COMPARISON OF METHODS FOR REGRESSION INTERVAL MAPPING IN QTL ANALYSISWITH NONNORMAL TRAITS

We compare the powers of three methods for the QTL analysis of non-nor mally distributed traits. We describe the nonparametric and the logist ic regression approaches recently proposed in the literature and study the properties of the standard regression interval mapping method whe n the trait is not normally distributed. It is shown that the standard approach is robust against nonnormality and behaves quite well for bo th continuous and discrete characters. The loss of power compared with the nonparametric or the logistic approach is generally minor. Moreov er, 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 distri bution is very skewed.