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Phenotypic and genome-wide association with the local environment of Arabidopsis

Nature Ecology & Evolutionvolume 3pages274285 (2019) | Download Citation

Abstract

The environment imposes critical selective forces on all living organisms, and the sessile nature of plants makes them particularly useful for investigating the relationship between genetic variation and environmental adaptation. In the model plant Arabidopsis thaliana, extensive information on phenotypic and genotypic variation is available, but comparable information on environmental variation within the native range of the species is lacking. Here, we compile 204 geoclimatic variables to describe the local environments of Arabidopsis accessions with known collection sites encompassing a wide geo-environmental range, and fully sequenced genomes from the 1001 Genomes Project. We identify candidate adaptive genetic variation associated with these environmental variables, and validate this approach through comparison with previous experimental studies, and by targeted confirmation of a role of the heterotrimeric G-protein γ subunit, AGG3, in cold tolerance, as newly predicted from our environmental genome wide association study (GWAS). To facilitate identification of adaptive variation, we created Arabidopsis CLIMtools: interactive web-based databases of the environment × genome associations and correlations between the local environments and 131 phenotypes compiled from previous experimental GWASs. Our study presents an extensive analysis of the local environments, landscape genomics and phenotypic variation of Arabidopsis, and illustrates how ‘in silico GWAS’ approaches can inform and complement experimental phenomics studies.

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Data availability

Summary statistics for each of the 204 environmental and 131 phenotypic variables (Supplementary Table 1) used in this study were produced using the package skimr115 in R90. To ensure the complete reproducibility of this study, the raw data used for the curated environmental and phenotypic variables described here are available in a public repository (https://github.com/CLIMtools/), along with the code for the respective interactive web tools.

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Acknowledgements

We thank J. R. Lasky, S. Mackenzie and P. C. Bevilacqua for helpful comments on an early version of this manuscript. We also thank D. C. Tack for bioinformatic assistance. This work was supported by an Agriculture and Food Research Initiative competitive grant (2013–02379) from the USDA National Institute of Food and Agriculture (to S.M.A.) and by NSF grant IOS-1339282 (to S.M.A. and P. C. Bevilacqua).

Author information

Affiliations

  1. Biology Department, Pennsylvania State University, University Park, PA, USA

    • Ángel Ferrero-Serrano
    •  & Sarah M. Assmann

Authors

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Contributions

S.M.A. and A.F.-S. conceived and designed the study. A.F.-S. performed the spatial data analysis, bioinformatics, data collection and statistical analyses. A.F.-S. was responsible for the web tools development. A.F.-S. conducted the wet-bench experiments and generated the data. S.M.A. provided critical comments on data analysis, collection and interpretation. A.F.-S. and S.M.A. wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Ángel Ferrero-Serrano or Sarah M. Assmann.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–3 and Supplementary Discussion

  2. Reporting Summary

  3. Supplementary Figure 4

    Interactive hierarchical cluster plot 1

  4. Supplementary Figure 5

    Interactive hierarchical cluster plot 2

  5. Supplementary Table 1

    Description of the environmental and phenotypic variables used in this study

  6. Supplementary Table 2

    Curation details for the 1,131 geo-referenced accessions

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https://doi.org/10.1038/s41559-018-0754-5