Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes.

Genomic prediction involves characterization of chromosome fragments in a training population to predict merit. Confidence in the predictions relies on their evaluation in a validation population. Many commercial animals are multibreed (MB) or crossbred, but seedstock populations tend to be purebred (PB). Training in MB allows selection of PB for crossbred performance. Training in PB to predict MB performance quantifies the potential for across-breed genomic prediction. Efficiency of genomic selection was evaluated for a trait with heritability 0.5 simulated using actual SNP genotypes. The PB population had 1,086 Angus animals, and the MB population had 924 individuals from 8 sire breeds. Phenotypic values were simulated for scenarios including 50, 100, 250, or 500 additive QTL randomly selected from 50K SNP panels. Panels containing various numbers of SNP, including or excluding the QTL, were used in the analysis. A Bayesian model averaging method was used to simultaneously estimate the effects of all markers on the panels in MB (or PB) training populations. Estimated effects were utilized to predict genomic merit of animals in PB (or MB) validation populations. Correlations between predicted and simulated genomic merit in the validation population was used to reflect predictive ability. Panels that included QTL were able to account for 50% or more of the within-breed genetic variance when the trait was influenced by 50 QTL. The predictive power eroded as the number of QTL increased. Panels that composed the QTL and no other markers were able to account for 50% or more genetic variance even with 500 QTL. Panels that included genomic markers as well as QTL had less predictive power as the number of markers on the panel was increased. Panels that excluded the QTL and relied on markers in linkage disequilibrium (LD) to predict QTL effects performed more poorly than marker panels with QTL. Real-life situations with 50K panels that excluded the QTL could account for no more than 20% genetic variation for 50 QTL and less than 10% for 500 QTL. The difference between panels that included and excluded QTL indicates that the predictive ability of existing panels is limited by their LD. Training in PB to predict MB tended to be more predictive than training in MB to predict PB due to greater average levels of LD in PB than in MB populations. Improved across breed prediction of genomic merit will require panels with more than 50,000 markers.