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Theoretical and Applied Genetics

, Volume 125, Issue 5, pp 1033–1046 | Cite as

Fine mapping and identification of candidate rice genes associated with qSTV11 SG , a major QTL for rice stripe disease resistance

  • Tackmin Kwon
  • Jong-Hee Lee
  • Soo-Kwon Park
  • Un-Ha Hwang
  • Jun-Hyun Cho
  • Do-Yeon Kwak
  • Yeong-Nam Youn
  • Un-Sang Yeo
  • You-Chun Song
  • Jaesung Nam
  • Hang-Won Kang
  • Min-Hee Nam
  • Dong-Soo ParkEmail author
Original Paper

Abstract

Rice stripe disease, caused by rice stripe virus (RSV) is a serious constraint to rice production in subtropical regions of East Asia. We performed fine mapping of a RSV resistance QTL on chromosome 11, qSTV11 SG , using near-isogenic lines (NILs, BC6F4) derived from a cross between the highly resistant variety, Shingwang, and the highly susceptible variety, Ilpum, using 11 insertion and deletion (InDel) markers. qSTV11 SG was localized to a 150-kb region between InDel 11 (17.86 Mbp) and InDel 5 (18.01 Mbp). Among the two markers in this region, InDel 7 is diagnostic of RSV resistance in 55 Korean japonica and indica rice varieties. InDel 7 could also distinguish the allele type of Nagdong, Shingwang, Mudgo, and Pe-bi-hun from Zenith harboring the Stv-b i allele. As a result, qSTV11 SG is likely to be the Stv-b i allele. There were 21 genes in the 150-kb region harboring the qSTV11 SG locus. Three of these genes, LOC_Os11g31430, LOC_Os11g31450, and LOC_Os11g31470, were exclusively expressed in the susceptible variety. These expression profiles were consistent with the quantitative nature along with incomplete dominance of RSV resistance. Sequencing of these genes showed that there were several amino acid substitutions between susceptible and resistant varieties. Putative functions of these candidate genes for qSTV11 SG are discussed.

Keywords

Quantitative Trait Locus Rice Variety InDel Marker Rice Stripe Virus Indica Variety 
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.

Notes

Acknowledgments

This study was supported by grants from the Rural Development Administration (PJ0086852012).

Supplementary material

122_2012_1893_MOESM1_ESM.docx (15 kb)
Supplementary material 1 (DOCX 14 kb)

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Tackmin Kwon
    • 1
  • Jong-Hee Lee
    • 1
  • Soo-Kwon Park
    • 1
  • Un-Ha Hwang
    • 1
  • Jun-Hyun Cho
    • 1
  • Do-Yeon Kwak
    • 1
  • Yeong-Nam Youn
    • 1
  • Un-Sang Yeo
    • 1
  • You-Chun Song
    • 1
  • Jaesung Nam
    • 2
  • Hang-Won Kang
    • 1
  • Min-Hee Nam
    • 1
  • Dong-Soo Park
    • 1
    Email author
  1. 1.National Institute of Crop ScienceMilyangKorea
  2. 2.Dong-A UniversityBusanKorea

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