A computationally simple test of homogeneity of odds ratios for twin data

It is of interest to compare measures of association of binary traits among samples of bivariate data. One example is the comparison of association wi thin a sample of monozygotic (MZ) twins to that within a sample of dizygoti c (DZ) twins. A larger association in the MZ twins suggests that the trait of interest may have a genetic component. The Bivariate data in this exampl e are binary traits for the twins in each pair. Another example is the comp arison of a measure of Hardy-Weinberg disequilibrium across several populat ions. The bivariate data in this example are the two alleles comprising the genotype of interest. We propose using derived logistic regression equatio ns from the full exponential model for the bivariate outcomes to test for h omogeneity. We adjust for correlation among outcomes via generalized estima ting equations. This modeling approach allows for adjustment for individual -level and pair-level covariates and thereby allows for testing for gene x environment interactions. Further, we extend the model to allow for simulta neous analysis of two diseases, which allows for testing for a genetic comp onent to the coaggregation of two diseases. In contrast to approaches propo sed by previous authors, no special software is required; our approach can be easily implemented in standard software packages. We compare our results to those of other methods proposed in the literature for data from the Vie tnam Era Twins Study. We apply our methods also to the Anqing Twin Study an d to data on major depression and generalized anxiety disorder from the Vir ginia Twin Register. (C) 2001 Wiley-Liss, Inc.