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Map** and identification of QTL in 5601T × U99-310255 RIL population using SNP genoty**: soybean seed quality traits

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Abstract

Background

Molecular markers have played and will continue to play a major role in the genetic characterization and improvement of soybeans. They have helped identify major loci for tolerance to abiotic stressors, disease resistance, herbicide resistance, soybean seed quality traits, and yield. However, most yield quantitative trait loci (QTL) are specific to a certain population, and the genetic variation found in the specific bi-parental population is not always shared in other populations. A major objective in soybean breeding is to develop high yielding cultivars. Unfortunately, soybean seed yield, as well as protein and oil content, are complex quantitative traits to characterize from the phenotypic and genotypic perspectives. The objectives of this study are to detect soybean genomic regions that increase protein content, while maintaining oil content and seed yield and to successfully identify soybean QTL associated with these seed quality traits.

Methods and results

To achieve these objectives, data from the 138 recombinant inbred lines grown in six environments were used to perform QTL detection analyses in search of significant genomic regions affecting soybean seed protein, oil, and yield.

Conclusions

A total of 21 QTL were successfully identified for yield, protein, oil, methionine, threonine, lodging, maturity, and meal. Knowledge of their locations and flanking markers will aid in marker assisted selection for plant breeders. This will lead to a more valuable soybean for farmers, processors, and animal nutritionists.

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Aoyagi A, Shurtleff W (2004) History of world soybean production and trade—part 1. Soyinfo Center. Available at http://www.soyinfocenter.com/HSS/production_and_trade1.php

  2. Diers BW, Keim P, Fehr WR, Shoemaker RC (1992) RFLP analysis of soybean seed protein and oil content. Theor Appl Genet 83:608–612

    CAS  PubMed  Google Scholar 

  3. Miller-Garvin J, Naeve SL (2015) United States soybean quality annual report 2015. Available at http://ussec.org/wp-content/uploads/2016/01/2015-Final-US-Commodity-Soybean-Quality-Report.pdf

  4. Warrington CV, Abdel-Haleem H, Hyten DL, Cregan PB, Orf JH, Killam AS, Bajjalieh N, Li Z, Boerma HR (2015) QTL for seed protein and amino acids in the Benning x Danbaekkong soybean population. Theor Appl Genet 128:2474

    Google Scholar 

  5. Wilcox JR (1998) Increasing seed protein in soybean with eight cycles of recurrent selection. Crop Sci 38:1536–1540

    Google Scholar 

  6. Wilcox JR, Shibles RM (2001) Interrelationships among seed quality attributes in soybean. Crop Sci 41:11–14

    CAS  Google Scholar 

  7. Wilson RF (2004) Seed composition. In: Boerma HR, Specht JE (eds) Soybeans: improvement, production, and uses, 3rd edn. ASA Inc., Madison, pp 621–669

    Google Scholar 

  8. Chung J, Babka HL, Graef GL, Staswick PE, Lee DJ, Cregan PB, Shoemaker RC, Specht JE (2003) The seed protein, oil and yield QTL on soybean linkage group I. Crop Sci 43:1053–1067

    CAS  Google Scholar 

  9. Cromwell GL (2012) Soybean meal—an exceptional protein source. Soybean Meal INFOcenter. Available at http://www.soymeal.org/ReviewPapers/SBMExceptionalProteinSource.pdf

  10. Soy Stats (2018) 2017 Soystats: a reference guide to important soybean facts and figures. The American Soybean Association. Available at http://soystats.com/2017-soystats/

  11. Friedman M, Brandon DL (2001) Nutritional and health benefits of soy proteins. J Agric Food Chem 49:1069–1086

    CAS  PubMed  Google Scholar 

  12. Thakur M, Hurburgh CR (2007) Quality of US soybean meal compared to the quality of soybean meal from other origins. J Am Oil Chem Soc 84:835–843

    CAS  Google Scholar 

  13. Baker KM, Utterback PL, Parsons CM, Stein HH (2011) Nutritional value of soybean meal produced from conventional, high-protein, or low-oligosaccharide varieties of soybeans and fed to broiler chicks. Poult Sci 90:390–395

    CAS  PubMed  Google Scholar 

  14. Qi Z, Wu Q, Han X, Sun Y, Du X, Liu C, Jiang H, Hu G, Chen Q (2011) Soybean oil content QTL map** and integrating with meta-analysis method for mining genes. Euphytica 179:499–514

    Google Scholar 

  15. Eskandari M, Cober ER, Rajcan I (2013) Genetic control of soybean seed oil: I. QTL and genes associated with seed oil concentration in RIL populations derived from crossing moderately high-oil parents. Theor Appl Genet 126:483–495

    CAS  PubMed  Google Scholar 

  16. Yesudas CR, Bashir R, Geisler MB, Lightfoot DA (2013) Identification of germplasm with stacked QTL underlying seed traits in an inbred soybean population from cultivars Essex and Forrest. Mol Breeding 31:693–703

    CAS  Google Scholar 

  17. Brummer EC, Graef GL, Orf J, Wilcox JR, Shoemaker RC (1997) Map** QTL for seed protein and oil content in eight soybean populations. Crop Sci 37:370–378

    Google Scholar 

  18. Cober ER, Voldeng HD (2000) Develo** high-protein, high-yield soybean populations and lines. Crop Sci 40:39–42

    Google Scholar 

  19. Eskandari M, Cober ER, Rajcan I (2013) Genetic control of soybean seed oil: II. QTL and genes that increase oil concentration without decreasing protein or with increased yield. Theor Appl Genet 126:1677–1687

    CAS  PubMed  Google Scholar 

  20. Krishnan HB, Natarajan SS, Mahmoud AA, Nelson RL (2007) Identification of glycinin and β-conglycinin subunits that contribute to the increased protein content of high-protein soybean lines. J Agric Food Chem 55:1839–1845

    CAS  PubMed  Google Scholar 

  21. Lee J-D, Shannon G, Choung M-G (2010) Selection for protein content in soybean from single F2 seed by near infrared reflectance spectroscopy. Euphytica 172:117–123

    CAS  Google Scholar 

  22. Phansak P, Soonsuwon W, Hyten DL, Song Q, Cregan PB, Graef GL, Specht JE (2016) Multi-population selective genoty** to identify soybean [Glycine max (L.) Merr.] seed protein and oil QTLs. G3-Genes Genom Genet 6:1635–1646

    CAS  Google Scholar 

  23. Yaklich RW (2001) β-Conglycinin and glycinin in high-protein soybean seeds. J Agric Food Chem 49:729–735

    CAS  PubMed  Google Scholar 

  24. Morrison MJ, Cober EK, Saleem MF, McLaughlin NB, Fregeau-Reid J, Ma BL, Yan W, Woodrow L (2008) Changes in isoflavone concentration with 58 years of genetic improvement of short-season soybean cultivars in Canada. Crop Sci 48:2201–2208

    Google Scholar 

  25. Bernardo R (2014) Process of plant breeding. Essentials of plant breeding. Stemma Press, Woodbury, pp 23–69

    Google Scholar 

  26. Bernardo R (2014) Genetic basis of plant improvement. Essentials of plant breeding. Stemma Press, Woodbury, pp 71–93

    Google Scholar 

  27. Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) map** and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142:169–196

    CAS  Google Scholar 

  28. Hyten DL, Cannon SB, Song Q, Weeks N, Fickus EW, Shoemaker RC, Specht JE, Farmer AD, May GD, Cregan PB (2010) High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. BMC Genomics 11:38–45

    PubMed  PubMed Central  Google Scholar 

  29. Pollard DA (2012) Design and construction of recombinant inbred lines. Methods Mol Biol 871:31–39

    CAS  PubMed  Google Scholar 

  30. Broman KW (2005) The genomes of recombinant inbred lines. Genetics 169:1133–1146

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Pantalone V, Cunicelli M, Wyman C (2021) Registration of soybean cultivar TN15-5007 with high meal protein. J Plant Regist 14:139–143

    Google Scholar 

  32. Pantalone VR, Allen FL, Landau-Ellis D (2003) Registration of ‘5601T’ soybean. Crop Sci 43:1123–1124

    Google Scholar 

  33. Bernardo R (2014) Genetic basis of plant improvement. Essentials of plant breeding. Stemma Press, Woodbury, pp 119–146

    Google Scholar 

  34. Wiggins B, Wiggins S, Cunicelli M, Smallwood C, Allen F, West D, Pantalone V (2019) Genetic gain for soybean seed protein, oil, and yiled in a recombinant inbred line population. J Am Oil Chem Soc 96:43–50

  35. Nyquist WE (1991) Estimation of heritability and prediction of selection response in plant populations. CRC Crit Rev Plant Sci 10:235–322

    Google Scholar 

  36. Contreras-Soto RI, Berwanger de Oliveira M, Costenaro-da-Silva D, Scapim CA, Schuster I (2017) Population structure, genetic relatedness and linkage disequilibrium bocks in cultivars of tropical soybean (Glycine max). Euphytica 213:173–184

    Google Scholar 

  37. Van Ooijen JW (2006) JoinMap ® 4 Software for the calculation of genetic linkage maps in experimental populations. Kyazma B.V., Wageningen

    Google Scholar 

  38. Kosambi DD (1944) The estimation of map distance from recombination values. Ann Eugen 12:172–175

    Google Scholar 

  39. Wang S, Basten CJ, Gaffney P, Zeng ZB (2010) Windows QTL Cartographer 2.5 user manual. Bioinformatics Research Center, North Carolina State Univ., Raleigh

    Google Scholar 

  40. Song Q, Hyten DL, Jia G, Quigley CV, Fickus EW, Nelson RL (2015) Fingerprinting soybean germplasm and its utility in genomic research. G3: Genes Genom Genet 50:1999–2006

    Google Scholar 

  41. Grant D, Nelson RT, Cannon SB, Shoemaker RC (2010) SoyBase, the USDA-ARS soybean genetics and genomics database. Nucl Acids Res 38(suppl 1):D843–D846. https://doi.org/10.1093/nar/gkp798

    Article  CAS  PubMed  Google Scholar 

  42. Fasoula VA, Harris DK, Boerma HR (2004) (2004) Validatioon and designation of quantitative trait loci for seed protein, seed oil, and seed weight from two soybean populations. Crop Sci 44:1218–1225

    CAS  Google Scholar 

  43. Lee SH, Bailey MA, Mian MAR, Carter TE Jr., Shipe ER, Ashley DA, Parrott WA, Hussey RS, Boerma HR (1996) RFLP loci associated with soybean seed protein and oil content across populations and locations. Theor Appl Genet 93:649–657

    CAS  PubMed  Google Scholar 

  44. Mao T, Jiang Z, Han Y, Teng W, Zhao X, Li W (2013) Identification of quantitative trait loci underlying seed protein and oil contents of soybean across multi-genetic backgrounds and environments. Plant Breed 132:630–641

    CAS  Google Scholar 

  45. Sun Y, Luan H, Qi Z, Shan D, Liu C, Hu G, Chen Q, Qi Z, Wu Q, Han X, Sun Y, Du X, Liu C, Jiang H, Hu G, Chen Q, Shan D (2011) An integrated quantitative trait locus map of oil content in soybean, Glycine max (L.) Merr., generated using a meta-analysis method for mining genes. Euphytica 179:499–514

    Google Scholar 

  46. Reinprecht Y, Poysa V, Yu K, Rajcan I, Ablett G, Pauls K (2006) Seed and agronomic QTL in low linolenic acid, lipoxygenase-free soybean (Glycine max (L.) Merrill) germplasm. Genome 49:1510–1527

    CAS  PubMed  Google Scholar 

  47. Zhang W, Wang Y, Luo G, Zhang J, He C, Wu X, Gai J, Chen S (2004) QTL map** of ten agronomic traits on the soybean (Glycine max L. Merr) genetic map and their association with EST markers. Theor Appl Genet 108:1131–1139

    CAS  PubMed  Google Scholar 

  48. Song Q, Jenkins J, Jia G, Hyten DL, Pantalone V, Jackson SA, Schmutz J, Cregan PB (2016) Construction of high resolution genetic linkage maps to improve the soybean genome sequence assembly Glyma1.01. BMC Genomics 17:33–43

    PubMed  PubMed Central  Google Scholar 

  49. Brim CA, Burton JW (1979) Recurrent selection in soybeans. II. Selection for increased percent in protein in seeds. Crop Sci 19:494–498

    Google Scholar 

  50. Pantalone VR, Smallwood CJ (2018) Registration of ‘TN11-5102’ soybean cultivar with high yield and high protein meal. J Plant Regist 12:304–308

    Google Scholar 

  51. Panthee DR, Pantalone VR, Sams CE, Saxton AM, West DR, Orf JH, Killam AS (2006) Quantitative trait loci controlling sulfur containing amino acids, methionine and cysteine, in soybean seeds. Theor Appl Genet 112:546–553

    CAS  PubMed  Google Scholar 

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Acknowledgements

The Tennessee Soybean Promotion Board and the United Soybean Board are gratefully acknowledged for funding this project. We appreciate the support at all the Research and Education Centers and Experiment Stations that made this work possible.

Funding

This work was funded by the Tennessee Soybean Promotion Board and the United Soybean Board.

Author information

Authors and Affiliations

  1. USDA-ARS, 3127 Ligon St., Raleigh, NC, 27607, USA

    Mia Cunicelli

  2. Department of Plant Sciences, University of Tennessee, 112 Plant Biotechnology Bldg. 2505 EJ Chapman Dr., Knoxville, TN, 37996, USA

    Carl Sams, Dennis West & Vincent Pantalone

  3. Department of Entomology and Plant Pathology, University of Tennessee, 352 Plant Biotechnology Bldg. 2505 EJ Chapman Dr., Knoxville, TN, 37996, USA

    Bode A. Olukolu

  4. Department of Animal Sciences, University of Tennessee, 232 Brehm Animal Science Bldg. 2506 River Dr., Knoxville, TN, 37996, USA

    Liesel Schneider

Authors
  1. Mia Cunicelli
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  3. Carl Sams
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  4. Liesel Schneider
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  6. Vincent Pantalone
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Contributions

VP and MC were responsible for study conception and design. Material preparation, data collection, and analysis were performed by MC. BO and LS consulted on statistical analysis. CS and DW contributed through editing and sitting on project committee. The first draft of the manuscript was written by MC and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mia Cunicelli.

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Cunicelli, M., Olukolu, B.A., Sams, C. et al. Map** and identification of QTL in 5601T × U99-310255 RIL population using SNP genoty**: soybean seed quality traits. Mol Biol Rep 49, 6623–6632 (2022). https://doi.org/10.1007/s11033-022-07505-y

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