MAXIMUM-LIKELIHOOD ANALYSIS OF RARE BINARY TRAITS UNDER DIFFERENT MODES OF INHERITANCE

Maximum likelihood methodology was applied to determine the mode of in heritance of rare binary traits with data structures typical for swine populations. The genetic models considered included a monogenic, a di genic, a polygenic, and three mixed polygenic and major gene models. T he main emphasis was on the detection of major genes acting on a polyg enic background. Deterministic algorithms were employed to integrate a nd maximize likelihoods. A simulation study was conducted to evaluate model selection and parameter estimation. Three designs were simulated that differed in the number of sires/number of dams within sires (10/ 10, 30/30, 100/30). Major gene effects of at least one SD of the liabi lity were detected with satisfactory power under the mixed model of in heritance, except for the smallest design. Parameter estimates were em pirically unbiased with acceptable standard errors, except for the sma llest design, and allowed to distinguish clearly between the genetic m odels. Distributions of the likelihood ratio statistic were evaluated empirically, because asymptotic theory did not hold. For each simulati on model, the Average Information Criterion was computed for all model s of analysis. The model with the smallest value was chosen as the bes t model and was equal to the true model in almost every case studied.