Theoretical and Applied Genetics

, Volume 128, Issue 9, pp 1839–1854 | Cite as

Fine mapping of Msv1, a major QTL for resistance to Maize Streak Virus leads to development of production markers for breeding pipelines

  • Sudha K. Nair
  • Raman Babu
  • Cosmos Magorokosho
  • George Mahuku
  • Kassa Semagn
  • Yoseph Beyene
  • Biswanath Das
  • Dan Makumbi
  • P. Lava Kumar
  • Michael Olsen
  • Prasanna M. Boddupalli
Original Paper


Key message

Msv1 , the major QTL for MSV resistance was delimited to an interval of 0.87 cM on chromosome 1 at 87 Mb and production markers with high prediction accuracy were developed.


Maize streak virus (MSV) disease is a devastating disease in the Sub-Saharan Africa (SSA), which causes significant yield loss in maize. Resistance to MSV has previously been mapped to a major QTL (Msv1) on chromosome 1 that is germplasm and environment independent and to several minor loci elsewhere in the genome. In this study, Msv1 was fine-mapped through QTL isogenic recombinant strategy using a large F 2 population of CML206 × CML312 to an interval of 0.87 cM on chromosome 1. Genome-wide association study was conducted in the DTMA (Drought Tolerant Maize for Africa)-Association mapping panel with 278 tropical/sub-tropical breeding lines from CIMMYT using the high-density genotyping-by-sequencing (GBS) markers. This study identified 19 SNPs in the region between 82 and 93 Mb on chromosome 1(B73 RefGen_V2) at a P < 1.00E-04, which coincided with the fine-mapped region of Msv1. Haplotype trend regression identified a haplotype block significantly associated with response to MSV. Three SNPs in this haplotype block at 87 Mb on chromosome 1 had an accuracy of 0.94 in predicting the disease reaction in a collection of breeding lines with known responses to MSV infection. In two biparental populations, selection for resistant Msv1 haplotype demonstrated a reduction of 1.03–1.39 units on a rating scale of 1–5, compared to the susceptible haplotype. High-throughput KASP assays have been developed for these three SNPs to enable routine marker screening in the breeding pipeline for MSV resistance.


Quantitative Trait Locus Major Quantitative Trait Locus Linkage Disequilibrium Decay Gray Leaf Spot Maize Streak Virus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge the financial support received from the Bill and Melinda Gates Foundation (BMGF) as part of the project, “Drought Tolerant Maize for Africa (DTMA)”. We thank CGIAR Research Program (CRP) on MAIZE for co-sponsoring this research work. The biparental populations and disease data from Kevin Pixley, CIMMYT and Jean-Marcel Ribaut, GCP, used in initial QTL mapping and validation study are thankfully acknowledged. Assistance for data analysis provided by Jyothsna Tejomurthula is appreciated. The authors would also like to thank the technical assistance from Carlos Martinez, Alberto Vergara and Jose Simon Marias of CIMMYT in carrying out this research work. The authors also gratefully acknowledge the two reviewers for their valuable comments that could improve the value of this manuscript.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The authors declare no ethical standards have been violated in the course of the study.

Supplementary material

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Supplementary material 1 (TIFF 1717 kb)
122_2015_2551_MOESM2_ESM.pdf (237 kb)
Supplementary material 2 (PDF 237 kb)
122_2015_2551_MOESM3_ESM.pdf (334 kb)
Supplementary material 3 (PDF 333 kb)


  1. Abalo G, Tongoona P, Derera J, Edema R (2009) A comparative analysis of conventional and marker-assisted selection methods in breeding maize streak virus resistance in maize. Crop Sci 49:509–520CrossRefGoogle Scholar
  2. Bent AF (1996) Plant disease resistance genes: function meets structure. Plant Cell 8:1757–1771PubMedCentralCrossRefPubMedGoogle Scholar
  3. Bishop DT, Williamson JA (1990) The power of identity-by-state methods for linkage analysis. Am J Hum Genet 46:254–265PubMedCentralPubMedGoogle Scholar
  4. Cairns JE, Crossa J, Zaidi PH et al (2013) Identification of drought, heat, and combined drought and heat tolerant donors in maize. Crop Sci 53:1335–1346CrossRefGoogle Scholar
  5. Ching A, Caldwell KS, Jung M et al (2002) SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genet 3:19PubMedCentralCrossRefPubMedGoogle Scholar
  6. CIMMYT (2001) Laboratory protocols: CIMMYT applied molecular genetics laboratory protocols. CIMMYT, MexicoGoogle Scholar
  7. Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6(5):e19379PubMedCentralCrossRefPubMedGoogle Scholar
  8. Ersoz E, Yu J, Buckler E (2009) Applications of linkage disequilibrium and association mapping in maize. In: Kirz A, Larkins B (eds) Molecular genetic approaches to maize improvement. Springer, Berlin, pp 173–195CrossRefGoogle Scholar
  9. Excoffier L, Slatkin M (1995) Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Biol Evol 12:921–927PubMedGoogle Scholar
  10. Gabriel SB, Schaffner SF, Nguyen H et al (2002) The structure of haplotype blocks in the human genome. Science 296:2225–2229CrossRefPubMedGoogle Scholar
  11. 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
  12. Guthrie EJ (1978) Measurement of yield losses caused by maize streak disease. Plant Dis Rep 62:839–840Google Scholar
  13. Hill WG, Weir BS (1988) Variances and covariances of squared linkage disequilibria in finite populations. Theor Popul Biol 33:54–78CrossRefPubMedGoogle Scholar
  14. Johnson GCL, Esposito L, Barratt BJ et al (2001) Haplotype tagging for the identification of common disease genes. Nat Genet 29:233–237CrossRefPubMedGoogle Scholar
  15. Kim SK, Efron Y, Khadr F, Fajemisin J, Lee MH (1987) Registration of 16 maize streak resistant tropical maize parental inbred lines. Crop Sci 27:824–825CrossRefGoogle Scholar
  16. Kim SK, Efron Y, Fajemisin JM, Buddenhagen IW (1989) Mode of gene action for resistance in maize to maize streak virus. Crop Sci 29:890–894CrossRefGoogle Scholar
  17. Kyetere DT, Ming R, McMullen MD et al (1999) Genetic analysis of tolerance to maize streak virus in maize. Genome 42:20–26CrossRefGoogle Scholar
  18. Lagat M, Danson M, Kimani M, Kuria A (2008) Quantitative trait loci for resistance to maize streak virus in maize genotypes used in hybrid development. Afric J Biotech 7:2573–2577Google Scholar
  19. Martin DP, Shepherd DN (2009) The epidemiology, economic impact and control of maize streak disease. Food Sec 1:305–315CrossRefGoogle Scholar
  20. Martin DP, Willment JA, Billharz R et al (2001) Sequence diversity and virulence in Zea mays of Maize streak virus isolates. Virology 288:247–255CrossRefPubMedGoogle Scholar
  21. Myles S, Peiffer J, Brown P et al (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202PubMedCentralCrossRefPubMedGoogle Scholar
  22. Nordborg M, Weigel D (2008) Next-generation genetics in plants. Nature 456:720–723CrossRefPubMedGoogle Scholar
  23. Peleman JD, Wye C, Zethof J et al (2005) Quantitative trait locus (QTL) isogenic recombinant analysis: a method for high resolution mapping of QTL within a single population. Genetics 171:1341–1352PubMedCentralCrossRefPubMedGoogle Scholar
  24. Pernet A, Hoisington D, Franco J et al (1999a) Genetic mapping of maize streak virus resistance from the Mascarene source. I. Resistance in line D211 and stability against different virus clones. Theor Appl Genet 99:524–539CrossRefPubMedGoogle Scholar
  25. Pernet A, Hoisington D, Dintinger J et al (1999b) Genetic mapping of maize streak virus resistance from the Mascarene source. II. Resistance in line CIRAD390 and stability across germplasm. Theor Appl Genet 99:540–553CrossRefPubMedGoogle Scholar
  26. Prasanna BM, Chaikam V, Mahuku G (2012) Doubled haploid (DH) technology in maize breeding: an overview. In: Prasanna BM, Chaikam V, Mahuku G (eds) Doubled haploid technology in maize breeding: theory and practice. CIMMYT, Mexico, pp 1–8Google Scholar
  27. Prasanna BM, Babu R, Nair S et al (2014) Molecular marker-assisted breeding for tropical maize improvement. In: Wusurika R, Bohn M, Lai J, Kole C (eds) Genetics, genomics and breeding of maize. CRC Press, London, pp 89–119Google Scholar
  28. Pratt RC, Gordon SG (2006) Breeding for resistance to maize foliar pathogens. Plant Breed Rev 27:119–174Google Scholar
  29. R Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL
  30. Rafalski A (2002) Applications of single nucleotide polymorphisms in crop genetics. Curr Opin Plant Biol 5:94–100CrossRefPubMedGoogle Scholar
  31. Rafalski JA (2010) Association genetics in crop improvement. Curr Opin Plant Biol 13:174–180CrossRefPubMedGoogle Scholar
  32. Remington DL, Thornsberry JM, Matsuoka Y et al (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc Nat Acad Sci USA 98:11479–11484PubMedCentralCrossRefPubMedGoogle Scholar
  33. Rodier A, Assie J, Marchand JL, Herve Y (1995) Breeding maize lines for complete and partial resistance to maize streak virus (MSV). Euphytica 81:57–70CrossRefGoogle Scholar
  34. Romay MC, Millard MJ, Glaubitz JC et al (2013) Comprehensive genotyping of the USA national maize inbred seeds bank. Genome Biol 14:R55PubMedCentralCrossRefPubMedGoogle Scholar
  35. Semagn K, Beyene Y, Makumbi D et al (2012) Quality control genotyping for assessment of genetic identity and purity in diverse tropical maize inbred lines. Theor Appl Genet 125:1487–1501CrossRefPubMedGoogle Scholar
  36. Shepherd DN, Mangwende T, Martin DP et al (2007) Maize streak virus-resistant transgenic maize: a first for Africa. Plant Biotech J 5:759–767CrossRefGoogle Scholar
  37. Shepherd DN, Martin DP, Van der walt E et al (2010) Maize streak virus: an old and complex ‘emerging’ pathogen. Mol Plant Path 11:1–12CrossRefGoogle Scholar
  38. Soto PE, Buddenhagen IW, Asnani VL (1982) Development of streak virus-resistant maize populations through improved challenge and selection methods. Ann Appl Biol 100:539–546CrossRefGoogle Scholar
  39. Storey HH, Howland AK (1967) Inheritance of resistance in maize to the virus of streak disease in East Africa. Ann Appl Biol 59:429–436CrossRefGoogle Scholar
  40. Stuber CW, Lincoln SF, Wolff DW (1987) Molecular markers facilitated investigations of quantitative trait loci in maize. II. Factors influencing yield and its component traits. Crop Sci 27:639–648CrossRefGoogle Scholar
  41. Tang CY, Bjarnason MJ (1993) Two approaches for the development of maize germplasm resistant to maize streak virus. Maydica 38:301–307Google Scholar
  42. Tenaillon MI, Sawkins MC, Long AD et al (2001) Patterns of DNA sequence polymorphism along chromosome 1 of maize (Zea mays ssp. mays L.). Proc Natl Acad Sci USA 98:9161–9166PubMedCentralCrossRefPubMedGoogle Scholar
  43. Wambugu F, Wafula J (2000) Advances in maize streak virus disease research in Eastern and Southern Africa. Workshop Report, 15–17 September 1999, KARI and ISAAA AfriCenter, Nairobi, Kenya. ISAAA Briefs No. 16. ISAAA, Ithaca, NY, p 43Google Scholar
  44. Welz HG, Schechert A, Pernet A et al (1998) A gene for resistance to maize streak virus in the African CIMMYT maize inbred CML202. Mol Breed 4:147–154CrossRefGoogle Scholar
  45. Yan J, Shah T, Warburton M et al (2009) Genetic characterization and linkage disequilibrium estimation of a global maize collection using SNP markers. PLoS One 04:e8451CrossRefGoogle Scholar
  46. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551PubMedCentralCrossRefPubMedGoogle Scholar
  47. Zeng LR, Vega-Sanchez ME, Zhu T, Wang GL (2006) Ubiquitinization-mediated protein degradation and modification: an emerging theme in plant-microbe interactions. Cell Res 16:413–426CrossRefPubMedGoogle Scholar
  48. Zhu C, Gore M, Buckler E, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sudha K. Nair
    • 1
  • Raman Babu
    • 1
  • Cosmos Magorokosho
    • 2
  • George Mahuku
    • 3
  • Kassa Semagn
    • 3
  • Yoseph Beyene
    • 3
  • Biswanath Das
    • 3
  • Dan Makumbi
    • 3
  • P. Lava Kumar
    • 4
  • Michael Olsen
    • 3
  • Prasanna M. Boddupalli
    • 3
  1. 1.International Maize and Wheat Improvement Center (CIMMYT), ICRISAT CampusGreater HyderabadIndia
  2. 2.International Maize and Wheat Improvement Center (CIMMYT)HarareZimbabwe
  3. 3.International Maize and Wheat Improvement Center (CIMMYT), ICRAF CampusNairobiKenya
  4. 4.International Institute for Tropical Agriculture (IITA)IbadanNigeria

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