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

Theoretical and Applied Genetics volume 129, pages 431–444 (2016)Cite this article

Abstract

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.

Abstract

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.

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Acknowledgments

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.

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Author notes

  1. Michael Stange

    Present address: Strube Research GmbH and Co. KG, Hauptstraße 1, 38387, Söllingen, Germany

Affiliations

  1. Institute of Plant Breeding, Seed Science and Population Genetics (350a), University of Hohenheim, 70593, Stuttgart, Germany

    Sen Han, H. Friedrich Utz, Tobias A. Schrag, Michael Stange & Albrecht E. Melchinger

  2. Crop Genetics and Breeding Department, China Agricultural University, Beijing, 100193, China

    Wenxin Liu

  3. State Plant Breeding Institute (720), University of Hohenheim, 70593, Stuttgart, Germany

    Tobias Würschum & Thomas Miedaner

  4. Department of Plant Breeding, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85350, Freising, Germany

    Eva Bauer & Chris-Carolin Schön

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  1. Sen Han
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  2. H. Friedrich Utz
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  3. Wenxin Liu
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  10. Albrecht E. Melchinger
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Correspondence to Albrecht E. Melchinger.

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

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Communicated by M. Frisch.

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Han, S., Utz, H.F., Liu, W. et al. Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize. Theor Appl Genet 129, 431–444 (2016). https://doi.org/10.1007/s00122-015-2637-3

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Keywords

  • Quantitative Trait Locus
  • Quantitative Trait Locus Mapping
  • Quantitative Trait Locus Effect
  • Genomic Prediction
  • Quantitative Trait Locus Detection