Identification of single nucleotide polymorphisms in the bovine follicle‐stimulating hormone receptor and effects of genotypes on superovulatory response traits
Bibliographic record
Abstract
In dairy cows, there is evidence that failure to respond to superovulation protocols is a heritable trait. In women, genotyping for the p.N680S single nucleotide polymorphism (SNP) in the follicle-stimulating hormone receptor (FSHR) gene may help identify poor responders before ovarian stimulation is initiated. Our objectives were to identify SNPs in the coding region of the bovine FSHR gene and to investigate the effect of FSHR genotypes on superovulatory response in Holstein cattle. Sequencing of FSHR exons 1-10 revealed seven SNPs. Three were non-synonymous mutations (c.337C>G, c.871A>G and c.1973C>G). SNP c.337C>G encodes for a proline-to-alanine (p.Pro113Ala) amino acid replacement in the extracellular ligand-binding domain of the receptor. PCR-RFLP analyses showed that homozygous GG Holstein cows present a higher percentage of viable embryos, whereas GG and CG animals have less unfertilised oocytes. SNP c.871A>G results in an isoleucine-to-valine (p.Ile291Val) modification, and homozygous AA animals present lower embryo yield after superovulatory treatments. SNP c.1973C>G corresponds to a threonine-to-serine (p.The658Ser) modification in the intracellular carboxyl-terminal domain of the FSHR protein, and homozygous GG Holstein cows were associated with a lower embryo yield and a higher percentage of unfertilised oocytes. Our results suggest that specific alleles of the bovine FSHR gene are associated with variations in embryo yield and in the number of unfertilised oocytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".