ArticleVeterinary and animal science2026
SNP-based selection strategies reveal antagonistic and favorable genetic architectures between milk production and fertility traits in dairy cattle: A simulation study.
Article in Veterinary and animal science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study evaluated genetic responses to selection based on single-nucleotide polymorphism (SNP) subsets with different effect directions on milk production and fertility in dairy cattle. A population of 1000 individuals was simulated using 50,000 SNP markers sampled from the Illumina BovineSNP50 BeadChip panel to represent a realistic genomic architecture. SNP effects were derived from previously published genome-wide association study (GWAS) results, in which the direction of SNP effects was inferred from reported test statistics (t-values). Five selection scenarios were evaluated over 50 generations, including selection based on milk-only SNPs, fertility-only SNPs, antagonistic SNP combinations, and SNPs with favorable effects on both traits. Results showed that single-trait selection maximized genetic gain for the target trait, with the milk_only and fert_only scenarios achieving the highest responses for milk production and fertility, respectively. Antagonistic SNP scenarios generated asymmetric trade-offs, where improvement in one trait was accompanied by deterioration, or where selection failed to improve the intended trait despite modest progress in the correlated trait. In contrast, selection based on SNPs with favorable effects on both traits produced simultaneous and sustained improvement in milk production and fertility, although at lower rates than single-trait selection. Genetic correlations were positive under favorable scenarios but strongly negative under antagonistic scenarios, indicating persistent genetic conflict between traits. These findings demonstrate that SNP effect direction and pleiotropy play a critical role in shaping long-term genetic responses and highlight the importance of multi-trait genomic selection strategies for achieving balanced and sustainable genetic improvement.
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