Theoretical and Applied Genetics

, Volume 124, Issue 2, pp 261–275 | Cite as

Evaluation of genome-wide selection efficiency in maize nested association mapping populations

  • Zhigang Guo
  • Dominic M. Tucker
  • Jianwei Lu
  • Venkata Kishore
  • Gilles Gay
Original Paper


In comparison to conventional marker-assisted selection (MAS), which utilizes only a subset of genetic markers associated with a trait to predict breeding values (BVs), genome-wide selection (GWS) improves prediction accuracies by incorporating all markers into a model simultaneously. This strategy avoids risks of missing quantitative trait loci (QTL) with small effects. Here, we evaluated the accuracy of prediction for three corn flowering traits days to silking, days to anthesis, and anthesis-silking interval with GWS based on cross-validation experiments using a large data set of 25 nested association mapping populations in maize (Zea mays). We found that GWS via ridge regression-best linear unbiased prediction (RR-BLUP) gave significantly higher predictions compared to MAS utilizing composite interval mapping (CIM). The CIM method may be selected over multiple linear regression to decrease over-estimations of the efficiency of GWS over a MAS strategy. The RR-BLUP method was the preferred method for estimating marker effects in GWS with prediction accuracies comparable to or greater than BayesA and BayesB. The accuracy with RR-BLUP increased with training sample proportion, marker density, and heritability until it reached a plateau. In general, gains in accuracy with RR-BLUP over CIM increased with decreases of these factors. Compared to training sample proportion, the accuracy of prediction with RR-BLUP was relatively insensitive to marker density.



Anthesis silking interval


Breeding value


Composition interval mapping


Day till anthesis


Days till silking


Genome-wide selection


Iterative conditional expectation


Logarithm of odds


Marker-assisted selection


Multiple linear regression


Monte Carlo Markov chain


Nested association mapping


Quantitative trait loci


Recombinant inbred line


Ridge regression-best linear unbiased prediction


Single nucleotide polymorphisms



The authors of the current manuscript would like to thank researchers and institutions who contributed to the Panzea database ( In addition, the authors would like express gratitude to the two anonymous reviewers for their detailed input in assessment of the manuscript.


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Zhigang Guo
    • 1
  • Dominic M. Tucker
    • 2
  • Jianwei Lu
    • 1
  • Venkata Kishore
    • 2
  • Gilles Gay
    • 1
  1. 1.Syngenta Biotechnology, Inc.Research Triangle ParkUSA
  2. 2.Syngenta Seeds, Inc.ClintonUSA

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