Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program.

ABSTRACT:

Key message

Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accur

SUBMITTER: Atanda SA 

PROVIDER: S-EPMC7813723 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature