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 transf ormation was studied using data from two trials in which mice were cha llenged at three months of age with a cloned isolate of Trypanosoma co ngolense and survival time was recorded. Data included records from th ree inbred strains (C57BL/6 (tolerant), A/J, and BALB/c (both suscepti ble)) and their crosses. Data were standardized and normalized using a modified power transformation. Segregation analysis was applied to bo th untransformed and transformed data to determine the genetic inherit ance 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 varianc e, and a mixed model with different variances for each major genotype. Even though the separate data sets and the combined data set all supp orted the hypothesis of a major gene (or a tightly linked cluster of g enes) with different variances within each genotype, parameter estimat es were highly sensitive to data transformation and several sets of pa rameter estimates gave similar likelihood values because of high depen dency between parameters. Based on the results segregation analysis ca n 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.