Using the simulated general population data sets, we first examined the effect of sampling strategies on the power of identifying linkage by selecting samples with (A) two affected sibs in a nuclear family and (B) one affected sib and one sib with an intermediate trait value in the upper quantiles. Second, we evaluated the improvement in power when analyzing correlated traits simultaneously. Under each selection criteria, 100 replicates of 300 nuclear families were sampled and analyzed with two-point linkage analysis for ten markers (1 cM apart) from each of the candidate regions. Different genes were identified under different sampling strategies. When a gene has a pleitropic effect, it is more powerful to analyze correlated traits simultaneously, either by using a linear combination or the larger value of standardized traits, than to analyze each trait separately.