Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers

Norbert Reinsch,Dörte Wittenburg,Nina Melzer

doi:10.1186/1471-2156-12-74

Norbert Reinsch, Dörte Wittenburg + Show 1 more

Open Access

https://doi.org/10.1186/1471-2156-12-74

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Abstract

BackgroundMolecular 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.MethodsWe 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.ResultsIf 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.ConclusionsThis 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.

Highlights

Molecular marker information is a common source to draw inferences about the relationship between genetic and phenotypic variation
The Bayesian methods commonly used for the estimation of additive effects apply Markov chain Monte Carlo (MCMC) simulations which require a lot of computing time, but they convince in terms of accuracy in predicting genetic values
We extend the fast Bayesian method, which was developed under pure additivity [20], to include non-additive effects. fBayesB is used to estimate the genetic effects on the basis of simulated datasets which resemble a dairy cattle population

Summary

Introduction

Molecular marker information is a common source to draw inferences about the relationship between genetic and phenotypic variation. The Bayesian methods commonly used for the estimation of additive effects apply Markov chain Monte Carlo (MCMC) simulations which require a lot of computing time, but they convince in terms of accuracy in predicting genetic values. An approximate Bayesian approach is available which applies the analytically derived posterior density for a marker effect rather than samples thereof [20] This approach (called fBayesB) was shown to be slightly less accurate, because in an iterative procedure only a single marker effect is studied at a time while the vector of phenotypes is corrected for all other previously estimated effects. The fBayesB strategy is much faster than the conventional Bayesian methods using MCMC This solving approach offers the possibility to account for genome-wide interacting effects and to estimate them with reasonable computational effort

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