Abstract
Simple SummaryThis study investigated the informative regions and the efficiency of genomic predictions for backfat thickness, days to 90 kg body weight, loin muscle area, and lean percentage in Korean Duroc pigs. The several regions of the genome were identified and a significant marker was found near the MC4R gene for growth and production-related traits. No differences in genomic accuracy were identified on the basis of the Bayesian approaches in these four growth and production-related traits. The genomic accuracy is improved by using deregressed estimated breeding values including parental information as a response variable in Korean Duroc pigs.Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.
Highlights
Genomic selection (GS) has been widely applied to several species, for example, pigs, chickens, beef, and dairy cattle, using commercial single nucleotide polymorphism (SNP) genotyping platforms from Illumina, GeneSeek-Neogen, and Affymetrix
The most important parameter in genomic prediction modeling is the accuracy of genomic prediction for the estimation of GE-estimated breeding values (EBVs) because the weights are determined on the basis of that parameter when blended with traditional EBVs and molecular breeding values (MBVs) in a “correlated traits” approach [1]
These results are consistent with the results of Badke et al [21], who showed that the proportion of correctly imputed alleles decreased by increasing the number of SNPs with a high minor allele frequency (MAF) in Yorkshire pigs
Summary
Genomic selection (GS) has been widely applied to several species, for example, pigs, chickens, beef, and dairy cattle, using commercial single nucleotide polymorphism (SNP) genotyping platforms from Illumina, GeneSeek-Neogen, and Affymetrix. These arrays estimate genomic-enhanced estimated breeding values (GE-EBVs), which are blended with classical estimated breeding values (EBVs) from classical genomic best linear unbiased prediction (BLUP) and molecular breeding values (MBVs) from summation of single nucleotide polymorphism (SNP) marker effects for genotyped animals. Genetic improvements are achieved by reducing the generation interval and increasing the accuracy through genomic selection modeling in dairy and beef cattle. In pigs, genetic improvements with the generational interval parameter are limited by rapid generational turnover
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