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Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism

Nature Genetics volume 46, pages 714–721 (2014)Cite this article

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Abstract

Plant metabolites are important to world food security in terms of maintaining sustainable yield and providing food with enriched phytonutrients. Here we report comprehensive profiling of 840 metabolites and a further metabolic genome-wide association study based on ∼6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. We observed substantial heterogeneity in the natural variation of metabolites and their underlying genetic architectures among different subspecies of rice. Data mining identified 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof of concept, we functionally identified or annotated five candidate genes influencing metabolic traits. Our study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.

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Figure 1: Manhattan plots of mGWAS results with genetic association.
Figure 2: Two-locus interactions between significant loci and mGWAS genetic architecture analysis between the indica and japonica subspecies.
Figure 3: Functional identification of Os02g57760 (O-methyltransferase) and the assignment of possible causative sites.
Figure 4: Functional annotation of Os07g32060 (UDP-glucosyl transferase) and the assignment of possible causative sites.

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Acknowledgements

We appreciate the critical reading and helpful comments on the manuscript made by C. Martin from John Innes Centre, UK and Q. Zhang from Huazhong Agricultural University, China. We thank W. Yan for kindly providing 148 varieties from a mini-core subset of the US Department of Agriculture rice gene bank. This work was supported by the Major State Basic Research Development Program of China (973 Program) (number 2013CB127001), the National High Technology R&D Program of China (863 Program) (numbers 2012AA10A303 and 2012AA10A304), the National Natural Science Foundation of China (numbers 31070267 and 31100962) and the Program for New Century Excellent Talents in University of Ministry of Education in China (NCET-09-0401). We are also thankful for support from the Ministry of Science and Technology (numbers 2010CB125901 and 2011CB100304).

Author information

Author notes

  1. Wei Chen, Yanqiang Gao, Weibo Xie, Liang Gong and Kai Lu: These authors contributed equally to this work.

Authors and Affiliations

  1. National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China

    Wei Chen, Yanqiang Gao, Weibo Xie, Liang Gong, Kai Lu, Wensheng Wang, Yang Li, Huaxia Dong, Wan Zhang, Lejing Zhang, Sibin Yu, Gongwei Wang, Xingming Lian & Jie Luo

  2. College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China

    Xianqing Liu

  3. Key Laboratory of Horticultural Plant Biology (Ministry of Education), Huazhong Agricultural University, Wuhan, China

    Hongyan Zhang

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  1. Wei Chen
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Contributions

J.L. conceived the project and supervised the study. X. Lian and K.L. prepared the material for genotyping. W.C., Y.G., L.G. and Y.L. performed most of the experiments. W.C., X. Liu, H.Z. and J.L. carried out the metabolite analyses. X. Lian, S.Y., H.D., W.Z., L.Z. and G.W. participated in the material preparation. W.C., W.X., W.W. and J.L. analyzed the data. J.L. wrote the paper. All of the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Xingming Lian or Jie Luo.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1-15 and Supplementary Tables 6, 7 and 23. (PDF 16370 kb)

Supplementary Table 1

Metabolite reporting checklist and recommendations for LC-MS (XLSX 11 kb)

Supplementary Table 2

The (almost) non-redundant MS2T library of rice leaf (XLSX 124 kb)

Supplementary Table 3

Scheduled MRM (multiple reaction monitoring) transitions for widely targeted metabolite analysis in rice leaf (XLSX 147 kb)

Supplementary Table 4

The list of collected 529 rice accessions (XLSX 84 kb)

Supplementary Table 5

Data matrix of 840 metabolites in 524 accessions of rice germplasms (including repeat1 and repeat2) (XLSX 10486 kb)

Supplementary Table 8

The list of total 2,947 significant SNPs detected in at least one of the populations (XLSX 267 kb)

Supplementary Table 9

The list of 634 loci detected in at least one of the populations (XLSX 56 kb)

Supplementary Table 10

The list of 551 lead SNPs that were repeatedly detected (XLSX 110 kb)

Supplementary Table 11

The list of 356 loci that were repeatedly detected (XLSX 77 kb)

Supplementary Table 12

The full lists of significant associations of metabolic GWAS (mGWAS) (XLSX 4949 kb)

Supplementary Table 13

The full lists of significant associations of metabolic GWAS (mGWAS) (XLSX 19291 kb)

Supplementary Table 14

Manhattan plots of 356 loci that were repeatedly detected (XLSX 39 kb)

Supplementary Table 15

Results of analysis of two-locus interactions (XLSX 205 kb)

Supplementary Table 16

Statistics of significant loci on the chromosomes (XLSX 16 kb)

Supplementary Table 17

The list of significant loci detected in indica and japonica subspecies (XLSX 58 kb)

Supplementary Table 18

The list of significant loci detected for subspecies differentiation metabolites in indica and japonica subspecies (XLSX 21 kb)

Supplementary Table 19

The full list of identified or annotated metabolites that were supported by GWAS (XLSX 40 kb)

Supplementary Table 20

The full list of candidate genes (XLSX 20 kb)

Supplementary Table 21

Metabolic profiling of transgenic lines overexpressing the candidate genes (XLSX 47 kb)

Supplementary Table 22

The results of overlap between GWAS loci and mQTL (XLSX 24 kb)

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Chen, W., Gao, Y., Xie, W. et al. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46, 714–721 (2014). https://doi.org/10.1038/ng.3007

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