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This article in CS

  1. Vol. 42 No. 4, p. 1316-1323
     
    Received: July 13, 2001
    Published: July, 2002


    * Corresponding author(s): j.r.witcombe@bangor.ac.uk
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doi:10.2135/cropsci2002.1316

Mapping Quantitative Trait Loci for Resistance to Downy Mildew in Pearl Millet

  1. E. S. Jonesa,
  2. W. A. Breeseb,
  3. C. J. Liuc,
  4. S. D. Singhd,
  5. D. S. Shawb and
  6. J. R. Witcombe *b
  1. a Plant Biotechnology Centre, Agriculture Victoria, La Trobe Univ., Bundoora VIC 3083, Australia
    b School of Biological Sciences, Univ. of Wales, Bangor, Gwynedd LL57 2UW, UK
    c CSIRO Plant Industry, Long Pocket Lab., 120 Meiers Road, Indooroopilly QLD 4068, Australia
    d Int. Crops Research Inst. for the Semi-Arid Tropics, Patancheru AP 502 324, India

Abstract

Downy mildew, caused by the pathogen Sclerospora graminicola (Sacc.) J. Schröt, can cause devastating yield losses in pearl millet [Pennisetum glaucum (L.) R. Br.]. Breeding for resistance to downy mildew is facilitated by an artificial glasshouse screening method that can be used worldwide. Quantitative trait loci (QTLs) mapping was used to determine whether resistance QTLs identified under field conditions in India were also detected in glasshouse screens carried out in India and the UK. Quantitative trait loci were mapped using 114 individual pearl millet progeny derived from a resistant × susceptible cross: molecular marker mapping was carried out in an F2 population with restriction fragment length polymorphisms (RFLPs), and disease incidence was assessed on F4 families. Composite interval mapping (CIM) was used to detect associations between F4 family means and marker genotypes. Despite key environmental and methodological differences between the disease screens, the same two QTLs were detected in each screening environment. One QTL had a major effect and explained up to 60% of the phenotypic variation, while the other had a minor effect and explained up to 16% of the phenotypic variation. Two additional QTLs were also consistently detected across screens by examining pair-wise marker interactions. Multiple-trait interval mapping detected all of the QTLs that had been detected in individual screens, including the QTLs that had only been detected by examining pair-wise marker interactions, demonstrating its increased power over single trait mapping. Quantitative trait locus × environment interactions were significant at each QTL due to differences in the magnitude, rather than direction, of QTL effects. The differences in magnitude appeared to be a consequence of the degree of normality of the disease distribution, rather than any differences between screening methods.

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Copyright © 2002. Crop Science Society of AmericaPublished in Crop Sci.42:1316–1323.