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Euphytica

, 214:6 | Cite as

Genomic prediction and GWAS of Gibberella ear rot resistance traits in dent and flint lines of a public maize breeding program

  • Sen Han
  • Thomas Miedaner
  • H. Friedrich Utz
  • Wolfgang Schipprack
  • Tobias A. Schrag
  • Albrecht E. Melchinger
Article
  • 483 Downloads

Abstract

Gibberella ear rot (GER) is a serious threat to maize cultivation, causing grain yield losses and contamination with mycotoxins. Genomic prediction (GP) has great potential to accelerate resistance breeding against GER. However, small training sets (TS) consisting of both phenotyped and genotyped individuals are a challenge for obtaining high prediction accuracy (ρ) in GP. A potential solution would be combining small-size populations across heterotic pools. However, genetic heterogeneity between populations in terms of segregating QTL, linkage disequilibrium (LD) pattern and genomic relationships can impair ρ of GP. In this study, we investigated the genetic architecture of GER severity, deoxynivalenol concentration (DON) and days to silking with genome-wide association studies within two elite panels of 130 dent and 114 flint lines from the maize breeding program of the University of Hohenheim tested in four environments. We also assessed the consistency of LD pattern and genomic relationships between the two heterotic pools. Furthermore, we compared four GP approaches differing in the composition of the TS with lines from a single or combined pool(s) and statistical models with marker effects identical or different but correlated between pools. We detected two and six QTL for DON within the dent and flint pool, respectively, but no common QTL. The LD pattern was consistent between pools for marker pairs ≤ 10 kb apart. GP across pools yielded low or even negative ρ. Combined-pool GP had no higher ρ than within-pool GP, regardless of the statistical model. Our findings underline the importance of investigating the genetic heterogeneity between populations prior to implementing GP using combined TS.

Keywords

Maize Fusarium GWAS Genomic prediction Training set Heterotic pools 

Abbreviations

AP

Across-pool prediction

CD

Combined-pool prediction, assuming marker effects different but correlated across pools

CI

Combined-pool prediction, assuming identical marker effects across pools

DON

Deoxynivalenol

DS

Days to silking

GBLUP

Genomic best linear unbiased prediction

GEBV

Genomic estimated breeding values

GER

Gibberella ear rot

GP

Genomic prediction

GWAS

Genome-wide association studies

LD

Linkage disequilibrium

TS

Training set

VS

Validation set

WP

Within-pool prediction

Notes

Acknowledgements

This research was supported by Deutsche Forschungsgemeinschaft (DFG) Grant No. ME 2260/6-1. Part of the SNP genotyping was funded by the German Ministry of Education and Research within the AgroClustEr “Synbreed – Synergistic plant and animal breeding” (FKZ: 0315528d). We are grateful to Vilson Mirdita and the staff of the Agricultural Research Station at Eckartsweier and Hohenheim for conducting the field trials for this study. We acknowledge Christian Riedelsheimer’s documentation on using the GenABEL R package and Haixiao Hu’s suggestions for performing genome-wide association studies. We appreciate advices from Pedro Correa Brauner and Willem Molenaar for improving the text of the manuscript.

Compliance with ethical standards

Ethical approval

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

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10681_2017_2090_MOESM1_ESM.docx (794 kb)
Supplementary material 1 (DOCX 794 kb)

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Sen Han
    • 1
    • 3
  • Thomas Miedaner
    • 2
  • H. Friedrich Utz
    • 1
  • Wolfgang Schipprack
    • 1
  • Tobias A. Schrag
    • 1
  • Albrecht E. Melchinger
    • 1
  1. 1.Institute of Plant Breeding, Seed Science and Population Genetics (350a)University of HohenheimStuttgartGermany
  2. 2.State Plant Breeding Institute (720)University of HohenheimStuttgartGermany
  3. 3.Monsanto Holland B.VBergschenhoekThe Netherlands

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