QTL detection is a good way to assess the genetic basis of quantitative traits such as the plant response to its environment, but requires large mapping populations. Experimental constraints, however, may require a restriction of the population size, risking a decrease in the quality level of QTL mapping. The purpose of this paper was to test if an advanced backcross population sample chosen by MapPop 1.0 could limit the effect of size restriction and improve the QTL detection when compared to random samples. We used the genotypic and phenotypic data obtained for 280 genotypes, considered as the reference population. The "MapPop sample" of 100 genotypes was first compared to the reference population, and genetic maps, genotypic and phenotypic data and QTL results were analysed. Despite the increase in donor allele frequency in the MapPop sample, this did not lead to an increase of the genetic map length or a biased phenotypic distribution. Three QTL among the 10 QTL found in the reference population were also detected in the MapPop sample. Next, the MapPop sample results were compared to those from 500 random samples of the same size. The main conclusion was that the MapPop software avoided the selection of biased samples and the detection of false QTL and appears particularly interesting to select a sample from an unbalanced population.