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Crop Science Abstract -

Relationships among Early European Maize Inbreds: II. Comparison of Pedigree and RFLP Data


This article in CS

  1. Vol. 33 No. 5, p. 944-950
    Received: Sept 30, 1992

    * Corresponding author(s):
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  1. Monika M. Messmer,
  2. Albrecht E. Melchinger ,
  3. Reinhold G. Herrmann and
  4. Jürgen Boppenmaier
  1. Inst. of Plant Breeding, Seed Science, and Population Genetics, Univ. of Hohenheim, P. O. Box 700562, W-7000 Stuttgart 70, Germany
    Inst. of Botany, Ludwig-Maximilians Univ., Menzinger Str. 67, W-8000 München 19, Germany



Information on genetic relationships among genotypes is of great importance to breeders. In this study we analyzed 29 maize (Zea mays L.) inbreds (18 flint and 11 dent lines) from the European germplasm by means of pedigree and RFLP analyses. Our main objective was to compare Malécot's coancestry (f based on pedigree data with genetic similarity (GS) based on RFLP data of 188 clone-enzyme combinations (CECs) for their ability to quantify the degree of relatedness among inbreds. Rank correlations between f and GS were highly significant (P < 0.01) for 87 related (f > 0) pairs of flint lines (rs = 0. 71) an for 30 related pairs of dent lines (rs = 0.86). One dent line was excluded because of likely errors in pedigree detected by RFLP data and confirmed by heterosis data. Based on linear regression of GS on f, coancestry explained 82 and 70% of the variation in GS for related pairs of flint and dent lines, respectively. Therefore, both the coancestry and marker approach are well-suited to (i) measure the average level of relatedness and (ii) identify closely related lines. Observed deviations of GS from its regression on f for individual pairs of lines can be explained by (i) random genetic drift and selection causing unequal genomic contributions of the parents, (ii) random variation of GS estimates due to sampling of the genome by the CECs used, (iii) wide variation in the GS estimates of unrelated lines, and (iv) experimental errors. Provided the genome is sampled by an adequate number (> 100) of CECs, RFLP-based GS estimates reflect more accurately the true genetic similarity of a given line pair than f.

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