Barley genetic architecture conditionally impacts recruitment of rhizosphere microorganisms and crop performance has been submitted

Microorganisms recruited to the rhizosphere from the surrounding soil can benefit the fitness of their host. Variation in plant genetics is associated with variation in rhizosphere microbial community composition leading to changes in fitness and crop productivity. However, what impact the abiotic environment has on connections between microbes and host genetics, and whether those connections in turn impact crop performance in realistic agricultural scenarios remains unclear. We assessed agronomic performance and 16S and ITS amplicon-based rhizosphere bacterial and fungal community composition on a large diverse barley population grown in seven field trials across four locations and two years. Within adapted regions, we observed consistent rhizosphere compositions across diverse soils, whereas in an unadapted environment, distinct microbial communities were recruited, indicating environmental specificity in microbial assembly. A Genome-wide association study (GWAS) identified 864 associations of barley genetic markers with bacterial or fungal taxa abundance. A total of 108 microbe-associated quantitative trait loci (QTL) co-localized with agronomic traits, suggesting pleiotropy or genetic linkage. Associated taxa varied considerably across field trial environments, whereas the mapped host QTL were more consistent. Members of the nitrogen cycling bacterial phylum, Nitrospirota were the most extensively associated. This phylum had 25 marker associations across five of the field trial location-years in the GWAS. These included a locus on chromosome 2H that associated with Nitrospirota bacteria and grain protein in two location-years, and the Leohumicola fungal genus and grain protein in a third trial. These findings support the genetic manipulation of rhizosphere microbiomes to enhance crop adaptation, whether with consistent or environment-specific microbial taxa. Such breeding advances will support phenotyping and selection strategies to improve crop resilience and productivity across variable environments.