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.
Highlights
Multi-marker methods, which fit all markers simultaneously, were originally tailored for genomic selection purposes, but have proven to be useful in association analyses, especially the so-called BayesC Bayesian methods
For each single nucleotide polymorphisms (SNP), a test statistic and Bennewitz et al Genet Sel Evol (2017) 49:7 an error probability for the trait association is obtained in a ‘frequentist’ manner, which can conveniently be used for post-Genome-wide association studies (GWAS) analyses, such as false discovery rate calculations [3], or for meta-analyses, e.g. [4]
Power decreased as the window posterior probabilities of association (WPPA) level increased for all simulated configurations and for both methods, as expected
Summary
Multi-marker methods, which fit all markers simultaneously, were originally tailored for genomic selection purposes, but have proven to be useful in association analyses, especially the so-called BayesC Bayesian methods. The effect of a gene can be captured only in part by a single marker due to imperfect LD, but might be better explained by using jointly the SNPs that surround the gene To overcome these problems, multi-marker methods have been proposed, which fit all SNPs simultaneously as random effects in the model [5]. Multi-marker methods have been proposed, which fit all SNPs simultaneously as random effects in the model [5] These models were originally tailored for genomic prediction or selection purposes [6], but have proven to be useful in association analyses [7]. They did not report a clear superiority of one method, but recommended to apply more than one method to real data
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