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

Authors

Zhikai Yang, University of Nebraska-Lincoln
Tianjing Zhao, University of Nebraska-Lincoln, University of California DavisFollow
Hao Cheng, University of California Davis
Jinliang Yang, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

12-5-2023

Citation

G3, 2024, 14(3), jkad286. https://doi.org/10.1093/g3journal/jkad286

Comments

Open access.

Abstract

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4–40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-ofconcept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.