Yang, Ye3; Christensen, Ole Fredslund4; Sorensen, Daniel4
1 Department of Molecular Biology and Genetics - Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Science and Technology, Aarhus University2 Quantitative Genetics and Bioinformatics, Faculty of Agricultural Sciences, Aarhus University, Aarhus University3 Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, Aarhus University4 Department of Molecular Biology and Genetics - Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Science and Technology, Aarhus University
Over recent years, statistical support for the presence of genetic factors operating at the level of the environmental variance has come from fitting a genetically structured heterogeneous variance model to field or experimental data in various species. Misleading results may arise due to skewness of the marginal distribution of the data. To investigate how the scale of measurement affects inferences, the genetically structured heterogeneous variance model is extended to accommodate the family of Box–Cox transformations. Litter size data in rabbits and pigs that had previously been analysed in the untransformed scale were reanalysed in a scale equal to the mode of the marginal posterior distribution of the Box–Cox parameter. In the rabbit data, the statistical evidence for a genetic component at the level of the environmental variance is considerably weaker than that resulting from an analysis in the original metric. In the pig data, the statistical evidence is stronger, but the coefficient of correlation between additive genetic effects affecting mean and variance changes sign, compared to the results in the untransformed scale. The study confirms that inferences on variances can be strongly affected by the presence of asymmetry in the distribution of data. We recommend that to avoid one important source of spurious inferences, future work seeking support for a genetic component acting on environmental variation using a parametric approach based on normality assumptions confirms that these are met.
Genetics Research, 2011, Vol 93, Issue 01, p. 33-46