Haplotype analysis of genomic prediction by incorporating genomic pathway information based on high-density SNP marker in Chinese yellow-feathered chicken

Genomic selection using single nucleo-tide polymorphism (SNP) markers is now intensively investigated in breeding and has been widely utilized for genetic improvement. Currently, several studies have used haplotype (consisting of multiallelic SNPs) for genomic prediction and revealed its performance advan-tage. In this study, we comprehensively evaluated the performance of haplotype models for genomic prediction in 15 traits, including 6 growth, 5 carcass, and 4 feeding traits in a Chinese yellow-feathered chicken population. We adopted 3 methods to define haplotypes from high -density SNP panels, and our strategy included combin-ing Kyoto Encyclopedia of Genes and Genomes pathway information and considering linkage disequilibrium (LD) information. Our results showed an increase in prediction accuracy due to haplotypes ranging from -0.04 >> 27.16% in all traits, where the significant improvements were found in 12 traits. The estimates of haplotype epistasis heritability were strongly correlated with the accuracy increase by haplotype models. In addi-tion, incorporating genomic annotation information could further increase the accuracy of the haplotype model, where the further increase in accuracy is signifi-cantly relative to the increase of relative haplotype epis-tasis heritability. The genomic prediction using LD information for constructing haplotypes has the best prediction performance among the 4 traits. These results uncovered that haplotype methods were beneficial for genomic prediction, and the accuracy could be further increased by incorporating genomic annotation informa-tion. Moreover, using LD information would potentially improve the performance of genomic prediction.

Automated summary

In this study, the performance of haplotype models for genomic prediction in a Chinese yellow-feathered chicken population was comprehensively evaluated. It was found that haplotype methods were beneficial for genomic prediction, and the accuracy could be further increased by incorporating genomic annotation information. Moreover, using LD information would potentially improve the performance of genomic prediction.