Basics of Genomic Selection 4. Selection Intensity and Cycle Time

Giovanny Covarrubias-Pazaran presents how to implement genomic prediction to change selection intensity. It is possible to increase selection intensity at additional cost (although lower than the cost of phenotyping), or maintain the same intensity and accuracy with reduced costs. This implies predicting individuals not observed before while also predicting haplotypes that have been observed given the population structure (closely related individuals observed). This is especially useful when programs need to decrease their costs. Additionally, Covarrubias-Pazaran presents how to implement genomic prediction to reduce cycle time. This implies predicting individuals that not observed before while also predicting haplotypes that have not been observed before. Due to the reduced accuracy of models in this case, a closed recurrent selection system is proposed to ensure that the model works. This is especially useful when programs want to increase their genetic gains About this training: Genomic selection/prediction is becoming an important tool to maximize genetic gain in breeding programs. The method provides multiple ways to improve different aspects of the breeders’ equation. In this training, the basics of genomic selection are explained and the different scenarios where the methodology can be used are presented. Experts from the CGIAR system including CIMMYT, IRRI, and ICRISAT present case example on the proper deployment of GS in running programs. Recorded during the workshop "Basics of Genomic Selection: Towards increasing selection accuracy, selection intensity and reducing cycle time in self and cross-pollinated crops," 9-11 August 2021. Organized and coordinated by Sanjay Katiyar, EiB ICAR project coordinator.