Utilizing Variants Identified with Multiple Genome-Wide Association Study Methods Optimizes Genomic Selection for Growth Traits in Pigs

Simple Summary The accurate prediction of growth traits in genomic selection (GS) is essential for pig breeding. Here, we performed GS using variants identified with three genome-wide association study methods on four growth-related traits in Yorkshire and Landrace pigs. A total of 1485 loci related to these traits and 24 candidate genes were mapped. Compared with using 60K SNP-chip data, GS with the pre-selected variants significantly improved prediction accuracies by 4 to 46% in genomic best linear unbiased prediction (GBLUP) models, and 5 to 27% in a two-kernel based GBLUP model for the four traits. Improving the prediction accuracies of economically important traits in genomic selection (GS) is a main objective for researchers and breeders in the livestock industry. This study aims at utilizing potentially functional SNPs and QTLs identified with various genome-wide association study (GWAS) models in GS of pig growth traits. We used three well-established GWAS methods, including the mixed linear model, Bayesian model and meta-analysis, as well as 60K SNP-chip and whole genome sequence (WGS) data from 1734 Yorkshire and 1123 Landrace pigs to detect SNPs related to four growth traits: average daily gain, backfat thickness, body weight and birth weight. A total of 1485 significant loci and 24 candidate genes which are involved in skeletal muscle development, fatty deposition, lipid metabolism and insulin resistance were identified. Compared with using all SNP-chip data, GS with the pre-selected functional SNPs in the standard genomic best linear unbiased prediction (GBLUP), and a two-kernel based GBLUP model yielded average gains in accuracy by 4 to 46% (from 0.19 +/- 0.07 to 0.56 +/- 0.07) and 5 to 27% (from 0.16 +/- 0.06 to 0.57 +/- 0.05) for the four traits, respectively, suggesting that the prioritization of preselected functional markers in GS models had the potential to improve prediction accuracies for certain traits in livestock breeding.