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

Abstract

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.

Abbreviations

ASI

Anthesis silking interval

BV

Breeding value

CIM

Composition interval mapping

DA

Day till anthesis

DS

Days till silking

GWS

Genome-wide selection

ICE

Iterative conditional expectation

LOD

Logarithm of odds

MAS

Marker-assisted selection

MLR

Multiple linear regression

MCMC

Monte Carlo Markov chain

NAM

Nested association mapping

QTL

Quantitative trait loci

RIL

Recombinant inbred line

RR-BLUP

Ridge regression-best linear unbiased prediction

SNP

Single nucleotide polymorphisms

Notes

Acknowledgments

The authors of the current manuscript would like to thank researchers and institutions who contributed to the Panzea database (http://www.panzea.org/). 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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