03639naa a2200337 a 450000100080000000500110000800800410001902400300006010000190009024500980010926000090020750000740021652026360029065000200292665000330294665000100297965300160298965300220300565300110302765300200303870000180305870000190307670000200309570000160311570000190313170000160315070000170316670000190318370000160320277300830321810540022019-10-15       2015    bl uuuu     u00u1 u    #d7 a10.2527/jas2014-88322DOI1 aCARDOSO, F. F.  aGenomic prediction for tick resistance in Braford and Hereford cattle.h[electronic resource]  c2015  aArticle history: Received December 19, 2014 / Accepted April 6, 2015.  aABSTRACT.  One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and 928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for k-means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breedspecific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance.   © 2015 American Society of Animal Science. All rights reserved.  aGANADO DE CARNE  aMEJORAMIENTO GENETICO ANIMAL  aSALUD  aBEEF CATLLE  aGENOMIC SELECTION  aHEALTH  aTICK RESISTANCE1 aGOMES, C.C.G.1 aSOLLERO, B. P.1 aOLIVEIRA, M. M.1 aROSO, V. M.1 aPICCOLI, M. L.1 aHIGA, R. H.1 aYOKOO, M. J.1 aCAETANO, A. R.1 aAGUILAR, I.  tJournal of Animal Science, 2015.gv. 95, p. 2693-2705. Published June 25, 2015