Accuracy of genomic evaluation using imputed high-density genotypes for carcass traits in commercial Hanwoo population

Abstract

This study aimed to evaluate the accuracy of genomic prediction using single-trait and multi-trait single-step genomic best linear unbiased prediction (ssGBLUP) based on 50 K SNP chip dataset and imputed high-density (HD) genotypes for various carcass traits in commercial Hanwoo population such as backfat thickness (BFT), carcass weight (CWT), rib eye area (REA), and marbling score (MS). Analyses involved phenotypes from 18,269 animals born between April 2006 and June 2017 from different commercial herds in South Korea as well as genomic information from 16,892 animals genotyped using customized Hanwoo 50 K SNP chip and imputed to HD genotypes. Cross-validation was performed on 3,041 animals in the validation population born from November 2016 to June 2017. The results showed that HD genotypes led to a marginal increase in prediction accuracies (0.6 to 2%) than 50 K genotypes for BFT, REA, and MS, while no improvement was noted for CWT. Compared to the single-trait model, the use of multi-trait model based on 50 K or HD genotypes produced only a small improvement in prediction accuracies (1.2 to 2%) for BFT and REA but without improvement for CWT. For MS, the accuracy of genomic prediction using the multi-trait model based on 50 K or HD genotype datasets was slightly lower than the single-trait model. Generally, inflation of predictions increased using the HD genotypes and multi-trait model. Therefore, we recommend the use of 50 K genotypes and single-trait model in the estimation of genomic breeding values for carcass traits in commercial Hanwoo population.