Skip to main content
ARS Home » Midwest Area » Columbia, Missouri » Plant Genetics Research » Research » Publications at this Location » Publication #347488

Research Project: Soybean Seed Improvement Through Quantitative Analysis of Phenotypic Diversity in Response to Environmental Fluctuations

Location: Plant Genetics Research

Title: Multivariate analysis of the cotton seed ionome reveals a shared genetic architecture

Author
item Pauli, Duke - Cornell University - New York
item Ziegler, Gregory
item Ren, Min - Purdue University
item Jenks, Matthew - West Virginia University
item Hunsaker, Douglas - Doug
item Zhang, Min - Purdue University
item Baxter, Ivan
item Gore, Michael - Cornell University - New York

Submitted to: G3, Genes/Genomes/Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/28/2018
Publication Date: 4/1/2018
Citation: Pauli, D., Ziegler, G.R., Ren, M., Jenks, M.A., Hunsaker, D.J., Zhang, M., Baxter, I.R., Gore, M.A. 2018. Multivariate analysis of the cotton seed ionome reveals a shared genetic architecture. G3, Genes/Genomes/Genetics. 8(4):1147-1160. https://doi.org/10.1534/g3.117.300479.
DOI: https://doi.org/10.1534/g3.117.300479

Interpretive Summary: To mitigate the effects of heat and drought stress, an understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton mapping population under water-limited and well-watered conditions in a hot, arid environment. We profiled the elemental concentrations (ionome) of both seed from the population, as well as soil samples taken from throughout the field site to help control for environmental variation. We demonstrated both genetic and environmental effects on elemental accumulation. We also showed that groups of elements move in concert, suggesting that the underlying processes affect multiple elements. We took multiple approaches to identifying the loci controlling the elements individually and groups of elements, identifying at least 14 loci responsible for elemental accumulation in this population. Models based on ionomic data were able to predict the plants irrigation regime with high accuracies. Taken together, this work represents a first step in unraveling the integrated genetic nature of the ionome, and demonstrate that it is capable of capturing the physiological status of the plant.

Technical Abstract: To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions.