Summer/2024 
Optimization of breeding programs via Simulations and Genomic Selection
Marco Antonio Peixoto
Federal University of Vicosa
27-29/May/2024
Schedule
Monday (05/27) - 5:00 pm - 9:00 pm
Tuesday (05/28) - 5:00 pm - 9:00 pm
Wednesday (05/29) - 5:00 pm - 9:00 pm
Introduction
Designing a breeding program is a complex task. It requires simultaneously considering multiple interdependent breeding objectives (costs, size, target traits, etc.). Towards this aim, simulations have been demonstrated as a powerful tool for generating data-driven evidence for breeding decisions. One among several tools in simulations is the AlphaSimR package (Gaynor et al. 2021). The package uses stochastic simulations for the design and optimization of breeding programs. It offers a fast, simple, and inexpensive way to test alternative breeding programs.
In addition, genomic selection is a tool that has been shown as a game changer in animal and plant breeding. It has been used to optimize several steps of a breeding program, such as advancements, early selections, hybrid prediction, parent selection, cross-selection, and trait introgression, among others.
Here, we will use both tools together (simulations and genomic selection) to guide discussions on how to optimize breeding programs.
Recommended literature
We recommend reading the following papers:
- Paper 1: Gaynor CR, Gorjanc G, and Hickey JM (2021). AlphaSimR: an R package for breeding program simulations. G3. https://doi.org/10.1093/g3journal/jkaa017
- Paper 2: Werner, CR et al. (2023). Genomic selection strategies for clonally propagated crops. Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-023-04300-6
- Paper 3: Meuwissen et al (2001). Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps. https://doi.org/10.1093/genetics/157.4.1819
- Paper 4: Bancic et al. (2024) Plant breeding simulations with AlphaSimR. https://doi.org/10.1101/2023.12.30.573724
Tentative program
| Subject | Sections | Time |
|---|---|---|
| AlphasimR: Base population and Global parameters | Block 1 | 4 hr |
| -Genetic basis of base populations | ||
| -Trait characteristics | ||
| -QTL (and SNPs) for traits | ||
| -Non-additive effects | ||
| -Population characteristics | ||
| -Functions for modeling a breeding program | ||
| Simulating Breeding pipelines | Block 2 | 3 hr |
| -From crosses to the release of varieties | ||
| -Recurrent Selection breeding program | ||
| -Reciprocal Recurrent Selection breeding program | ||
| How to deploy Genomic selection | Block 3 | 5 hr |
| -The genome and phenotypes | ||
| -Mixed models and genomic models | ||
| -Models and predictions into AlphaSimR | ||
| -Factors affecting prediction accuracy | ||
| -Training populations and model deployment | ||
| -Using external packages for predictions |
Topics and Content
I. AlphasimR: Base population and Global parameters
Importing external data
- Content [html]
- Script [rmd]
- Data [externalData]
II. Simulating Breeding pipelines
Genetic trends over generations
Line breeding pipeline
Maize breeding pipeline
III. How to deploy Genomic selection
Intro to GS in AlphaSimR
Maize breeding pipeline II (with GS now)