To ensure uniform analyses across datasets, the coordinating site at Washington University developed analysis scripts in SAS® and R. Scripts were distributed to collaborating sites, which then analyzed their datasets locally. Results were returned to the coordinating site for meta-analyses. We used standard inverse-variance-weighted meta-analysis as implemented in the rmeta package in R (Lumley; R Development Core Team 2012). Additionally, to be included in the meta-analysis of a given model, each dataset was required to have at least five cases and five controls available. This requirement was intended to reduce noise when some subgroups became very small after phenotypic filtering. All samples included for general dependence in fact met a higher threshold of at least 20 cases and 20 controls. We report fixed effect estimates together with Cochran’s Q and I2 to evaluate heterogeneity for each meta-analyzed model. No significant heterogeneity was observed among the studies analyzed (p-value for Q > 0.05, Table S1 and Tables 3 and 4). Correspondingly, Q values were close to the respective degrees of freedom (number of studies) and I2 values were small with no values greater than 26% (Supplementary Table S1).
