Theoretical and Applied Genetics

, Volume 117, Issue 7, pp 1167–1179 | Cite as

Association mapping in multiple segregating populations of sugar beet (Beta vulgaris L.)

  • Benjamin Stich
  • Albrecht E. Melchinger
  • Martin Heckenberger
  • Jens Möhring
  • Axel Schechert
  • Hans-Peter Piepho
Original Paper


Association mapping in multiple segregating populations (AMMSP) combines high power to detect QTL in genome-wide approaches of linkage mapping with high mapping resolution of association mapping. The main objectives of this study were to (1) examine the applicability of AMMSP in a plant breeding context based on segregating populations of various size of sugar beet (Beta vulgaris L.), (2) compare different biometric approaches for AMMSP, and (3) detect markers with significant main effect across locations for nine traits in sugar beet. We used 768 F n (n = 2, 3, 4) sugar beet genotypes which were randomly derived from 19 crosses among diploid elite sugar beet clones. For all nine traits, the genotypic and genotype × location interaction variances were highly significant (P < 0.01). Using a one-step AMMSP approach, the total number of significant (P < 0.05) marker-phenotype associations was 44. The identification of genome regions associated with the traits under consideration indicated that not only segregating populations derived from crosses of parental genotypes in a systematic manner could be used for AMMSP but also populations routinely derived in plant breeding programs from multiple, related crosses. Furthermore, our results suggest that data sets, whose size does not permit analysis by the one-step AMMSP approach, might be analyzed using the two-step approach based on adjusted entry means for each location without losing too much power for detection of marker-phenotype associations.


Quantitative Trait Locus Sugar Beet Quantitative Trait Locus Detection Allele Substitution Effect Testcross Progeny 
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.



This research was conducted within the Breeding and Informatics (BRAIN) project of the Genome Analysis of the Plant Biological System (GABI) initiative ( The authors appreciate the editorial work of Dr. J. Muminović, whose suggestions considerably improved the style of the manuscript. The authors thank two anonymous reviewers for their valuable suggestions.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Benjamin Stich
    • 1
  • Albrecht E. Melchinger
    • 1
  • Martin Heckenberger
    • 1
  • Jens Möhring
    • 2
  • Axel Schechert
    • 3
  • Hans-Peter Piepho
    • 2
  1. 1.Institute for Plant Breeding, Seed Science, and Population GeneticsUniversity of HohenheimStuttgartGermany
  2. 2.Institute for Crop Production and Grassland ResearchUniversity of HohenheimStuttgartGermany
  3. 3.Fr. Strube Saatzucht GmbH & Co. KGSöllingenGermany

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