1. Introduction. Molecular markers and prediction. Predictive inference. Cross-validation. Overview of some penalized methods.

2. Review of least-squares, maximum likelihood and best linear unbiased prediction.

3. GWAS (genome-wide association study) and pitfalls.

4. Review of Bayesian inference, MCMC and Bayesian regression. Bayesian predictive distributions

5. Challenges from complexity: over-parameterization, instrumental models, errors in gene action specification.

6. Genomic BLUP and genomic studentized prediction (GSTUP). The Bayesian alphabet (Bayes A, B, C, Bayesian Lasso, Bayes R).

7. The problem of dealing with gene-gene-gene-....-gene interactions.

8. Introduction to non-parametric regression: kernel methods and neural networks.

9. Estimating distributions of prediction errors via re-sampling.