Page 357 - Linear Models for the Prediction of Animal Breeding Values 3rd Edition
P. 357

eigenvalues 105–106                  total genetic merit 208
                MME 106                           dominance relationship
                reduced rank 106–108                    matrix 204–205
            transformation                        epistasis see epistasis
                canonical see canonical           inversion, dominance matrix
                      transformation                    see dominance matrix
                Cholesky see Cholesky             statistical framework 204
                      transformation          numerator relationship matrix
        multivariate animal model                 decomposing 23–25
            conditional probabilities 267–268     inbreeding coefficient 23
            description 266–267                   inverse
                                                       −1
            numerical illustration 269–270           A  to zero 26
            prior distributions 267                  accounting, inbreeding 28–30
        multivariate best linear unbiased prediction   animals, pedigree 27
              (MBLUP)                                inbreeding ignorance 27
            advantages and disadvantages 70          pedigree 27–28
            environments                          pedigree, six animals 23
                different traits recorded,        probability, identical genes 22
                      relatives 84–86
                DRB 84, 89
                genetic correlation 84        ordered categorical traits
                MACE see multi-trait across-country   animal breeding 219
                      evaluations (MACE)          binary trait 219–220
                milk yield 84                     joint analysis, quantitative and binary
            equal design matrices with missing          traits
                  records                            Bayesian approach 230
                differences, sex solutions 80        data and model definition 230–233
                genetic parameters 78                numerical application 234–239
                loss of traits 78                 linear and non-linear models 219
                MME 79                            threshold model see threshold model
                WWG and PWG 78–79
            equal design matrices with no missing
                  records                     PCG see preconditioned conjugate gradient
                accuracy 76–77                      (PCG)
                calculation, DYDs 77–78       post-weaning gain (PWG) 72–73, 78–79
                defining 71–72                pre-weaning gain (WWG) 72–73, 78–79,
                MME calculation 73–74               252–253, 257–259
                partitioning animal           preconditioned conjugate gradient (PCG)
                      evaluations 74–76           computation strategy 293
                WWG and PWG 72–73                 computing starting values 294–295
            trait evaluation 70                   convergence criteria 296–297
            unequal design matrices               genetic evaluation models 292
                computation, DYD 82–83            iterative stage 295–296
                FAT1 and FAT2 81, 82              multivariate accumulation
                HYS effects 80, 81                      technique 294
                MME 81–82                         pseudo-code 293
                                              progeny records
                                                  breeding value 7
        non-additive animal models                calculations 7
            and dominance effect                  description 6
                genetic effects 206–208           EBV 8
                MME and BLUP 205                  half-sib progeny 8


        Index                                                                341
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