Estimation of quantitative genetic parameters under non‐normal models

In traditional quantitative genetics, the relationship between the observed value of a quantitative trait in a set of families and its genetic and environmental contributions can be described as a linear additive relationship. Generally, it is assumed that the genetic and environmental effects are independent and normally distributed. Consequently, the quantitative trait also has a normal distribution. However, there are some situations where the phenotype does not follow the normal distribution. To deal with this problem the author suggests the families of distributions that belong to the Johnson Translation System (JTS). As an example, two dependent quantitative traits, weight and height, are investigated assuming that they have a lognormal and normal distribution, respectively. Computational methods for estimating the genotypic variances and covariances are presented.