Genomic Scan for Runs of Homozygosity and Selective Signature Analysis to Identify Candidate Genes in Large White Pigs
Bibliographic record
Abstract
Large White pigs are extensively utilized in China for their remarkable characteristics of rapid growth and the high proportion of lean meat. The economic traits of pigs, comprising reproductive and meat quality traits, play a vital role in swine production. In this study, 2295 individuals, representing three different genetic backgrounds Large White pig populations were used: 500 from the Canadian line, 295 from the Danish line, and 1500 from the American line. The GeneSeek 50K GGP porcine HD array was employed to genotype the three pig populations. Firstly, genomic selective signature regions were identified using the pairwise fixation index (FST) and locus-specific branch length (LSBL). By applying a top 1% threshold for both parameters, a total of 888 candidate selective windows were identified, harbouring 1571 genes. Secondly, the investigation of regions of homozygosity (ROH) was performed utilizing the PLINK software. In total, 25 genomic regions exhibiting a high frequency of ROHs were detected, leading to the identification of 1216 genes. Finally, the identified potential functional genes from candidate genomic regions were annotated, and several important candidate genes associated with reproductive traits (ADCYAP1, U2, U6, CETN1, Thoc1, Usp14, GREB1L, FGF12) and meat quality traits (MiR-133, PLEKHO1, LPIN2, SHANK2, FLVCR1, MYL4, SFRP1, miR-486, MYH3, STYX) were identified. The findings of this study provide valuable insights into the genetic basis of economic traits in Large White pigs and may have potential use in future pig breeding programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".