Genomic regions underlying variation in wattles, horns, and supernumerary teats phenotypes in Egyptian goats
Bibliographic record
Abstract
Goats play a crucial role in providing humans with various types of valuable products including milk and meat. The underlying genetic mechanisms of important morphological aspects remain largely unknown in goats, highlighting the need for further investigation. A genome-wide association analysis (GWAS) was conducted for three morphological phenotypes in Egyptian goats. All animals were genotyped using the Illumina 65 K SNP BeadChips. Results of GWAS for wattles identified two significant ( P ≤ 1.4 × 10–6, false discovery rate (FDR) ≤ 0.05) single nucleotide polymorphisms (SNPs) on chromosome 10 within a region (72–74 Mb) containing FMN1 and GREM1 genes that are important for limb development and growth. For horns, three significant SNPs were identified on chromosome 1 (119–131 Mb) harbouring candidate genes for embryonic development and tissue differentiation, such as CEP70, DZIP1L, CLDN18, SOX14, and SLC35G2. For supernumerary teats, four significant SNPs located on chromosomes 25 (8.7 Mb), 9 (47.8 Mb), 17 (45.1 Mb), and 28 (6.7 Mb) were identified, harbouring candidate genes involved in morphogenesis and reproductive traits such as EMP2, MDN1, PCDH10, and GHITM. This study identified novel candidate genes alongside previously reported ones in other goat breeds, suggesting their potential as candidate genes for the studied traits in Egyptian goats.
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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) | 0.002 | 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".