Linkage detection of a trait involves detecting regions of the genome that influence the trait. A wide variety of statistical models are currently employed for linkage analysis of quantitative traits. Many of these models are developed under some assumptions of the trait distributions. Violation of the assumptions about the trait generally affects the type I error and power for linkage detection. In this paper, we have proposed a trait-model-free approach for linkage analysis of a quantitative trait in general pedigrees. The conditional segregation of marker alleles given the trait is modeled using a latent-variable logistic model. A likelihood-ratio test is used to test for linkage under our model. The main applicability of this approach lies in the fact that it always provides correct type I error no matter what the trait distribution is and thus can be used for nonnormal traits or for selected samples. By means of simulation studies, we have compared the power of our proposed model with existing approaches for nonnormal traits. The performance of these methods was also studied on a real dataset. We have demonstrated the usefulness of our approach in terms of power and robustness for linkage detection of quantitative traits in general pedigrees.