With the expected development of thousands of molecular markers in most crops, the marker-assisted selection theory has recently shifted from the use of a few markers targeted in QTL regions (or derived from candidate or validated genes) to the use of many more markers covering the whole genome. These genome-wide markers are already used for association analysis between polymorphisms for anonymous markers and qualitative or quantitative traits. The condition for success is that a sufficient level of linkage disequilibrium (LD) exists between the adjacent markers used for genotyping and the true genes or QTLs. This LD is known to vary among species and type of genetic material. In selfing species, particularly among breeding lines, LD has been reported to range up to 1 cM or more. In such conditions, uncharacterized markers can be used to predict the breeding value of a trait without referring to actual QTLs. We present an example applying DArT markers to the INRA wheat breeding material in an attempt to implement whole genome selection as an alternative to phenotypic selection. This study assesses different models (GBLUP, Ridge Regression BLUP, Bayesian Ridge Regression and Lasso) and their ability to predict the yields of genotypes evaluated in a multi-site network from 2000 to 2009 in a highly unbalanced design. The prediction coefficients obtained by cross-validation techniques are encouraging, given the small size of the training population.