Theoretical and Applied Genetics

, Volume 129, Issue 2, pp 431–444 | Cite as

Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize

  • Sen Han
  • H. Friedrich Utz
  • Wenxin Liu
  • Tobias A. Schrag
  • Michael Stange
  • Tobias Würschum
  • Thomas Miedaner
  • Eva Bauer
  • Chris-Carolin Schön
  • Albrecht E. Melchinger
Original Article


Key message

QTL analysis for Fusarium resistance traits with multiple connected families detected more QTL than single-family analysis. Prediction accuracy was tightly associated with the kinship of the validation and training set.


QTL mapping has recently shifted from analysis of single families to multiple, connected families and several biometric models have been suggested. Using a high-density consensus map with 2472 marker loci, we performed QTL mapping with five connected bi-parental families with 639 doubled-haploid (DH) lines in maize for ear rot resistance and analyzed traits DON, Gibberella ear rot severity (GER), and days to silking (DS). Five biometric models differing in the assumption about the number and effects of alleles at QTL were compared. Model 2 to 5 performing joint analyses across all families and using linkage and/or linkage disequilibrium (LD) information identified all and even further QTL than Model 1 (single-family analyses) and generally explained a higher proportion p G of the genotypic variance for all three traits. QTL for DON and GER were mostly family specific, but several QTL for DS occurred in multiple families. Many QTL displayed large additive effects and most alleles increasing resistance originated from a resistant parent. Interactions between detected QTL and genetic background (family) occurred rarely and were comparatively small. Detailed analysis of three fully connected families yielded higher p G values for Model 3 or 4 than for Model 2 and 5, irrespective of the size N TS of the training set (TS). In conclusion, Model 3 and 4 can be recommended for QTL-based prediction with larger families. Including a sufficiently large number of full sibs in the TS helped to increase QTL-based prediction accuracy (r VS) for various scenarios differing in the composition of the TS.


Quantitative Trait Locus Quantitative Trait Locus Mapping Quantitative Trait Locus Effect Genomic Prediction Quantitative Trait Locus Detection 
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 supported by Deutsche Forschungsgemeinschaft (DFG) grant no. ME 2260/6-1. The DH lines used in this study were produced by KWS SAAT SE (Einbeck, Germany). We are indebted to M. Martin and W. Schipprack and the staff of the Agricultural Research Station at Eckartsweier and Hohenheim for conducting the field trials for this study. We acknowledge the support of T. Wimmer in providing the software for cross-validation. We are grateful to S. Jasson and B. Mangin for generously providing technical assistance with software MCQTL_LD and D. Leroux with the “clusthaplo” R package; MCAM Bink and F. van Eeuwijk for giving constructive suggestions for our analyses; J. Li, L. Moreau and H. Giraud for answering questions about multiple regression analysis.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

The experiments reported in this study comply with the current laws of Germany.

Supplementary material

122_2015_2637_MOESM1_ESM.pdf (129 kb)
Supplementary material 1 (PDF 129 kb)
122_2015_2637_MOESM2_ESM.pdf (1.9 mb)
Supplementary material 2 (PDF 1903 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sen Han
    • 1
  • H. Friedrich Utz
    • 1
  • Wenxin Liu
    • 2
  • Tobias A. Schrag
    • 1
  • Michael Stange
    • 1
    • 5
  • Tobias Würschum
    • 3
  • Thomas Miedaner
    • 3
  • Eva Bauer
    • 4
  • Chris-Carolin Schön
    • 4
  • Albrecht E. Melchinger
    • 1
  1. 1.Institute of Plant Breeding, Seed Science and Population Genetics (350a)University of HohenheimStuttgartGermany
  2. 2.Crop Genetics and Breeding DepartmentChina Agricultural UniversityBeijingChina
  3. 3.State Plant Breeding Institute (720)University of HohenheimStuttgartGermany
  4. 4.Department of Plant Breeding, Center of Life and Food Sciences WeihenstephanTechnische Universität MünchenFreisingGermany
  5. 5.Strube Research GmbH and Co. KGSöllingenGermany

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