Detailed characterization of the porcine <i>MC4R</i> gene in relation to fatness and growth
Bibliographic record
Abstract
In contrast to the human MC4R gene, where multiple variants have been described, several of which are associated with appetite and obesity, few MC4R variants have been reported in the pig. The most interesting polymorphism reported to date in the pig is p.Asp298Asn, which is significantly associated with variation in growth and fatness traits in most breeds and crosses. However, some reports have seemingly failed to confirm this association. The discrepancy of p.Asp298Asn associations in some pig populations suggested that further discovery of SNPs in MC4R would be useful. Utilizing the recently released pig genome sequence information, we obtained the whole MC4R genome sequence and detected five additional SNPs, a variable (CA)(n) repeat and a C indel in the ISU Berkshire x Yorkshire pig resource family. Linkage disequilibrium (LD) analysis revealed that the additional five SNPs were not in strong LD with p.Asp298Asn, but single marker association analysis indicated that they were significantly (P < 0.05) associated with fatness measures and very highly significantly (P < 0.0001) associated with average daily gain on test (ADGTEST). Three major haplotypes were identified and the subsequent association analyses suggested that the two non-synonymous SNPs had different effects, e.g. p.Arg236His influenced back fat and growth on test while p.Asp298Asn was primarily associated with variation in growth rate in this population. An interaction effect between these two SNPs was found for ADGTEST, which may partly explain some of the previous discrepancies reported for MC4R in different pig populations. Examination of the p.Arg236His polymorphism in populations where the effect of p.Asp298Asn is limited is warranted.
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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".