Page 353 - Linear Models for the Prediction of Animal Breeding Values

Index

adjusted right-hand side (ARHS) 280 solutions and variances 196–197
animal evaluation model t-distribution 194
accuracy, evaluations 44–46 BayesB 197–199
construction, mixed model equations BayesC 199–200
breeding values, progeny 42 BayesCp 201
EBV 42 description 193
least squares equations (LSE) 38–39 genetic variances 193
male calves 40 best linear unbiased prediction (BLUP)
Mendelian sampling 41 animal evaluation model see animal
MME 39–40 evaluation model
non-zero off-diagonals 40 animal models and groups 54–60
predicted breeding values 42 breeding value 36
progeny contribution 40–41 data span 37
description 37 derivation 311–312
in groups description 34
assigned parents 56 genetic evaluation 34
data set and genetic parameters 56 identity matrix 36
design matrices and MME 57–60 minimization 35
G1 recoding 57 mixed linear model 35
matrix notation 55 MME 36, 312–313
Mendelian sampling 56 numerator relationship matrix 35
pedigree file 57 progeny contribution (PC) 313
relationship matrix 54 properties 34
sires 55 reduced animal model 49–54
strategy 55 selection index 36
pre-weaning gain 37 sire model see sire model
PYD see progeny yield deviation (PYD) birth weight (BW) 235–238
approximate reliability, genetic evaluations BLUP see best linear unbiased prediction
animal model 314–315 (BLUP)
random regression models breeding value prediction
ancestors 317 correlated response (CR) 11
observation, animal 316 genetic markers see genetic markers
ARHS see adjusted right-hand side (ARHS) pedigree 9–10
associative breeding value (SBV) progeny records see progeny records
122–123, 127–128 repeated records
accuracy, EBV 5
environmental correlations 4
Bayesian methods, SNP heritability and repeatability 4
BayesA mean yield, milk 6
chi distribution 194 multiple measurements 3
conditional distribution 194 percentage 5
Gibbs sampling chain 197 ratio, accuracy 5
random number 196 single record 2–3
reference population 195 BW see birth weight (BW)

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