Molecular Breeding

, 37:80 | Cite as

Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids

  • Danilo Hottis LyraEmail author
  • Leandro de Freitas Mendonça
  • Giovanni Galli
  • Filipe Couto Alves
  • Ítalo Stefanine Correia Granato
  • Roberto Fritsche-Neto


In maize breeding, genomic prediction may be an efficient tool for selecting single-crosses evaluated under abiotic stress conditions. In addition, a promising strategy is applying multiple-trait genomic prediction using selection indices (SIs), increasing genetics gains and reducing time per cycles. In this study, we aimed (i) to compare accuracy of single- and multi-trait genomic prediction (STGP; MTGP) in two maize datasets, (ii) to evaluate prediction of four selection indices that could contribute to the selection of tropical maize hybrids under contrasting nitrogen conditions, and (iii) to compare the use of linear (GBLUP) and nonlinear (RKHS/GK) kernels in STGP and MTGP analyses. For either single-trait GBLUP and RKHS analyses, the highest values obtained for accuracy were 0.40 and 0.41 using harmonic mean (HM), respectively. From multi-trait GBLUP and GK, using the combination of selection indices in MTGP seems to be suitable, increasing the accuracy. Adding grain yield and plant height in MTGP showed a slight improvement in accuracy compared to STGP. In general, there was a modest benefit of using single-trait RKHS and GK multi-trait, rather than GBLUP.


Abiotic stress Single-trait genomic prediction Gaussian kernel GBLUP Genomic heritability 



We thank Helix Sementes® (São Paulo, Brazil) for the dataset II, and the Allogamous Plant Breeding Laboratory for the technical and scientific support.

Compliance with ethical standards


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).

Supplementary material

11032_2017_681_Fig5_ESM.gif (660 kb)
Supplemental Fig. S1

Population structure analysis of tropical maize inbred lines. (a) First two principal components of 49 inbred lines, and BIC values of k-means clustering. (b) First two principal components of 106 inbred lines, and BIC values of k-means clustering. The dashed black line shows the number of groups inferred in each method. (GIF 660 kb)

11032_2017_681_MOESM1_ESM.tiff (2.6 mb)
High Resolution Image (TIFF 2630 kb)
11032_2017_681_Fig6_ESM.gif (5.7 mb)
Supplemental Fig. S2

Scatterplot between the combinations of four selection indices (a-f). N-agronomic efficiency (NAE, ton ton−1 N ha−1), low-N tolerance index (LNTI, %), low-N agronomy efficiency (LNAE, ton ha−1), and harmonic mean (HM). The solid red line is the regression slope and 95% confidence interval (light gray band). (GIF 5864 kb)

11032_2017_681_MOESM2_ESM.tiff (9.1 mb)
High Resolution Image (TIFF 9363 kb)


  1. Abdel-Ghani AH, Kumar B, Reyes-Matamoros J, Gonzalez-Portilla PJ, Jansen C, San Martin JP, Lee M, Lubberstedt T (2013) Genotypic variation and relationships between seedling and adult plant traits in maize (Zea mays L.) inbred lines grown under contrasting nitrogen levels. Euphytica 189:123–133CrossRefGoogle Scholar
  2. Alimi NA, Bink MC, Dieleman JA, Magan JJ, Wubs AM, Palloix A, van Eeuwijk FA (2013) Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper. Theor Appl Genet 126(10):2597–2625CrossRefPubMedGoogle Scholar
  3. Bernardo R (2014) Genomewide selection when major genes are known. Crop Sci 54:68–75CrossRefGoogle Scholar
  4. Beyene Y, Semagn K, Mugo S, Tarekegne A, Babu R, Meisel B, Sehabiague P, Makumbi D, Magorokosho C, Oikeh S, Gakunga J, Vargas M, Olsen M, Prasanna BM, Banziger M, Crossa J (2015) Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci 55(1):154–163CrossRefGoogle Scholar
  5. Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual. Department of Primary Industries, Queensland, AustraliaGoogle Scholar
  6. Calus MPL, Veerkamp RF (2011) Accuracy of multi-trait genomic selection using different methods. Genet Sel Evol 43(26):1–14Google Scholar
  7. Cantelmo NF, Von Pinho RV, Balestre M (2017) Genome-wide prediction for maize single-cross hybrids using the GBLUP model and validation in different crop seasons. Mol Breeding 37(4):51CrossRefGoogle Scholar
  8. Cecarelli S, Grando S, Impiglia A (1998) Choice of selection strategy in breeding barley for stress environments. Euphytica 103:307–318CrossRefGoogle Scholar
  9. Ceron-Rojas JJ, Crossa J, Arief VN, Basford K, Rutkoski J, Jarquin D, Alvarado G, Beyene Y, Semagn K, DeLacy I (2015) A genomic selection index applied to simulated and real data. G3-Genes Genom Genet 5(10):2155–2164Google Scholar
  10. Cerón-Rojas JJ, Crossa J, Toledo FH, Sahagún-Castellanos J (2016) A predetermined proportional gains eigen selection index method. Crop Sci 56:2436–2447CrossRefGoogle Scholar
  11. Chen K, Camberato JJ, Tuinstra MR, Kumudini SV, Tollenaar M, Vyn TJ (2016) Genetic improvement in density and nitrogen stress tolerance traits over 38 years of commercial maize hybrid release. Field Crop Res 196:438–451CrossRefGoogle Scholar
  12. Cooper M, Gho C, Leafgren R, Tang T, Messina C (2014) Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J Exp Bot 65(21):6191–6204CrossRefPubMedGoogle Scholar
  13. Craswell ET, Godwin DC (1984) The efficiency of nitrogen fertilizers applied to cereals grown in different climates. In: Tinker PB, Lauchli A (eds) Advances in plant nutrition. Praeger publishers, NY 1:1–55Google Scholar
  14. Crossa J, Campos Gde L, Perez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186(2):713–724CrossRefPubMedPubMedCentralGoogle Scholar
  15. Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, Ceron-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K (2014) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112(1):48–60CrossRefPubMedGoogle Scholar
  16. Cuevas J, Crossa J, Montesinos-López OA, Burgueño J, Pérez-Rodríguez P, de los Campos G (2017) Bayesian genomic prediction with genotype x environment interaction kernel models. G3-Genes Genom Genet 7:41–53Google Scholar
  17. Cuevas J, Crossa J, Soberanis V, Pérez-Elizalde S, Pérez-Rodríguez P, de los Campos G, Montesinos-López OA, Burgueño J (2016) Genomic prediction of genotype x environment interaction kernel regression models. The Plant Genome 9(3):1–20CrossRefGoogle Scholar
  18. Da Y, Wang CK, Wang SW, Hu G (2014) Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers. PLoS One 9(1):e87666CrossRefPubMedPubMedCentralGoogle Scholar
  19. Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3(10):e3395CrossRefPubMedPubMedCentralGoogle Scholar
  20. de los Campos G, Grüneberg A (2016) MTM (Multiple-Trait Model) package. Available at: Accessed 29 May 2017
  21. de los Campos G, Sorensen D, Gianola D (2015) Genomic heritability: what is it? PLoS Genet 11(5):e1005048CrossRefPubMedCentralGoogle Scholar
  22. Dekkers JCM (2007) Prediction of response to marker-assisted and genomic selection using selection index theory. J Anim Breed Genet 124(6):331–341CrossRefPubMedGoogle Scholar
  23. dos Santos JPR, Vasconcellos RCD, Pires LPM, Balestre M, Von Pinho RG (2016) Inclusion of dominance effects in the multivariate GBLUP model. PLoS One 11(4):1–21Google Scholar
  24. Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255CrossRefGoogle Scholar
  25. Fritsche-Neto R, DoVale JC, Lanes ECM, Resende MDV, Miranda GV (2012) Genome-wide selection for tropical maize root traits under conditions of nitrogen and phosphorus stress. Acta Scientiarum 34(4):389–395Google Scholar
  26. Gong FP, Wu XL, Zhang HY, Chen YH, Wang W (2015) Making better maize plants for sustainable grain production in a changing climate. Front Plant Sci 6:1–6Google Scholar
  27. Granato ISC, Bermudez FP, Reis GG, Dovale JC, Miranda GV, Fritsche-Neto R (2014) Index selection of tropical maize genotypes for nitrogen use efficiency. Bragantia 73(2):153–159CrossRefGoogle Scholar
  28. Guo G, Zhao FP, Wang YC, Zhang Y, Du LX, Su GS (2014) Comparison of single-trait and multiple-trait genomic prediction models. BMC Genet 15(30):1–7Google Scholar
  29. Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177(4):2389–2397PubMedPubMedCentralGoogle Scholar
  30. Habier D, Fernando RL, Dekkers JCM (2009) Genomic selection using low-density marker panels. Genetics 182(1):343–353CrossRefPubMedPubMedCentralGoogle Scholar
  31. Harfouche A, Meilan R, Altman A (2014) Molecular and physiological responses to abiotic stress in forest trees and their relevance to tree improvement. Tree Physiol 34(11):1181–1198CrossRefPubMedGoogle Scholar
  32. Hayashi T, Iwata H (2013) A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinformatics 14:34CrossRefPubMedPubMedCentralGoogle Scholar
  33. He D, Kuhn D, Parida L (2016) Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction. Bioinformatics 32(12):i37–i43CrossRefPubMedPubMedCentralGoogle Scholar
  34. Heslot N, Akdemir D, Sorrells ME, Jannink JL (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127(2):463–480CrossRefPubMedGoogle Scholar
  35. Isidro J, Jannink JL, Akdemir D, Poland J, Heslot N, Sorrells ME (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128(1):145–158CrossRefPubMedGoogle Scholar
  36. Jafari A, Paknejad F, Al-Ahmadi MJ (2009) Evaluation of selection indices for drought tolerance of corn (Zea mays L.) hybrids. Int J Plant Prod 3(4):33–38Google Scholar
  37. Jia Y, Jannink JL (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192(4):1513–1522CrossRefPubMedPubMedCentralGoogle Scholar
  38. Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201(2):759–768CrossRefPubMedPubMedCentralGoogle Scholar
  39. Jombart T, Collins C, Kamvar ZN, Lustrik R, Solymos P, Ahmed I, Jombart MT (2015) Adegenet: exploratory analysis of genetic and genomic data. R package version 201Google Scholar
  40. Khan FU, Mohammad F (2016) Application of stress selection indices for assessment of nitrogen tolerance in wheat (Triticum aestivum L.). J Anim Plant Sci 26(1):201–210Google Scholar
  41. Kumar B, Guleria SK, Khanorkar SM, Dubey RB, Pater J, Kumar V, Parihar CM, Jat SL, Singh V, Yatish KR, Das A, Sekhar JC, Bhati P, Kaur H, Kumar M, Singh AK, Varghese E, Yadav OP (2016) Selection indices to identify maize (Zea mays L.) hybrids adapted under drought-stress and drought-free conditions in a tropical climate. Crop Pasture Sci 67(10):1087–1095Google Scholar
  42. Lee SH, van der Werf JH (2016) MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information. Bioinformatics 32(9):1420–1422CrossRefPubMedPubMedCentralGoogle Scholar
  43. Lian L, Jacobson A, Zhong S, Bernardo R (2014) Genomewide prediction accuracy within 969 maize biparental populations. Crop Sci 54(4):1514–1522CrossRefGoogle Scholar
  44. Liu ZY, Zhu CS, Jiang Y, Tian YL, Yu J, An HZ, Tang WJ, Sun J, Tang JP, Chen GM, Zhai HQ, Wang CM, Wan JM (2016) Association mapping and genetic dissection of nitrogen use efficiency-related traits in rice (Oryza sativa L.). Funct Integr Genomic 16(3):323–333CrossRefGoogle Scholar
  45. Massman JM, Gordillo A, Lorenzana RE, Bernardo R (2013) Genomewide predictions from maize single-cross data. Theor Appl Genet 126(1):13–22CrossRefPubMedGoogle Scholar
  46. Mendes MP, de Souza CL (2016) Genomewide prediction of tropical maize single-crosses. Euphytica 209(3):651–663CrossRefGoogle Scholar
  47. Miti F, Tongoona P, Derera J (2010) S1 selection of local maize landraces for low soil nitrogen tolerance in Zambia. African J Plant Sci 4:67–81Google Scholar
  48. Montesinos-Lopez OA, Montesinos-Lopez A, Crossa J, Toledo FH, Perez-Hernandez O, Eskridge KM, Rutkoski J (2016) A genomic bayesian multi-trait and multi-environment model. G3-Genes Genom Genet 6(9):2725–2744Google Scholar
  49. Morota G, Boddhireddy P, Vukasinovic N, Gianola D, DeNise S (2014) Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits. Front Genet 5:56PubMedPubMedCentralGoogle Scholar
  50. Morota G, Gianola D (2014) Kernel-based whole-genome prediction of complex traits: a review. Front Genet 5:363PubMedPubMedCentralGoogle Scholar
  51. Mueller SM, Vyn TJ (2016) Maize plant resilience to N stress and post-silking N capacity changes over time: a review. Front Plant Sci 7(53):1–38Google Scholar
  52. Pérez-Elizalde S, Cuevas J, Pérez-Rodríguez P, Crossa J (2015) Selection of the bandwidth parameter in a Bayesian kernel regression model for genomic-enabled prediction. J Agric Biol Environ Stat 20:512–532CrossRefGoogle Scholar
  53. Perez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–U463CrossRefPubMedPubMedCentralGoogle Scholar
  54. Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink JL (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. The Plant Genome 5(3):103–113CrossRefGoogle Scholar
  55. Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44(2):217–220CrossRefPubMedGoogle Scholar
  56. Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194(2):493–503CrossRefPubMedPubMedCentralGoogle Scholar
  57. Rincent R, Laloe D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodriguez VM, Moreno-Gonzalez J, Melchinger A, Bauer E, Schoen CC, Meyer N, Giauffret C, Bauland C, Jamin P, Laborde J, Monod H, Flament P, Charcosset A, Moreau L (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics 192(2):715–728CrossRefPubMedPubMedCentralGoogle Scholar
  58. Schulthess AW, Wang Y, Miedaner T, Wilde P, Reif JC, Zhao YS (2016) Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor Appl Genet 129(2):273–287CrossRefPubMedGoogle Scholar
  59. Spinelli VM, Dias LAS, Rocha RB, Resende MDV (2015) Estimates of genetic parameters with selection within and between half-sib families of Jatropha curcas L. Ind Crop Prod 69:355–361CrossRefGoogle Scholar
  60. Trachsel S, Leyva M, Lopez M, Suarez EA, Mendoza A, Montiel NG, Macias MS, Burgueno J, San Vicente F (2016) Identification of tropical maize germplasm with tolerance to drought, nitrogen deficiency, and combined heat and drought stresses. Crop Sci 56(6):3031–3045CrossRefGoogle Scholar
  61. Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, Meitinger T, Strom TM, Fries R, Pausch H, Bertani C, Davassi A, Mayer KFX, Schon CC (2014) A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics 15(823):1–15Google Scholar
  62. VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423CrossRefPubMedGoogle Scholar
  63. Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z (2016) Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity 118:302–310CrossRefPubMedGoogle Scholar
  64. Wu YS, Liu WG, Li XH, Li MS, Zhang DG, Hao ZF, Weng JF, Xu YB, Bai L, Zhang SH, Xie CX (2011) Low-nitrogen stress tolerance and nitrogen agronomic efficiency among maize inbreds: comparison of multiple indices and evaluation of genetic variation. Euphytica 180(2):281–290CrossRefGoogle Scholar
  65. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28(24):3326–3328CrossRefPubMedPubMedCentralGoogle Scholar
  66. Ziyomo C, Bernardo R (2013) Drought tolerance in maize: indirect selection through secondary traits versus genomewide selection. Crop Sci 53(4):1269–1275CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Danilo Hottis Lyra
    • 1
    Email author
  • Leandro de Freitas Mendonça
    • 1
  • Giovanni Galli
    • 1
  • Filipe Couto Alves
    • 1
  • Ítalo Stefanine Correia Granato
    • 1
  • Roberto Fritsche-Neto
    • 1
  1. 1.University of São Paulo, Luiz de Queiroz College of AgricultureDepartment of GeneticsSão PauloBrazil

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