, 215:33 | Cite as

Genotypic main effect and genotype-by-environment interaction effect on seed protein concentration and yield in food-grade soybeans (Glycine max (L.) Merrill)

  • Rachel Whaley
  • Mehrzad EskandariEmail author


As consumers look for healthier dietary alternatives, many have recognized soybean [Glycine max (L.) Merrill] as a prominent source of high quality protein. The major obstacles to developing commercially competitive high protein food-grade soybean cultivars are the complex inheritance of seed protein concentration and its inverse association with yield. A better understanding of the genetic background and environmental conditions influencing soybean seed protein, and its relationship with yield, can facilitate the development of superior high-protein cultivars. Therefore, the main objective of this research was to study the influence of genotype and genotype-by-environment interaction effects (GGE) on seed protein and yield, and their relationship, using two recombinant inbred line (RIL) populations. The RIL populations were derived from crosses between a high-protein cultivar, AC X790P (486 g kg−1, dry weight basis), and two moderate-protein elite cultivars, S18-R6 (404 g kg−1) and S23-T5 (413 g kg−1), and were evaluated in a multi-environment trial in southwestern Ontario, Canada, in 2015 and 2016. Significant (P < 0.05) phenotypic variation was observed for seed protein and yield in both populations within and across environments. The effects of genotype, environment, and GE interactions on both traits were significant in both populations. Genotypic main effect plus GGE biplot analyses led to the identification of stable high yielding high-protein RILs for the sub-regions and individual testing environments. These results indicated that the association between seed protein and yield can be manipulated using specific genetic backgrounds and environments, and superior genotypes for target regions can be identified using GGE biplots.


Soybean [Glycine max (L.) Merrill] Genotypic main effect Genotype by environment interaction GGE biplots Seed protein concentration Seed yield 



The assistance and technical support by Bryan Stirling, John Kobler, and the entire soybean crew at the University of Guelph, Ridgetown Campus is gratefully acknowledged. Funding for this project was provided, in part, by SeCan and the Grain Farmers of Ontario.

Authors’ contributions

ME: designed and supervised the research, and contributed in preparing and reviewing the manuscript; RW: executed the experimental designs, helped in phenotyping the RILs, carried out statistical analyses, and prepared the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.

Supplementary material

10681_2019_2344_MOESM1_ESM.docx (170 kb)
Supplementary material 1 (DOCX 169 kb)


  1. Baianu I, You T, Costescu D, Lozano P, Prisecaru V, Nelson RL (2012) Determination of soybean oil, protein and amino acid residues in soybean seeds by high resolution nuclear magnetic resonance (NMRS) and near infrared (NIRS). Nat Proc 1:1–62. CrossRefGoogle Scholar
  2. Bandillo N, Jarquin D, Song Q, Nelson R, Cregan P, Specht J et al (2015) A population structure and genome-wide association analysis on the USDA soybean germplasm collection. Plant Genome 8:1–13. CrossRefGoogle Scholar
  3. Bhan MK, Pal S, Rao BL, Dhar AK, Kang MS (2005) GGE biplot analysis of oil yield in lemongrass. J New Seeds 7:127–139CrossRefGoogle Scholar
  4. Bhathena SJ, Velasquez MT (2002) Beneficial role of dietary phytoestrogens in obesity and diabetes. Am J Clin Nutr 76:1191–1201. CrossRefPubMedGoogle Scholar
  5. Bowley SR (2008) A Hitchhiker’s guide to statistics in plant biology, 2nd edn. Any Old Subject Books, GuelphGoogle Scholar
  6. Brzostowski LF, Diers BW (2017) Agronomic evaluation of a high protein allele from PI407788A on chromosome 15 across two soybean backgrounds. Crop Sci 57:2972–2978CrossRefGoogle Scholar
  7. Burton JW (1985) Breeding soybeans for improved protein quantity and quality. In: Shibles R (ed) 3rd soybean research conference: Ames, IA, 12–17 August 1984. Westview Press Boulder, pp 361–367Google Scholar
  8. Burton JW (1987) Quantitative genetics: results relevant to soybean breeding. In: Wilcox JR (ed) Soybeans: improvement, production, and uses. American Society of Agronomy, Madison, pp 211–247Google Scholar
  9. Cai T, Chang KC (1999) Processing effect on soybean storage proteins and their relationship with tofu quality. J Agric Food Chem 47:720–727CrossRefGoogle Scholar
  10. Canadian Food Inspection Agency (2011a) S18-R6. Government of Canada. Accessed 13 Oct 2016
  11. Canadian Food Inspection Agency (2011b) S23-T5. Government of Canada. Accessed 13 Oct 2016
  12. Carter TE, Wilson RF (1998) Soybean quality for human consumption. In: Andrew J (ed) Proceedings of the 10th Australian soybean conference. CSIRO Tropical Agriculture, St. Lucia, pp 1–16Google Scholar
  13. Carter A, Rajcan I, Woodrow L, Navabi A, Eskandari M (2018) Genotype, environment, and genotype by environment interaction for seed isoflavone concentration in soybean grown in soybean cyst nematode infested and non-infested environments. Field Crop Res 216:189–196. CrossRefGoogle Scholar
  14. Casanoves F, Baldessari J, Balzarini M (2005) Evaluation of multi-environment trials of peanut cultivars. Crop Sci 45:18–26. CrossRefGoogle Scholar
  15. Cornelius PL, Crossa J, Seyedsadr MS (1996) Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. In: Kang MS, Gauch HG (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 199–234Google Scholar
  16. Cui Z, James AT, Mizazaki S, Wilson RF, Carter TE (2004) Breeding specialty soybeans for traditional and new soyfoods. In: Liu K (ed) Soybeans as functional foods and ingredients. AOCS Press, Champaign, pp 264–322Google Scholar
  17. Dardanelli JL, Balzarini M, Martinez MJ, Cuniberti M, Resnik S, Ramunda SF et al (2006) Soybean maturity groups, environments, and their interaction define mega-environments for seed composition in Argentina. Crop Sci 46:1939–1947. CrossRefGoogle Scholar
  18. Eskandari M, Ablett GR, Rajcan I, Fischer D, Stirling BT (2016a) OAC Prosper soybean. Can J Plant Sci 97:337–339. CrossRefGoogle Scholar
  19. Eskandari M, Ablett GR, Rajcan I, Stirling BT, Fischer D (2016b) Candor soybean. Can J Plant Sci 97:390–392. CrossRefGoogle Scholar
  20. Eskandari M, Ablett GR, Rajcan I, Stirling BT, Fischer D (2017) OAC Brooke soybean. Can J Plant Sci 97:199–201. CrossRefGoogle Scholar
  21. Fu YB, Peterson GW, Morrison MJ (2007) Genetic diversity of Canadian soybean cultivars and exotic germplasm revealed by simple sequence repeat markers. Crop Sci 47:1947–1954CrossRefGoogle Scholar
  22. Gauch HG, Zobel RW (1988) Predictive and postdictive success of statistical analysis of yield trials. Theor Appl Genet 76:1–10CrossRefGoogle Scholar
  23. Hasler CM (1998) Scientific status summary on functional foods: their role in disease prevention and health promotion. Food Technol 52:63–70. CrossRefGoogle Scholar
  24. Helms TC, Orf JH (1998) Protein, oil, and yield of soybean lines selected for increased protein. Crop Sci 38:707–711. CrossRefGoogle Scholar
  25. Kang MS, Aggarwal VD, Chirwa RM (2006) Adaptability and stability of bean cultivars as determined via yield-stability statistic and GGE biplot analysis. J Crop Improv 15:97–120CrossRefGoogle Scholar
  26. Kim Y, Wicker L (2005) Soybean cultivars impact quality and function of soymilk and tofu. J Sci Food Agric 85:2514–2518. CrossRefGoogle Scholar
  27. Kim M, Schultz S, Nelson RL, Diers BW (2016) Identification and fine mapping of a soybean seed protein QTL from PI407788A on chromosome 15. Crop Sci 56:219–225. CrossRefGoogle Scholar
  28. Malvar RA, Revilla P, Butron A, Gouesnard B, Boyat A, Soengas P et al (2005) Performance of crosses among French and Spanish maize populations across environments. Crop Sci 45:1052–1057CrossRefGoogle Scholar
  29. Messina M (1995) Modern applications for an ancient bean: soybeans and the prevention and treatment of chronic disease. J Nutr 125:567–569. CrossRefGoogle Scholar
  30. Mian R, McHale L, Li Z, Dorrance AE (2017) Registration of ‘Highpro1’ soybean with high protein and high yield developed from a north × south cross. J Plant Reg 11:51–54CrossRefGoogle Scholar
  31. Microsoft Canada Inc. “Microsoft Excel®.” Microsoft Canada Inc., Mississauga, Ontario, CanadaGoogle Scholar
  32. Ontario Ministry of Agriculture Food and Rural Affairs (2016) Provincial field crop production and prices. Government of Ontario. Accessed 21 Dec 2017
  33. Ontario Soybean and Canola Committee (2017) Soybean food quality report. Accessed 21 Dec 2017
  34. Pantalone VR, Smallwood C (2018) Registration of ‘TN11-5102’ soybean cultivar with high yield and high protein meal. J Plant Regist 12:1–5CrossRefGoogle Scholar
  35. Panthee DR, Pantalone VR (2006) Registration of soybean germplasm lines TN03-350 and TN04-5321 with improved protein concentration and quality registration by CSSA. Crop Sci 46:2328–2329CrossRefGoogle Scholar
  36. Perten Instruments Canada. “DA 7250 NIR analyzer.” Perten Instruments Canada, Winnipeg, Manitoba, CanadaGoogle Scholar
  37. Phuke et al (2017) Genetic Variability, Genotype × Environment Interaction, Correlation, and GGE BiplotAnalysis for Grain Iron and Zinc Concentration and Other Agronomic Traits in RIL Population of Sorghum (Sorghum bicolor L. Moench). Front Plant Sci. CrossRefGoogle Scholar
  38. Poysa V, Buzzell RI (2001) AC X790P soybean. Can J Plant Sci 81:447–448. CrossRefGoogle Scholar
  39. Poysa V, Woodrow L (2002) Stability of soybean seed composition and its effect on soymilk and tofu yield and quality. Food Res Int 35:337–345. CrossRefGoogle Scholar
  40. Poysa V, Woodrow L, Yu K (2013) AAC Malden soybean. Can J Plant Sci 93:1277–1279. CrossRefGoogle Scholar
  41. Primomo VS, Falk DE, Ablett GR, Tanner JW, Rajcan I (2002) Genotype × environment interactions, stability, and agronomic performance of soybean with altered fatty acid profiles. Crop Sci 42:37–44CrossRefGoogle Scholar
  42. Rao MS, Mullinix BG, Rangappa M, Cebert E, Bhagsari AS, Sapra VT et al (2002) Genotype × environment interactions and yield stability of food-grade soybean genotypes. Agron J 94:72–80CrossRefGoogle Scholar
  43. Samonte SO, Wilson LT, McClung AM, Medley JC (2005) Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Sci 45:2414–2424CrossRefGoogle Scholar
  44. SAS Institiute Inc. “SAS 9.4.” SAS Institute Inc., Cary, North Carolina, USAGoogle Scholar
  45. Schaefer MJ, Love J (1992) Relationships between soybean components and tofu texture. J Food Qual 15:53–66. CrossRefGoogle Scholar
  46. Shannon G, Wilcox JR, Probst AH (1972) Estimated gains from selection for protein and yield in the F4 generation of six soybean populations. Crop Sci 12:824–826. CrossRefGoogle Scholar
  47. Shen CF, DeMan L, Buzzell RI, DeMan JM (1991) Yield and quality of tofu as affected by soybean and soymilk characteristics: glucono-δ-lactone coagulant. J Food Sci 56:109–112. CrossRefGoogle Scholar
  48. Stanojevic SP, Barac MB, Pesic MB, Vucelic-Radovic BV (2011) Assessment of soy genotype and processing method on quality of soybean tofu. J Agric Food Chem 59:7368–7376. CrossRefPubMedGoogle Scholar
  49. Sudarić A, Šimić D, Vratarić M (2006) Characterization of genotype by environment interactions in soybean breeding programmes of southeast Europe. Plant Breed 125:191–194. CrossRefGoogle Scholar
  50. Vollmann J, Fritz CN, Wagentristl H, Ruckenbauer P (2000) Environmental and genetic variation of soybean seed protein content under Central European growing conditions. J Sci Food Agric 80:1300–1306.;2-I CrossRefGoogle Scholar
  51. Vratarić M, Sudarić A, Sudar R (2001) Soybean breeding on grain yield and grain quality. In: Lelas V (ed) Proceedings of the fourth Croatian congress of food technologists, biotechnologists and nutritionists central European meeting. Cereal Research Communications, Opatija, pp 233–238Google Scholar
  52. Wang HL, Hesseltine CW (1982) Coagulation conditions in tofu processing. Process Biochem 17:7–12Google Scholar
  53. Wilcox JR, Cavins JF (1995) Backcrossing high seed protein to a soybean cultivar. Crop Sci 35:1036–1041. CrossRefGoogle Scholar
  54. Wilkinson GN, Eckert SR, Hancock TW, Mayo O (1983) Nearest neighbor (NN) analysis of field experiments. J R Stat Soc B 45:151–211Google Scholar
  55. Wilson RF (2004) Seed composition. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses, Monograph 16, 2004. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, pp 621–668.Google Scholar
  56. Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca RatonGoogle Scholar
  57. Yan W, Hunt L, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40(3):597. CrossRefGoogle Scholar
  58. Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643–655. CrossRefGoogle Scholar
  59. Yang R, Juskiw P (2011) Analysis of covariance in agronomy and crop research. Can J Plant Sci 91:621–641. CrossRefGoogle Scholar
  60. Yan W, Rajcan I (2002) Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci 42:11–20. CrossRefPubMedGoogle Scholar
  61. Yin X, Vyn TJ (2005) Relationships of isoflavone, oil, and protein in seed with yield of soybean. Agron J 97:1314–1321. CrossRefGoogle Scholar
  62. Yu K, Woodrow L, Poysa V (2015) AAC 26-15 soybean. Can J Plant Sci 95:441–443. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Plant AgricultureUniversity of GuelphGuelphCanada

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