Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome

Authors: ASARO FIKAS, ALEXANDRA - Donald Danforth Plant Science Center; DILKES, BRIAN - Purdue University; Baxter, Ivan

Submitted to: Plant Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/2/2019
Publication Date: 5/10/2019
Citation: Asaro Fikas, A., Dilkes, B.P., Baxter, I.R. 2019. Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome. Plant Journal. 3(5);1-15. https://doi.org/10.1002/pld3.139.
DOI: https://doi.org/10.1002/pld3.139

Interpretive Summary: Plants take up elements from the soil, a process that is highly regulated by the plant’s genome. This complicated process is affected by multiple different factors, each which affects multiple different elements. In order to look at how maize alters its elemental uptake in response to different environments, we analyzed the kernel elemental content of a population derived from a cross grown 10 different times in six locations. We found that treating the elements as interconnected traits (instead of individual elements) revealed novel genetic loci and that the environment had a profound effect on which genetic loci were important for elemental accumulation in the kernel. Our results suggest that to have a full understanding of elemental accumulation in maize kernels and other food crops, we will need to understand the interactions identified here at the level of the genes and the environmental variables that contribute to loading essential nutrients into seeds.

Technical Abstract: The integrated responses of biological systems to genetic and environmental variation result in substantial covariance in multiple phenotypes. The resultant pleiotropy, environmental effects, and genotype-by-environmental interactions (GxE) are foundational to our understanding of biology and genetics. Yet, the treatment of correlated characters, and the identification of the genes encoding functions that generate this covariance, has lagged. As a test case for analyzing the genetic basis underlying multiple correlated traits, we analyzed maize kernel ionomes from Intermated B73 x Mo17 (IBM) recombinant inbred populations grown in 10 environments. Plants obtain elements from the soil through genetic and biochemical pathways responsive to physiological state and environment. Most perturbations affect multiple elements which leads the ionome, the full complement of mineral nutrients in an organism, to vary as an integrated network rather than a set of distinct single elements. We compared quantitative trait loci (QTL) determining single-element variation to QTL that predict variation in principal components (PCs) of multiple-element covariance. Single-element and multivariate approaches detected partially overlapping sets of loci. QTL influencing trait covariation were detected at loci that were not found by mapping single-element traits. Moreover, this approach permitted testing environmental components of trait covariance, and identified multi-element traits that were determined by both genetic and environmental factors as well as genotype-by-environment interactions. Growth environment had a profound effect on the elemental profiles and multi-element phenotypes were significantly correlated with specific environmental variables.