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.
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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
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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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Communicated by M. Sillanpaa.
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Guo, Z., Tucker, D.M., Lu, J. et al. Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124, 261–275 (2012). https://doi.org/10.1007/s00122-011-1702-9
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DOI: https://doi.org/10.1007/s00122-011-1702-9