This report summarizes the contributions of Genetic Analysis Workshop 13 (GAW13) related to the use of covariates in genetic analysis. Seven papers are summarized, five of which analyzed the Framingham Heart Study Data, and two the simulated data. Five papers examined the role of covariates in linkage analysis, using a variety of statistical approaches including affected sibling pair analysis, conditional logistic regression, and variance components methods. One paper examined the impact of covariates on family-based association analysis. In each of these papers, the detection of genetic effects could be influenced by the incorporation of covariates. The final paper examined the role of transmission ratio distortion in the analysis of complex traits and the role of covariates in the variability in transmission ratio distortion. While each paper takes a different approach to the genetic analysis of complex traits, a common thread running through each is that the inclusion of covariates can have a substantial impact on the results of the analysis. Care must be taken to understand how the covariates are being used in each analysis, what assumptions are being made, and how these assumptions might affect the results and their interpretation. Finally, the results of Group 5 studies show that inclusion of covariates can increase the power to detect genes for complex traits, and has the potential to advance an understanding of the role of genes in these complex traits.