Fig. 1
From: A phenome-wide scan reveals convergence of common and rare variant associations
Study design. a Leveraging the empirical data (e.g., UK Biobank/GWAS ATLAS) and simulations of different genetic architectures, the signals from common and rare variants were projected to genes via MAGMA and Burden/SKAT-O, respectively. b Non-parametric and parametric tests were used to estimate the correlation between the effective sample size and the Common variant and Rare variant Convergence (CORAC) signature and an alternative chance-corrected convergence coefficient (CORACmodified). Sensitivity analysis was performed by varying the number of top-ranked significant genes. The standard error of the convergence coefficient was estimated using bootstrap, enabling downstream statistical inference. Posterior inference can be performed on the signature using a Bayesian framework (Methods). c Visualization of the convergence signature