In this report, we present a simple and powerful way to incorporate individual-specific liability classes into linkage analysis. The proposed method is applicable to both quantitative and qualitative traits. In linkage studies, we may have information about different covariates. Incorporation of these covariates along with the estimates of residual familial effects, age-at-onset effects, and susceptibility in the definition of liability classes can increase the power to detect genetic linkage. In this study, we show how one can form individual-specific liability classes and use these classes in standard linkage-analysis programs, such as the widely used LINKAGE package, to perform more powerful genetic linkage analysis. Our simulation study shows that this approach yields higher LOD scores and more-accurate estimates of the recombination fraction in the families showing linkage. The proposed method is also applied to kindreds collected, at the M. D. Anderson Cancer Center, through probands with childhood soft-tissue sarcoma. Confirmed germ-line mutations in the p53 tumor-suppressor gene have been identified in these families. Application of our method to these families yielded significantly higher LOD scores and more-accurate recombination fractions than did analysis that did not account for individual-specific covariate information.