RICOPILI: Rapid Imputation for COnsortias PIpeLIne
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Abstract
Summary: 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. Availability and implementation: RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home Supplementary information: Supplementary data are available at Bioinformatics online.
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The authors used FUSION and EpiXcan to check whether the SMR results were robust.
“We used FUSION44 and EpiXcan45 as tests of robustness of the SMR results.”
From Mapping genomic loci implicates genes and synaptic biology in schizophrenia (2022) · cites this paper as [44]
The study performed systematic quality control on raw genotyping data to filter out unqualified samples and SNPs using the Ricopili pipeline.
“We performed systematic quality control on the raw genotyping data to filter out both unqualified samples and SNPs using the Ricopili pipeline for case-control groups43.”
From A regulatory variant at 19p13.3 is associated with primary biliary cholangitis risk and ARID3A expression (2023) · cites this paper as [43]
Age, sex, and ten principal components were used as covariates in the association analysis to correct for population stratification.
“Association analyses of each cohort were performed with logistic regression in the Ricopili pipeline and age, sex and ten principal components were used as covariates in the association analysis to correct for the population stratification43.”
From A regulatory variant at 19p13.3 is associated with primary biliary cholangitis risk and ARID3A expression (2023) · cites this paper as [43]
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- Authors
- Lam, M., Awasthi, S., Watson, H. J., Goldstein, J., Panagiotaropoulou, G., Trubetskoy, V., Karlsson, R., Frei, O., Fan, C.-C., De Witte, W., Mota, N. R., Mullins, N., Brügger, K., Lee, S. H., Wray, N. R., Skarabis, N., Huang, H., Neale, B., Daly, M. J., Mattheisen, M., Walters, R., & Ripke, S.
- Affiliations
- Broad Institute (US)
- University of North Carolina at Chapel Hill (US)
- Karolinska Institutet (SE)
- University of Oslo (NO)
- Department of Human Genetics, Donders Institute for Brain , Cognition and Behaviour
- Radboud University Nijmegen (NL)
- Icahn School of Medicine at Mount Sinai (US)
- University of Bergen (NO)
- University of South Australia (AU)
- The University of Queensland (AU)
- Charité – Universitätsmedizin Berlin (DE)
- Aarhus University (DK)
- Corresponding author
- Ripke, Stephan
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-08
- Volume
- 36
- Issue
- 3
- Pages
- 930-933
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7868045
- OpenAlex
- W2967846176
- Cited by
- 315
- References
- 20
- Citations by year
- 2026: 39 · 2025: 49 · 2024: 56 · 2023: 57 · 2022: 47 · 2021: 36 · 2020: 23 · 2019: 7 · 2018: 1
- FWCI
- 17.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by-nc)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion, other-oa)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by-nc)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (U01 MH109528, U01 MH1095320)
- National Medical Research Council (NMRC/TCR/003, MH095: 003/008-1014, NMRC/CG/004/2013, NMRC/SEEDFD/019/2017)
- NIH/NIMH (U01MH109528)
- Broad Institute of MIT and Harvard (691099)
- German Research Foundation (RI2846/1-1)
- NIH
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genotype imputation, quality control, psychiatric genomics, polygenic risk scoring, replication analysis, meta-analysis, high-performance computing, parallelization, Perl
- MeSH terms
- Algorithms, Software, Genome, Genomics, Genome-Wide Association Study
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