A power calculation is crucial in planning genetic studies. In genetic association studies, the power is often calculated using the expected number of individuals with each genotype calculated from an assumed allele frequency under Hardy-Weinberg equilibrium. Since the allele frequency is often unknown, the number of individuals with each genotype is random and so a power calculation assuming a known allele frequency may be incorrect. Ambrosius et al. recently showed that the power ignoring this randomness may lead to studies with insufficient power and proposed averaging the power due to the randomness. We extend the method of averaging power in two directions. First, for testing association in case-control studies, we use the Cochran-Armitage trend test and find that the time needed for calculating the averaged power is much reduced compared to the chi-square test with two degrees of freedom studied by Ambrosius et al. A real study is used for illustration of the method. Second, we extend the method to linkage analysis, where the number of identical-by-descent alleles shared by siblings is random. The distribution of identical-by-descent numbers depends on the underlying genetic model rather than the allele frequency. The robust test for linkage analysis is also examined using the averaged powers. We also recommend a sensitivity analysis when the true allele frequency or the number of identical-by-descent alleles is unknown.
Copyright 2005 S. Karger AG, Basel.