A multiomics discriminatory analysis approach to identify drought-related signatures in maize nodal roots

Maize is one of the major food crops grown in the continental US, and as such, major interest is directed towards understanding its adaptability to drought stress. Certain cultivars of maize have shown increased resistance to water shortages, by continuing to maintain root growth even when under severe water stress. To better understand the molecular mechanisms which lead to such adaptation, we analyzed multiomics datasets generated from the growth zone of nodal roots from FR697, an inbred line that shows a superior capacity for root growth maintenance under drought stress. We used a research pipeline consisting of a discriminatory multiomics data integration approach, which uses a combination of sparse Generalized Canonical Correlation Analysis (sGCCA) and generalized Partial Least Square (PLS) analysis instead of traditional “filter funnel” approaches, to incorporate all datasets into one holistic global network and form clusters spanning all omics levels. We then linked significant elements from these clusters to various observations associated with drought stress in the root tip samples, reinforced by their roles in biological pathways.