03895naa a2200277 a 450000100080000000500110000800800410001902200140006002400310007410000210010524501470012626000090027350004720028252026000075465000150335465000200336965000340338965000270342365000250345070000160347570000130349170000160350470000160352070000190353677300620355510501472019-10-30       2013    bl uuuu     u00u1 u    #d  a0021-88127 a10.2527/jas.2012-58262DOI1 aLOURENCO, D.A.L.  aPrediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models.h[electronic resource]  c2013  aArticle history: Published online July 26, 2013. This study was partially funded by the American Angus Association (St. Joseph, MO) and the USDA Agriculture and Food Research Initiative (Grant no. 2009-65205-05665 from the USDA National Institute of Food and Agriculture Animal Genome Program). Helpful comments and suggestions from W. M. Snelling (U.S. Meat Animal Research Center, ARS, USDA, Clay Center, NE) and two anonymous reviewers are gratefully acknowledged.  aABSTRACT.  Different methods for genomic evaluation were compared for accuracy and feasibility of evaluation using phenotypic, pedigree, and genomic information for a trait influenced by a maternal effect. A simulated population was constructed that included 15,800 animals in 5 generations. Genotypes from 45,000 SNP were available for 1,500 animals in the last 3 generations. Genotyped animals in the last generation had no phenotypes. Weaning weight data were simulated using an animal model with direct and maternal effects. Additive direct and maternal effects were considered either noncorrelated (Graphic) or negatively correlated (Graphic). Methods of analysis were traditional BLUP, BayesC using phenotypes and ignoring maternal effects (BayesCPR), BayesC using deregressed EBV (BayesCDEBV), and single-step genomic BLUP (ssGBLUP). Whereas BayesCPR can be used when phenotypes of only genotyped animals are available, BayesCDEBV can be used when BLUP EBV of genotyped animals are available, and ssGBLUP is suitable when genotypes, phenotypes, and pedigrees are jointly available. For all genotyped and young genotyped animals, mean accuracies from BayesCPR and BayesCDEBV were lower than accuracies from BLUP for direct and maternal effects. The differences in mean accuracy were greater when genetic correlation was negative. Gains in accuracy were observed when ssGBLUP was compared with BLUP; for the direct (maternal) effect the average gain was 0.01 (0.02) for all genotyped animals and 0.03 (0.02) for young genotyped animals without phenotypes. Similar gains were observed for 0 and negative genetic correlation. Accuracy with BayesCPR was affected by ignoring phenotypes of nongenotyped animals and maternal effect and by not accounting for parent average. Accuracy with BayesCDEBV was affected by approximations needed for deregression, not accounting for parent average, and sequential rather than simultaneous fitting of genomic and nongenomic information. Whereas BayesCDEBV presented a considerable bias, especially for maternal effect, ssGBLUP was unbiased for both effects. The computing time was 1 s for BLUP, 44 s for ssGBLUP, and over 2,000 s for BayesC. Greatest computational efficiency and accuracy of genomic prediction for a maternally affected trait was obtained when information from all nongenotyped but related individuals was included and phenotypes, pedigree, and genotypes were available and considered jointly. Increasing the gain in accuracy of genomic predictions obtained by ssGBLUP over BLUP may require an increase in the number of genotyped animals.  aGANADERÍA  aGANADO DE CARNE  aMEJORAMIENTO GENÉTICO ANIMAL  aMODELOS DE SIMULACIÓN  aSELECCIÓN GENÓMICA1 aMISZTAL, I.1 aWANG, H.1 aAGUILAR, I.1 aTSURUTA, S.1 aBERTRAND, J.K.  tJournal of Animal Science, 2013gv.91, no.9, p.4090-4098.