Since GENEVA studies a wide range of complex traits utilizing various study designs, the power to detect genetic effects will vary substantially. Given study-specific parameters (i.e. study design, sample size and baseline risk) and assuming a minor allele frequency of 0.3, all studies have 80% power to detect additive variant relative risks of > 1.57 and proportions of variance explained for primary quantitative traits ranging from 0.0097 to 0.02 (Table I). Combining data on common outcomes and environmental measures across studies will also allow tests with greater power for even modest effect sizes. The availability of phenotype data common to multiple studies also provides a platform for exploring other, potentially novel, gene-trait associations. For example, anthropometric measures, such as height, weight and body mass index (BMI), are uniformly available across a majority of the studies and we anticipate genome-wide scan data for ~40,000 subjects for cross-study analysis of these traits. Assuming an effect allele frequency of 0.3, we are sufficiently powered to detect a marginal correlation coefficient of at least 0.0011 when BMI is the outcome of interest. Investigators are
