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Distinct genetic architectures for phenotype means and plasticities in Zea mays

Nature Plants volume 3, pages 715–723 (2017)Cite this article

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Abstract

Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in a maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single-nucleotide polymorphisms. We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.

Phenotypic plasticity describes the phenotypic variation of a trait that occurs when a genotype is exposed to different environments1. Variation in plastic responses among genotypes is described as genotype–environment interaction (GxE)2, which is an important factor in plant breeding3,4. Understanding these interactions is important because agricultural systems will need to feed ~9 billion people by 2050, requiring a 60% increase in productivity compared to current levels5, but also different and increasingly variable weather patterns will need to be considered6. If plasticity is under genetic control, then genes exist that control the phenotypic mean and plastic response separately or together7; thus, it may be possible to breed optimized, locally adapted cultivars that take advantage of certain environmental conditions and outperform more widely adapted cultivars8. Conversely, a lack of a plastic response may be better for some phenotypes such as disease resistance.

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Fig. 1: Quartile coefficients of dispersion for the linear and non-linear plasticities of 23 phenotypes.
Fig. 2: The mean percentage variance explained by genome-wide SNPs hierarchically assigned to annotation categories.
Fig. 3: Plasticity-associated loci are pervasive across phenotypes.
Fig. 4: Candidate genes for mean phenotypes and plasticity are structurally and functionally distinct.

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Acknowledgements

We thank the Panzea group for making their genotypic and phenotypic data on the US-NAM population publically available, W. Wu for conducting the RNA-Seq experiments, C.-T. ‘Eddy’ Yeh for SNP calling and J. Yang for preparing the imputed SNPs. We also thank anonymous reviewers for helpful comments. This material is based upon work supported in part by the National Science Foundation (grant number 1027527) and the National Institute of General Medical Sciences of the National Institutes of Health (grant number 1R01GM109458-01) to P.S.S. and D.N. and to D.N., respectively. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Author notes

  1. Srikant Srinivasan

    Present address: School of Computing and Electrical Engineering, IIT Mandi, Mandi, Himachal Pradesh, 175005, India

Authors and Affiliations

  1. Department of Agronomy, Iowa State University, Ames, IA, 50010-3650, USA

    Aaron Kusmec & Patrick S. Schnable

  2. Plant Sciences Institute, Iowa State University, Ames, IA, 50010-3650, USA

    Srikant Srinivasan, Dan Nettleton & Patrick S. Schnable

  3. Department of Statistics, Iowa State University, Ames, IA, 50010-3650, USA

    Dan Nettleton

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Contributions

A.K., S.S. and P.S.S. conceived the study. S.S. contributed to data collection and quality control. A.K. analysed the data and interpreted the results. D.N. provided guidance with statistical analyses. A.K. and P.S.S. wrote the manuscript.

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Correspondence to Patrick S. Schnable.

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Supplementary Information

Supplementary Figure 1–6, Supplementary Table 1, Supplementary References.

Life Sciences Reporting Summary

Supplementary Table 2

Significant SNPs for the mean phenotype, linear plasticity, and non-linear plasticity of 23 phenotypes with their estimated effect sizes, standard errors, p-values and q-values.

Supplementary Table 3

Candidate genes for the mean phenotype, linear plasticity, and non-linear plasticity of 23 phenotypes with their genomic locations and associated SNPs.

Supplementary Table 4

Enriched GO terms from the biological process sub-ontology for candidate genes grouped by mean phenotype, linear plasticity, and non-linear plasticity.

Supplementary Table 5

Enriched GO terms from the cellular component sub-ontology for candidate genes grouped by mean phenotype, linear plasticity, and non-linear plasticity.

Supplementary Table 6

Enriched GO terms from the molecular function sub-ontology for candidate genes grouped by mean phenotype, linear plasticity, and non-linear plasticity.

Supplementary Table 7

Enriched GO terms from the biological process sub-ontology for the protein products of candidate genes and their primary predicted interaction partners for the mean phenotype, linear plasticity, and non-linear plasticity of 23 phenotypes.

Supplementary Table 8

Enriched GO terms from the cellular component sub-ontology for the protein products of candidate genes and their primary predicted interaction partners for the mean phenotype, linear plasticity, and non-linear plasticity of 23 phenotypes.

Supplementary Table 9

Enriched GO terms from the molecular function sub-ontology for the protein products of candidate genes and their primary predicted interaction partners for the mean phenotype, linear plasticity, and non-linear plasticity of 23 phenotypes.

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Kusmec, A., Srinivasan, S., Nettleton, D. et al. Distinct genetic architectures for phenotype means and plasticities in Zea mays. Nature Plants 3, 715–723 (2017). https://doi.org/10.1038/s41477-017-0007-7

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