RICOPILI: Rapid Imputation for COnsortias PIpeLIne

Max Lam, Swapnil Awasthi, Hunna J. Watson, Jackie Goldstein, Georgia Panagiotaropoulou, Vassily Trubetskoy, Robert Karlsson, Oleksander Frei, Chun Chieh Fan, Ward De Witte, Nina R. Mota, Niamh Mullins, Kim Brügger, S. Hong Lee, Naomi R. Wray, Nora Skarabis, Hailiang Huang, Benjamin Neale, Mark J. Daly, Manuel MattheisenRaymond Walters, Stephan Ripke

Research output: Contribution to journalArticlepeer-review

136 Citations (Scopus)


Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.

Original languageEnglish
Pages (from-to)930-933
Number of pages4
Issue number3
Publication statusPublished - Feb 2020


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