AlphaSimR is a GitLab repositorium with R code addressing multiple simulation questions for designing breeding programs. This includes proper program size, experimental designs, surrogates of genetic merit, reduction of cycle time, among others (this is only accessible to a small number of users on request).
Link: https://gitlab.com/excellenceinbreeding/module2
Contact: g.covarrubias@cgiar.org
What is the best selection method?
Selection methods for multiple traits aim to find the balance to increase the population means for multiple traits.
These simulation reports demonstrate the differences between classical methods such independent culling and selection indices, to support the practical implementation of indices.
Contact: g.covarrubias@cgiar.org
What is an optimal program size?
The size of the breeding program is something that can increase the genetic gain by taking advantage of factors such as selection intensity or the among- and within-family variance.
These simulation reports demonstrate the trade-offs between of number of parents, crosses and progeny per cross, and provide practical advice on how to set the level of each.
Contact: d.gemenet@cgiar.org
What is the right number of testers?
Hybrid breeding aims to keep and increase the non-additive interactions in the final products while increasing the additive genetic value in a recurrent selection program.
The following simulation reports and retrospective analyses explore questions such as how many testers should be used to capture the general combining ability (GCA) that maximizes additive and non-additive effects.
Contact: d.gemenet@cgiar.org
Should we move to hybrid breeding?
Hybrid breeding aims to keep and increase the non-additive interactions in the final products while increasing the additive genetic value in a recurrent selection program.
The following simulation reports show how the hybrid genetic model applies to non-inbred crops and different ploidies. In addition, we provide practical advice for related questions related to testers, 3-way crosses, etc.
Contact: m.r.labroo@cgiar.org
How to implement genomic selection?
Using genome-assisted prediction to increase accuracy, manage diversity, intensity or reduce cycle time is normally referred as genomic selection (GS).
The following simulation reports provide practical recommendations for a successful implementation of these methodologies.
Contact: g.covarrubias@cgiar.org
Breeding programs rely on accurate selections to move population means along time. Understanding the best experimental design across environments is pivotal to achieve this goal.
These simulation reports and retrospective analyses demonstrate practical recommendations on the proper experimental design.
Contact: c.werner@cgiar.org
What is the effect of reducing cycle time?
Breeding programs aim to increase productivity of varieties in farmers fields. EiB proposes a fast and accurate population improvement approach as a strategy to release such improved varieties at a faster rate.
These simulation reports show the impact of reducing the cycle time in the short and long-term genetic gain and improve variety release.
Contact: g.covarrubias@cgiar.org
Today 10/08/2019 we focused on discussing the analogy of plant breeding with other sectors like the automotive industry to show that breeders and QGs are couple that should work towards designing the pipeline to deliver a product (squares 2 and 3). The people participating felt they have to work towards defining clear boundaries on where and when the breeder comes to scene.
