Introduction | 1) MET: main effect model | 2.1) MET: diagonal model (DG) | 2,2) Heterogeneous residuals | 3) MET: compund symmetry model (CS) | 3.2) MET: main + diagonal (M+DG) | 4) MET: unstructured model (US) | 5) MET: random regression model | 5.2) Finlay-Wilkinson regression | 6) Factor analytic (reduced rank) model | 7) Two stage analysis | 8) How to deal with massive GxE models or a massive number of individuals | Literature

1) Extracting fixed effect coefficients and their standard errors | 2) Extracting variance-covariance components | 3) Extracting random effect coefficients and their standard errors | 4) Computing predictions or linear combinations | Literature

1) Single environment model | 2) MET: main effect model | 3) MET: diagonal model (DG) | 4) MET: compund symmetry model (CS) | 5) MET: unstructured model (US) | 6) MET: random regression model | 7) Other GxE covariance structures | 8) Finlay-Wilkinson regression | 9) Factor analytic (reduced rank) model | 10) Two stage analysis | Literature

SECTION 1: Introduction | Backgrounds in linear algebra | SECTION 2: Topics in quantitative genetics | 1) Marker and non-marker based heritability calculation | 2) Specifying heterogeneous variances in univariate models | 3) Using the vpredict calculator | 3.1) Standar error for heritability | 4) Half and full diallel designs (use of the overlay) | 5) Genomic selection: predicting mendelian sampling | 6) Indirect genetic effects | 7) Genomic selection: single cross prediction | 8) Multivariate genetic models and genetic correlations | SECTION 3: Special topics in Quantitative genetics | 1) Partitioned model | 2) UDU' decomposition | 3) Mating designs | North Carolina Design I (Nested design) | North Carolina Design II (Factorial design) | 4) GWAS by GBLUP | Final remarks | Literature

SECTION 1: Introduction | Backgrounds in tensor products | SECTION 2: Spatial models | 1) Two dimensional splines (multiple spatial components) | 2) Two dimensional splines in single field (single spatial component) | 3) Spatial models in multiple trials at once | Literature

1) Random slopes | 2) Random slopes and random intercepts (without correlation) | 3) Random slopes and random intercepts (with correlation) | 4) Random slopes with a different intercept | 4) Other models available in sommer but not in lme4 | Literature

1) Optimizing the selection of one feature with a constraint in another feature | 2) Obtaining subsample of a population to maximize a feature while constraining the relationship between individuals in the population | 2a) Best parents for the next generation | 2b) Obtaining optimal N crosses from a population for a given trait/feature | 3) Optimizing a subsample of size N to be representative | 3a) Optimizing a subsample of size N to be representative of its own | 3b) Optimizing a subsample of size N to be representative of another population | 6) How to specify constraints | Gender in breeding | Number of times a parent should be used | 6) Customizing a fitness function (linear regression example) | 7) Travel salesman problem | 8) How to force the initial solutions or founders | 9) How to optimize the number of progeny to produce per cross | Literature

SECTION 1: Basic topics in quantitative genetics | 1) Marker and non-marker based heritability calculation | 2) Specifying heterogeneous variances in univariate models | 3) Half and full diallel designs (use of the overlay) | 4) Genomic selection: predicting mendelian sampling | 5) Indirect genetic effects | 6) Genomic selection: single cross prediction | 8) Spatial modeling: using the 2-dimensional spline | 9) Multivariate genetic models and genetic correlations | SECTION 2: Special topics in Quantitative genetics | 1) Partitioned model | 2) UDU' decomposition | 3) Mating designs | North Carolina Design I (Nested design) | North Carolina Design II (Factorial design) | 4) GWAS by GBLUP | Literature