OBJECTIVES: Combining the analysis of family-based samples with unrelated individuals can enhance the power of genetic association studies. Various combined analysis techniques have been recently developed; as yet, there have been no comparisons of their power, or robustness to confounding factors. We investigated empirically the power of up to six combined methods using simulated samples of trios and unrelated cases/controls (TDTCC), trios and unrelated controls (TDTC), and affected sibpairs with parents and unrelated cases/controls (ASPFCC). METHODS: We simulated multiplicative, dominant and recessive models with varying risk parameters in single samples. Additionally, we studied false-positive rates and investigated, if possible, the coverage of the true genetic effect (TDTCC). RESULTS/CONCLUSIONS: Under the TDTCC design, we identified four approaches with equivalent power and false-positive rates. Combined statistics were more powerful than single-sample statistics or a pooled chi(2)-statistic when risk parameters were similar in single samples. Adding parental information to the CC part of the joint likelihood increased the power of generalised logistic regression under the TDTC but not the TDTCC scenario. Formal testing of differences between risk parameters in subsamples was the most sensitive approach to avoid confounding in combined analysis. Non-parametric analysis based on Monte-Carlo testing showed the highest power for ASPFCC samples.
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