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

View ORCID ProfileDuke Pauli, Greg Ziegler, Min Ren, View ORCID ProfileMatthew A. Jenks, Douglas J. Hunsaker, View ORCID ProfileMin Zhang, View ORCID ProfileIvan Baxter, View ORCID ProfileMichael A. Gore
doi: https://doi.org/10.1101/213777
Duke Pauli
*Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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Greg Ziegler
†Donald Danforth Plant Science Center, St. Louis, Missouri, USA
‡United States Department of Agriculture–Agricultural Research Service (USDA-ARS), Plant Genetics Research Unit, St. Louis, Missouri, USA
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Min Ren
§Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
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Matthew A. Jenks
**Division of Plant and Soil Sciences, West Virginia University, Morgantown, WV 26506, USA
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Douglas J. Hunsaker
††“United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Arid-Land Agricultural Research Center, Maricopa, AZ 85138, USA
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Min Zhang
§Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
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Ivan Baxter
†Donald Danforth Plant Science Center, St. Louis, Missouri, USA
‡United States Department of Agriculture–Agricultural Research Service (USDA-ARS), Plant Genetics Research Unit, St. Louis, Missouri, USA
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Michael A. Gore
*Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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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.

One sentence summary The cotton seed ionome has a shared genetic basis that provides insight into the physiological status of the plant.

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Posted January 25, 2018.
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Multivariate analysis of the cotton seed ionome reveals a shared genetic architecture
Duke Pauli, Greg Ziegler, Min Ren, Matthew A. Jenks, Douglas J. Hunsaker, Min Zhang, Ivan Baxter, Michael A. Gore
bioRxiv 213777; doi: https://doi.org/10.1101/213777
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Multivariate analysis of the cotton seed ionome reveals a shared genetic architecture
Duke Pauli, Greg Ziegler, Min Ren, Matthew A. Jenks, Douglas J. Hunsaker, Min Zhang, Ivan Baxter, Michael A. Gore
bioRxiv 213777; doi: https://doi.org/10.1101/213777

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