GSE106098
Hybrid performance in maize predicted with combinations of omics data
We explored genomics, transcriptomics (mRNA and sRNA) and metabolomics of maize parent lines as predictors for agronomic performance of single-cross hybrids. Our results indicate that the merit of any individual predictor is trait dependent and that combining predictors has advantages for application across traits. We conclude that downstream “omics” can complement genomics for hybrid prediction and thereby contribute to more efficient selection of hybrid candidates. Overall design: This dataset reports on the analysis of sRNAs in 64 Dent and 41 Flint inbred lines from a maize breeding population with 10 replicated samples