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

Abstract

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.

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs

“…Our results indicated that the use of preselected variants from a large GWAS meta-analysis has shown small and non-robust improvements in the accuracy of genomic prediction compared to using all variants from the SNP chip data in pigs. A previous study [22] based on preselected variants by p-value ranking from GWAS results demonstrated an improvement in the accuracy of genomic prediction for BFT, ADG, and loin muscle area across different pig breeds. The improvement ranged from 4 to 13.2% compared to the SNP chip data, which was slightly higher than our results.…”

“…One of the most successful strategies to improve the accuracy of genomic predictions is the utilization of preselected variants associated with the trait of interest from WGS data. In recent years, preselected potential causal variants identified through genome-wide association studies (GWASs) have already been applied in genomic prediction to improve the prediction accuracy for a variety of traits in livestock and poultry [19,20], including pigs [21,22]. In certain instances, this strategy has resulted in improved accuracy of genomic prediction for growth and carcass traits in pigs, with improvements ranging from 0.9 to 46% for multi-breed populations [21][22][23].…”

“…In recent years, preselected potential causal variants identified through genome-wide association studies (GWASs) have already been applied in genomic prediction to improve the prediction accuracy for a variety of traits in livestock and poultry [19,20], including pigs [21,22]. In certain instances, this strategy has resulted in improved accuracy of genomic prediction for growth and carcass traits in pigs, with improvements ranging from 0.9 to 46% for multi-breed populations [21][22][23]. However, it should be noted that this strategy did not result in improved prediction accuracy in all cases [21,24,25].…”

Genomic prediction and genome‐wide association studies for additive and dominance effects for body composition traits using 50 K and imputed high‐density SNP genotypes in Yunong‐black pigs

“…Zhang et al. (2023) found that, although some improvement in trait prediction accuracy was occasionally observed by increasing marker density, the increase in marker density did not translate into an increase in prediction accuracy in most cases, particularly for small population sizes.…”