Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle

Abstract

Whole-genome sequence (WGS) data are increasingly being applied into genomic predictions, offering a higher predictive ability by including causal mutations or single-nucleotide polymorphisms (SNPs) putatively in strong linkage disequilibrium with causal mutations affecting the trait. This study aimed to improve the predictive performance of the customized Hanwoo 50 k SNP panel for four carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes (i.e., backfat thickness, carcass weight, longissimus muscle area, and marbling score), 50 k genotypes, and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation [intergenic (IGR), intron (ITR), regulatory (REG), synonymous (SYN), and non-synonymous (NSY)] to characterize the genomic regions that will deliver higher predictive power for the traits investigated. Animals were assigned into two groups, the discovery set (7324 animals) used for predictive variant detection and the cross-validation set for genomic prediction. Genome-wide association studies were performed by trait to every genomic region and entire WGS data for the pre-selection of variants. Each set of pre-selected SNPs with different density (1000, 3000, 5000, or 10,000) were added to the 50 k genotypes separately and the predictive performance of each set of genotypes was assessed using the genomic best linear unbiased prediction (GBLUP). Results showed that the predictive performance of the customized Hanwoo 50 k SNP panel can be improved by the addition of pre-selected variants from the WGS data, particularly 3000 variants from each trait, which is then sufficient to improve the prediction accuracy for all traits. When 12,000 pre-selected variants (3000 variants from each trait) were added to the 50 k genotypes, the prediction accuracies increased by 9.9, 9.2, 6.4, and 4.7% for backfat thickness, carcass weight, longissimus muscle area, and marbling score compared to the regular 50 k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were close to 1, indicating an unbiased prediction. The strategy used to select variants based on functional annotation did not show a clear advantage compared to using whole-genome. Nonetheless, such pre-selected SNPs from the IGR region gave the highest improvement in prediction accuracy among genomic regions and the values were close to those obtained using the WGS data for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent, and using WGS data remained more accurate compared to using a specific genomic region.

Genomic prediction with whole-genome sequence data in intensely selected pig lines

“…In our study, the most robust results were obtained for the ChipPlusSign set, where variants that showed statistically significant associations to the trait were preselected and added to the information from the marker array. This is consistent with previous reports that showed an improvement of prediction accuracy under similar approaches [29][30][31][32].…”

“…Preselecting predictor variants based on functional annotation was not useful, as it reduced prediction accuracy in several traits and lines. Previous studies showed that subsets of variants based on functionality either did not improve or reduced prediction accuracy [20] and that adding preselected variants from coding regions to marker arrays produced lower prediction accuracy than just adding the same number of variants without considering functional classification [32]. A plausible explanation is that functional variants are enriched for lower minor allele frequency, which can be less informative for prediction [13].…”

“…Adding the 16k SNPs with largest estimated effects to a 60k array increased prediction reliability on average by 2.7 (up to 4.8) percentage points in Holstein cattle [30]. For the custom 50k array for Hanwoo cattle, it has been reported that adding at least around 12k SNPs (3k for each of four traits) improved prediction accuracy by up to ∼6 percentage points [32]. The addition of ∼400 variants preselected by GWAS with regional heritability mapping to a 50k array increased prediction accuracy by 9 percentage points in sheep [31].…”

“…Because the signal of some variants may go undetected for some traits but not for other correlated traits, combining GWAS information of several traits can also help identifying weak or moderate associations [25]. We did not test whether combining the significant markers from the different single-trait GWAS yielded greater improvements in prediction accuracy [29,32]. Multi-trait GWAS models could be more suited for that purpose [72,75].…”

Rare and population-specific functional variation across pig lines

“…Therefore, it is also expected that missense and LOF variants are responsible for differences among breeds, genetic lines, and varieties of livestock and crop species that have undergone diverse selection histories. Identification of such functional variants would have direct applications in gene-assisted and genomic selection [23][24][25]. Furthermore, strategies based on genome editing have been theorized to either promote favourable alleles [26] or remove deleterious alleles [27] in selection candidates.…”