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Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize

Zhikai Yang, Tianjing Zhao, Hao Cheng, Jinliang Yang
doi: https://doi.org/10.1101/2023.03.03.530932

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doi 
https://doi.org/10.1101/2023.03.03.530932
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  • November 14, 2023.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

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  1. Zhikai Yang1,2,
  2. Tianjing Zhao3,
  3. Hao Cheng3 and
  4. Jinliang Yang1,2,*
  1. 1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
  2. 2Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
  3. 3Department of Animal Science, University of California Davis, Davis, CA 95616, USA
  1. ↵*Corresponding author: jinliang.yang{at}unl.edu
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Posted November 14, 2023.
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Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize
Zhikai Yang, Tianjing Zhao, Hao Cheng, Jinliang Yang
bioRxiv 2023.03.03.530932; doi: https://doi.org/10.1101/2023.03.03.530932
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