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Abstract
Genetic and environmental factors collectively determine plant growth and yield. In the past 20 years, genome-wide association studies (GWAS) have been conducted on crops to decipher genetic loci that contribute to growth and yield, however, plant genotype appears to be insufficient to explain the trait variations. Here, we unravel the associations between genotypic, phenotypic, and rhizoplane microbiota variables of 827 foxtail millet cultivars by an integrated GWAS, microbiome-wide association studies (MWAS) and microbiome genome-wide association studies (mGWAS) method. We identify 257 rhizoplane microbial biomarkers associated with six key agronomic traits and validated the microbial-mediated growth effects on foxtail millet using marker strains isolated from the field. The rhizoplane microbiota composition is mainly driven by variations in plant genes related to immunity, metabolites, hormone signaling and nutrient uptake. Among these, the host immune gene FLS2 and transcription factor bHLH35 are widely associated with the microbial taxa of the rhizoplane. We further uncover a plant genotype-microbiota interaction network that contributes to phenotype plasticity. The microbial-mediated growth effects on foxtail millet are dependent on the host genotype, suggesting that precision microbiome management could be used to engineer high-yielding cultivars in agriculture systems.
Plant genotype alone appears to be insufficient to explain trait variations. This study integrates GWAS, MWAS and mGWAS in 827 foxtail millet cultivars, revealing that root-associated microbiota affect plant phenotypes in a host genotype-dependent manner.
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1 State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China (GRID:grid.21155.32) (ISNI:0000 0001 2034 1839)
2 Chinese Academy of Sciences, National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Shanghai, China (GRID:grid.9227.e) (ISNI:0000000119573309)
3 State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China (GRID:grid.21155.32) (ISNI:0000 0001 2034 1839); BGI-Qingdao, Qingdao, China (GRID:grid.21155.32) (ISNI:0000 0001 2034 1839)
4 State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China (GRID:grid.21155.32) (ISNI:0000 0001 2034 1839); University of Chinese Academy of Sciences, College of Life Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419)
5 Shanxi Agricultural University, Center for Agricultural Genetic Resources Research, Taiyuan, China (GRID:grid.412545.3) (ISNI:0000 0004 1798 1300)
6 State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China (GRID:grid.21155.32) (ISNI:0000 0001 2034 1839); Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen, China (GRID:grid.21155.32) (ISNI:0000 0001 2034 1839)
7 Technical University of Denmark, Department of Biotechnology and Biomedicine, Lyngby, Denmark (GRID:grid.5170.3) (ISNI:0000 0001 2181 8870)
8 State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China (GRID:grid.21155.32) (ISNI:0000 0001 2034 1839); Northeast Forestry University, BGI Life Science Joint Research Center, Harbin, China (GRID:grid.412246.7) (ISNI:0000 0004 1789 9091)