Abstract: Quantitative trait loci (QTL) analysis is the precondition and basis for positional cloning of major genes underlying quantitative traits. Normality transformation of quantitative trait data using BoxCox formula has significant effect on QTL detection. In this paper, a case study on poplar is carried out to demonstrate the application of BoxCox formula for normality transformation and the effect of data distribution on QTL analysis. The results are as followings: QTL detection is significantly affected by the data distribution. Without normality transformation, some significant QTLs may be missed. Similar variation trends are observed on the heat plots of LOD scores and LOD peaks are found to appear in the same positions in the charts established by LOD scores derived before and after the normality transformation. On chromosome 4, 3 LOD peaks appear at 40—60 cm, 80— 100 cm and 130—150 cm intervals respectively both before and after transformation. However, the corresponding LOD peaks are found to increase to the significant level after transformation. Especially, the increment of the first peak is about 3 folds higher than that before BoxCox transformation, which means a relatively strong QTL in the corresponding position. The same scenario is also observed on chromosome 8 in this study.