Theoretical and Applied Genetics

, Volume 130, Issue 9, pp 1927–1939 | Cite as

Omics-based hybrid prediction in maize

  • Matthias Westhues
  • Tobias A. Schrag
  • Claas Heuer
  • Georg Thaller
  • H. Friedrich Utz
  • Wolfgang Schipprack
  • Alexander Thiemann
  • Felix Seifert
  • Anita Ehret
  • Armin Schlereth
  • Mark Stitt
  • Zoran Nikoloski
  • Lothar Willmitzer
  • Chris C. Schön
  • Stefan ScholtenEmail author
  • Albrecht E. MelchingerEmail author
Original Article


Key message

Complementing genomic data with other “omics” predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.


Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream “omics” data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of “omics” data. Here, we evaluate several “omics” predictors—genomic, transcriptomic and metabolic data—measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream “omics” data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.



We thank the staff of the Agricultural Experimental Research station, University of Hohenheim, for excellent technical assistance in conducting the field experiments. We are indebted to the group of R. Fries from Technische Universität München for the SNP genotyping of the parent inbred lines, to X. Mi for his assistance in preparing auxiliary figures based on the Mathematica software, to C. Zenke for advice on the computation of transcriptomic BLUEs and to P. Schopp for advice on prediction models.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

122_2017_2934_MOESM1_ESM.pdf (1.8 mb)
Supplementary material 1 (pdf 1877 KB)


  1. Argillier O, Méchin V, Barrière Y (2000) Inbred line evaluation and breeding for digestibility-related traits in forage maize. Crop Sci 40(6):1596–1600. doi: 10.2135/cropsci2000.4061596x CrossRefGoogle Scholar
  2. Arrivault S, Guenther M, Ivakov A, Feil R, Vosloh D, Van Dongen JT, Sulpice R, Stitt M (2009) Use of reverse-phase liquid chromatography, linked to tandem mass spectrometry, to profile the Calvin cycle and other metabolic intermediates in Arabidopsis rosettes at different carbon dioxide concentrations. Plant J 59(5):824–839. doi: 10.1111/j.1365-313X.2009.03902.x CrossRefGoogle Scholar
  3. Arroyo-Currás N, Somerson J, Vieira PA, Ploense KL, Kippin TE, Plaxco KW (2017) Real-time measurement of small molecules directly in awake, ambulatory animals. Proc Natl Acad Sci USA 114(4):645–650. doi: 10.1073/pnas.1613458114 PubMedPubMedCentralCrossRefGoogle Scholar
  4. Bernardo R (1996) Best linear unbiased prediction of maize single-cross performance. Crop Sci 36:50–56CrossRefGoogle Scholar
  5. Brem RB, Storey JD, Whittle J, Kruglyak L (2005) Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436(7051):701–3. doi: 10.1038/nature03865 PubMedPubMedCentralCrossRefGoogle Scholar
  6. Brown AA, Buil A, Vinuela A, Lappalainen T, Zheng HF, Richards JB, Small KS, Spector TD, Dermitzakis ET, Durbin R (2014) Genetic interactions affecting human gene expression identified by variance association mapping. eLife 3:1–16. doi: 10.7554/eLife.01381 Google Scholar
  7. Browning BL, Browning SR (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84(2):210–223. doi: 10.1016/j.ajhg.2009.01.005 PubMedPubMedCentralCrossRefGoogle Scholar
  8. Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) Mixed models for S language environments, ASReml-R reference manual. Training and development series, No QE02001. QLD Department of Primary Industries and Fisheries, BrisbaneGoogle Scholar
  9. Caldana C, Degenkolbe T, Cuadros-Inostroza A, Klie S, Sulpice R, Leisse A, Steinhauser D, Fernie AR, Willmitzer L, Hannah MA (2011) High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions. Plant J 67(5):869–884. doi: 10.1111/j.1365-313X.2011.04640.x PubMedCrossRefGoogle Scholar
  10. Civelek M, Lusis AJ (2014) Systems genetics approaches to understand complex traits. Nat Rev Genet 15(1):34–48. doi: 10.1038/nrg3575 PubMedCrossRefGoogle Scholar
  11. Cox TS, Murphy JP, Rodgers DM (1986) Changes in genetic diversity in the red winter wheat regions of the United States. Proc Natl Acad Sci USA 83(15):5583–5586. doi: 10.1073/pnas.83.15.5583 PubMedPubMedCentralCrossRefGoogle Scholar
  12. Dalchau N, Baek SJ, Briggs HM, Robertson FC, Dodd AN, Gardner MJ, Stancombe MA, Haydon MJ, Stan GB, Gonçalves JM, Webb AAR (2011) The circadian oscillator gene GIGANTEA mediates a long-term response of the Arabidopsis thaliana circadian clock to sucrose. Proc Natl Acad Sci USA 108(12):5104–5109. doi: 10.1073/pnas.1015452108. arXiv:1408.1149 PubMedPubMedCentralCrossRefGoogle Scholar
  13. Dan Z, Hu J, Zhou W, Yao G, Zhu R, Zhu Y, Huang W (2016) Metabolic prediction of important agronomic traits in hybrid rice (Oryza sativa L.). Nat Sci Rep 6(October 2015):1–9. doi: 10.1038/srep21732
  14. de Abreu e Lima F, Westhues M, Willmitzer L, Melchinger AE, Nikoloski Z (2017) Metabolic robustness in young roots underpins a predictive model of maize hybrid performance in the field. Plant J 90(2):319–329. doi: 10.1111/tpj.13495 PubMedCrossRefGoogle Scholar
  15. Duvick DN (2005) Genetic progress in yield of United States maize (Zea mays L.). Maydica 50:193–202Google Scholar
  16. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Pearson Prentice, Harlow, UKGoogle Scholar
  17. Fernie AR (2007) The future of metabolic phytochemistry: larger numbers of metabolites, higher resolution, greater understanding. Phytochemistry 68(22–24):2861–2880. doi: 10.1016/j.phytochem.2007.07.010 PubMedCrossRefGoogle Scholar
  18. Fernie AR, Stitt M (2012) On the discordance of metabolomics with proteomics and transcriptomics: coping with increasing complexity in logic, chemistry, and network interactions scientific correspondence. Plant Physiol 158(3):1139–45. doi: 10.1104/pp.112.193235 PubMedPubMedCentralCrossRefGoogle Scholar
  19. Fiévet JB, Dillmann C, de Vienne D (2010) Systemic properties of metabolic networks lead to an epistasis-based model for heterosis. Theor Appl Genet 120(2):463–73. doi: 10.1007/s00122-009-1203-2 PubMedCrossRefGoogle Scholar
  20. Francesconi M, Lehner B (2014) The effects of genetic variation on gene expression dynamics during development. Nature 505(7482):208–11. doi: 10.1038/nature12772 PubMedCrossRefGoogle Scholar
  21. 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(2):441–450. doi: 10.1007/s00122-009-1204-1 PubMedCrossRefGoogle Scholar
  22. Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner EM, Hansen M, Joets J, Le Paslier MC, McMullen MD, Montalent P, Rose M, Schön CC, Sun Q, Walter H, Martin OC, Falque M (2011) A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PloS One 6(12):e28,334. doi: 10.1371/journal.pone.0028334
  23. Geiger HH, Melchinger AE, Schmidt G (1986) Analysis of factorial crosses between flint and dent maize inbred lines for forage performance and quality traits. In: Dolstra O, Miedema P (eds) Breeding of silage maize. Pudoc, Wageningen, pp 147–154Google Scholar
  24. Genz A, Bretz F, Miwa T, Mi X, Leisch F, Scheipl F, Hothorn T (2017) mvtnorm: multivariate normal and t distributions.
  25. Gerke JP, Edwards JW, Guill KE, Ross-Ibarra J, McMullen MD (2015) The genomic impacts of drift and selection for hybrid performance in maize. Genetics 201(3):1201–1211. doi: 10.1534/genetics.115.182410. arXiv:1307.7313
  26. Gottesman II, Gould TD (2003) The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 160(4):636–645. doi: 10.1176/appi.ajp.160.4.636 PubMedCrossRefGoogle Scholar
  27. Grieder C, Mittweg G, Dhillon B, Montes J, Orsini E, Melchinger AE (2011) Determination of methane fermentation yield and its kinitics by near infrared spectroscopy and chemical composition in maize. J Near Infrared Spectrosc 19(6):463–477CrossRefGoogle Scholar
  28. Grieder C, Dhillon BS, Schipprack W, Melchinger AE (2012) Breeding maize as biogas substrate in Central Europe: II. Quantitative-genetic parameters for inbred lines and correlations with testcross performance. Theor Appl Genet 124(6):981–988. doi: 10.1007/s00122-011-1762-x PubMedCrossRefGoogle Scholar
  29. Guo M, Rupe MA, Yang X, Crasta O, Zinselmeier C, Smith OS, Bowen B (2006) Genome-wide transcript analysis of maize hybrids: allelic additive gene expression and yield heterosis. Theor Appl Genet 113(5):831–845. doi: 10.1007/s00122-006-0335-x PubMedCrossRefGoogle Scholar
  30. Guo Z, Magwire MM, Basten CJ, Xu Z, Wang D (2016) Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize. Theor Appl Genet 129(12):2413–2427. doi: 10.1007/s00122-016-2780-5 PubMedCrossRefGoogle Scholar
  31. Hadfield JD (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 33(2):1–22. doi: 10.1002/ana.22635. arXiv:1501.0228 CrossRefGoogle Scholar
  32. Hall BD, Fox R, Zhang Q, Baumgarten A, Nelson B, Cummings J, Drake B, Phillips D, Hayes K, Beatty M, Zastrow-Hayes G, Zeka B, Hazebroek J, Smith S (2016) Comparison of genotypic and expression data to determine distinctness among inbred lines of maize for granting of plant variety protection. Crop Sci 56(4):1443–1459CrossRefGoogle Scholar
  33. Henderson C (1985) Best linear unbiased prediction of nonadditive genetic merits in noninbred populations. J Anim Sci 60:111–117CrossRefGoogle Scholar
  34. Henderson CR (1984) Applications of linear models in animal breeding models. University of Guelph, GuelphGoogle Scholar
  35. Heuer C (2015) cpgen: parallelized genomic prediction and GWAS.
  36. Hill WG, Goddard ME, Visscher PM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4(2):1–10. doi: 10.1371/journal.pgen.1000008 CrossRefGoogle Scholar
  37. Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M, Burgueño J, de los Campos G (2014) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 127(3):595–607. doi: 10.1007/s00122-013-2243-1 PubMedCrossRefGoogle Scholar
  38. Jiang Y, Reif JC (2015) Modelling epistasis in genomic selection. Genetics 201(2):759–768. doi: 10.1534/genetics.115.177907 PubMedPubMedCentralCrossRefGoogle Scholar
  39. Kacser H, Burns JA (1981) The molecular basis of dominance. Genetics 97:639–666PubMedPubMedCentralGoogle Scholar
  40. Kadam D, Potts S, Bohn MO, Lipka AE, Lorenz A (2016) Genomic prediction of hybrid combinations in the early stages of a maize hybrid breeding pipeline. G3(6):3443–3453. doi: 10.1101/054015
  41. Kang HM, Sul JH, Service SK, Zaitlen Na, Kong SY, Freimer NB, Sabatti C, Eskin E (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42(4):348–354. doi: 10.1038/ng.548 PubMedCrossRefGoogle Scholar
  42. Kelliher T, Starr D, Richbourg L, Chintamanani S, Delzer B, Nuccio ML, Green J, Chen Z, McCuiston J, Wang W, Liebler T, Bullock P, Martin B (2017) MATRILINEAL, a sperm-specific phospholipase, triggers maize haploid induction. Nature 542(7639):105–109. doi: 10.1038/nature20827 PubMedCrossRefGoogle Scholar
  43. Kerr MK, Churchill GA (2001) Experimental design for gene expression microarrays. Biostatistics 2(2):183–201. doi: 10.1093/biostatistics/2.2.183 PubMedCrossRefGoogle Scholar
  44. Larièpe A, Moreau L, Laborde J, Bauland C, Mezmouk S, Décousset L, Mary-Huard T, Fiévet JB, Gallais A, Dubreuil P, Charcosset A (2017) General and specific combining abilities in a maize (Zea mays L.) test-cross hybrid panel: relative importance of population structure and genetic divergence between parents. Theor Appl Genet 130(2):403–417. doi: 10.1007/s00122-016-2822-z PubMedCrossRefGoogle Scholar
  45. Lisec J, Römisch-Margl L, Nikoloski Z, Piepho HP, Giavalisco P, Selbig J, Gierl A, Willmitzer L (2011) Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns. Plant J 68(2):326–336. doi: 10.1111/j.1365-313X.2011.04689.x PubMedCrossRefGoogle Scholar
  46. Longin CFH, Mi X, Würschum T (2015) Genomic selection in wheat: optimum allocation of test resources and comparison of breeding strategies for line and hybrid breeding. Theor Appl Genet 128(7):1297–1306. doi: 10.1007/s00122-015-2505-1 PubMedCrossRefGoogle Scholar
  47. Mackay TFC (2014) Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat Rev Genet 15(1):22–33. doi: 10.1038/nrg3627 PubMedCrossRefGoogle Scholar
  48. Mackay TFC, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10(8):565–77. doi: 10.1038/nrg2612 PubMedCrossRefGoogle Scholar
  49. Martini JWR, Wimmer V, Erbe M, Simianer H (2016) Epistasis and covariance: how gene interaction translates into genomic relationship. Theor Appl Genet 129(5):963–976. doi: 10.1007/s00122-016-2675-5 PubMedCrossRefGoogle Scholar
  50. Massman JM, Gordillo A, Lorenzana RE, Bernardo R (2013) Genomewide predictions from maize single-cross data. Theor Appl Genet 126(1):13–22. doi: 10.1007/s00122-012-1955-y PubMedCrossRefGoogle Scholar
  51. Melchinger AE, Gumber RK (1998) Overview of heterosis and heterotic groups in agronomic crops. In: Lamkey K, Staub J (eds) Concepts and breeding of heterosis in crop plants. CSSA, Madison, p 16Google Scholar
  52. Mele M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ, Johnson R, Segre AV, Djebali S, Niarchou A, Consortium TG, Wright FA, Lappalainen T, Calvo M, Getz G, Dermitzakis ET, Ardlie KG, Guigo R (2015) The human transcriptome across tissues and individuals. Science 348(6235):660–665. doi: 10.1126/science.aaa0355 CrossRefGoogle Scholar
  53. Mrode RA (2014) Linear models for the prediction of animal breeding values, 3rd edn. CABI, Oxfordshire. doi: 10.1017/CBO9781107415324.004. arXiv:1011.1669v3
  54. Patti GJ, Yanes O, Siuzdak G (2012) Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13(4):263–9. doi: 10.1038/nrm3314 PubMedPubMedCentralCrossRefGoogle Scholar
  55. Pérez P, de Los Campos G (2014) Genome-wide regression & prediction with the BGLR statistical package. Genetics 198(October):483–495. doi: 10.1534/genetics.114.164442 PubMedPubMedCentralCrossRefGoogle Scholar
  56. R Core Team (2016) R: a language and environment for statistical computing.
  57. Reif JC, Gumpert F, Fischer S, Melchinger AE (2007) Impact of interpopulation divergence on additive and dominance variance in hybrid populations. Genetics 176(3):1931–1934. doi: 10.1534/genetics.107.074146 PubMedPubMedCentralCrossRefGoogle Scholar
  58. 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):217–20. doi: 10.1038/ng.1033 PubMedCrossRefGoogle Scholar
  59. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D (2015a) Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet 16:85–97. doi: 10.1038/nrg3868 PubMedCrossRefGoogle Scholar
  60. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK (2007) A comparison of background correction methods for two-colour microarrays. Bioinformatics 23(20):2700–2707. doi: 10.1093/bioinformatics/btm412 PubMedCrossRefGoogle Scholar
  61. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015b) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47. doi: 10.1093/nar/gkv007 PubMedPubMedCentralCrossRefGoogle Scholar
  62. Robson DS, Powers L, Urquhart NS (1967) The proportion of genetic deviates in the tails of a normal population. Der Züchter 37(4):205–216. doi: 10.1007/BF00329530 Google Scholar
  63. Rudd JJ, Kanyuka K, Hassani-Pak K, Derbyshire M, Andongabo A, Devonshire J, Lysenko A, Saqi M, Desai NM, Powers SJ, Hooper J, Ambroso L, Bharti A, Farmer A, Hammond-Kosack KE, Dietrich RA, Courbot M (2015) Transcriptome and metabolite profiling of the infection cycle of Zymoseptoria tritici on wheat reveals a biphasic interaction with plant immunity involving differential pathogen chromosomal contributions and a variation on the hemibiotrophic lifestyle def. Plant Physiol 167(3):1158–1185. doi: 10.1104/pp.114.255927 PubMedPubMedCentralCrossRefGoogle Scholar
  64. Sackton TB, Hartl DL (2016) Perspective genotypic context and epistasis in individuals and populations. Cell 166:279–287. doi: 10.1016/j.cell.2016.06.047 PubMedPubMedCentralCrossRefGoogle Scholar
  65. Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M, Schölkopf B, Weigel D, Lohmann JU (2005) A gene expression map of Arabidopsis thaliana development. Nat Genet 37(5):501–506. doi: 10.1038/ng1543 PubMedCrossRefGoogle Scholar
  66. Schnell F (1965) Die Covarianz zwischen Verwandten in einer gen-orthogonalen population. I. Allgemeine Theorie. Biom Z 7(1):2–49CrossRefGoogle Scholar
  67. Schopp P, Müller D, Technow F, Melchinger AE (2017) Accuracy of genomic prediction in synthetic populations depending on the number of parents, relatedness and ancestral linkage disequilibrium. Genetics 205:441–454. doi: 10.1534/genetics.116.193243 PubMedCrossRefGoogle Scholar
  68. Searle BC, Gittelman RM, Manor O, Akey JM (2016) Detecting sources of transcriptional heterogeneity in large-scale RNA-Seq data sets. Genetics 204(December):1391–1396. doi: 10.1534/genetics.116.193714 PubMedCrossRefGoogle Scholar
  69. Smyth G (2004) Linear models and empirical bayes methods for assessing differential expression in microarrays experiments. Stat Appl Genet Mol Biol 3(1):1–26CrossRefGoogle Scholar
  70. Smyth GK, Speed T (2003) Normalization of cDNA microarray data. Methods 31(4):265–273. doi: 10.1016/S1046-2023(03)00155-5 PubMedCrossRefGoogle Scholar
  71. Springer NM, Stupar RM (2007a) Allele-specific expression patterns reveal biases and embryo-specific parent-of-origin effects in hybrid maize. Plant Cell 19(8):2391–2402. doi: 10.1105/tpc.107.052258 PubMedPubMedCentralCrossRefGoogle Scholar
  72. Springer NM, Stupar RM (2007b) Allelic variation and heterosis in maize: how do two halves make more than a whole? Genome Res 17(3):264–275. doi: 10.1101/gr.5347007 PubMedCrossRefGoogle Scholar
  73. Stuber CW, Cockerham CC (1966) Gene effects and variances in hybrid populations. Genetics 54(6):1279–1286PubMedPubMedCentralGoogle Scholar
  74. Stupar RM, Gardiner JM, Oldre AG, Haun WJ, Chandler VL, Springer NM (2008) Gene expression analyses in maize inbreds and hybrids with varying levels of heterosis. BMC Plant Biol 8(33):1–19. doi: 10.1186/1471-2229-8-33 Google Scholar
  75. Swanson-Wagner RA, Jia Y, DeCook R, Borsuk LA, Nettleton D, Schnable PS (2006) All possible modes of gene action are observed in a global comparison of gene expression in a maize F1 hybrid and its inbred parents. Proc Natl Acad Sci USA 103(18):6805–6810. doi: 10.1073/pnas.0510430103 PubMedPubMedCentralCrossRefGoogle Scholar
  76. Technow F, Schrag TA, Schipprack W, Bauer E, Simianer H, Melchinger AE (2014) Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197:1343–1355. doi: 10.1534/genetics.114.165860 PubMedPubMedCentralCrossRefGoogle Scholar
  77. Thiemann A, Fu J, Schrag TA, Melchinger AE, Frisch M, Scholten S (2010) Correlation between parental transcriptome and field data for the characterization of heterosis in Zea mays L. Theor Appl Genet 120(2):401–413. doi: 10.1007/s00122-009-1189-9 PubMedCrossRefGoogle Scholar
  78. Thiemann A, Fu J, Seifert F, Grant-Downton RT, Schrag TA, Pospisil H, Frisch M, Melchinger AE, Scholten S (2014) Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci. BMC Plant Biol 14(88):1–14. doi: 10.1186/1471-2229-14-88 Google Scholar
  79. Tzin V, Fernandez-Pozo N, Richter A, Schmelz EA, Schoettner M, Schäfer M, Ahern KR, Meihls LN, Kaur H, Huffaker A, Mori N, Degenhardt J, Mueller LA, Jander G (2015) Dynamic maize responses to aphid feeding are revealed by a time series of transcriptomic and metabolomic assays. Plant Physiol 169(November):1727–1743. doi: 10.1104/pp.15.01039 PubMedPubMedCentralGoogle Scholar
  80. VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423. doi: 10.3168/jds.2007-0980 PubMedCrossRefGoogle Scholar
  81. van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom 7:1–15. doi: 10.1186/1471-2164-7-142 CrossRefGoogle Scholar
  82. Vazquez AI, Veturi YC, Behring M, Shrestha S, Kirst M, Resende MFR, de los Campos G (2016) Increased proportion of variance explained and prediction accuracy of survival of breast cancer patients with use of whole-genome multi-omic profiles. Genetics 203(3):1425–1438. doi: 10.1534/genetics.115.185181 PubMedPubMedCentralCrossRefGoogle Scholar
  83. Vinuela A, Snoek LB, Riksen JAG, Kammenga JE (2010) Genome-wide gene expression regulation as a function of genotype and age in C. elegans. Genome Res 20:929–937. doi: 10.1101/gr.102160.109 PubMedPubMedCentralCrossRefGoogle Scholar
  84. Wedzony M, Forster B, Zur I, Golemiec E, Scechynska-Hebda M, Dubas E, Gotebiowska G (2009) Progress in doubled haploid technology in higher plants. In: Touarev A, Forster BP, Mohan JS (eds) Advances in haploid production in higher plants. Springer, Berlin, pp 1–33Google Scholar
  85. Witt S, Galicia L, Lisec J, Cairns J, Tiessen A, Araus JL, Palacios-Rojas N, Fernie AR (2012) Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. Mol Plant 5(2):401–17. doi: 10.1093/mp/ssr102 PubMedCrossRefGoogle Scholar
  86. Xu S, Xu Y, Gong L, Zhang Q (2016) Metabolomic prediction of yield in hybrid rice. Plant J 88(2):219–227. doi: 10.1111/tpj.13242 PubMedCrossRefGoogle Scholar
  87. Yang J, Huang T, Petralia F, Long Q, Zhang B, Argmann C, Zhao Y, Mobbs CV, Schadt EE, Zhu J, Tu Z (2015) Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases. Nat Sci Rep 5(15):145. doi: 10.1038/srep15145 Google Scholar
  88. Zenke-Philippi C, Thiemann A, Seifert F, Schrag TA, Melchinger AE, Scholten S, Frisch M (2016) Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles. BMC Genom 17(1):262. doi: 10.1186/s12864-016-2580-y CrossRefGoogle Scholar
  89. Zhao Y, Li Z, Liu G, Jiang Y, Maurer HP, Würschum T, Mock HP, Matros A, Ebmeyer E, Schachschneider R, Kazman E, Schacht J, Gowda M, Longin CFH, Reif JC (2015) Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding. Proc Natl Acad Sci USA 112(51):15,624–15,629. doi: 10.1073/pnas.1514547112
  90. Zhu J, Sova P, Xu Q, Dombek KM, Xu EY, Vu H, Tu Z, Brem RB, Bumgarner RE, Schadt EE (2012) Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol 10(4):e1001301. doi: 10.1371/journal.pbio.1001301

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© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institute of Plant Breeding, Seed Science and Population GeneticsUniversity of HohenheimStuttgartGermany
  2. 2.Institute of Animal Breeding and HusbandryChristian-Albrechts-University KielKielGermany
  3. 3.Inguran LLC dba STGeneticsNavasotaUSA
  4. 4.Biocenter Klein Flottbek, Developmental Biology and BiotechnologyUniversity of HamburgHamburgGermany
  5. 5.Max-Planck Institute of Molecular Plant PhysiologyPotsdamGermany
  6. 6.Plant BreedingTechnische Universität MünchenFreisingGermany

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