Cargando…
Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix
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 t...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439142/ https://www.ncbi.nlm.nih.gov/pubmed/28253844 http://dx.doi.org/10.1186/s12711-017-0302-9 |
_version_ | 1783237890262695936 |
---|---|
author | Holmes, John B. Dodds, Ken G. Lee, Michael A. |
author_facet | Holmes, John B. Dodds, Ken G. Lee, Michael A. |
author_sort | Holmes, John B. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-017-0302-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5439142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54391422017-05-23 Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix Holmes, John B. Dodds, Ken G. Lee, Michael A. Genet Sel Evol Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-017-0302-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-02 /pmc/articles/PMC5439142/ /pubmed/28253844 http://dx.doi.org/10.1186/s12711-017-0302-9 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Holmes, John B. Dodds, Ken G. Lee, Michael A. Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix |
title | Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix |
title_full | Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix |
title_fullStr | Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix |
title_full_unstemmed | Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix |
title_short | Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix |
title_sort | estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439142/ https://www.ncbi.nlm.nih.gov/pubmed/28253844 http://dx.doi.org/10.1186/s12711-017-0302-9 |
work_keys_str_mv | AT holmesjohnb estimationofgeneticconnectednessdiagnosticsbasedonpredictionerrorswithoutthepredictionerrorvariancecovariancematrix AT doddskeng estimationofgeneticconnectednessdiagnosticsbasedonpredictionerrorswithoutthepredictionerrorvariancecovariancematrix AT leemichaela estimationofgeneticconnectednessdiagnosticsbasedonpredictionerrorswithoutthepredictionerrorvariancecovariancematrix |