BACKGROUND Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative pr...

A common analysis objective is estimation of a realized random effect. The parameter underlying such an effect is usually defined as an average response of a realized unit, such as a cluster mean, domain mean, small area mean, or subject effect. The effects are called random effects since their occurrence is the result of some (actual or assumed) random sampling process. In mixed models, random...

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the av...

Emslie While it is difficult to pinpoint exactly when selection indices were first used in the poultry breeding industry, it is generally recognized that “in the late 1940s. . . a few of the breeders began to develop poultry breeding as a business” (Hunton, 1990). Certainly by the mid-50’s, progressive poultry egg breeders were applying index and complex breeding strategies to their selection p...

In the current post-genomic era, the genetic basis of pig growth can be understood by assessing SNP marker effects and genomic breeding values (GEBV) based on estimates of these growth curve parameters as phenotypes. Although various statistical methods, such as random regression (RR-BLUP) and Bayesian LASSO (BL), have been applied to genomic selection (GS), none of these has yet been used in a...

BACKGROUND Genomic breeding value estimation is the key step in genomic selection. Among many approaches, BLUP methods and Bayesian methods are most commonly used for estimating genomic breeding values. Here, we applied two BLUP methods, TABLUP and GBLUP, and three Bayesian methods, BayesA, BayesB and BayesCπ, to the common dataset provided by the 15th QTL-MAS Workshop to evaluate and compare t...

Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between estimated genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attentio...

Abstract The efficacy of selection programs in sheep depends on accurate evaluation genetic merit. Globally, evaluations are based best linear unbiased prediction (BLUP) which fixed effects and breeding values estimated simultaneously. With genotyping becoming increasingly routine, pedigree, performance, genomic data being combined single-step BLUP (ssGBLUP) to generate enhanced (GEBV). Since G...

Data analysis using the General linear model assumes factors to be fixed effects, and BLUE method, which is based on their mean performance, appropriate select best performing genotypes. The mixed incorporates random effects that are very important compare a genotype’s performance through BLUP. purpose of this study was identify genotypes provided high grain yield model, BLUP BLUE, determine im...

Genomic selection (GS) has become a very intense field of research during recent years. GS may be defined as the simultaneous selection for many (tens or hundreds of thousands of) markers, which cover the entire genome in a dense manner so that all genes are expected to be in linkage disequilibrium with at least some of the markers. In a sense, GS is marker assisted selection on a genome wide s...