Abstract
Fitness traits described as a ratio often display non-normal distributions; consequently, transformations are frequently applied to improve normality prior to the estimation of genetic parameters. However, the impact of different transformations on genetic parameter estimates depends on the dataset at hand. The objective of this study was to evaluate the effects of eight common transformations (z-score, log, square root, probit, arcsine, logit, Box-Cox and Yeo-Johnson) on genetic parameter estimates for non-normal fitness traits in turkeys. Three fertility traits in turkeys were analysed. Egg production rate, egg fertility rate and hatch of fertile eggs rate phenotypes were collected on 6667 turkeys. All three phenotypes exhibited a significant level of non-normality. An informative pedigree file for the phenotyped birds was generated and consisted of 8612 animals. A mixed linear model that included the hatch year and regression on body weight at 18 weeks of age as fixed effects was used to analyse the transformed and untransformed phenotypes. To make the untransformed and transformed data comparable, they were all standardised to the same mean and variance. Results showed that the transformations significantly impacted genetic parameter estimates. In fact, the percentage variations in the estimates of the heritabilities of the three traits compared to the non-transformed data ranged from -80% to 45%. Across the different comparison criteria, the Box-Cox transformation seems to have the advantage compared to the other methods. Furthermore, it resulted in the highest heritability estimates. Although the genetic correlations showed fewer differences across transformations, the Spearman rank correlations ranged between 0.87 and 1, indicating some re-ranking. These findings suggest that the choice of data transformation impacts inferences on the genetic properties of non-normal traits, and careful consideration of the transformation method is needed prior to genetic analysis of skewed fitness data in turkeys and potentially other agricultural species. The results provide guidelines for the appropriate choice of transformations given observed levels of deviation from normality.