Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis

Abstract

BackgroundMulti-marker methods, which fit all markers simultaneously, were originally tailored for genomic selection purposes, but have proven to be useful also in association analyses, especially the so-called BayesC Bayesian methods. In a recent study, BayesD extended BayesC towards accounting for dominance effects and improved prediction accuracy and persistence in genomic selection. The current study investigated the power and precision of BayesC and BayesD in genome-wide association studies by means of stochastic simulations and applied these methods to a dairy cattle dataset. MethodsThe simulation protocol was designed to mimic the genetic architecture of quantitative traits as realistically as possible. Special emphasis was put on the joint distribution of the additive and dominance effects of causative mutations. Additive marker effects were estimated by BayesC and additive and dominance effects by BayesD. The dependencies between additive and dominance effects were modelled in BayesD by choosing appropriate priors. A sliding-window approach was used. For each window, the R. Fernando window posterior probability of association was calculated and this was used for inference purpose. The power to map segregating causal effects and the mapping precision were assessed for various marker densities up to full sequence information and various window sizes.ResultsPower to map a QTL increased with higher marker densities and larger window sizes. This held true for both methods. Method BayesD had improved power compared to BayesC. The increase in power was between −2 and 8% for causative genes that explained more than 2.5% of the genetic variance. In addition, inspection of the estimates of genomic window dominance variance allowed for inference about the magnitude of dominance at significant associations, which remains hidden in BayesC analysis. Mapping precision was not substantially improved by BayesD.ConclusionsBayesD improved power, but precision only slightly. Application of BayesD needs large datasets with genotypes and own performance records as phenotypes. Given the current efforts to establish cow reference populations in dairy cattle genomic selection schemes, such datasets are expected to be soon available, which will enable the application of BayesD for association mapping and genomic prediction purposes.

An approximate Bayesian significance test for genomic evaluations

“…This was observed for the approximate Bayesian approaches used in this study, for MCMC‐based methods (e.g. Bennewitz et al., ) and for other SNP‐selection approaches (e.g. Sabourin, Nobel, & Valdar, ).…”

“…For the identification of loci with significant additive or nonadditive genetic impact on a trait, MCMC-based stochastic variable selection methods are useful (e.g. Bennewitz, Edel, Fries, Meuwissen, & Wellmann, 2017;Yi et al, 2005). Depending on the number of iterations and on the model dimension, such methods are exact but may need exhausting computing time.…”

Benefits of Dominance over Additive Models for the Estimation of Average Effects in the Presence of Dominance

“…Only few studies have used the AD-model in GWAS to explicitly estimate $a$ and $d$ ( e.g. , Lopes et al 2014; Aliloo et al 2015; Huang et al 2015; Bennewitz et al 2017) and, to our knowledge, none have investigated differences in accuracy of estimated average effects between the A-model and AD-model. The effects of sampling genotypes on $\stackrel{true^}{\alpha }$ shown in this study apply to ${\stackrel{true^}{\alpha }}_m$ in GWAS, because ${\stackrel{true^}{\alpha }}_m$ are usually estimated by ordinary least squares.…”

Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

“…Gibbs sampling was used to draw samples from the posterior distributions using the program BayesDsamples (Wellmann and Bennewitz, 2012). The SNP effect estimates were used to calculate window genomic breeding values for windows of five consecutive SNPs (GEBV W ) using standard notations (Falconer and Mackay 2007;Bennewitz et al, 2017). From these, the expected GEBV W (E(GEBV W )) was subtracted in order to pinpoint trait-associated chromosomal regions.…”