Molecular Breeding

, Volume 34, Issue 2, pp 739–748 | Cite as

Transferring a major QTL for oil content using marker-assisted backcrossing into an elite hybrid to increase the oil content in maize

Article

Abstract

Maize oil is highly valued as a human vegetable oil and in animal feed. The normal hybrid, Zhengdan958, is one of the most widely distributed varieties in China. To increase its oil content, a favorable allele of a major quantitative trait locus for oil content, qHO6 on chromosome 6 from the high-oil inbred line, By804, was transferred into the two parents of Zhengdan958, Zheng58, and Chang7-2, using marker-assisted backcrossing. Two improved inbred lines, Zheng58-qHO6 and Chang7-2-qHO6, were developed through six generations of backcrosses guided by molecular markers. An approximately 260-kb fragment from the donor parent was transferred into recurrent lines, and over 99 % of recurrent genomes were recovered. Both of the improved inbred lines showed increased oil content of roughly 1 % without a change in grain weight. Consequently, the oil content in improved Zhengdan958-qHO6, crossed from Zheng58-qHO6 to Chang7-2-qHO6, reached 4.5 %, with increases in absolute and relative content of 0.71 and 18 %, respectively, compared with the original Zhengdan958. The grain yield of the improved Zhengdan958-qHO6 ranged from 5,928 to 11,826 kg/ha in ten environments, similar to the original Zhengdan958. This study provides a practical example of the feasibility of improving quantitative traits by transferring desirable alleles using marker-assisted backcrossing.

Keywords

Maize Oil content QTL Marker-assisted backcrossing Zhengdan958 

Notes

Acknowledgments

We greatly appreciate the helpful comments on the manuscript from two anonymous reviewers. Financial support was provided by the Chinese High Technology Project (2012AA101104) and the National Natural Science Foundation of China (31361140362).

Supplementary material

11032_2014_71_MOESM1_ESM.doc (245 kb)
Supplementary material 1 (DOC 245 kb)

References

  1. Andersen JR, Lübberstedt T (2003) Functional markers in plant. Trends Plant Sci 8:554–560PubMedCrossRefGoogle Scholar
  2. Babu R, Nair SK, Kumar A, Venkatesh S, Sekhar JC, Singh NN, Srinivasan G, Gupta HS (2005) Two-generation marker-aided backcrossing for rapid conversion of normal maize lines to quality protein maize (QPM). Theor Appl Genet 111(5):888–897PubMedCrossRefGoogle Scholar
  3. Berke T, Rocheford TR (1995) Quantitative trait loci for flowering, plant and ear height, and kernel traits in maize. Crop Sci 35:1542–1549CrossRefGoogle Scholar
  4. Butlin RK, Ritchie MG (1989) Genetic coupling in mate recognition systems: what is the evidence? Biol J Linn Soc Lond 37(3):237–246CrossRefGoogle Scholar
  5. Chai YC, Hao XM, Yang XH, Allen WB, Li JM, Yan JB, Shen B, Li JS (2012) Validation of DGAT1-2 polymorphisms associated with oil content and development of functional markers for molecular breeding of high-oil maize. Mol Breed 29:939–949CrossRefGoogle Scholar
  6. Clark D, Dudley JW, Rocheford TR, Ledeaux JR (2006) Genetic analysis of corn kernel chemical composition in the random mated 10 generation of the cross of generation 70 of IHO 9 ILO. Crop Sci 46:807–819CrossRefGoogle Scholar
  7. Collard BCY, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Phil Trans R Soc B 363:557–572PubMedCentralPubMedCrossRefGoogle Scholar
  8. Cook JP, McMullen MD, Holland JB, Tian F, Bradbury P, Ross-Ibarra J, Buckler ES, Flint-Garcia FA (2012) Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels. Plant Physiol 158:824–834PubMedCentralPubMedCrossRefGoogle Scholar
  9. Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet 3:22–32PubMedCrossRefGoogle Scholar
  10. Dudley JW (1977) Seventy-six generation of selection for oil and protein percentage in maize. In: Pollak E (ed) Proceedings of international conference on quantitative genetics. Iowa State University Press, Ames, pp 459–473Google Scholar
  11. Dudley JW, Lambert RJ (2004) 100 generations of selection for oil and protein in corn. Plant Breed Rev 24:79–110Google Scholar
  12. Ganal MW, Durstewitz G, Polley A, Be′rard A, Buckler ES et al (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:e28334PubMedCentralPubMedCrossRefGoogle Scholar
  13. Gao S, Martinez C, Skinner DJ et al (2008) Development of a seed DNA-based genotyping system for marker-assisted selection in maize. Mol Breed 22:477–494CrossRefGoogle Scholar
  14. Goldman IL, Rocheford TR, Duldey JW (1994) Molecular markers associated with maize kernel oil content in an illinois high protein × illinois low protein cross. Crop Sci 34:908–915CrossRefGoogle Scholar
  15. Guo YQ, Yang XH, Chander S, Yan JB, Zhang J, Song TM, Li JS (2013) Identification of unconditional and conditional QTL for oil, protein and starch content in maize. Crop J 1:34–42Google Scholar
  16. Han Y, Parsons CM, Alexander DE (1987) Nutritive value of high oil for poultry. Poult Sci 66:103–111PubMedCrossRefGoogle Scholar
  17. Han J, Wang HW, Chen SJ (2008) QTL mapping of kernel oil content of chromosome 6 in a high oil maize mutant (Zea mays L.) genes. Genomics 30:373–382Google Scholar
  18. Harjes CE, Rocheford TR, Bai L, Brutnell TP, Kandianis CB, Sowinski SG, Stapleton AE, Vallabhaneni R, Williams M, Wurtzel ET, Yan JB, Buckler ES (2008) Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319:330–333PubMedCentralPubMedCrossRefGoogle Scholar
  19. Hospital F (2009) Challenges for effective marker-assisted selection in plants. Genetica 136:303–310PubMedCrossRefGoogle Scholar
  20. Hoy RR, Hahn J, Paul RC (1977) Hybrid cricket auditory behavior: evidence for genetic coupling in animal communication. Science 195(4273):82–84PubMedCrossRefGoogle Scholar
  21. Lambert RJ (2001) High-oil corn hybrids. In: Hallau AR (ed) Special corn. CRC Press, Boca Raton, pp 131–153Google Scholar
  22. Lambert RJ, Alexander DE, Mejaya IJ (2004) Single kernel selection for increased grain oil in maize synthetics and high-oil hybrid development. Plant Breed Rev 24:153–175Google Scholar
  23. Laurie CC, Chasalow SD, LeDeaux JR, McCarroll R, Bush D, Hauge B, Lai CQ, Clark D, Rocheford TR, Dudley JW (2004) The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics 168:2141–2155PubMedCentralPubMedCrossRefGoogle Scholar
  24. Li H, Peng ZY, Yang XH*, Wang WD, Fu JJ, Wang JH, Han YJ, Chai YC, Guo TT, Yang N, Liu J, Warburton ML, Cheng YB, Hao XM, Zhang P, Zhao JY, Liu YJ, Wang GY, Li JS, Yan JB (2013) Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet 45:43–50PubMedCrossRefGoogle Scholar
  25. Liang FS, Deng QY, Wang YG, Xiong YD, Jin DM, Li JM, Wang B (2004) Molecular marker-assisted selection for yield-enhancing genes in the progeny of “9311 × O. rufipogon” using SSR. Euphytica 139:159–165CrossRefGoogle Scholar
  26. Mangolin CA, de Souza CL, Garcia AAF Jr, Garcia AF, Sibov ST, de Souza AP (2004) Mapping QTLs for kernel oil content in a tropical maize population. Euphytica 137:251–259CrossRefGoogle Scholar
  27. Misevic D, Alexander DE (1989) Twenty-four cycles of phenotypic recurrent selection for percent oil in maize: I. Per se and test-cross performance. Crop Sci 29:320–324CrossRefGoogle Scholar
  28. Moose SP, Dudley JW, Rocheford TR (2004) Maize selection passes the century mark: a unique resource for 21st century genomics. Trends Plant Sci 9:358–364PubMedCrossRefGoogle Scholar
  29. Murry MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res 8:4321–4343CrossRefGoogle Scholar
  30. Ribaut JM, Ragot M (2007) Marker-assisted selection to improve drought adaptation in maize: the backcross approach, perspectives, limitations, and alternatives. J Exp Bot 58:351–360PubMedCrossRefGoogle Scholar
  31. Song TM, Chen SJ (2004) Long term selection for oil concentration in five maize populations. Maydica 49:9–14Google Scholar
  32. Song XF, Song TM, Dai JR, Rocheford TR, Li JS (2004) QTL mapping of kernel oil concentration with high-oil maize by SSR markers. Maydica 49:41–48Google Scholar
  33. Stearns FW (2010) One hundred years of pleiotropy: a retrospective. Genetics 186:767–773PubMedCentralPubMedCrossRefGoogle Scholar
  34. Sun F, Liu P, Ye J, Lo LC, Cao SY, Li L, Yue GH, Wang CM (2012) An approach for jatropha improvement using pleiotropic QTLs regulating plant growth and seed yield. Biotechnol Biofuels 2012(5):42–51CrossRefGoogle Scholar
  35. Toojinda T, Baird E, Booth A, Broers L, Hayes P, Powell W, Thomas W, Vivar H, Young G (1998) Introgression of quantitative trait loci (QTLs) determining stripe rust resistance in barley: an example of marker-assisted line development. Theor Appl Genet 96(1):123–131CrossRefGoogle Scholar
  36. Wassom JJ, Mikkelineni V, Bohn MO, Rocheford TR (2008a) QTL for fatty acid composition of maize kernel oil in Illinois High Oil 9 B73 backcross-derived lines. Crop Sci 48:69–78CrossRefGoogle Scholar
  37. Wassom JJ, Wong JC, Martinez E, King JJ, DeBaene J, Hotchkiss JR, Mikkilineni V, Bohnh MO, Rocheford TR (2008b) QTL associated with maize kernel oil, protein, and starch concentrations; kernel mass; and grain yield in Illinois High Oil 9 B73 backcross-derived lines. Crop Sci 48:243–252CrossRefGoogle Scholar
  38. Xu YB, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407CrossRefGoogle Scholar
  39. Yan JB, Kandianis CB, Harjes CE, Bai L, Kim EH, Yang XH, Skinner D, Fu ZY, Mitchell S, Li Q, Fernandez MGS, Zaharieva M, Babu R, Fu Y, Palacios N, Li JS, DellaPenna D, Brutnell Buckler ES, Warburton ML, Rocheford T (2010a) Rare genetic variation at Zea mays crtRB1 increases b-carotene in maize grain. Nat Genet 42:322–327PubMedCrossRefGoogle Scholar
  40. Yan JB, Yang XH, Shah T, Sánchez-Villeda H, Li JS, Warburton ML, Zhou Y, Crouch JH, Xu YB (2010b) High-throughput SNP genotyping with the GoldenGate assay in maize. Mol Breed 25:441–451CrossRefGoogle Scholar
  41. Yang XH, Guo YQ, Yan JB, Zhang J, Song TM, Rocheford T, Li JS (2010) Major and minor QTL and epistasis contribute to fatty acid compositions and oil concentration in high-oil maize. Theor Appl Genet 120:665–678PubMedCrossRefGoogle Scholar
  42. Yang XH, Ma HL, Zhang P, Yan JB, Guo YQ, Song TM, Li JS (2012) Characterization of QTL for oil content in maize kernel. Theor Appl Genet 125:1169–1179PubMedCrossRefGoogle Scholar
  43. Yang LQ, Wang W, Yang WP, Wang MC (2013) Marker-assisted selection for pyramiding the waxy and opaque-16 genes in maize using cross and backcross schemes. Mol Breeding 31:767–775CrossRefGoogle Scholar
  44. Zhang J, Lu XQ, Song XF, Yan JB, Song TM, Dai JR, Rocheford T, Li JS (2008) Mapping quantitative trait loci for oil, starch, and protein concentrations in grain with high-oil maize by SSR markers. Euphytica 162:335–344CrossRefGoogle Scholar
  45. Zhao XL, Tan GQ, Xing YX, Wei L, Chao Q, Zuo WL, Lübberstedt T, Xu ML (2012) Marker-assisted introgression of qHSR1 to improve maize resistance to head smut. Mol Breeding 30:1077–1088CrossRefGoogle Scholar
  46. Zheng PZ, Allen WB, Roesler K, Williams ME, Zhang SR, Li JM, Glassman K, Ranch J, Nubel D, Solawetz W, Bhattramakki D, Llaca V, Deschamps S, Zhong GY, Tarczynski MC, Shen B (2008) A phenylalanine in DGAT is a key determinant of oil content and composition in maize. Nat Genet 40:367–372PubMedCrossRefGoogle Scholar
  47. Zhou PH, Tan YF, He YQ, Xu CG, Zhang Q (2003) Simultaneous improvement for four quality traits of Zhenshan 97, an elite parent of hybrid rice, by molecular marker-assisted selection. Theor Appl Genet 106:326–331PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic ImprovementChina Agricultural UniversityBeijingChina

Personalised recommendations