Theoretical and Applied Genetics

, Volume 129, Issue 9, pp 1697–1710 | Cite as

Genomic selection for wheat traits and trait stability

  • Mao Huang
  • Antonio Cabrera
  • Amber Hoffstetter
  • Carl Griffey
  • David Van Sanford
  • José Costa
  • Anne McKendry
  • Shiaoman Chao
  • Clay Sneller
Original Article


Key message

Based on the estimates of accuracy, genomic selection would be useful for selecting for improved trait values and trait stability for agronomic and quality traits in wheat. Trait values and trait stability estimated by two methods were generally independent indicating a breeder could select for both simultaneously.


Genomic selection (GS) is a new marker-assisted selection tool for breeders to achieve higher genetic gain faster and cheaper. Breeders face challenges posed by genotype by environment interaction (GEI) pattern and selecting for trait stability. Obtaining trait stability is costly, as it requires data from multiple environments. There are few studies that evaluate the efficacy of GS for predicting trait stability. A soft winter wheat population of 273 lines was genotyped with 90 K single nucleotide polymorphism markers and phenotyped for four agronomic and seven quality traits. Additive main effect and multiplicative interaction (AMMI) model and  Eberhart and Russell regression (ERR) were used to estimate trait stability. Significant GEI variation was observed and stable lines were identified for all traits in this study. The accuracy of GS ranged from 0.33 to 0.67 for most traits and trait stability. Accuracy of trait stability was greater than trait itself for yield (0.44 using AMMI versus 0.33) and heading date (0.65 using ERR versus 0.56). The opposite trend was observed for the other traits. GS did not predict the stability of the quality traits except for flour protein, lactic acid and softness equivalent. Significant GS accuracy for some trait stability indicated that stability was under genetic control for these traits. The magnitude of GS accuracies for all the traits and most of the trait stability index suggests the possibility of rapid selection for these trait and trait stability in wheat breeding.



We thank Dr. C. Sneller’s lab members for helping with the field data collection. This project was supported by Triticeae Coordinated Agricultural Project (2011-68002-30029) of the USDA National Institute of Food and Agriculture.

Compliance with Ethical Standards

This research comply with the current laws of the United States of America.

Conflict of interest

The authors of this study declare that there is no conflict of interest for this study.

Supplementary material

122_2016_2733_MOESM1_ESM.docx (219 kb)
Supplementary material 1 (DOCX 216 kb)


  1. Akcura M, Kaya Y, Taner S, Ayranci R (2006) Parametric stability analyses for grain yield of durum wheat. Plant Soil Environ 52:254Google Scholar
  2. Aliyu OM, Adeigbe OO, Lawal OO (2014) Phenotypic stability analysis of yield components in cashew (Anacardium occidentale L.) using additive main effect and multiplicative interaction (AMMI) and GGE biplot analyses. Plant Breed Biotechnol 2:354–369CrossRefGoogle Scholar
  3. Amiri E, Farshadfar E, Jowkar MM (2013) AMMI analysis of wheat substitution lines for detecting genes controlling adaptability. Int J Adv Biol Biomed Res 1:1112–1123Google Scholar
  4. Asoro FG, Newell MA, Beavis WD, Scott MP, Jannink JL (2011) Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Genome 4:132–144CrossRefGoogle Scholar
  5. Ayers KL, Cordell HJ (2010) SNP Selection in genome-wide and candidate gene studies via penalized logistic regression. Genet Epidemiol 34:879–891CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bao Y, Vuong T, Meinhardt C, Tiffin P, Denny R, Chen S, Nguyen HT, Orf JH, Young ND (2014) Potential of association mapping and genomic selection to explore PI 88788 derived soybean cyst nematode resistance. Plant Genome 7(3). doi: 10.3835/plantgenome2013.11.0039 
  7. Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48CrossRefGoogle Scholar
  8. Berke TG, Baenziger PS, Morris R (1992) Chromosomal location of wheat quantitative trait loci affecting stability of six traits, using reciprocal chromosome substitutions. Crop Sci 32:628–633CrossRefGoogle Scholar
  9. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  10. Costa J, Bollero G (2001) Stability analysis of grain yield in barley (Hordeum vulgare) in the US mid-Atlantic region. Ann Appl Biol 139:137–143CrossRefGoogle Scholar
  11. Crossa J, de los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724CrossRefPubMedPubMedCentralGoogle Scholar
  12. Crossa J, de los Campos G, Maccaferri M, Tuberosa R, Burgueño J, Pérez P (2015) Extending the marker × environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat. Crop Sci. doi: 10.2135/cropsci2015.04.0260 Google Scholar
  13. de los Campos G, Pérez P (2014) BGLR: bayesian generalized linear regression. R package version 1.0.4.
  14. de Mendiburu F (2015) Agricolae: statistical procedures for agricultural research. R package version 1.2-3.
  15. Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601CrossRefPubMedGoogle Scholar
  16. Eberhart St, Russell W (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40CrossRefGoogle Scholar
  17. Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255CrossRefGoogle Scholar
  18. Fehr WR (1987) Heritability. In: Principles of cultivar development, vol 1, pp 95–105Google Scholar
  19. Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Crop Pasture Sci 14:742–754CrossRefGoogle Scholar
  20. Forkman J, Piepho HP (2014) Parametric bootstrap methods for testing multiplicative terms in GGE and AMMI models. Biometrics 70:639–647CrossRefPubMedGoogle Scholar
  21. Frashadfar E, Safari H, Jamshidi B (2012) GGE biplot analysis of adaptation in wheat substitution lines. Int J Agric Crop Sci 4:877–881Google Scholar
  22. Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22CrossRefPubMedPubMedCentralGoogle Scholar
  23. Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44(3):705–715CrossRefGoogle Scholar
  24. Gauch HG, Piepho H-P, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Sci 48:866–889CrossRefGoogle Scholar
  25. Goddard ME, Hayes B (2007) Genomic selection. J Anim Breed Genet 124:323–330CrossRefPubMedGoogle Scholar
  26. Grattapaglia D, Resende MD (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7:241–255CrossRefGoogle Scholar
  27. Hanson W (1970) Genotypic stability. Theor Appl Genet 40:226–231CrossRefPubMedGoogle Scholar
  28. Hayes B, Bowman P, Chamberlain A, Goddard M (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443CrossRefPubMedGoogle Scholar
  29. Hayes B, Lewin H, Goddard M (2013) The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet 29:206–214CrossRefPubMedGoogle Scholar
  30. He S, Schulthess AW, Mirdita V, Zhao Y, Korzun V, Bothe R, Ebmeyer E, Reif JC, Jiang Y (2016) Genomic selection in a commercial winter wheat population. Theor Appl Genet 129:641–651CrossRefPubMedGoogle Scholar
  31. Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  32. Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690CrossRefGoogle Scholar
  33. Heffner EL, Jannink J-L, Iwata H, Souza E, Sorrells ME (2011a) Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 51:2597–2606CrossRefGoogle Scholar
  34. Heffner EL, Jannink J-L, Sorrells ME (2011b) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4:65–75CrossRefGoogle Scholar
  35. Heslot N, Yang H-P, Sorrells ME, Jannink J-L (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160CrossRefGoogle Scholar
  36. Heslot N, Jannink J-L, Sorrells ME (2013) Using genomic prediction to characterize environments and optimize prediction accuracy in applied breeding data. Crop Sci 53:921–933CrossRefGoogle Scholar
  37. Heslot N, Akdemir D, Sorrells ME, Jannink J-L (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
  38. Holland JB, Nyquist WE, Cervantes-Martinez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed 22:9–112Google Scholar
  39. Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177CrossRefPubMedGoogle Scholar
  40. Kraakman AT, Niks RE, Van den Berg PM, Stam P, Van Eeuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168:435–446CrossRefPubMedPubMedCentralGoogle Scholar
  41. Lado B, Barrios PG, Quincke M, Silva P, Gutiérrez L (2015) Modeling genotype by environment interaction for genomic selection with unbalanced data from a wheat (Triticum aestivum L.) breeding program. Crop Sci. doi: 10.2135/cropsci2015.04.0207 Google Scholar
  42. Legarra A, Robert-Granié C, Manfredi E, Elsen J-M (2008) Performance of genomic selection in mice. Genetics 180:611–618CrossRefPubMedPubMedCentralGoogle Scholar
  43. Lin Z, Hayes B, Daetwyler H (2014) Genomic selection in crops, trees and forages: a review. Crop Pasture Sci 65:1177–1191CrossRefGoogle Scholar
  44. Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink J-L, Singh RP, Autrique E and de los Campos G (2015) Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model. G3 (Bethesda) 5(4):569–582Google Scholar
  45. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161CrossRefPubMedGoogle Scholar
  46. 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
  47. Meuwissen T, Hayes B, Goddard M (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  48. Mohamed NE, Said AA, Amein KA (2013) Additive main effects and multiplicative interaction (AMMI) and GGE-biplot analysis of genotype × environment interactions for grain yield in bread wheat (Triticum aestivum L.). Afr J Agric 8:5197–5203Google Scholar
  49. Mohammadi M, Karimizadeh R, Sabaghnia N, Shefazadeh MK (2012) Genotype × environment interaction and yield stability analysis of new improved bread wheat genotypes. Turk J Field Crops 17:67–73Google Scholar
  50. Mukherjee A, Mohapatra N, Bose L, Jambhulkar N, Nayak P (2013) Additive main effects and multiplicative interaction (AMMI) analysis of G × E interactions in rice-blast pathosystem to identify stable resistant genotypes. Afr J Agric 8:5492–5507Google Scholar
  51. Namorato H, Miranda GV, Souza L, Oliveira LR, DeLima RO, Mantovani EE (2009) Comparing biplot multivariate analysis with Eberhart and Russell’method for genotype × environment interaction. Crop Breed Appl Biot 9:299–307CrossRefGoogle Scholar
  52. Ogutu JO, Schulz-Streeck T, Piepho H-P (2012) Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. In: BMC proceedings. BioMed Central Ltd, p S10Google Scholar
  53. Pérez P, de los Campos G (2014) Genome-wide regression & prediction with the BGLR statistical package. Genetics 198:483–495CrossRefPubMedPubMedCentralGoogle Scholar
  54. Pérez P, de los Campos G, Crossa J, Gianola D (2010) Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome 3:106–116CrossRefPubMedPubMedCentralGoogle Scholar
  55. Perkins JM, Jinks J (1968) Environmental and genotype-environmental components of variability. 3. Multiple lines and crosses. Heredity 23:339–356CrossRefPubMedGoogle Scholar
  56. Piepho HP (1998) Methods for comparing the yield stability of cropping systems. J Agr Crop Sci 180:193–213CrossRefGoogle Scholar
  57. Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888CrossRefPubMedPubMedCentralGoogle Scholar
  58. Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1–2):209–228CrossRefGoogle Scholar
  59. Piepho HP, Möhring J, Schulz-Streeck T, Ogutu JO (2012) A stage-wise approach for the analysis of multi-environment trials. Biom J 54:844–860CrossRefPubMedGoogle Scholar
  60. Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5:103–113CrossRefGoogle Scholar
  61. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
  62. Rao PS, Reddy PS, Rathore A, Reddy BV, Panwar S (2011) Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype × environment interaction and seasonal adaptation. Indian J Agric Sci 81:438–444Google Scholar
  63. Resende MF, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190:1503–1510CrossRefPubMedPubMedCentralGoogle Scholar
  64. Reynolds M, Bonnett D, Chapman SC, Furbank RT, Manès Y, Mather DE, Parry MA (2011) Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies. J Exp Bot 62:439–452CrossRefPubMedGoogle Scholar
  65. Rezene Y, Bekele A, Goa Y (2014) GGE and ammi biplot analysis for field PEA yield stability in SNNPR State, Ethiopia. Int J Sust Agric Res 1:28–38Google Scholar
  66. Rharrabti Y, Villegas D, Royo C, Martos-Núñez V, Del Moral LG (2003) Durum wheat quality in Mediterranean environments: II. Influence of climatic variables and relationships between quality parameters. Field Crops Res 80:133–140CrossRefGoogle Scholar
  67. Rinaldo A, Bacanu SA, Devlin B, Sonpar V, Wasserman L, Roeder K (2005) Characterization of multilocus linkage disequilibrium. Genet Epidemiol 28:193–206CrossRefPubMedGoogle Scholar
  68. Rodrigues PC, Malosetti M, Gauch HG, van Eeuwijk FA (2014) A weighted AMMI algorithm to study genotype-by-environment interaction and QTL-by-environment interaction. Crop Sci 54:1555–1570CrossRefGoogle Scholar
  69. Rondanini DP, Gomez NV, Agosti MB, Miralles DJ (2012) Global trends of rapeseed grain yield stability and rapeseed-to-wheat yield ratio in the last four decades. Eur J Agron 37:56–65CrossRefGoogle Scholar
  70. Rutkoski JE, Heffner EL, Sorrells ME (2011) Genomic selection for durable stem rust resistance in wheat. Euphytica 179:161–173CrossRefGoogle Scholar
  71. Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink J-L, Sorrells M (2012) Evaluation of genomic prediction methods for Fusarium head blight resistance in wheat. Plant Genome 5:51–61CrossRefGoogle Scholar
  72. Rutkoski JE, Poland JA, Singh RP, Huerta-Espino J, Bhavani S, Barbier H, Rouse MN, Jannink J-L, Sorrells ME (2014) Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome. doi: 10.3835/plantgenome2014.02.0006 Google Scholar
  73. SAS Institute Inc. (2008) SAS/STAT User's Guide, Version 9.2. SAS Institute Inc, Cary, NCGoogle Scholar
  74. Sabaghnia N, Sabaghpour S, Dehghani H (2008) The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. J Agric Sci 146:571–581CrossRefGoogle Scholar
  75. Sabaghnia N, Mohammadi M, Karimizadeh R (2012) The evaluation of genotype × environment interactions of durum wheat’s yield using of the Ammi model. Agric For 55:5–21Google Scholar
  76. Sabaghpour SH, Razavi F, Fatemeh Danyali S, Tobe D, Ebadi A (2012) Additive main effect and multiplicative interaction analysis for grain yield of chickpea (Cicer arietinum L.) in Iran. ISRN Agron 2012:1–6CrossRefGoogle Scholar
  77. Sadeghi S, Samizadeh H, Amiri E, Ashouri M (2013) Additive main effects and multiplicative interactions (AMMI) analysis of dry leaf yield in tobacco hybrids across environments. Afr J Biotechnol 10:4358–4364Google Scholar
  78. Saleem N, Ahmad M, Vashnavi R, Bukhari A, Dar ZA (2015) Stability analysis in Wheat: an application of additive main effects and multiplicative interaction. Afr J Agric Res 10:295–300CrossRefGoogle Scholar
  79. Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78:629–644CrossRefPubMedPubMedCentralGoogle Scholar
  80. Schulz-Streeck T, Ogutu JO, Gordillo A, Karaman Z, Knaak C, Piepho H-P (2013) Genomic selection allowing for marker-by-environment interactions. Plant Breed. 132:532–538CrossRefGoogle Scholar
  81. Shukla G (1972) Some statistical aspects of partitioning genotype environmental components of variability. Heredity 29:237–245CrossRefPubMedGoogle Scholar
  82. Smith N, Guttieri M, Souza E, Shoots J, Sorrells M, Sneller C (2011) Identification and validation of QTL for grain quality traits in a cross of soft wheat cultivars Pioneer Brand 25R26 and Foster. Crop Sci 51:1424–1436CrossRefGoogle Scholar
  83. Sneller C, Kilgore-Norquest L, Dombek D (1997) Repeatability of yield stability statistics in soybean. Crop Sci 37:383–390CrossRefGoogle Scholar
  84. Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink J-L, McCouch SR (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet 11:e1004982CrossRefPubMedGoogle Scholar
  85. Tai GC (1971) Genotypic stability analysis and its application to potato regional trials. Crop Sci 11:184–190CrossRefGoogle Scholar
  86. Tarakanovas P, Ruzgas V (2006) Additive main effect and multiplicative interaction analysis of grain yield of wheat varieties in Lithuania. Agron Res 4:91–98Google Scholar
  87. Tollenaar M, Lee E (2002) Yield potential, yield stability and stress tolerance in maize. Field Crops Res 75:161–169CrossRefGoogle Scholar
  88. Tribout T, Larzul C, Phocas F (2012) Efficiency of genomic selection in a purebred pig male line. J Anim Sci 90:4164–4176CrossRefPubMedGoogle Scholar
  89. Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L (2014) Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol J 12:787–796CrossRefPubMedPubMedCentralGoogle Scholar
  90. Wang Y, Mette MF, Miedaner T, Wilde P, Reif JC, Zhao Y (2015) First insights into the genotype–phenotype map of phenotypic stability in rye. J Exp Bot 66:3275–3284CrossRefPubMedPubMedCentralGoogle Scholar
  91. Zali H, Farshadfar E, Sabaghpour SH, Karimizadeh R (2012) Evaluation of genotype × environment interaction in chickpea using measures of stability from AMMI model. Ann Biol Res 3:3126–3136Google Scholar
  92. Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP, Longin FH, Ranc N, Reif JC (2012) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776CrossRefPubMedGoogle Scholar
  93. Zhong S, Dekkers JC, Fernando RL, Jannink J-L (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364CrossRefPubMedPubMedCentralGoogle Scholar
  94. Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J 80:388–393CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Mao Huang
    • 1
  • Antonio Cabrera
    • 1
  • Amber Hoffstetter
    • 1
  • Carl Griffey
    • 2
  • David Van Sanford
    • 3
  • José Costa
    • 4
  • Anne McKendry
    • 5
  • Shiaoman Chao
    • 6
  • Clay Sneller
    • 1
  1. 1.Ohio Agriculture Research and Development CenterThe Ohio State UniversityWoosterUSA
  2. 2.University of Virginia TechBlacksburgUSA
  3. 3.University of KentuckyLexingtonUSA
  4. 4.USDA-ARSBeltsvilleUSA
  5. 5.University of MissouriColumbiaUSA
  6. 6.Cereal Crops Research UnitUSDA-ARSFargoUSA

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