C-GWAS a method for combining GWAS summary statistics of multiple potentially correlated traits

C-GWAS is a powerful method for combining GWAS summary statistics of multiple potentially related traits and detect SNPs with multi-trait effects.C-GWAS begins with GWASs summary as inputs and outputs a single vector of combined p-values testing if the null is deviated. For each SNP, the null is the absence of any effect on all traits, and the alternative is that its effect deviates from 0 for at least one trait. C-GWAS integrates two different statistical methods with complementary statistical features to ensure the optimal power under various and complex scenarios while keeping a stable study-wide type-I error rate. The first method is called iterative effect based inverse covariance weighting (i-EbICoW) and the second method is called truncated Wald test (TWT).C-GWAS controls the study-wide type-I error rate in an empirical manner via simulations and adjust the resultant p-values in such a way that they are directly comparable with those from traditional GWAS of a single trait.