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This article in TPG

  1. Vol. 3 No. 3, p. 142-153
    OPEN ACCESS
     
    Received: May 27, 2010
    Published: Nov, 2010


    * Corresponding author(s): Peter.Balint-Kurti@ars.usda.gov
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doi:10.3835/plantgenome2010.05.0011

Joint Analysis of Near-Isogenic and Recombinant Inbred Line Populations Yields Precise Positional Estimates for Quantitative Trait Loci

  1. Kristen L. Kump,
  2. James B. Holland,
  3. Mark T. Jung,
  4. Petra Wolters and
  5. Peter J. Balint-Kurti 
  1. K.L. Kump, Dep. of Crop Science, North Carolina State Univ., Raleigh, NC 27695; J.B. Holland, USDA-ARS, Plant Science Research Unit and Dep. of Crop Science, North Carolina State Univ., Raleigh NC 27695; M.T. Jung and P. Wolters, DuPont Crop Genetics Research, Experimental Station, P.O. Box 80353, Wilmington, DE 19880; P.J. Balint-Kurti, USDA-ARS, Plant Science Research Unit and Dep. of Plant Pathology, North Carolina State Univ., Raleigh NC 27695

Abstract

Data generated for initial quantitative trait loci (QTL) mapping using recombinant inbred line (RIL) populations are usually ignored during subsequent fine-mapping using near-isogenic lines (NILs). Combining both datasets would increase the number of recombination events sampled and generate better position and effect estimates. Previously, several QTL for resistance to southern leaf blight of maize were mapped in two RIL populations, each independently derived from a cross between the lines B73 and Mo17. In each case the largest QTL was in bin 3.04. Here, two NIL pairs differing for this QTL were derived and used to create two distinct F2:3 family populations that were assessed for southern leaf blight (SLB) resistance. By accounting for segregation of the other QTL in the original RIL data, we were able to combine these data with the new genotypic and phenotypic data from the F2:3 families. Joint analysis yielded a narrower QTL support interval compared to that derived from analysis of any one of the data sets alone, resulting in the localization of the QTL to a less than 0.5 cM interval. Candidate genes identified within this interval are discussed. This methodology allows combined QTL analysis in which data from RIL populations is combined with data derived from NIL populations segregating for the same pair of alleles. It improves mapping resolution over the conventional approach with virtually no additional resources. Because data sets of this type are commonly produced, this approach is likely to prove widely applicable.

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