|ZIEGLER, GREG - Danforth Plant Science Center|
|BECKER, ANTHONY - Danforth Plant Science Center|
Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/7/2012
Publication Date: 12/12/2012
Citation: Ziegler, G., Terauchi, A.M., Becker, A., Armstrong, P.R., Hudson, K.A., Baxter, I.R. 2012. Ionomic screening of field-grown soybeans identifies mutants with altered seed elemental composition. The Plant Genome. 6(2):1-9.
Interpretive Summary: The mineral content of soybeans is an important component of the human diet. We developed a method of screening large, field grown mutant populations of soybeans for lines with altered elemental profiles. We test several methods of sorting through the data to find the optimal method for identifying potential mutant lines, which are then regrown to confirm the predictions. We demonstrate that this method is successful at identifying mutant lines. This method can therefore be used to identify genes that can be used to produce lines with improved nutritional properties, an important goal of U.S. plant breeders and one that impacts the competitive health of the soybean industry.
Technical Abstract: Soybean seeds contain high levels of mineral nutrients essential for human and animal nutrition. High throughput elemental profiling (ionomics) has identified mutants in model plant species grown in controlled environments. Here, we describe a method for identifying potential soybean ionomics mutants grown in a field setting and apply it to 975 NMU mutagenized lines. After performing a spatial correction, we identified mutants using either visual scoring of z-score plots or computational ranking of putative mutants followed by visual confirmation. Although there was a large degree of overlap between the methods, each method identified unique lines. The visual scoring approach identified 22/427 (5%) potential mutants, 70% (16/22) of which were confirmed when seeds from the same parent plant were re-grown in the field. We also performed simulations to determine an optimal strategy for screening large populations. All data from the screen is available at www.ionomicshub.org.