The relative effect of genomic information on efficiency of Bayesian analysis of the mixed linear model with unknown variance

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The relative effect of genomic information on efficiency of Bayesian analysis of the mixed linear model with unknown variance. / Milkevych, Viktor; Madsen, Per; Gao, Hongding; Jensen, Just.

I: Journal of Animal Breeding and Genetics (Online), 30.07.2020.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{bc4a5c569028428d8af88b372ebe0e52,
title = "The relative effect of genomic information on efficiency of Bayesian analysis of the mixed linear model with unknown variance",
abstract = "This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.",
author = "Viktor Milkevych and Per Madsen and Hongding Gao and Just Jensen",
note = "{\textcopyright} 2020 Blackwell Verlag GmbH.",
year = "2020",
month = jul,
day = "30",
doi = "10.1111/jbg.12497",
language = "English",
journal = "Journal of Animal Breeding and Genetics (Online)",
issn = "1439-0388",
publisher = "Wiley-Blackwell Verlag GmbH",

}

RIS

TY - JOUR

T1 - The relative effect of genomic information on efficiency of Bayesian analysis of the mixed linear model with unknown variance

AU - Milkevych, Viktor

AU - Madsen, Per

AU - Gao, Hongding

AU - Jensen, Just

N1 - © 2020 Blackwell Verlag GmbH.

PY - 2020/7/30

Y1 - 2020/7/30

N2 - This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

AB - This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

U2 - 10.1111/jbg.12497

DO - 10.1111/jbg.12497

M3 - Journal article

C2 - 32729965

JO - Journal of Animal Breeding and Genetics (Online)

JF - Journal of Animal Breeding and Genetics (Online)

SN - 1439-0388

ER -