Genome-wide association studies (GWASs) have enabled the discovery of genetic variants underlying a plethora of complex traits (https://www.ebi.ac.uk/gwas/diagram). GWASs have highlighted previously unknown biological mechanisms associated with complex diseases and traits (Breen et al., 2016). The Psychiatric Genomics Consortium (PGC) (http://www.med.unc.edu/pgc) the largest umbrella organization for psychiatric genetics (Sullivan et al., 2018)—have made possible to advance the objectives of (i) revealing biological insights of psychiatric illness, (ii) informing clinical practice and (iii) presenting new therapeutic targets through sheer number of cohorts for GWASs across various psychiatric traits (Breen et al., 2016; Sullivan et al., 2012). The exponential availability of cohorts requires efficient, consistent and standardized approaches for various aspects of GWAS data management and analysis. Here, we introduce RICOPILI, the pipeline that automates rapid GWAS analysis workflow across various PGC workgroups. The pipeline is state-of-art, constantly incorporating latest available GWAS computational techniques and methods. With open-sourced simulated GWAS datasets and training tutorials packaged with the pipeline, RICOPILI is ideal for those contributing to large-scale genetic studies.
