Genomic selection has become a powerful tool for accelerating genetic improvement in animal breeding, yet its broader application is often limited by high genotyping costs and reduced predictive reliability across populations. Functional genomic annotations offer a promising strategy to prioritize biologically meaningful variants and improve prediction robustness. Here, using Pacific white shrimp (Litopenaeus vannamei) as a model, we conducted a proof-of-concept study integrating whole-genome resequencing and phenotypic data from 972 individuals. High-resolution epigenomic maps were generated by profiling four histone marks (H3K4me1, H3K4me3, H3K27me3, and H3K27ac) across multiple embryonic stages and adult muscle tissue using CUT&Tag, and were then used to guide SNP prioritization for genomic prediction. Among the evaluated strategies, SNPs located in the muscle-specific bivalent promoter/enhancer (E6) state, which is characterized by the co-occurrence of active and repressive marks, consistently yielded the highest prediction accuracy. Notably, even at a moderate density (15k), E6-derived SNPs outperformed substantially larger genome-wide SNP sets. Importantly, in cross-population validation using an independent strain, the E6 SNP subset increased prediction accuracy by 47.6% (increasing from 0.21 ± 0.05 to 0.31 ± 0.04, P < 0.05) relative to random subsets at equivalent density. These results demonstrate that epigenetic annotation–guided SNP prioritization can enhance prediction accuracy and robustness while reducing marker requirements, providing a practical framework for integrating functional genomic information into aquaculture breeding.
Jiale et al. (Thu,) studied this question.