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Efficiency of Recurrent Genomic Selection in Panmictic Populations
José Marcelo Soriano Viana1, Jean Paulo Aparecido da Silva1, Paulo Sávio Lopes2
1Department of General Biology, Federal University of Viçosa, Viçosa 36570-900, MG, Brazil.
Animals : an Open Access Journal From MDPI
|October 16, 2025
Summary
Recurrent genomic selection boosts genetic gain in broiler chickens, with effectiveness tied to linkage disequilibrium levels. Model updates and sufficient training data are key for optimal genomic selection efficiency.
Area of Science:
- Animal Breeding and Genetics
- Quantitative Genetics
- Genomic Selection
Background:
- Simulation studies offer cost-effective breeder decision support.
- Recurrent genomic selection (RGS) is a powerful tool for genetic improvement.
- Understanding RGS efficiency under different genetic models is crucial.
Purpose of the Study:
- To evaluate the efficiency of recurrent genomic selection in panmictic populations.
- To compare RGS performance under additive-dominance and additive-dominance with epistasis models.
- To assess the impact of linkage disequilibrium (LD) levels on RGS efficacy.
Main Methods:
- Simulated two broiler chicken populations with varying LD levels.
- Utilized 38,500 SNPs and 1000 genes for feed conversion ratio.
- Applied recurrent genomic selection over seven cycles.
Main Results:
- RGS efficacy correlated positively with LD and genotypic variance.
- Model updating improved RGS efficacy; training set size of 10%/generation yielded near-maximum efficacy.
- Additive prediction accuracy and genetic gain were highly correlated; inbreeding remained low.
- Epistasis, particularly dominant epistasis, significantly reduced RGS efficacy.
Conclusions:
- RGS is effective in broiler chickens, influenced by LD and genetic architecture.
- Low-cost RGS strategies are feasible with minimal training data.
- Epistatic effects necessitate careful consideration in genomic selection models for accurate genetic gain prediction.
