PT  - JOURNAL ARTICLE
AU  - Alexandra Asaro
AU  - Brian P. Dilkes
AU  - Ivan Baxter
TI  - Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome
AID  - 10.1101/241380
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 241380
4099  - http://biorxiv.org/content/early/2017/12/31/241380.short
4100  - http://biorxiv.org/content/early/2017/12/31/241380.full
AB  - 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. To examine the genetic basis of covariation in the accumulation of multiple elements, we analyzed maize kernel ionomes from Intermated B73 × Mo17 (IBM) recombinant inbred populations grown in 10 environments. 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. In addition to loci co-localizing with single-element QTL, multivariate traits within environments were controlled by loci with significant multi-element effects not detectable using single-element traits. Gene-by-environment interactions underlying multiple-element covariance were identified through QTL analyses of principal component models of ionome variation. In addition to interactive effects, growth environment had a profound effect on the elemental profiles and multi-element phenotypes were significantly correlated with specific environmental variables.Author Summary A multivariate approach to the analysis of element accumulation in the maize kernel shows that elements are not regulated independently. By describing relationships between element accumulation we identified new genetic loci invisible to single-element approaches. The mathematical combinations of elements distinguish groups of plants based on environment, demonstrating that observed variation derives from interactions between genetically controlled factors and environmental variables. These results suggest that successful application of ionomics to improve human nutrition and plant productivity requires simultaneous consideration of multiple-element effects and variation of such effects in response to environment.