Combining unrelated family studies to improve the power of genetic association test

Family-based studies provide powerful inferences regarding associations between genetic variants and risks, but have limitations. Since very often, the availability of the parental genotypes can pose a problem for using family-based design, especially when the disease of interest has a late age of onset. To improve the efficiency of the studies, a popular approach is to reconstruct the missing genotypes from the genotypes of their offspring and correct the biases resulting from the reconstruction. In this paper, the author shows that two or more unrelated family studies, for the same candidate marker but different diseases, can also be combined to construct a more efficient test for association analysis. The usual case-control study with parental genotypes is a special case of the data discussed here. The author used a simulation study to compare the performance of the new method with other well-known methods. The results showed that the new test has an advantage of having larger power when there is no effect of population stratification between two study samples. However, if there is effect of population stratification between the two samples, the new test still maintains the expected type I error rate and has comparable power performance. Since the unrelated family studies not for the disease of interest are often readily accessible with minimal cost, the proposed method has practical value. The new approach can also be easily modified to allow for missing parental data.