To compare the mCLR method with the combined association approach, we simulated a simple scenario including population stratification by sampling the trio data in proportion, q and 1–q, from C = 2 subpopulations. The unrelated controls were sampled with equal probability from the two subpopulations. For this sampling scenario, the two samples were combinable without concern for population heterogeneity only when q ≈ 0.5. To examine the false positive rate when the sampling proportions in subpopulations are not the same, we varied q between 0.1 and 0.5, and set the GRR at ψ = 1 (under a multiplicative model with no risk). Each simulation included 500 controls and 500 trios. Three levels of stratification were simulated: Fst = 0.001, 0.01, 0.05. As expected the mCLR did a better job than the combined association analysis in controlling for spurious associations in the presence of population stratification (Figure 5). When Fst =0.05, the combined association analysis produced unacceptably high Type I errors at every level of q. Even when the two populations are quite similar genetically (Fst = 0.001), the combined association
