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For a long time,purebred pigs were evalua-ted in a nucleus for several growth,meat qualityand reproduction traits including growth,backfatand number of piglets alive[1].The evaluationwas using BLUP with all traits treated as linearand also assuming a normal distribution.Ani-mals down the pyramid were not evaluated;itwas assumed that most if not all of the gains ofselection at the nucleus level transferred to thecommercial level.The selection based on the e-valuations seemed to be successful …  相似文献   
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Preconditioned conjugate gradient method can be used to solve large mixed model equations quickly. Convergence of the method depends on the quality of the preconditioner. Here, the effect of simple preconditioners on the number of iterations until convergence was studied by solving breeding values for several test day models. The test day records were from a field data set, and several simulated data sets with low and high correlations among regression coefficients. The preconditioner matrices had diagonal or block diagonal parts. Transformation of the mixed model equations by diagonalization of the genetic covariance matrix was studied as well. Preconditioner having the whole block of the fixed effects was found to be advantageous. A block diagonal preconditioner for the animal effects reduced the number of iterations the higher the correlations among animal effects, but increased memory usage of the preconditioner. Diagonalization of the animal genetic covariance matrix often reduced the number of iterations considerably without increased memory usage.  相似文献   
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