Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-calle...

Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|G...

Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to "correct" for population stratification. However, these methods induce a mean correction that does not account for heterogeneit...

Genomic selection has been widely used for complex quantitative trait in farm animals. Estimations of breeding values for slaughter traits are most important to beef cattle industry, and it is worthwhile to investigate prediction accuracies of genomic selection for these traits. In this study, we assessed genomic predictive abilities for average daily gain weight (ADG), live weight (LW), carcas...

We examined whether or not the predictive ability of genomic best linear unbiased prediction (GBLUP) could be improved via a resampling method used in machine learning: bootstrap aggregating sampling ("bagging"). In theory, bagging can be useful when the predictor has large variance or when the number of markers is much larger than sample size, preventing effective regularization. After present...

Predicting disease resistance is one of the most prominent applications aquaculture selective breeding. Reductions in genotyping costs have allowed implementation genomic selection an abundance species and their related diseases showing promising results. Machine learning (ML) models can be value for prediction purposes, as suggested by several studies both plants livestock. The current study a...

This study reports the first preliminary results of genomic prediction with whole-genome sequence data (12,590,056 SNPs) for 5503 bulls with accurate phenotypes. Two methods were compared: genome-enabled best linear unbiased prediction (GBLUP) and a Bayesian approach (BSSVS). Results were compared with results using BovineHD genotypes (631,428 SNPs). Results were reported for somatic cell score...

The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels, containing 6,177, 2,480, 1,536, 768 and 384 SNPs, were simulated from the 50 k panel. A fraction of 0%...

The accuracy of genomic estimated breeding values (GEBV) was evaluated for sixteen meat quality traits in a Berkshire population (n = 1,191) that was collected from Dasan breeding farm, Namwon, Korea. The animals were genotyped with the Illumina porcine 62 K single nucleotide polymorphism (SNP) bead chips, in which a set of 36,605 SNPs were available after quality control tests. Two methods wer...

Abstract Plant breeders need efficient systems to identify which inbreds combine create new hybrid cultivars. The North Carolina Design II (NC DII) is a useful mating design evaluate the potential of varieties and their inbred parents. Genomic best linear unbiased prediction (GBLUP) models, either with or without inclusion dominance term in model, have been found be an method for using rich mar...