This course is designed for crop improvement scientists who wish to incorporate modern biometric and statistical approaches in their work. Areas covered include the following:
1. Introduction to statistics and principles of experimental design
- Introduction to statistics
- Components of an Experiment and terminology of Experimental Design
- Main principles of experimental design: the 3 Rs
- Experimental studies
- From the field to the data file – collecting and organizing experimental data
- Analysis of Variance (ANOVA)
- Statistical Analysis and Modeling
- Completely Randomized Design (CRD)
- RCBD (factorial designs)
2. Experiments with more than one random term
- Split-plot designs
- Estimate genetic variance components (introduction to REML analysis)
- Estimation of heritability and genetic correlations
3. Experiments for factors with many levels
- Incomplete block designs (part I). Balanced incomplete blocks.
- Incomplete block designs (part II). Lattice designs, alpha designs.
4. GxE analysis: Adaptability and Stability analysis / Finding structure in GxE.
- Introduction to GxE: basic definitions.
- Finlay-Wilkinson, AMMI, and GGE model.
- Stability analysis and variance covariance structure.
5. Single trait QTL mapping.
- Introduction to QTL mapping: the basic principles.
- QTL mapping: marker-based analysis, Simple Interval Mapping (SIM), Composite Interval Mapping (CIM). Estimation of QTL effects, explained variance and confidence intervals.
6. Advanced QTL analysis
- QTL x Environment
- Multi-trait QTL mapping
7. Using Selection indices
The training can be delivered in modules distributed over a period of time or at one go in keeping with the client’s needs and requirements. Please contact us here for more information.