Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations - Salek Ardestani, Siavash; Jafarikia, Mohsen; Sargolzaei, Mehdi; Sullivan, Brian; Miar, Younes (Frontiers in Genetics, 2021)
Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of backfat thickness (BFT), average daily gain (ADG) and loin muscle depth (LMD) in Canadian Duroc, Landrace and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total number of 41,304, 48,580 and 49,102 single nucleotide polymorphisms (SNPs) remained for Duroc (n=6,649), Landrace (n=5,362) and Yorkshire (n=5,008) breeds, respectively. The breeding values of animals in the validation groups (n=392 to n=774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their de-regressed breeding values (dEBV) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG and Yorkshire-BFT scenarios and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.