Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle - Lopez, Bryan Irvine M.; An, Narae; Srikanth, Krishnamoorthy; Lee, Seunghwan; Oh, Jae-Don; Shin, Dong-Hyun; Park, Woncheoul; Chai, Han-Ha; Park, Jong-Eun; Lim, Dajeong (Frontiers in Genetics, 2021)
This study aimed to improve the predictive performance of the customized Hanwoo 50k SNP panel for various carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes, 50k genotypes and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation (intergenic, intron, regulatory, synonymous, and non-synonymous) to characterize genomic regions that will deliver higher predictive power for the traits investigated. Animals were divided into two groups, assigned either to the discovery set (7,324 animals) used for predictive variant detection or cross-validation set for genomic prediction. Genome-wide association studies were performed by trait to every genomic region and entire WGS data for pre-selection of variants. Each set of pre-selected SNPs with different density (1,000, 3,000, 5,000 or 10,000) were added to the 50k genotypes separately, and assessed the predictive performance of each set of genotypes using genomic best linear unbiased prediction (GBLUP). The results showed that the predictive performance of the customized Hanwoo 50k SNP panel can be improved by adding pre-selected variants from WGS data. Particularly, adding at least 3,000 variants from each trait is sufficient to improve the prediction accuracy for all traits. When ~12,000 pre-selected variants (3,000 variants from each trait) were added to 50k genotypes, the prediction accuracies increased by 9.9%, 9.2%, 6.4% and 4.7% for backfat thickness, carcass weight, eye muscle area and marbling score compared to regular 50k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were very close to 1, which indicates unbiased prediction. The strategy used in selecting variants based on functional annotation did not show a clear advantage compared to using whole-genome region. Nonetheless, pre-selected SNPs from intergenic region gave the highest improvement in prediction accuracy among genomic regions and the values were close with those of using whole-genome region for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent and using whole-genome region when selecting variants remained more accurate compared to using a specific genomic region.