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

Area of Science:

• Agricultural Science
• Microbiology
• Genetics

Background:

• Root-associated microbiomes (rhizobiomes) influence plant nutrient acquisition, stress tolerance, and disease resistance.
• The contribution of rhizobiomes to plant genotype trait variation and their utility in genomic selection remains largely unexplored.

Purpose of the Study:

• To develop and evaluate a microbiome-enabled genomic selection method for maize.
• To assess the impact of rhizobiome data on prediction accuracy for plant growth and nitrogen response traits.

Main Methods:

• Developed a genomic selection model integrating host single nucleotide polymorphisms (SNPs) and rhizobiome amplicon sequence variants (ASVs).
• Applied the model to a maize diversity panel under high and low nitrogen field conditions.
• Utilized high-dimensional mediation analysis to identify microbial mediators linking plant genotype and phenotype.

Main Results:

• The microbiome-enabled genomic selection model significantly outperformed conventional genomic selection for most time-series traits.
• An average relative improvement of 3.7% in prediction accuracy was observed, with greater gains (8.4-40.2%) under low nitrogen conditions.
• Identified microbial mediators previously associated with plant growth promotion.

Conclusions:

• Microbiome-enabled genomic selection enhances prediction accuracy, demonstrating potential for improving crop breeding.
• The findings support the role of beneficial microbes in enhancing nutrient uptake, particularly under nutrient-limited conditions.
• This approach serves as a proof-of-concept for microbiome-enabled plant breeding in sustainable agriculture.