Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle

Abstract

BackgroundThe success of genomic selection depends mainly on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), the number of animals in the training set (TS) and the heritability (h2) of the trait. The extent of LD depends on the genetic structure of the population and the density of markers. The aim of this study was to calculate accuracy of direct genomic estimated breeding values (DGEBV) using best linear unbiased genomic prediction (GBLUP) for different marker densities, heritabilities and sizes of the TS in simulated populations that mimicked previously reported extent and pattern of LD in beef cattle.ResultsThe accuracy of DGEBV increased significantly (p < 0.05) with the increase in the number of bulls in the TS (480, 960 or 1920), trait h2 (0.10, 0.25 or 0.40) and marker densities (40 k or 800 k). Increasing the number of animals in the TS by 4-fold and using their phenotypes to estimate marker effects was not sufficient to maintain or increase the accuracy of DGEBV obtained using estimated breeding values (EBVs) when the trait h2 was lower than 0.40 for both marker densities. Comparing to expected accuracies of parent average (PA), the gains by using DGEBV would be of 27%, 13% and 10% for trait h2 equal to 0.10, 0.25 and 0.40, respectively, considering the scenario with 40 k markers and 1920 bulls in TS.ConclusionsAs reported in dairy cattle, the size of the TS and the extent of LD have major impact on the accuracy of DGEBV. Based on the findings of this simulation study, large TS, as well as dense marker panels, aiming to increase the level of LD between markers and QTL, will likely be needed in beef cattle for successful implementation of genomic selection.

The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle

“…Taken together, our current results (50K vs. 80K) and the results of other genomic evaluation studies (777K vs. 50K) suggest that there are no significant differences in prediction accuracies by simply increasing SNP density. These are contrary to the expectation that the accuracy of genomic prediction could improve as a result of an increased degree of linkage disequilibrium (ld) between SNP markers and QTL (Meuwissen and Goddard, 2010) and the result that the extent of LD had major impact on the prediction accuracy in simulation study (Brito et al, 2011). The reason why this expectation was not realized could be due to a general lack of strong LD between markers and QTL and the fact that adding additional noninformative SNP simply adds an additional source of noise to prediction equations.…”

Genomic selection in pigs: state of the art and perspectives

“…The reliability of GEBV prediction depends on the level of linkage disequilibrium between markers and QTL in both within family or population-wise scenarios (Hayes et al 2009). Increasing the density of markers would increase the level of linkage disequilibrium between markers and QTL that, in turn, would increase the accuracy of GEBV (Brito et al 2011) till a maximum of accuracy that depend on the genetic architecture of the traits. The level of linkage disequilibrium is larger in livestock populations than in humans.…”

Genomic selection using beef commercial carcass phenotypes

“…With GS in beef cattle in its infancy, there is limited evidence in the literature with which to compare the genetic gains predicted in this study. Two other simulation studies, Brito et al (2011) and Van Eenennaam et al (2011), predicted increases in accuracy from the use of GS in terminal beef traits of similar magnitude to this study. Saatchi et al (2011) reported GBV accuracies of between 0.2 and 0.6 in an evaluation of 3570 US Angus bulls, which used deregressed EBV as phenotypes.…”

“…Given the need to estimate these parameters used in constructing this model, a weighted bending procedure was incorporated to make the correlation matrices positivedefinite (Jorjani et al, 2003). The pedigree-based estimate of Ne by Bouquet et al (2010) for the Irish Limousin population (a population of similar size and genetic origin to that in the United Kingdom), of~300, was adopted for the current study. The bovine autosomal chromosome lengths were taken from Deukwhan and Vasco (2011).…”