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Marker-assisted prediction of non-additive genetic values

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Abstract

It has become increasingly clear from systems biology arguments that interaction and non-linearity play an important role in genetic regulation of phenotypic variation for complex traits. Marker-assisted prediction of genetic values assuming additive gene action has been widely investigated because of its relevance in artificial selection. On the other hand, it has been less well-studied when non-additive effects hold. Here, we explored a nonparametric model, radial basis function (RBF) regression, for predicting quantitative traits under different gene action modes (additivity, dominance and epistasis). Using simulation, it was found that RBF had better ability (higher predictive correlations and lower predictive mean square errors) of predicting merit of individuals in future generations in the presence of non-additive effects than a linear additive model, the Bayesian Lasso. This was true for populations undergoing either directional or random selection over several generations. Under additive gene action, RBF was slightly worse than the Bayesian Lasso. While prediction of genetic values under additive gene action is well handled by a variety of parametric models, nonparametric RBF regression is a useful counterpart for dealing with situations where non-additive gene action is suspected, and it is robust irrespective of mode of gene action.

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Acknowledgments

This work was supported by the Wisconsin Agriculture Experiment Station, Aviagen Ltd., and by grants NRICGP/USDA 2003-35205-12833, NSF DEB-0089742 and NSF DMS-044371. We thank the editor and the reviewers for their insightful comments.

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Authors and Affiliations

  1. Department of Animal Sciences, University of Wisconsin-Madison, 1675 Observatory Dr. Animal Science Bldg, Madison, WI, 53706, USA

    Nanye Long, Daniel Gianola & Guilherme J. M. Rosa

  2. Department of Dairy Science, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Daniel Gianola & Kent A. Weigel

  3. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Daniel Gianola & Guilherme J. M. Rosa

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  1. Nanye Long
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  2. Daniel Gianola
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  3. Guilherme J. M. Rosa
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  4. Kent A. Weigel
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Correspondence to Nanye Long.

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Long, N., Gianola, D., Rosa, G.J.M. et al. Marker-assisted prediction of non-additive genetic values. Genetica 139, 843–854 (2011). https://doi.org/10.1007/s10709-011-9588-7

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  • DOI: https://doi.org/10.1007/s10709-011-9588-7

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