Giovanny Covarrubias-Pazaran presents how to implement genomic prediction to properly manage genetic variance. The logistical challenges that will be faced during adoption are then discussed, along with a summary of the different uses of genomic selection (GS).
Breeding scheme optimization
Yoseph Beyene presents the current state of genomic prediction application in the CIMMYT maize breeding program in East Africa. Yoseph presents the use of GS to increase accuracy and change selection intensity in the maize program and discusses the challenges of implementation in an ongoing breeding program.
Eng Hwa Ng and Rajaguru Bohar present the current genotyping shared services available from EiB to enable low, mid and high-density genotyping for the application in genomic selection. The link to trait augmentation is presented and the logistical challenges involved in starting to genotype the early-stage materials from programs is discussed. A network strategy for genotyping is presented to breeders. Costs, workflows, panels available and crop status are discussed by Rajaguru.
Giovanny Covarrubias-Pazaran presents how to implement genomic prediction to change selection intensity. It is possible to increase selection intensity at an additional cost (although lower than the cost of phenotyping) or maintain the same intensity and accuracy with reduced costs.
Additionally, Covarrubias-Pazaran presents how to implement genomic prediction to reduce cycle time. This implies predicting individuals that have not been observed before while also predicting haplotypes that have not been observed before.
Giovanny Covarrubias-Pazaran presents how to implement genomic prediction to increase the accuracy of field trials by using a genomic relationship matrix to predict haplotypes and individuals. This is especially useful when heritabilities in field trials are low.
This session is focused on the basics of what genomic prediction implies in terms of data and models and applications. Common questions raised by CGIAR programs during interactions with the EiB platform are also covered.
New series of manuals developed by EiB is a practical guide to designing crossing, evaluation and selection strategies, and developing performance indicators such as genetic gain and heritability.
In the past, plant breeding has helped avert entire famines by changing a handful of genes in key crop varieties. But today's breeders must meet similar challenges with consistent excellence, making the right decisions each season to refine natural genetic diversity into a multitude of hard-working food crops.
The “Breeding Costing Tool” is a powerful solution for allowing users to estimate the cost of crop breeding and its associated research activities and to help breeders make decisions about resource allocation. The costing tool is designed to calculate the cost of running a crop breeding activity, or an entire breeding pipeline, using the prices, costs and salaries from a single year. The software is freely available from the Queensland Alliance for Agriculture and Food Innovation and the University of Queensland in Australia.
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).