As previously mentioned, pivotal issues WGs face when combining or harmonizing data from different studies are determining the analytic plan and the precise modeling strategy, adjusting for confounders, and performing stratified analysis. WGs continually consider, for instance: (a) whether waves of longitudinal data should be combined for comparability to cross-sectional studies; (b) whether analyses should be adjusted for covariates, such as ethnicity, or conducted separately in each group; (c) whether analyses should remove, truncate, or adjust for outliers (e.g. by normalizing distributions) and how these outliers are defined; (d) which statistical models accommodate the nuances of each dataset; and (e) whether studies should focus on main effects of individual SNPs, candidate genes, on gene systems or on gene-by-gene (G*G) and G*E analyses. Overarching these phenotype issues are comparable concerns regarding uniform quality control metrics for genotype data and comparable imputation statistics which add inherent complexity to the process. A pre-determined analytic strategy for the main gene discovery stage as well as approaches to G*E and G*G associations requires special planning as there is no standard approach though all agree that
