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Enviromics in breeding: applications and perspectives on envirotypic-assisted selection

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Abstract

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We propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an “omics” scale of environmental attributes drives the prediction of unobserved genotype performances.

Abstract

Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of “envirotypes” at multiple “enviromic” markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the “GIS–GEI”) which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.

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Availability of data and materials

This article is the peer-reviewed version of the preprint posted august 06, 2019 at: https://doi.org/10.1101/726513. Code used to generate the simulated data, and the envirotypic (File S1) and phenotypic data (File S2) are available in the repository: https://figshare.com/articles/Enviromics_data/8264132.

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Acknowledgements

We would like to acknowledge professors Gustavo E. Marcatti from UFSJ and Helio G. Leite from UFV for their valuable input regarding the use of GIS and landscape level plant management data. We also want to thank the editor Martin P. Boer and the four anonymous reviewers for their considerations and suggestions to improve the manuscript. This work was partly supported by a postdoctoral grant (project FAP-DF 0193.001198/2016) to RTR (grant 4026592013-9), DFG grant (PI 377/20-1) to HPP, and infrastructure funding from the Brazilian Council for Scientific and Technological Development (CNPq/PQ 307096/2018-1).

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Authors and Affiliations

  1. School of Agronomy, University of Goiás (UFG), Goiânia, GO, 74690-900, Brazil

    Rafael T. Resende

  2. Biostatistics Unit, University of Hohenheim, 70593, Stuttgart, Germany

    Hans-Peter Piepho

  3. Department of Animal and Dairy Sciences, University of Wisconsin-Madison, 53706, Madison, USA

    Guilherme J. M. Rosa

  4. EMBRAPA Genetic Resources and Biotechnology – EPqB, Brasília, DF, 70770‐910, Brazil

    Orzenil B. Silva-Junior & Dario Grattapaglia

  5. Department of Animal Science, University of Viçosa (UFV), Viçosa, MG, 36570-900, Brazil

    Fabyano F. e Silva

  6. Department of Statistics, University of Viçosa (UFV), Viçosa, MG, 36570-900, Brazil

    Marcos Deon V. de Resende

  7. EMBRAPA Coffee Research, Brasília, DF, 70770-901, Brazil

    Marcos Deon V. de Resende

  8. Genomic Sciences and Biotechnology Program, Catholic University of Brasília, Brasília, DF, 70790‐160, Brazil

    Dario Grattapaglia

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  1. Rafael T. Resende
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Contributions

RTR, HPP, OBSJ, GJMR and FFS were involved in simulation and data analysis. MDVR, RTR, HPP, GJMR and FFS developed mathematical–statistical procedures and notations. RTR, HPP, GJMR, OBSJ, FFS and DG wrote the manuscript. All authors reviewed the manuscript.

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Correspondence to Rafael T. Resende or Dario Grattapaglia.

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Communicated by Martin Boer.

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Resende, R.T., Piepho, HP., Rosa, G.J.M. et al. Enviromics in breeding: applications and perspectives on envirotypic-assisted selection. Theor Appl Genet 134, 95–112 (2021). https://doi.org/10.1007/s00122-020-03684-z

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