The study evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

The study identifies fungal effectors from Ustilago maydis that interact with plant TOPLESS corepressors and induce gall formation by hijacking maize lateral root initiation pathways, notably through upregulation of LBD transcription factors. Transgenic expression of class II effectors derepresses auxin signaling, leading to pluripotent calli without external hormones, and maize mutants in LBD genes show reduced gall development.