Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture

Authors

Duke Pauli, Cornell University
Greg Ziegler, Donald Danforth Plant Science Center & Plant Genetics Research Unit
Min Ren, Purdue University
Matthew A. Jenks, West Virgina University
Douglas J. Hunsaker, Arid-Land Agriculture Research Center
Min Zhang, Purdue University
Ivan Baxter, Donal Danforth Plant Science Center & Plant Genetics Research Unit
Michael A. Gore, Cornell UniversityFollow

Author ORCID Identifier

https://orcid.org/0000-0002-8292-2388

N/A

N/A

N/A

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https://orcid.org/0000-0002-0164-1948

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https://orcid.org/0000-0001-6896-8024

Document Type

Article

Publication Date

2018

College/Unit

Davis College of Agriculture, Natural Resources and Design

Department/Program/Center

Division of Plant and Soil Sciences

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 con- ditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the pop- ulation 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.

Digital Commons Citation

Pauli, Duke; Ziegler, Greg; Ren, Min; Jenks, Matthew A.; Hunsaker, Douglas J.; Zhang, Min; Baxter, Ivan; and Gore, Michael A., "Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture" (2018). Faculty & Staff Scholarship. 1738.
https://researchrepository.wvu.edu/faculty_publications/1738

Source Citation

Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., & 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

Comments

Copyright © 2018 Pauli et al. doi: https://doi.org/10.1534/g3.117.300479 Manuscript received November 26, 2017; accepted for publication January 28, 2018; published Early Online February 1, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Supplemental Material is available online at www.g3journal.org/lookup/suppl/ doi:10.1534/g3.117.300479/-/DC1.