Page 359 - Linear Models for the Prediction of Animal Breeding Values 3rd Edition
P. 359
multiple linear regression 12 schemes 121–122
phenotype and genetic marker square matrix 300
information 21 survival analysis
properties 11 censoring 240–241
restricted 19–20 definition 240
single records, individual and function 240
relatives 14–15 linear models 241
single nucleotide polymorphisms (SNPs) livestock production 240
chromosome segments 178 non-parametric estimation 245
DNA sequence variation 177 proportional hazard models
fixed effect model definition 243–244
animal breeding context 179 exponential distribution 244
and EDC 180 Weibull distribution 244–245
GEBV 182 random regression models 241–243
marker effects prediction 181 regression models see regression
MME 180 survival models
and polygenic effects 180 symmetric matrix 301
reference animals 181
steps 179
mixed linear model TDY see test day fat yields (TDY)
GBLUP 184–187 test day fat yields (TDY) 132
selection index threshold model
approach 187–188 calving ease score distribution 223
SNP-BLUP 183–184 categorical traits 223
sire model and data organization 221–222
description 46 functions, normal
design matrices and MME 47–48 distribution 220–221
in matrix notation 46 inverse, pedigree 224
sire-maternal grandsire (S-MGS) 119–120 iteration, subclass 225–230
SNPs see single nucleotide pedigree 223
polymorphisms (SNPs) probability, category 229–230
social interaction system of equations 224–225
animal model triangular matrix 300–301
associative effects 123
MME 123
pigs 125–127 univariate animal model
residual covariance 124–125 Gibbs sampling output 264–265
correlated error structure 128–129 joint and full conditional
DBV and SBV 122–123, 127–128 distributions 262–264
direct effects 121 numerical application 265–266
economic and welfare problems 121 prior distributions 261–262
food competition 121
IGE 121
non-additive components 122 WWG see pre-weaning gain (WWG)
Index 343
