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Characterizing introgression-by-environment interactions using maize near isogenic lines

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Abstract

Key message

Significant introgression-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome.

Abstract

Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to introgression-by-environment interactions. To study potential patterns of introgression-by-environment interactions, fifteen near-isogenic lines (NILs) with > 90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess introgression-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the introgression-by-environment interaction varies across traits. Some introgressed segments were more prone to introgression-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of introgression-by-environment interaction in NILs is useful in consideration of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.

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References

  • Allard RW, Bradshaw AD (1964) Implications of genotype-environmental interactions in applied plant breeding. Crop Sci 4:503–508

    Article  Google Scholar 

  • Balakrishnan D, Subrahmanyam D, Badri J et al (2016) Genotype × environment interactions of yield traits in backcross introgression lines derived from Oryza sativa cv. Swarna/Oryza nivara. Front Plant Sci 7:1530

    Article  PubMed  PubMed Central  Google Scholar 

  • Bänziger M, Betrán FJ, Lafitte HR (1997) Efficiency of high-nitrogen selection environments for improving maize for low-nitrogen target environments. Crop Sci 37:1103–1109

    Article  Google Scholar 

  • Bebber DP, Ramotowski MAT, Gurr SJ (2013) Crop pests and pathogens move polewards in a warming world. Nat Clim Chang 3:985–988

    Article  Google Scholar 

  • Browning BL, Browning SR (2016) Genotype imputation with millions of reference samples. Am J Hum Genet 98:116–126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chapman SC, Crossa J, Edmeades GO (1997) Genotype by environment effects and selection for drought tolerance in tropical maize. I. Two mode pattern analysis of yield. Euphytica 95:01–09

    Article  Google Scholar 

  • Crossa J, Fox PN, Pfeiffer WH, Rajaram S, Gauch HG Jr (1991) AMMI adjustment for statistical analysis of an international wheat yield trial. Theor Appl Genet. 81(1):27–37

    Article  CAS  PubMed  Google Scholar 

  • Crossa J (2012) From genotype × environment interaction to gene × environment interaction. Curr Genom 13:225–244

    Article  CAS  Google Scholar 

  • de Leon N, Jannink J-L, Edwards JW, Kaeppler SM (2016) Introduction to a special issue on genotype by environment interaction. Crop Sci 56:2081–2089

    Article  Google Scholar 

  • De Mendiburu F, Simon R (2015) Agricolae - Ten years of an open source statistical tool for experiments in breeding, agriculture and biology. PeerJ PrePrints

  • Deza MM, Deza E (2009) Encyclopedia of distances. In: Deza E, Deza MM (eds) Encyclopedia of distances. Springer, Berlin, pp 1–583

    Chapter  Google Scholar 

  • Diniz-Filho JAF, Soares TN, Lima JS et al (2013) Mantel test in population genetics. Genet Mol Biol 36:475–485

    Article  PubMed  PubMed Central  Google Scholar 

  • Duvick DN (2005a) Genetic progress in yield of United States maize (Zea mays L.). Maydica 50:193

    Google Scholar 

  • Duvick DN (2005b) The contribution of breeding to yield advances in maize (Zea mays L.). In: Advances in agronomy. Academic Press, pp 83–145

  • Edwards JW (2016) Genotype× environment interaction for plant density response in maize (Zea mays L.). Crop Sci 56:1493–1505

    Article  CAS  Google Scholar 

  • Eichten SR, Foerster JM, de Leon N et al (2011) B73-Mo17 near-isogenic lines demonstrate dispersed structural variation in maize. Plant Physiol 156:1679–1690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Elias AA, Robbins KR, Doerge RW, Tuinstra MR (2016) Half a century of studying genotype× environment interactions in plant breeding experiments. Crop Sci 56:2090–2105

    Article  Google Scholar 

  • Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • El-Soda M, Malosetti M, Zwaan BJ et al (2014) Genotype× environment interaction QTL mapping in plants: lessons from Arabidopsis. Trends Plant Sci 19:390–398

    Article  CAS  PubMed  Google Scholar 

  • Esuma W, Kawuki RS, Herselman L, Labuschagne MT (2016) Stability and genotype by environment interaction of provitamin A carotenoid and dry matter content in cassava in Uganda. Breed Sci 66(3):434–443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fan X-M, Kang MS, Chen H et al (2007) Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron J 99:220–228

    Article  Google Scholar 

  • Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742–754

    Article  Google Scholar 

  • Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44:705–715

    Article  Google Scholar 

  • Glaubitz JC, Casstevens TM, Lu F et al (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE. 9(2):e90346

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gonzalo M, Holland JB, Vyn TJ, McIntyre LM (2010) Direct mapping of density response in a population of B73 x Mo17 recombinant inbred lines of maize (Zea Mays L.). Heredity (Edinb) 104(6):583–599

    Article  CAS  Google Scholar 

  • Guo M, Rupe MA, Wei J et al (2014) Maize ARGOS1 (ZAR1) transgenic alleles increase hybrid maize yield. J Exp Bot 65:249–260

    Article  CAS  PubMed  Google Scholar 

  • Hirsch CN, Foerster JM, Johnson JM et al (2014) Insights into the maize pan-genome and pan-transcriptome. Plant Cell 26:121–135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Holland JB (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161

    Article  CAS  PubMed  Google Scholar 

  • Huang X, Feng Q, Qian Q et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lado B, Barrios PG, Quincke M et al (2016) Modeling genotype× environment interaction for genomic selection with unbalanced data from a wheat breeding program. Crop Sci 56:2165–2179

    Article  Google Scholar 

  • Li Z, Coffey L, Garfin J et al (2018) Genotype-by-environment interactions affecting heterosis in maize. PLoS ONE 13:e0191321

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lukens LN, Doebley J (1999) Epistatic and environmental interactions for quantitative trait loci involved in maize evolution. Genet Res 74:291–302

    Article  CAS  Google Scholar 

  • Malosetti M, Ribaut J-M, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front Physiol 4:44

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Malosetti M, Bustos-Korts D, Boer MP, van Eeuwijk FA (2016) Predicting responses in multiple environments: issues in relation to genotype× environment interactions. Crop Sci 56:2210–2222

    Article  Google Scholar 

  • Mansfield BD, Mumm RH (2014) Survey of plant density tolerance in U.S Maize Germplasm. Crop Sci 54:157–173

    Article  Google Scholar 

  • Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220

    CAS  PubMed  Google Scholar 

  • Messina C, Hammer G, Dong Z et al (2009) Modelling crop improvement in a G× E× M framework via gene-trait-phenotype relationships. In: Crop physiology: interfacing with genetic improvement and agronomy. Elsevier, The Netherlands, pp 235–265

  • Miller ND, Haase NJ, Lee J et al (2017) A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images. Plant J 89:169–178

    Article  CAS  PubMed  Google Scholar 

  • Mohammadi R, Amri A (2016) Genotype x environment interaction implication: a case study of durum wheat breeding in Iran. In: Al-Khayri JM, Jain SM, Johnson DV (eds) Advances in plant breeding strategies: agronomic, abiotic and biotic stress traits. Springer International Publishing, Cham, pp 515–558

    Chapter  Google Scholar 

  • Mohammadi R, Farshadfar E, Amri A (2016) Path analysis of genotype× environment interactions in rainfed durum wheat. Plant Prod Sci 19:43–50

    Article  CAS  Google Scholar 

  • Ndhlela T, Herselman L, Magorokosho C et al (2014) Genotype× environment interaction of maize grain yield using AMMI biplots. Crop Sci 54:1992–1999

    Article  Google Scholar 

  • Nuvunga JJ, Oliveira LA, Pamplona AKA et al (2015) Factor analysis using mixed models of multi-environment trials with different levels of unbalancing. Genet Mol Res 14:14262–14278

    Article  CAS  PubMed  Google Scholar 

  • Pilloud MA, Hefner JT (2016) Biological distance analysis: forensic and bioarchaeological perspectives. Academic Press, Cambridge

    Google Scholar 

  • Pordes R, Petravick D, Kramer B et al (2007) The open science grid. In: Journal of Physics: Conference Series. IOP Publishing, p 012057

  • Ribaut J-M, Hoisington D (1998) Marker-assisted selection: new tools and strategies. Trends Plant Sci 3:236–239

    Article  Google Scholar 

  • Ribaut J-M, Jiang C, Gonzalez-de-Leon D et al (1997) Identification of quantitative trait loci under drought conditions in tropical maize.2. Yield components and marker-assisted selection strategies. Theor Appl Genet 94:887–896

    Article  Google Scholar 

  • Thain D, Tannenbaum T, Livny M (2005) Distributed computing in practice: the Condor experience. Concurr Comput 17:323–356

    Article  Google Scholar 

  • Thiry AA, Chavez Dulanto PN, Reynolds MP, Davies WJ (2016) How can we improve crop genotypes to increase stress resilience and productivity in a future climate? A new crop screening method based on productivity and resistance to abiotic stress. J Exp Bot 67:5593–5603

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Trenberth KE, Dai A, van der Schrier G et al (2013) Global warming and changes in drought. Nat Clim Chang 4:17

    Article  Google Scholar 

  • Tuberosa R (2012) Phenotyping for drought tolerance of crops in the genomics era. Front Physiol 3:347

    Article  PubMed  PubMed Central  Google Scholar 

  • van Eeuwijk FA (1995) Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models. Euphytica 84:1–7

    Article  Google Scholar 

  • Xu Y (2016) Envirotyping for deciphering environmental impacts on crop plants. Theor Appl Genet 129:653–673

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yan W, Cornelius PL, Crossa J, Hunt LA (2001) Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci 41:656–663

    Article  Google Scholar 

  • Zhao F, Xu S (2012) Genotype by environment interaction of quantitative traits: a case study in barley. G3 Genes Genomes Genetics 2:779–788

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Jacob Garfin, Peter Hermanson, James Satterlee, Kimberly McFee, and Brad Keiter for technical assistance. This work was supported by the Minnesota Corn Research and Promotion Council (Project Number 4108-16SP), the Minnesota Agricultural Experiment Station (Project 13–113), the Iowa Corn Growers, and Iowa State University’s Plant Sciences Institute.

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Authors and Affiliations

  1. Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA

    Zhi Li, Sara B. Tirado, Dnyaneshwar C. Kadam, Aaron J. Lorenz & Candice N. Hirsch

  2. Department of Plant and Microbial Biology, University of Minnesota, 1479 Gortner Avenue, Saint Paul, MN, 55108, USA

    Sara B. Tirado & Nathan M. Springer

  3. Department of Agronomy, Iowa State University, 1111 WOI Rd, Ames, IA, 50011, USA

    Lisa Coffey & Patrick S. Schnable

  4. Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA

    Nathan D. Miller & Edgar P. Spalding

  5. Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA

    Natalia de Leon & Shawn M. Kaeppler

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  1. Zhi Li
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Contributions

CNH, NMS, PSS, SMK, and NdL conceived and designed the experiment. ZL, SBT, LC, NDM, and EPS conducted phenotypic data collection and analysis. ZL, DCK, and AJL performed genotypic data analysis. ZL, NMS, and CNH wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Candice N. Hirsch.

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Communicated by Martin Boer.

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Li, Z., Tirado, S.B., Kadam, D.C. et al. Characterizing introgression-by-environment interactions using maize near isogenic lines. Theor Appl Genet 133, 2761–2773 (2020). https://doi.org/10.1007/s00122-020-03630-z

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