Assessment of Heterozygosity and Genome-Wide Analysis of Heterozygosity Regions in Two Duroc Pig Populations
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Bibliographic record
Abstract
Heterozygosity can effectively reflect the diverse models of population structure and demographic history. However, the genomic distribution of heterozygotes and the correlation between regions of heterozygosity (runs of heterozygosity, ROHet) and phenotypes are largely understudied in livestock. The objective of this study was to identify ROHet in the Duroc pig genome, and investigate the relationships between ROHet and eight important economic traits. Here, we genotyped 3,770 American Duroc (S21) and 2,096 Canadian Duroc (S22) pigs using 50 K single nucleotide polymorphism array to analyze heterozygosity. A total of 145,010 and 84,396 ROHets were characterized for S21 and S22 populations, respectively. ROHet segments were mostly enriched in 1-2 Mb length classification (75.48% in S21 and 72.25% in S22). The average genome length covered by ROHet was 66.53 ± 12.20 Mb in S21 and 73.32 ± 13.77 Mb in S22 pigs. Additionally, we detected 20 and 13 ROHet islands in S21 and S22 pigs. Genes in these genomic regions were mainly involved in the biological processes of immunity and reproduction. Finally, the genome-wide ROHet-phenotypes association analysis revealed that 130 ROHets of S21 and 84 ROHets of S22 were significantly associated with eight economic traits. Among the candidate genes in the significant ROHet regions, 16 genes related to growth, metabolism, and meat quality were considered as candidate genes for important economic traits of pigs. This work preliminarily explores the effect of heterozygosity-rich regions in the pig genome on production performance and provides new insights for subsequent research on pig genetic improvement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it