Theoretical and Applied Genetics

, Volume 129, Issue 12, pp 2413–2427 | Cite as

Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize

  • Zhigang Guo
  • Michael M. Magwire
  • Christopher J. Basten
  • Zhanyou Xu
  • Daolong Wang
Original Article

Abstract

Key Message

Predictive ability derived from gene expression and metabolic information was evaluated using genomic prediction methods based on datasets from a public maize panel.

Abstract

With the rapid development of high throughput biological technologies, information from gene expression and metabolites has received growing attention in plant genetics and breeding. In this study, we evaluated the utility of gene expression and metabolic information for genomic prediction using data obtained from a maize diversity panel. Our results show that, when used as predictor variables, gene expression levels and metabolite abundances provided reasonable predictive abilities relative to those based on genetic markers, although these values were not as large as those with genetic markers. Integrating gene expression levels and metabolite abundances with genetic markers significantly improved predictive abilities in comparison to the benchmark genomic best linear unbiased prediction model using genome-wide markers only. Predictive abilities based on gene expression and metabolites were trait-specific and were affected by the time of measurement and tissue samples as well as the number of genes and metabolites included in the model. In general, our results suggest that, rather than being conventionally used as intermediate phenotypes, gene expression and metabolic information can be used as predictors for genomic prediction and help improve genetic gains for complex traits in breeding programs.

Supplementary material

122_2016_2780_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (PDF 1118 kb)

References

  1. Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schon CC (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123:339–350CrossRefPubMedGoogle Scholar
  2. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  3. Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296:752–755CrossRefPubMedGoogle Scholar
  4. Chan EKF, Rowe HC, Corwin JA, Joseph B, Kliebenstein DJ (2011) Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. Plos Biol 9(8):e1001125CrossRefPubMedPubMedCentralGoogle Scholar
  5. Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H, Zhang W, Zhang L, Yu S, Wang G, Lian X, Luo J (2014) Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46:714–721CrossRefPubMedGoogle Scholar
  6. 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 186:713–724CrossRefPubMedPubMedCentralGoogle Scholar
  7. Cubillos FA, Coustham V, Loudet O (2012) Lessons from eQTL mapping studies: non-coding regions and their role behind natural phenotypic variation in plants. Curr Opin Plant Biol 15:192–198CrossRefPubMedGoogle Scholar
  8. de Los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MP (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327–345CrossRefGoogle Scholar
  9. de Oliveira EJ, Vilela de Resende MD, Santos VdS, Ferreira CF, Fachardo Oliveira GA, da Silva MS, de Oliveira LA, Aguilar-Vildoso CI (2012) Genome-wide selection in cassava. Euphytica 187:263–276CrossRefGoogle Scholar
  10. Falconer DS, Mackay TFC (1996). Introduction to Quantitative Genetics, 4th edn. Pearson EducationLimited, Essex, EnglandGoogle Scholar
  11. Feher K, Lisec J, Romisch-Margl L, Selbig J, Gierl A, Piepho HP, Nikoloski Z, Willmitzer L (2014) Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach. PLoS One 9:e85435CrossRefPubMedPubMedCentralGoogle Scholar
  12. Fernie AR, Schauer N (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet 25:39–48CrossRefPubMedGoogle Scholar
  13. Frisch M, Thiemann A, Fu J, Schrag TA, Scholten S, Melchinger AE (2010) Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize. Theor Appl Genet 120:441–450CrossRefPubMedGoogle Scholar
  14. Fu J, Falke KC, Thiemann A, Schrag TA, Melchinger AE, Scholten S, Frisch M (2012) Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data. Theor Appl Genet 124:825–833CrossRefPubMedGoogle Scholar
  15. Fu J, Cheng Y, Linghu J, Yang X, Kang L, Zhang Z, Zhang J, He C, Du X, Peng Z, Wang B, Zhai L, Dai C, Xu J, Wang W, Li X, Zheng J, Chen L, Luo L, Liu J, Qian X, Yan J, Wang J, Wang G (2013) RNA sequencing reveals the complex regulatory network in the maize kernel. Nat Commun 4:2832 doi:10.1038/ncomms3832 PubMedGoogle Scholar
  16. Gartner T, Steinfath M, Andorf S, Lisec J, Meyer RC, Altmann T, Willmitzer L, Selbig J (2009) Improved heterosis prediction by combining information on DNA- and metabolic markers. PLoS One 4:e5220CrossRefPubMedPubMedCentralGoogle Scholar
  17. Guo Z, Tucker DM, Lu J, Kishore V, Gay G (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124:261–275CrossRefPubMedGoogle Scholar
  18. Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127:749–762CrossRefPubMedGoogle Scholar
  19. Habier D, Fernando RL, Garrick DJ (2013) Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 194:597–607CrossRefPubMedPubMedCentralGoogle Scholar
  20. Heffner EL, Jannink J-L, Iwata H, Souza E, Sorrells ME (2011) Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 51:2597–2606CrossRefGoogle Scholar
  21. Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph Press, Guelph, CanadaGoogle Scholar
  22. Henderson CR (1985) Best linear unbiased prediction of nonadditive genetic merits in noninbred populations. J Anim Sci 60:111–117CrossRefGoogle Scholar
  23. Holland JB, Nyquist WE, Cervantes-Martinez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed Rev 22:9–112Google Scholar
  24. Holloway B, Luck S, Beatty M, Rafalski JA, Li B (2011) Genome-wide expression quantitative trait loci (eQTL) analysis in maize. BMC Genom 12:336CrossRefGoogle Scholar
  25. Howard R, Carriquiry AL, Beavis WD (2014) Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures. G3-genes genomes. Genetics 4:1027–1046Google Scholar
  26. Hu Y, Morota G, Rosa GJ, Gianola D (2015) Prediction of plant height in Arabidopsis thaliana using DNA methylation data. Genetics 201:779–793CrossRefPubMedPubMedCentralGoogle Scholar
  27. Isidro J, Jannink J-L, Akdemir D, Poland J, Heslot N, Sorrells ME (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128:145–158CrossRefPubMedGoogle Scholar
  28. Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201:759–768CrossRefPubMedPubMedCentralGoogle Scholar
  29. Kliebenstein DJ, Kroymann J, Brown P, Figuth A, Pedersen D, Gershenzon J, Mitchell-Olds T (2001) Genetic control of natural variation in Arabidopsis glucosinolate accumulation. Plant Physiol 126:811–825CrossRefPubMedPubMedCentralGoogle Scholar
  30. Kruijer W, Boer MP, Malosetti M, Flood PJ, Engel B (2015) Marker-based estimation of heritability in immortal populations. Genet 199:379–398CrossRefGoogle Scholar
  31. Lehermeier C, Schoen C-C, de los Campos G (2015) Assessment of genetic heterogeneity in structured plant populations using multivariate whole-genome regression models. Genet 201:323–337. doi:10.1534/genetics.115.177394 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Li H, Peng Z, Yang X, Wang W, Fu J, Wang J, Han Y, Chai Y, Guo T, Yang N, Liu J, Warburton ML, Cheng Y, Hao X, Zhang P, Zhao J, Liu Y, Wang G, Li J, Yan J (2013) Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet 45:43–50CrossRefPubMedGoogle Scholar
  33. Makowsky R, Pajewski NM, Klimentidis YC, Vazquez AI, Duarte CW, Allison DB, de los Campos G (2011) Beyond missing heritability: prediction of complex traits. PLoS Genet 7:e1002051CrossRefPubMedPubMedCentralGoogle Scholar
  34. Massman JM, Jung H-JG, Bernardo R (2013) Genomewide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53:58–66CrossRefGoogle Scholar
  35. Matsuda S, Funabiki A, Furukawa K, Komori N, Koike M, Tokuji Y, Takamure I, Kato K (2012) Genome-wide analysis and expression profiling of half-size ABC protein subgroup G in rice in response to abiotic stress and phytohormone treatments. Mol Genet Genom 287:819–835CrossRefGoogle Scholar
  36. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  37. Meyer RC, Steinfath M, Lisec J, Becher M, Witucka-Wall H, Torjek O, Fiehn O, Eckardt A, Willmitzer L, Selbig J, Altmann T (2007) The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proc Natl Acad Sci USA 104:4759–4764CrossRefPubMedPubMedCentralGoogle Scholar
  38. Nica AC, Dermitzakis ET (2013) Expression quantitative trait loci: present and future. Philos Trans R Soc Lond B Biol Sci 368:20120362CrossRefPubMedPubMedCentralGoogle Scholar
  39. Ober U, Ayroles JF, Stone EA, Richards S, Zhu D, Gibbs RA, Stricker C, Gianola D, Schlather M, Mackay TFC, Simianer H (2012) Using whole-genome sequence data to predict quantitative trait phenotypes in drosophila melanogaster. PLoS Genet 8(5):e1002685CrossRefPubMedPubMedCentralGoogle Scholar
  40. Piyasatian N, Fernando RL, Dekkers JCM (2007) Genomic selection for marker-assisted improvement in line crosses. Theor Appl Genet 115:665–674CrossRefPubMedGoogle Scholar
  41. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal component analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CrossRefPubMedGoogle Scholar
  42. 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 44:217–220CrossRefPubMedGoogle Scholar
  43. Technow F, Buerger A, Melchinger AE (2013) Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups. G3-Genes Genomes Genet 3:197–203Google Scholar
  44. Van der Vaart AW (2000) Asymptotic statistics. Cambridge University Press, CambridgeGoogle Scholar
  45. VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423CrossRefPubMedGoogle Scholar
  46. Visscher PM, Yang J, Goddard MEA et al (2010) A commentary on ‘Common SNPs Explain a Large Proportion of the Heritability for Human Height’ by Yang et al. (2010). Twin Res Hum Genet 13:517–524CrossRefPubMedGoogle Scholar
  47. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63CrossRefPubMedPubMedCentralGoogle Scholar
  48. Wang Y-P, Chang K-W, Chen R-K, Lo J-C, Shen Y (2010) Large-area rice yield forecasting using satellite imageries. Int J Appl Earth Obs Geoinf 12:27–35CrossRefGoogle Scholar
  49. Wen W, Li D, Li X, Gao Y, Li W, Li H, Liu J, Liu H, Chen W, Luo J, Yan J (2014) Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun 5:3438PubMedPubMedCentralGoogle Scholar
  50. West MA, Kim K, Kliebenstein DJ, van Leeuwen H, Michelmore RW, Doerge RW, St Clair DA (2007) Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics 175:1441–1450CrossRefPubMedPubMedCentralGoogle Scholar
  51. Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink J-L, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3-Genes Genomes Genet 2:1427–1436Google Scholar
  52. Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM (2013) Pitfalls of predicting complex traits from SNPs. Nat Rev Genet 14:507–515CrossRefPubMedPubMedCentralGoogle Scholar
  53. Xu S (2013) Mapping quantitative trait loci by controlling polygenic background effects. Genetics 195:1209–1222CrossRefPubMedPubMedCentralGoogle Scholar
  54. Yang N, Lu Y, Yang X, Huang J, Zhou Y, Ali F, Wen W, Liu J, Li J, Yan J (2014) Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet 10:e1004573CrossRefPubMedPubMedCentralGoogle Scholar
  55. Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467CrossRefPubMedPubMedCentralGoogle Scholar
  56. Zhong S, Dekkers JC, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study. Genetics 182:355–364CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Zhigang Guo
    • 1
  • Michael M. Magwire
    • 1
  • Christopher J. Basten
    • 1
  • Zhanyou Xu
    • 2
  • Daolong Wang
    • 1
  1. 1.Syngenta Crop Protection, LLCResearch Triangle ParkUSA
  2. 2.Syngenta Crop Protection, LLCSlaterUSA

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