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    Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

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    Author
    Holmes, JB; Dodds, KG; Lee, MA
    Date
    2017-03-02
    Source Title
    Genetics Selection Evolution
    Publisher
    Springer Science and Business Media LLC
    University of Melbourne Author/s
    Holmes, John
    Affiliation
    Melbourne School of Population and Global Health
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Holmes, J. B., Dodds, K. G. & Lee, M. A. (2017). Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.. Genet Sel Evol, 49 (1), pp.29-. https://doi.org/10.1186/s12711-017-0302-9.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255171
    DOI
    10.1186/s12711-017-0302-9
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439142
    Abstract
    BACKGROUND: An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. RESULTS: In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. CONCLUSIONS: Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

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