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Accounting for competition in genetic analysis, with particular emphasis on forest genetic trials

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Abstract

Available experimental evidence suggests that there are genetic differences in the abilities of trees to compete for resources, in addition to non-genetic differences due to micro-site variation. The use of indirect genetic effects within the framework of linear mixed model methodology has been proposed for estimating genetic parameters and responses to selection in the presence of genetic competition. In this context, an individual’s total breeding value reflects the effects of its direct breeding value on its own phenotype and its competitive breeding value on the phenotype of its neighbours. The present study used simulated data to investigate the relevance of accounting for competitive effects at the genetic and non-genetic levels in terms of the estimation of (co)variance components and selection response. Different experimental designs that resulted in different genetic relatedness levels within a neighbourhood and survival were other key issues examined. Variances estimated for additive genetic and residual effects tended to be biased under models that ignored genetic competition. Models that fitted competition at the genetic level only also resulted in biased (co)variance estimates for direct additive, competitive additive and residual effects. The ability to detect the correct model was reduced when relatedness within a neighbourhood was very low and survival decreased. Selection responses changed considerably between selecting on breeding value estimates from a model ignoring genetic competition and total breeding estimates using the correct model. Our results suggest that considering a genetic basis to competitive ability will be important to optimise selection programmes for genetic improvement of tree species.

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Acknowledgments

We are grateful to Piter Bijma for the very helpful suggestions and comments which greatly contributed to improve the manuscript. We also thank William Muir and Bruce Walsh for general comments on a previous draft of this manuscript. The financial supports given to João Costa e Silva by Fundação para a Ciência e Tecnologia (Lisboa, Portugal) through the Ciência 2007 initiative, and by the University of Tasmania (Tasmania, Australia) for a visiting scholarship at the School of Plant Science, are gratefully acknowledged. We also gratefully acknowledge the financial support received from Forest and Wood Products Australia through the project grant PNC076-0809. We are indebted to Forest & Landscape, Faculty of Life Sciences, University of Copenhagen, Denmark and Southern Tree Breeding Association, Australia, who contributed actual field trial data for preliminary analyses. We would like to thank the helpful support given by Brad Potts, Greg Dutkowski, Viggo Jensen, Jon Kehlet Hansen, Erik Kæjr, Tony McRae and Lars Graudal during this work.

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Authors and Affiliations

  1. Centro de Estudos Florestais, Departamento dos Recursos Naturais, Ambiente e Território, Instituto Superior de Agronomia, Universidade Técnica de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal

    João Costa e Silva

  2. PlantPlan Genetics Pty Ltd, School of Plant Science, University of Tasmania, Private Bag 55, Hobart, Tasmania, 7001, Australia

    Richard J. Kerr

Authors
  1. João Costa e Silva
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  2. Richard J. Kerr
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Correspondence to João Costa e Silva or Richard J. Kerr.

Additional information

Communicated by R. Burdon

João Costa e Silva and Richard J. Kerr contributed equally to this work.

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Online Resource 2

Number of likelihood-ratio tests that were not statistically significant at the 5% level from a total of 100 comparisons between the AR and GC-AR models in a given simulation scenario. (PDF 22.9 kb)

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Costa e Silva, J., Kerr, R.J. Accounting for competition in genetic analysis, with particular emphasis on forest genetic trials. Tree Genetics & Genomes 9, 1–17 (2013). https://doi.org/10.1007/s11295-012-0521-8

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  • DOI: https://doi.org/10.1007/s11295-012-0521-8

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