Polymorphism detection of porcine <i>PSMC3</i>, <i>PSMC6</i> and <i>PSMD3</i> genes and their association with partial growth, carcass traits, meat quality and immune traits
Bibliographic record
Abstract
PSMC3, PSMC6 and PSMD3 genes encode the proteasome 26S ATPase subunit 3, subunit 6 and the non-ATPase subunit 3, respectively, and exert the function in antigen processing and presentation. So these three genes were considered as the candidate genes that have the effect on porcine production and immune traits in this study. Genetic variations of these three genes were investigated and the single nucleotide polymorphism-based (SNPs-based) association analyses were studied initially. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) revealed that there is a MvaI polymorphic site in intron3 of PSMC3 gene and a MboI polymorphic site within intron 5 of PSMC6 gene, as well as a RsaI polymorphic site within exon 10 of the PSMD3 gene. χ2 analysis presented that allele frequencies differed among four breeds (Meishan, Erhualian, Qingping pig and Duroc) at PSMC3 and PSMD3 loci (P < 0.01). Association analysis showed that the PSMC3 gene has an effect on average backfat thickness (P < 0.01), total erythrocytes (P < 0.01) and hematocrit (P < 0.01), and there is a significant association between the PSMD3 genotypes and the mean corpuscular volume. PSMC6 has no effect on the production and immune traits we studied. Key words: Porcine, PSMC3, PSMC6, PSMD3, polymorphism, association analysis
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 | 0.000 | 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) | 0.001 | 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".