Predicting complex phenotypes using multi-omics data in maize
Authors: Creach, M., Webster, B., Newton, L., Turkus, J., Schnable, J., Thompson, A., VanBuren, R.
Date: 2025-10-01 · Version: 1
DOI: 10.1101/2025.09.30.679283 Category: Plant Biology
Model Organism: Zea mays
▶ AI Summary
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.
multi-omics trait prediction transcriptomics phenomics genotype-by-environment interaction