RICOPILI takes dataset with unfiltered genotype calls, through trait association analysis, multi-cohort meta-analysis, linkage disequilibrium (LD) score regression (Bulik-Sullivan et al., 2015), conditional analysis, replication analysis and polygenic risk scoring (Supplementary Fig. S1). Little intermediate interaction is required, allowing for efficient standardized analysis of genome-wide data and results. Standardized file naming conventions are designed to optimize overview and analysis record tracking within large-scale genetic projects. Publication-ready data visualizations and reports (in PDF and Excel format) permits easy evaluation of the results. Simulated datasets are also available with the pipeline for training and development purposes. In the ensuing sections, we describe the main components of the pipeline.
