linkage disequilibrium (LD) in 1061 individuals representing the five main cattle breeds from China. We also investigated haplotype frequencies and LD coefficients for five SVs in all study populations. LD and haplotype structure of SREBP-1c were different between breeds. The result of haplotype analysis of five SVs showed that 27 different haplotypes were identified by all breeds. Two haplotypes (Hap1 and Hap2) shared by all five populations accounted for 42.75%, 35.68%, 36.44%, 25.43%, and 96.26% of all haplotypes observed in the cattle breeds Nanyang, Qinchuan, Jiaxian, Jinnan, and Chinese Holstein, respectively. The statistical analyses indicated that one single SV and 38 combined haplotypes were significantly associated with growth traits in the Nanyang cattle population (P < 0.05 or P < 0.01). The results of this study suggest that the SREBP-1c gene possibly is a strong candidate gene that affects growth traits in the Chinese beef cattle breeding program.
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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".