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While for this specific dataset, this has little impact on the accuracy of the GEBV, it may prove a good property to discriminate true effect from noise, and thereby, improve overall prediction under different scenarios. The Horseshoe prior showed a different shrinkage pattern than the other methods. The Horseshoe tended to select smaller number of SNP and assigning them larger effects, while strongly shrinking the remaining SNP to have an effect closer to zero. GEBV with the highest accuracy were obtained with Bayes A, Bayes B and the Horseshoe prior. The accuracy for all methods ranged from 0.74 to 0.83, representing an improvement of 44% to 78% over the traditional BLUP evaluation. The implementation of all methods (except GBLUP) was done using a MCMC approach, where the relevant parameters defining the prior distributions were jointly estimated from the data. It has an infinite spike at zero and heavy tail that decay by β -2 (slower than the Laplace or the Student-t). The distribution for the Horseshoe prior behaves like log(1+1/β 2) (up to a constant). The distribution of the Bayesian Lasso is a Laplace distribution for Bayes A is a Student-t for Bayes B and Bayes C is a spike and slab prior combining a proportion of SNP without effect and a proportion with effect distributed as a Student-t or Gaussian for Bayes B and C, respectively for GBLUP is similar to a ridge regression. The main difference between the methods is the prior distribution assumed during the estimation of the SNP effects. The method was compared with five commonly used methods: Bayes A, Bayes B, Bayes C, Bayesian Lasso and GLUP. A method for estimating genomic breeding values (GEBV) based on the Horseshoe prior was introduced and used on the analysis of the 16 th QTLMAS workshop dataset, which resembles three milk production traits.