bioRxivPlant BiologyPreprint
Phyllosphere and rhizosphere microbiomes empower Nicotiana tobacum complex traits dissection and prediction
Qiao Du, Yu HaniD, Zhengwen LiuiD, Huan SiiD, Yan JiiD, Li LiuiD, Zhiliang XiaoiD, Lirui ChengiD, Aiguo YangiD, Dan LiuiD, Yanjun ZaniD
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Abstract
Understanding how plant-associated microbiomes interact with host genome variation to influence agronomic traits is essential for advancing microbiomeassisted crop improvement. In this study, we characterized the phyllosphere and rhizosphere microbiomes of 164 diverse Nicotiana tabacum accessions using 16S rRNA sequencing and integrated these data with host genomic variation and 22 agronomic traits. The two microbiomes exhibited distinct taxonomic structures, diversity patterns, and predicted metabolic functions. Microbiome genomewide association studies identified extensive host genetic control over microbial abundance, including 49 shared genomic loci that explained nearly half of the heritable variation in both microbiomes. Microbiomewide association studies revealed biologically meaningful associations between specific ASVs and agronomic traits. However, network analysis demonstrated that microbial subcommunities, rather than individual taxa, contributed substantially to phenotypic variation. Then, colocalization analysis further identified genetic variants jointly influencing microbial abundance and metabolite traits, highlighting potential host-microbe-trait causal links. Incorporating microbiome data into genomic selection models, we successfully improved prediction accuracy for several traits, especially plant architecture and flowering. Together, this work provides a comprehensive populationlevel framework linking host genetics, microbiome composition, and agronomic traits in tobacco, offering new insights for microbiomeinformed breeding strategies.
- organism: plant (other)
- assay: metagenomics
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant calling
- approach: statistical model
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