title: Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle.
abstract: The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.
authors.affiliation: Department of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, T6G 2P5, Canada.
authors.forename: F D N
authors.initials: FD
authors.lastname: Mujibi
authors.forename: J D
authors.initials: JD
authors.lastname: Nkrumah
authors.forename: O N
authors.initials: ON
authors.lastname: Durunna
authors.forename: P
authors.initials: P
authors.lastname: Stothard
authors.forename: J
authors.initials: J
authors.lastname: Mah
authors.forename: Z
authors.initials: Z
authors.lastname: Wang
authors.forename: J
authors.initials: J
authors.lastname: Basarab
authors.forename: G
authors.initials: G
authors.lastname: Plastow
authors.forename: D H
authors.initials: DH
authors.lastname: Crews
authors.forename: S S
authors.initials: SS
authors.lastname: Moore
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding / Q000379:methods*; D002417:Cattle / Q000235:genetics / Q000502:physiology*; D003433:Crosses, Genetic; D004435:Eating / Q000235:genetics / Q000502:physiology*; D014644:Genetic Variation; D016678:Genome; D008297:Male; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D040641:Quantitative Trait Loci; D011897:Random Allocation