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Theoretical and Applied Genetics

, Volume 132, Issue 1, pp 273–288 | Cite as

Modeling copy number variation in the genomic prediction of maize hybrids

  • Danilo Hottis LyraEmail author
  • Giovanni Galli
  • Filipe Couto Alves
  • Ítalo Stefanine Correia Granato
  • Miriam Suzane Vidotti
  • Massaine Bandeira e Sousa
  • Júlia Silva Morosini
  • José Crossa
  • Roberto Fritsche-Neto
Original Article

Abstract

Key message

Our study indicates that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids. Moreover, predicting hybrid phenotypes by combining additive–dominance effects with copy variants has the potential to be a viable predictive model.

Abstract

Non-additive effects resulting from the actions of multiple loci may influence trait variation in single-cross hybrids. In addition, complementation of allelic variation could be a valuable contributor to hybrid genetic variation, especially when crossing inbred lines with higher contents of copy gains. With this in mind, we aimed (1) to study the association between copy number variation (CNV) and hybrid phenotype, and (2) to compare the predictive ability (PA) of additive and additive–dominance genomic best linear unbiased prediction model when combined with the effects of CNV in two datasets of maize hybrids (USP and HELIX). In the USP dataset, we observed a significant negative phenotypic correlation of low magnitude between copy number loss and plant height, revealing a tendency that more copy losses lead to lower plants. In the same set, when CNV was combined with the additive plus dominance effects, the PA significantly increased only for plant height under low nitrogen. In this case, CNV effects explicitly capture relatedness between individuals and add extra information to the model. In the HELIX dataset, we observed a pronounced difference in PA between additive (0.50) and additive–dominance (0.71) models for predicting grain yield, suggesting a significant contribution of dominance. We conclude that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids, although the inclusion of CNVs into datasets does not return significant gains concerning PA.

Notes

Acknowledgements

This project was supported by São Paulo Research Foundation-FAPESP (Process: 2013/24135-2; 2014/26326-2; 2015/14376-8), and Coordination for the Improvement of Higher Level Personnel (CAPES). We thank Helix Sementes® for the partnership and dataset.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Data archiving

The phenotypic and genotypic data for the maize hybrids included in this study can be found at Mendeley (https://data.mendeley.com/datasets/shzpgd4ytw/4). We provided the adjusted mean of the traits from the 906 (File USP set) and 452 hybrids (File HELIX set), the additive and dominance GBLUP matrix (File USP_GB; HELIX_GB), CNV matrix (File USP_CNV; HELIX_CNV), and the total number of CNV (copy gain, copy loss, and gain/loss ratio) (File NumberCNV).

Supplementary material

122_2018_3215_MOESM1_ESM.tiff (2.7 mb)
Population structure analysis and diallel scheme. First two principal components of a 49 (USP) and b128 (HELIX) inbred lines. The plot inside each graph represents the BIC values of k-means clustering, where thelowest value indicates the number of groups inferred. Diagram of the crosses in the diallel scheme from c 49 and d128 inbred lines, where the hybrids tested in the field are represented by black squares Supplementary material 1 (TIFF 2808 kb)
122_2018_3215_MOESM2_ESM.tiff (1.4 mb)
Example of copy number estimation based on the log2 ratios from Axiom CNV Tool software. USPdataset (49 inbred lines) was used for the analysis. Each probe is represented as a small blue dot along the length of theten chromosomes. Two horizontal lines (thresholds) was determined for copy gain (green) and copy loss (red) Supplementary material 2 (TIFF 1424 kb)
122_2018_3215_MOESM3_ESM.tiff (717 kb)
Heatmap of genomic relationship matrix from 906 maize single-crosses (USP set). a additive GBLUPmatrix, b dominance GBLUP matrix, and c CNV matrix. Numbers bellow the legends represent the mean,minimum, and maximum of the genomic matrix scaled and centered, respectively Supplementary material 3 (TIFF 717 kb)
122_2018_3215_MOESM4_ESM.tiff (738 kb)
Heatmap of genomic relationship matrix from 452 maize single-crosses (HELIX set). a additive GBLUPmatrix, b dominance GBLUP matrix, and c CNV matrix. Numbers bellow the legends represent the mean,minimum, and maximum of the genomic matrix scaled and centered, respectively Supplementary material 4 (TIFF 737 kb)
122_2018_3215_MOESM5_ESM.tiff (182 kb)
Boxplot of phenotypic traits. BLUP mean values of a grain yield (GY, ton ha−1) and harmonic mean (HM,ton ha−1), b plant and ear height (PH/EH, cm), and c stay green (SG, score) for USP and HELIX datasets Supplementary material 5 (TIFF 182 kb)
122_2018_3215_MOESM6_ESM.tiff (354 kb)
Deviance information criterion (DIC) from 906 maize single-crosses (USP dataset). Traits evaluated inlow and optimum N are a grain yield (GY, ton ha−1), b plant height (PH, cm), c stay green (SG, score), and dharmonic mean (HM, ton ha−1). Black numbers above bars represent the mean estimated from fifty replications in T1validation. Models reported are GBa, GBad, CNV, GBa+CNV, and GBad+CNV Supplementary material 6 (TIFF 353 kb)
122_2018_3215_MOESM7_ESM.tiff (293 kb)
Deviance information criterion (DIC) from 452 maize single-crosses (HELIX dataset). Traits are a grainyield (GY, ton ha-1), b plant height (PH, cm), and c ear height (PH, cm). Black numbers above bars represent themean estimated from fifty replications in T1 validation. Models reported are GBa, GBad, CNV, GBa+CNV, andGBad+CNV Supplementary material 7 (TIFF 293 kb)
122_2018_3215_MOESM8_ESM.docx (40 kb)
Supplementary material 8 (DOCX 40 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Danilo Hottis Lyra
    • 1
    • 2
    Email author
  • Giovanni Galli
    • 1
  • Filipe Couto Alves
    • 1
  • Ítalo Stefanine Correia Granato
    • 1
  • Miriam Suzane Vidotti
    • 1
  • Massaine Bandeira e Sousa
    • 1
  • Júlia Silva Morosini
    • 1
  • José Crossa
    • 3
  • Roberto Fritsche-Neto
    • 1
  1. 1.Department of GeneticsLuiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP)PiracicabaBrazil
  2. 2.Department of Computational and Analytical SciencesRothamsted ResearchHarpendenUK
  3. 3.Biometrics and Statistics UnitInternational Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico

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