Joint multivariate segregation and linkage analysis provides a method for simultaneously analyzing data on affection status, correlated phenotypic traits, environmental risk factors, and other covariates. The power of this approach for mapping disease susceptibility loci of small effect (oligogenes) was evaluated by analyzing the GAW9 Problem 2 data set. The program REGRESS, which assumes a pleiotropy model in which one locus influences both affection status (AF) and a quantitative trait, was used to conduct joint segregation and linkage analysis of bivariate phenotypes, each comprising AF and one quantitative trait (Q2, Q3, Q4). A genome-wide search using markers spaced approximately 10 cM apart was conducted and regions on chromosomes 1, 2, and 5 were identified as demonstrating linkage with three respective bivariate phenotypes at the following markers: AF/Q2-D1G2; AF/Q3-D2G10; and AF/Q4-D5G18. The effects of other loci were included in a general model by specifying the quantitative traits they influenced as covariates along with age, sex, and an environmental effect. Use of covariate and quantitative trait data in each analysis resulted in respective chi 2 values with 1 df of 38.4, 65.4, and 22.0 to reject the no linkage hypothesis at theta = 0, with respective equivalent lod scores of 8.3, 14.2, and 4.8. Rejection at p < 0.0002 occurred using markers as far away as 20 cM. These loci were not detected when AF alone was analyzed.