1471-2156-12-74.pdf 629,66KB
1000 Titel
  • Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers
1000 Autor/in
  1. Melzer, Nina |
  2. Reinsch, Norbert |
  3. Wittenburg, Dörte |
1000 Erscheinungsjahr 2011
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2011-08-25
1000 Erschienen in
1000 Quellenangabe
  • 12: 74
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2011
1000 Lizenz
1000 Verlagsversion
  • |
  • |
1000 Ergänzendes Material
  • |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • BACKGROUND: Molecular marker information is a common source to draw inferences about the relationship between genetic and phenotypic variation. Genetic effects are often modelled as additively acting marker allele effects. The true mode of biological action can, of course, be different from this plain assumption. One possibility to better understand the genetic architecture of complex traits is to include intra-locus (dominance) and inter-locus (epistasis) interaction of alleles as well as the additive genetic effects when fitting a model to a trait. Several Bayesian MCMC approaches exist for the genome-wide estimation of genetic effects with high accuracy of genetic value prediction. Including pairwise interaction for thousands of loci would probably go beyond the scope of such a sampling algorithm because then millions of effects are to be estimated simultaneously leading to months of computation time. Alternative solving strategies are required when epistasis is studied. METHODS: We extended a fast Bayesian method (fBayesB), which was previously proposed for a purely additive model, to include non-additive effects. The fBayesB approach was used to estimate genetic effects on the basis of simulated datasets. Different scenarios were simulated to study the loss of accuracy of prediction, if epistatic effects were not simulated but modelled and vice versa. RESULTS: If 23 QTL were simulated to cause additive and dominance effects, both fBayesB and a conventional MCMC sampler BayesB yielded similar results in terms of accuracy of genetic value prediction and bias of variance component estimation based on a model including additive and dominance effects. Applying fBayesB to data with epistasis, accuracy could be improved by 5% when all pairwise interactions were modelled as well. The accuracy decreased more than 20% if genetic variation was spread over 230 QTL. In this scenario, accuracy based on modelling only additive and dominance effects was generally superior to that of the complex model including epistatic effects. CONCLUSIONS: This simulation study showed that the fBayesB approach is convenient for genetic value prediction. Jointly estimating additive and non-additive effects (especially dominance) has reasonable impact on the accuracy of prediction and the proportion of genetic variation assigned to the additive genetic source.
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
1000 Label
1000 Förderer
  1. German Federal Ministry of Education and Research (BMBF) |
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. FUGATO project "Bovine Integrative Bioinformatics for Genomic Selection (BovIBI)"
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer German Federal Ministry of Education and Research (BMBF) |
    1000 Förderprogramm FUGATO project "Bovine Integrative Bioinformatics for Genomic Selection (BovIBI)"
    1000 Fördernummer -
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1000 Erstellt am 2017-06-26T10:53:57.648+0200
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