Resolution of the genetic components of complex disorders may require simultaneous analysis of the contribution of individual quantitative trait loci (QTLs) to multiple variables. A likelihood approach is used to illustrate how the complexities of multivariate data may be resolved with multipoint linkage analysis. Sibling pair data were simulated from a model in which two QTLs and trait-specific polygenic effects explained all the sibling resemblance within and between five variables. Multipoint linkage analysis was used to obtain individual pair probabilities of having zero, one, or two alleles identical by descent, and these probabilities were applied in a weighted maximum-likelihood fit function. The results were compared with those obtained using conventional linear structural equation modeling to estimate the contribution of latent genetic factors to the genetic covariance in the multiple measures. Both analyses were conducted using the Mr package. Relatively poor agreement was found between genetic factors defined in purely statistical terms by varimax rotation of the first two factors of the genetic covariance matrix and the structure obtained by fitting a model jointly to the phenotypic and the multipoint linkage data.