Effect of Winsorization on Power and Type 1 Error of Variance Components and Related Methods of QTL Detection

Variance components analysis provides an efficient method for performing linkage analysis for quantitative traits. However, power and type 1 error of variance components-based likelihood ratio testing may be affected when phenotypic data are nonnormally distributed (especially with high values of kurtosis) and there is moderate to high correlation among the siblings. Winsorization can reduce the effect of outliers on statistical analyses. Here, we considered the effect of winsorization on variance components-based tests. We considered the likelihood ratio test (LRT), the Wald test, and some robust variance components tests. We compared these tests with Haseman-Elston least squares-based tests. We found that power to detect linkage is significantly increased after winsorization of the nonnormal phenotypes. Winsorization does not greatly diminish the type 1 error for the variance components-based tests for markedly non-normal data. A robust version of the LRT that adjusts for sample kurtosis showed the best power for nonnormal data. Finally, phenotype winsorization of nonnormal data reduces the bias in estimation of the major gene variance component.