Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.

ABSTRACT:

Key message

We validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations. Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybr

SUBMITTER: Lorenzi A 

PROVIDER: S-EPMC11341662 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature