Improving wheat grain yield genomic prediction accuracy using historical data.

ABSTRACT: Genomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data from 6 selection environments were analyzed, with the populations of 5 years (2018-2023) as the validation population and earlier years (back to 2013-2014) as the training population. Generally, we observed that as the number of training years increased, the prediction accuracy tended to improve or stabilize. For instance, in the late heat stress selection environment (beds late heat stress), prediction accuracy increased from 0.11 (1 training year) to 0.23 (5 years), stabilizing at 0.26. Simila

SUBMITTER: Vitale P 

PROVIDER: S-EPMC12005153 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature