Comparison of methods for regression interval mapping in QTL analysis with non-normal traits

Abstract

We compare the powers of three methods for the QTL analysis of non-normally distributed traits. We describe the nonparametric and the logistic regression approaches recently proposed in the literature and study the properties of the standard regression interval mapping method when the trait is not normally distributed. It is shown that the standard approach is robust against non-normality and behaves quite well for both continuous and discrete characters. The loss of power compared with the nonparametric or the logistic approach is generally minor. Moreover, the least squares estimation procedure of the regression interval mapping is not affected by departure from normality. The use of other approaches could be restricted to extreme cases where the trait distribution is very skewed.