AbstractSegregation analysis frequently is used to test for the presence of major gene effects and to estimate the various genetic and environmental components contributing to diseases. Recent advances in both theoretical models and computational algorithms have provided a number of new programs for performing segregation analyses. We compared two newer programs: REGC (part of the package “SAGE”) and FISHER/MENDEL with an older established program (PAP) to determine relative accuracy in recovering parameter values and asymptotic standard errors, ability to discriminate between alternative transmission models, and execution speeds. Each program was applied to a set of computer simulations of a quantitative trait generated under a variety of genetic models. The results of these comparisons indicated that all the programs provided very similar parameter estimates, but that they differed in their abilities to identify the correct mode of transmission. In our simulations, PAP more often led to the selection of the correct transmission model, whereas REGC frequently indicated the presence of a major gene in simulations of purely polygenic transmission. Relative speeds for the programs differed, and their rank ordering varied with the complexity of the model being fitted. Although REGC was the fastest program for fitting a major gene or mixed model, it was by far the slowest program for estimating parameters in a sporadic or polygenic mo
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