In this paper we investigate the use of a two-stage case-control design to test for linkage disequilibrium in a large sample with a large number of null makers and one potential candidate marker. The scores, or signals, obtained at the markers, at the same stage, are assumed to be independent. The aim is to reduce the cost due to the number of laboratory analyses. In the first stage, the test is carried out at all markers of a randomly selected proportion λ of the sample at hand. Then the markers showing a score over a specified threshold, say, the median score, along with an average random proportion p of the makers with scores below the median are selected for the second stage of the study. Combined scores are then computed at the second stage and these cross-stage scores are not assumed to be necessarily additive or independent. This, partially, extends Satagopan et al. (Biometrics 58 (2002) 163–170) analysis in the case of independent marker outcomes. The aim is to identify optimal values for p and λ that maximize the probability to detect association in the case of association. The transmission-disequilibrium test is considered in the analysis and analytical formulas for the underlying probabilities are derived throughout. Furthermore, simulation results on the performance of the two designs are presented.