The power to detect a quantitative trait locus (QTL) in sib-pair data is investigated. We assume that we have at our disposal 3 or 4 related phenotypic measures in a sample of sib-pairs. Individual differences in these phenotypes are due to a common QTL and specific (i.e., unique to each phenotype) nonshared environmental and specific polygenic additive effects. In addition, models are considered that include common nonshared environmental effects and/or common polygenic additive effects. We calculate the power to detect the QTL in a genetic covariance structure analysis of the multivariate data, of the mean phenotypic data, and of factor scores. The use of factor scores is shown to be universally more powerful than the use of multivariate or mean phenotypic data. We also investigate the effect of using a single sample of sib-pairs to both calculate the factor score regression matrix and to carry out the QTL analysis. The use of a single sample to both these ends results in a loss of power compared to the theoretical, expected power. The gain in power attributable to the use of factor scores, however, outweighs this observed loss in power. The advantages of using factor scores in selecting sib-pairs are discussed.