Preface; Software Requirements; Contents; About the Authors; Chapter 1: Introduction to ASReml Software; Why ASReml?; ASReml Workflow; Setting Up ConTEXT Editor to Create and Execute ASReml Command Files; Starting with ASReml; Data Field Definitions; Transformation of Response Variables; Data File and Job Control Qualifiers; Specifying Terms in the Linear Model; Variance Header Line and Random Model Terms; Running ASReml; ASReml Output Files; Tabulation; Prediction; Processing Multiple Analyses with One Command File; Linear Combinations of Variance Components; A Brief Introduction to ASReml-R

Data Set Used in the AnalysisFitting a Model in ASReml-R; Chapter 2: A Review of Linear Mixed Models; Mixed Models Compared to Traditional ANOVA; Balanced Data: ANOVA with SAS Proc GLM; Balanced Data: ANOVA with R; Balanced Data: ANOVA with ASReml; Balanced Data: Mixed Models Analysis with SAS Proc MIXED; Balanced Data: Mixed Models Analysis with R; Balanced Data: Mixed Models Analysis with ASReml; Hypothesis Testing with Mixed Models; Prediction: BLUE and BLUP; Unbalanced Data; ANOVA with SAS Proc GLM; Unbalanced Data: Mixed Models Analysis with SAS

Mixed Models in a Nutshell: Theory and ConceptsThe Model; Fixed and Random Effects; Expectations and Variance-Covariance for the Random Effects; A Trivial Example: Daughters Lactation Yield; Solving the Model; The Mixed Model Equations; Estimability in Models with Multiple Fixed Effects; Standard Errors and Accuracy of the Estimates; A Brief Note on REML; Chapter 3: Variance Modeling in ASReml; Variance Model Specifications; Gamma and Sigma Parameterization in ASReml; Homogenous Variance Models; Heterogeneous R Variance Structures; Sections May Have Different Residual Variances

Error Effects May Not Be IndependentHeterogeneous G Variance Structures; Block Diagonal G Structure; Nested and Interaction Terms in the G Structure; Effects in G Can Be Correlated; Correlated Effects Due to Genetics; Initial Values; Chapter 4: Breeding Values; Family Selection; Causal Variance Components and Resemblance; The GCA (Family) Model; Analysis of Half-Sib Progeny Data Using GCA Model; Variance Components and Their Linear Combinations; Variation Among Family Means; Within-Family Variation; The Accuracy of Breeding Values; Individual ("Animal) Model

Animal Model for Half-Sib Family DataThe Animal Model with Deep Pedigrees and Maternal Effects; Accounting for Genetic Groups Effect in Predictions; Treating Genetic Groups as a Fixed Effect in GCA model; Fitting Genetic Groups as Pedigree Information in Individual Model; Effect of Self-Fertilization on Variance Components; Chapter 5: Genetic Values; Specific Combining Ability (SCA) and Genetic Values; Diallel Mating Designs; Diallel Example; Specific Combining Ability (SCA) Effect; Reciprocal Effects; Interpretation of Observed Variances from Diallels