ArticleJournal of animal science2025
Genomic prediction accounting for dominance and epistatic genetic effects on litter size traits in Large White pigs.
Article in Journal of animal science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integrating Epistatic Interactions into Genomic Prediction of Growth and Fillet Fat Content in Common Carp.International journal of molecular sciences · 2026Article
- Candidate Gene Identification and Genomic Prediction for Key Reproductive Traits in Yorkshire, Landrace, and Duroc Pigs.Animals : an open access journal from MDPI · 2026Article
- Genome-Wide Association Studies on the Autosomes and Chromosome X Uncover Genetic Basis of Reproductive Traits in Yorkshire Pigs.Animals : an open access journal from MDPI · 2026Article
- Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.G3 (Bethesda, Md.) · 2025Article
- Genomic Analysis of Reproductive Trait Divergence in Duroc and Yorkshire Pigs: A Comparison of Mixed Models and Selective Sweep Detection.Veterinary sciences · 2025Article
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15 authors.
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Abstract
Litter size traits of sows are crucial for the economic benefits of the pig industry. Three phenotypic traits of 1,206 Large White pigs, the total number born (TNB), number born alive (NBA), and number of healthy piglets (NHP), were recorded. We evaluated a series of genomic best linear unbiased prediction models that sequentially added additive effects (model A), dominance effects (model A + D), and epistatic effects (model A + D + AA, model A + D + AA + AD, and model A + D + AA + AD + DD) using chip data and imputed whole-genome sequencing (WGS) data to estimate genetic parameters and predictive accuracy. The reproductive traits of sows showed low heritability in this study, with narrow heritability of the 3 traits ranging from 0.030 to 0.064, and broad heritability ranging from 0.125 to 0.145. The inclusion of nonadditive effects in the model improved the accuracy of genomic selection. In the chip data, compared with that of the A model, the A + D + AA + AD + DD model showed the greatest increase in accuracy for TNB, NBA, and NHP, with improvements of 1.78%, 1.67%, and 1.74%, respectively. Additionally, the accuracy of the imputed WGS data was greater compared to the chip data. For the TNB, NBA, and NHP traits, the predictive accuracy of the imputed WGS data improved by 3.26%, 7.72%, and 3.00%, respectively, compared with that of the chip data. In summary, these results suggest that nonadditive effects in genomic selection could improve prediction accuracy and should be considered in pig genomic evaluation procedures.
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