Utilizing Variants Identified with Multiple Genome-Wide Association Study Methods Optimizes Genomic Selection for...

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Livestock Breeding

Background:

• Genomic selection (GS) aims to improve prediction accuracy for economically important traits in livestock.
• Genome-wide association studies (GWAS) identify genetic markers associated with traits.

Purpose of the Study:

• To enhance prediction accuracies in pig genomic selection using functional SNPs and QTLs identified via GWAS.
• To evaluate the impact of pre-selected functional markers on GS prediction accuracy for pig growth traits.

Main Methods:

• Utilized three GWAS methods (mixed linear model, Bayesian, meta-analysis) on SNP-chip and whole genome sequence data from Yorkshire and Landrace pigs.
• Identified significant loci and candidate genes related to growth traits (average daily gain, backfat thickness, body weight, birth weight).
• Applied standard genomic best linear unbiased prediction (GBLUP) and a two-kernel GBLUP model incorporating pre-selected functional markers.

Main Results:

• Detected 1485 significant loci and 24 candidate genes involved in muscle development, fat deposition, lipid metabolism, and insulin resistance.
• GS using pre-selected functional SNPs improved prediction accuracies by 4-46% (standard GBLUP) and 5-27% (two-kernel GBLUP) compared to using all SNP-chip data.
• Significant accuracy gains were observed for average daily gain, backfat thickness, body weight, and birth weight.

Conclusions:

• Prioritizing pre-selected functional markers in GS models has the potential to significantly improve prediction accuracies for specific pig growth traits.
• This strategy offers a valuable tool for livestock breeders aiming to enhance genetic gain in economically important traits.