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

Publication Date:
2018-03-29
Publisher:
Genetics Society of America (GSA)
Electronic ISSN:
2160-1836
Topics:
Biology
Published by:
http://www.genetics-gsa.org/
_version_ 1836398868586037248
autor Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
beschreibung 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.
citation_standardnr 6220363
datenlieferant ipn_articles
feed_id 169615
feed_publisher Genetics Society of America (GSA)
feed_publisher_url http://www.genetics-gsa.org/
insertion_date 2018-03-29
journaleissn 2160-1836
publikationsjahr_anzeige 2018
publikationsjahr_facette 2018
publikationsjahr_intervall 7984:2015-2019
publikationsjahr_sort 2018
publisher Genetics Society of America (GSA)
quelle G3: Genes, Genomes, Genetics
relation http://www.g3journal.org/cgi/content/short/8/4/1147?rss=1
search_space articles
shingle_author_1 Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
shingle_author_2 Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
shingle_author_3 Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
shingle_author_4 Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
shingle_catch_all_1 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
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.
Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_2 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
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.
Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_3 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
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.
Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_4 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
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.
Pauli, D., Ziegler, G., Ren, M., Jenks, M. A., Hunsaker, D. J., Zhang, M., Baxter, I., Gore, M. A.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_title_1 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
shingle_title_2 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
shingle_title_3 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
shingle_title_4 Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
timestamp 2025-06-30T23:33:54.721Z
titel Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
titel_suche Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture
topic W
uid ipn_articles_6220363