Short communication: Genomic prediction based on unbiased estimation of the genomic relationship matrix in pigs

Abstract

The traditional genomic relationship matrix (GRM) has shown to be a biased estimation of true kinship, which can affect subsequent genetic analyses. In this study, we employed an unbiased kinship (UKin) estimation method within the genomic best linear unbiased prediction framework to evaluate its prediction performance on both a simulated dataset and a Large White pig dataset. The simulated dataset encompasses six traits, 900 quantitative trait loci, and 36 000 single nucleotide polymorphisms (SNPs). Two scenarios (small effect genes; major genes and small effect genes) and three heritabilities (0.1, 0.3 and 0.5) were considered. The Large White pig dataset includes two traits, 3 290 animals and 35 172 SNPs. The prediction performance of the Ukin method was compared with several other GRM construction methods, including VanRaden1 and 2 methods, Goudet method, and the runs of homozygosity (ROH) method. In the simulated dataset, VanRaden2 method and the UKin+VanRaden1 method achieved relatively higher prediction accuracies, averaging 0.561 and 0.558 for the six traits, respectively. Apart from the ROH method, all methods demonstrated similar levels of unbiasedness, around 1.10. In the Large White pig dataset, the accuracy of two traits hovered around 0.780, and the unbiasedness around 0.99, again with the ROH method as an exception. This study underscores the potential of the unbiased kinship estimation method in animal breeding.