Comparative analysis of the <i>IGF2</i> and <i>ZBED6</i> gene variants and haplotypes reveals significant effect of growth traits in cattle
Bibliographic record
Abstract
Muscle growth is a complex phenomenon regulated by many factors, whereby net growth results from the combined action of synthesis and turnover. Insulin-like growth factor 2 (IGF2) is a fetal growth and differentiation factor that plays an important role in muscle growth and in myoblast proliferation and differentiation; Zinc finger, BED-type containing 6 (ZBED6) is a novel transcription factor that was identified and shown to act as a repressor of IGF2 transcription in skeletal muscle. In this study, a total of seven single nucleotide polymorphisms (SNPs) were identified, four SNPs in intron 8 of IGF2 and one promoter SNP and two missense mutations in the coding region of ZBED6, two of which were in complete linkage disequilibrium (LD) in the bovine IGF2. The 58 haplotypes were inferred in 1522 individuals representing four purebred cattle breeds from China. The seven SNPs, 79 and 66 combined diplotypes were revealed for association with body mass in Nanyang and Jiaxian cattle populations at five different ages (P < 0.05 or 0.01). The mutant-type variants and haplotype 58 (likely in LD with the beneficial quantitative trait nucleotide allele) was superior for body mass; the heterozygote diplotype of the most common haplotypes 58 was associated with higher body mass compared to either heterozygote or homozygote. The statistical analyses indicated that the mutant-type variants and haplotypes are significantly associated with body mass in study cattle populations at different ages. These data demonstrate that variants and haplotypes are associated with growth traits, and these results may provide important biological insights into the phenotypic differentiation that is associated with adaptation and specialization of cattle breeds.
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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 | 0.001 |
| 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".