In 1990, Lande and Thompson [1] defined a two steps marker assisted selection procedure. Firstly, apparent effects of markers were estimated in a reference population. Secondly, during *n* generations, breeding values of candidates to selection were calculated from these estimated effects giving a so called Molecular Scores. These ideas, which founded the genomic selection, were more recently made operational by SNP chips which provide tens of thousands genotypes per individual. The seminal paper of Meuwissen *et al*. [2] presented a few statistical approaches of these Genomic Estimated Breeding Values (GEBV). A large literature followed, describing and comparing various methods.

These methods could be classified according to the assumption made concerning the variance of chromosome segments effects. The simplest assumption, assumed in BLUP methodology [2] or Ridge Regression [3], is that the variance of these effects is equal for all chromosome segments. However, this hypothesis is not consistent with classical genetic prior, and observations, that only a few chromosome segments contain QTL, with various extent of their effects, while most chromosome segments do not contain QTL.

Variable selection procedures were proposed to better fit this biological knowledge. In [2], a stepwise procedure, including a QTL detection step through single segments regression analyses, was envisaged in the least square framework. The efficiency for genomic evaluation of more advanced penalized regression approaches were evaluated, like sparse PLS [4], LASSO [5] or Elastic Net [6], which all allow the vast majority of loci to have null regression coefficients.

On the other hand, Bayesian methods were proposed to take into account the between chromosome segments variances heterogeneity. In BayesA [2], each chromosome segment is given its own variance, all segments contributing to the variability. This last hypothesis is made free with other Bayesian techniques which assume that only a fraction π of the segments carry QTL: BayesB keeps the between segments variance heterogeneity, while BayesC considers a single variance for the active segments. In BayesCπ, the proportion π is estimated from the data [7].

The QTLMAS XV^{th} dataset consisted of the pedigree, marker genotypes and quantitative trait performances of animals with a sib family structure [8]. Pedigree and genotypes concerned 3,000 progenies among those 2,000 were phenotyped. The trait was regulated by 8 QTLs which displayed additive, imprinting or epistatic effects. The 1,000 unphenotyped progenies were considered as candidates to selection. Participants of the XV^{th} QTLMAS workshop were invited to predict GEBV of these 1,000 individuals and to send to the organizers the description of their methods and results before the meeting. This paper aims at comparing the GEBV estimations obtained by participants to the workshop. Comparing the results obtained by the different groups should provide insight into determining which method is best fitted to analyze this kind of data set.