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 also looking across studies at smoking and alcohol consumption behavior, female reproductive history and oral health. GENEVA investigators also plan to assess genetic loci associated with novel traits such as caffeine consumption, physical activity and “wellness” (i.e. protection against disease). As one of the first GWAS of caffeine intake, mega-analysis of data on 22,000 genome-wide scans will afford 80% power to detect additive genetic variants that explain marginal effects as small as 0.0019 while satisfying a type 1 error level of 1E-08.
