Sensitivity of segregation analysis to data structure and transformation: a case study of trypanotolerance in mice

Sensitivity of segregation analysis for data structure and data transformation was studied using data from two trials in which mice were challenged at three months of age with a cloned isolate of Trypanosoma congolense and survival time was recorded. Data included records from three inbred strains (C57BL/6 (tolerant), A/J, and BALB/c (both susceptible)) and their crosses. Data were standardized and normalized using a modified power transformation. Segregation analysis was applied to both untransformed and transformed data to determine the genetic inheritance of trypanotolerance in these mice. Data from the two trials were analysed separately and combined. Four genetic models were compared; a one locus model, a polygenic model, a mixed model with common variance, and a mixed model with different variances for each major genotype. Even though the separate data sets and the combined data set all supported the hypothesis of a major gene (or a tightly linked cluster of genes) with different variances within each genotype, parameter estimates were highly sensitive to data transformation and several sets of parameter estimates gave similar likelihood values because of high dependency between parameters. Based on the results segregation analysis can be very sensitive to data structure in a crossbreeding design and to data transformation. Interpretation of the results can be misleading if the entire parameter space is not studied carefully.