Controlling elemental composition is critical for plant growth and development as well as the nutrition of humans who utilize plants for food. Uncovering the genetic architecture underlying mineral ion homeostasis in plants is a critical first step towards understanding the biochemical networks that regulate a plant's elemental composition (ionome). Natural accessions of Arabidopsis thaliana provide a rich source of genetic diversity that leads to phenotypic differences. We analyzed the concentrations of 17 different elements in 12 A. thaliana accessions and three recombinant inbred line (RIL) populations grown in several different environments using high-throughput inductively coupled plasma- mass spectroscopy (ICP-MS). Significant differences were detected between the accessions for most elements and we identified over a hundred QTLs for elemental accumulation in the RIL populations. Altering the environment the plants were grown in had a strong effect on the correlations between different elements and the QTLs controlling elemental accumulation. All ionomic data presented is publicly available at www.ionomicshub.org.
Quantitative trait loci (QTL) mapping is a popular statistical method that is often used in agricultural applications to identify genomic regions associated with phenotypic traits of interest. In its most common form, a QTL analysis tests one phenotypic trait at a time using a variety of research hypotheses that depend on the application. When multiple traits are available, there are considerable benefits to analyzing subsets of biologically related traits in a multipletrait QTL mapping framework. Determining the most informative subset(s) of traits is the critical challenge that we address in this work. We present our approach, as well as simulations that demonstrate the performance. We also discuss an application of our approach as applied to an Arabidopsis thaliana data set.
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