Optimizing The Production Genetic Evaluation System Of Canadian Beef Cattle

Genomic selection is revolutionizing livestock breeding by obtaining a higher accuracy of selection and shorter generation interval.Quality beef cattle breeding may benefit from increasing the accuracy of genomic selection.The objectives of this study were to optimize genetic evaluation system of canadian beef cattle.The results and conclusions of the current study are summarized as follow:(1)Crossbreeding is an effective method for improving the efficiency of production in commercial cow-calf operations.It exploits available heterosis(hybrid vigour)and complementarity between different breeds or populations(lines).Before adopting a crossbreeding system,commercial cattle producers should evaluate available genetic resources and feasible crossbreeding systems,and choose one that is most beneficial for their own environment,resources,and management.This study compared profitability of alternative crossbreeding systems based on Beefbooster beef cattle breeding strains through computer simulation.Biological and economic data were collected from commercial customers of Beefbooster in Montana and western Canada,and breeding records from the database of Beefbooster,Inc.Three maternal strains(M1,M2 and M4)and two specialized paternal strains(M3 and TX),were evaluated with two simulated crossbreeding systems.System 1 uses a rotational cross between M1 and M4 with yearling crossbred heifers bred to M3 sires.System 2 is based on a three-strain rotation of M1,M2 and M4 with yearling crossbred heifers bred to M3 to facilitate ease of calving and crossbred cows bred to a classical terminal sire strain TX.Simulated base profit from system 2 was $29.57 greater(215.21 vs.185.64 yr-1 per cow)than from system 1.(2)Social interactions among animals are widely existed in livestock population.However,some studies showed that the selection of social genetic effect leaded to extra increase of inbreeding.In this study,two optimization methods(SBLUP+GA1,SBLUP+GA2)based on genetic algorithm were used to obtain the optimal genetic contributions of seed stocks and maximize the average genetic gains of direct and social genetic component while minimizing the inbreeding.In SBLUP+GA1,only the contributions of sires were optimized.In SBLUP+GA2,the contributions of sires and dams were optimized together.There results showed SBLUP+GA1 and SBLUP+GA2 resulted in 18.52%and 25.93%lower inbreeding rate than common social genetic effect selection based on BLUP method(SBLUP)under base parameters,respectively.Under that situation,the average gains for direct,social and total genetic effect component in SBLUP+GA1 were actually improved 3.59%,10.02%and 4.32%relative to SBLUP,respectively.In SBLUP+GA2,they were 1.28%,10.00%and 2.02%,respectively.SBLUP+GA2 resulted in lower inbreeding rate,but,obtained slightly less genetic gain than SBLUP+GA1.(3)The Igenity(?)genotyping panel with a total of 233 SNP markers was used to genotype 2749 animals of the Beefbooster breeding stock population.A total of 144 SNP makers were used in conducting the association of the SNP markers and the growth traits based on multiple markers regression using stepwise method.The numbers of SNP markers that significantly(<0.05)associated with birth weight(BWT),direct genetic effect of weaning weight(WWT),maternal genetic effect of weaning weight(Milk),yearling weight(YWT),mature weight(MWT),and scrotal circumference(SC)were 139,135,12,89,129 and 105,respectively.Marker score of each individual was calculated as the linear regression on the number of copies of specific allele of all significant(<0.05)SNP markers.Bi-variable analysis of marker scores and phenotypes for all traits were conducted using ASReml Software and the genetic parameters for each trait were estimated.The genetic correlations between phenotype and marker score for BWT,WWT,Milk,YWT,MWT and SC were 0.61±0.08,0.39 ± 0.10,0.14±0.03,0.38 ± 0.04,0.57±0.09 and 0.54 ±0.10,respectively.The developed two-trait marker-assisted evaluation(TMAE)model increased the estimation accuracy of the phenotypic EBV of the animal when the genetic correlations between phenotype and marker score were high,even with the current limited genotypic and phenotypic information.The average prediction accuracy of phenotypic EBV for BWT,WWT,Milk,YWT,MWT and SC using TMAE were increased by 0.07,0.07,0.001,0.42,0.08 and 0.22,respectively.(4)Residual feed intake(RFI)and carcass merit(CM)are both complex traits emerging as critical targets for beef genetic improvement.RFI and CM traits are difficult and expensive to measure and genetic improvement for these traits through traditional selection methods is not very effective.Therefore,genome-wide selection using DNA markers may be a potential alternative for genetic improvement of these traits.In this study,the efficiency of a genome-wide selection model for genetic improvement of RFI and CM was assessed.The Illumina Bovine50K bead chip was used to genotype 922 beef cattle from the Kinsella Beef Research Ranch of the University of Alberta.A Bayes model and multiple marker regression using a stepwise method were used to conduct the association test.The number of significant SNP markers for carcass weight(CWT),carcass back fat(BF),carcass rib eye area(REA),carcass grade fat(GDF),lean meat yield(LMY),and residual feed intake(RFI)were 75,54,67,57,44 and 50,respectively.Bi-variate analysis of marker scores and phenotypes for all traits were made using DMU Software.The genetic parameter for each trait was estimated.The genetic correlations of marker score and phenotype for CWT,BF,REA,GDF,LMY and RFI were 0.75,0.69,0.87,0.77,0.78,and 0.85,respectively.The average prediction accuracies of phenotypic EBV for the six traits were increased by 0.05,0.16,0.24,0.23,0.17 and 0.19,respectively.The results of this study indicated that the two-trait marker-assisted evaluation model used was a suitable alternative of genetic evaluation for these traits in beef cattle.